Should I retire early?

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Much has been written of the widespread concern among America’s physicians over upcoming changes in our health care system. Dire predictions of impending doom have prompted many to consider early retirement.

I do not share such concerns, for what that is worth; but if you do, and you are serious about retiring sooner than planned, now would be a great time to take a close look at your financial situation.

Many doctors have a false sense of security about their money; most of us save too little. We either miscalculate or underestimate how much we’ll need to last through retirement.

We tend to live longer than we think we will, and as such we run the risk of outliving our savings. And we don’t face facts about long-term care. Not nearly enough of us have long-term care insurance, or the means to self-fund an extended long-term care situation.

Many people lack a clear idea of where their retirement income will come from, and even when they do, they don’t know how to manage their savings correctly. Doctors in particular are notorious for not understanding investments. Many attempt to manage their practice’s retirement plans with inadequate knowledge of how the investments within their plans work.

So how will you know if you can safely retire before Obamacare gets up to speed? Of course, as with everything else, it depends. But to arrive at any sort of reliable ballpark figure, you’ll need to know three things: (1) how much you realistically expect to spend annually after retirement; (2) how much principal you will need to generate that annual income; and (3) how far your present savings are from that target figure.

An oft-quoted rule of thumb is that in retirement you should plan to spend about 70% of what you are spending now. In my opinion, that’s nonsense. While a few significant expenses, such as disability and malpractice insurance premiums, will be eliminated, other expenses, such as travel, recreation, and medical care (including long-term care insurance, which no one should be without), will increase. My wife and I are assuming we will spend about the same in retirement as we spend now, and I suggest you do too.

Once you know how much money you will spend per year, you can calculate how much money – in interest- and dividend-producing assets – will be needed to generate that amount.

Ideally, you will want to spend only the interest and dividends; by leaving the principal untouched you will never run short, even if you retire at an unusually young age, or longevity runs in your family (or both). Most financial advisers use the 5% rule: You can safely assume a minimum average of 5% annual return on your nest egg. So if you want to spend $100,000 per year, you will need $2 million in assets; for $200,000, you’ll need $4 million.

This is where you may discover – if your present savings are a long way from your target figure – that early retirement is not a realistic option. Better, though, to make that unpleasant discovery now, rather than face the frightening prospect of running out of money at an advanced age. Don’t be tempted to close a wide gap in a hurry with high-return/high-risk investments, which often backfire, leaving you further than ever from retirement.

Of course, it goes without saying that debt can destroy the best-laid retirement plans. If you carry significant debt, pay it off as soon as possible, and certainly before you retire.

Even if you have no plans to retire in the immediate future, it is never too soon to think about retirement. Young physicians often defer contributing to their retirement plans because they want to save for a new house, or college for their children. But there are tangible tax benefits that you get now, because your contributions usually reduce your taxable income, and your investment grows tax-free until you take it out.

For long-term planning, the most foolproof strategy – seldom employed, because it’s boring – is to sock away a fixed amount per month (after your retirement plan has been funded) in a mutual fund. For example, $1,000 per month for 25 years with the market earning 10% overall comes to almost $2 million, with the power of compounded interest working for you.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.

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Much has been written of the widespread concern among America’s physicians over upcoming changes in our health care system. Dire predictions of impending doom have prompted many to consider early retirement.

I do not share such concerns, for what that is worth; but if you do, and you are serious about retiring sooner than planned, now would be a great time to take a close look at your financial situation.

Many doctors have a false sense of security about their money; most of us save too little. We either miscalculate or underestimate how much we’ll need to last through retirement.

We tend to live longer than we think we will, and as such we run the risk of outliving our savings. And we don’t face facts about long-term care. Not nearly enough of us have long-term care insurance, or the means to self-fund an extended long-term care situation.

Many people lack a clear idea of where their retirement income will come from, and even when they do, they don’t know how to manage their savings correctly. Doctors in particular are notorious for not understanding investments. Many attempt to manage their practice’s retirement plans with inadequate knowledge of how the investments within their plans work.

So how will you know if you can safely retire before Obamacare gets up to speed? Of course, as with everything else, it depends. But to arrive at any sort of reliable ballpark figure, you’ll need to know three things: (1) how much you realistically expect to spend annually after retirement; (2) how much principal you will need to generate that annual income; and (3) how far your present savings are from that target figure.

An oft-quoted rule of thumb is that in retirement you should plan to spend about 70% of what you are spending now. In my opinion, that’s nonsense. While a few significant expenses, such as disability and malpractice insurance premiums, will be eliminated, other expenses, such as travel, recreation, and medical care (including long-term care insurance, which no one should be without), will increase. My wife and I are assuming we will spend about the same in retirement as we spend now, and I suggest you do too.

Once you know how much money you will spend per year, you can calculate how much money – in interest- and dividend-producing assets – will be needed to generate that amount.

Ideally, you will want to spend only the interest and dividends; by leaving the principal untouched you will never run short, even if you retire at an unusually young age, or longevity runs in your family (or both). Most financial advisers use the 5% rule: You can safely assume a minimum average of 5% annual return on your nest egg. So if you want to spend $100,000 per year, you will need $2 million in assets; for $200,000, you’ll need $4 million.

This is where you may discover – if your present savings are a long way from your target figure – that early retirement is not a realistic option. Better, though, to make that unpleasant discovery now, rather than face the frightening prospect of running out of money at an advanced age. Don’t be tempted to close a wide gap in a hurry with high-return/high-risk investments, which often backfire, leaving you further than ever from retirement.

Of course, it goes without saying that debt can destroy the best-laid retirement plans. If you carry significant debt, pay it off as soon as possible, and certainly before you retire.

Even if you have no plans to retire in the immediate future, it is never too soon to think about retirement. Young physicians often defer contributing to their retirement plans because they want to save for a new house, or college for their children. But there are tangible tax benefits that you get now, because your contributions usually reduce your taxable income, and your investment grows tax-free until you take it out.

For long-term planning, the most foolproof strategy – seldom employed, because it’s boring – is to sock away a fixed amount per month (after your retirement plan has been funded) in a mutual fund. For example, $1,000 per month for 25 years with the market earning 10% overall comes to almost $2 million, with the power of compounded interest working for you.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.

Much has been written of the widespread concern among America’s physicians over upcoming changes in our health care system. Dire predictions of impending doom have prompted many to consider early retirement.

I do not share such concerns, for what that is worth; but if you do, and you are serious about retiring sooner than planned, now would be a great time to take a close look at your financial situation.

Many doctors have a false sense of security about their money; most of us save too little. We either miscalculate or underestimate how much we’ll need to last through retirement.

We tend to live longer than we think we will, and as such we run the risk of outliving our savings. And we don’t face facts about long-term care. Not nearly enough of us have long-term care insurance, or the means to self-fund an extended long-term care situation.

Many people lack a clear idea of where their retirement income will come from, and even when they do, they don’t know how to manage their savings correctly. Doctors in particular are notorious for not understanding investments. Many attempt to manage their practice’s retirement plans with inadequate knowledge of how the investments within their plans work.

So how will you know if you can safely retire before Obamacare gets up to speed? Of course, as with everything else, it depends. But to arrive at any sort of reliable ballpark figure, you’ll need to know three things: (1) how much you realistically expect to spend annually after retirement; (2) how much principal you will need to generate that annual income; and (3) how far your present savings are from that target figure.

An oft-quoted rule of thumb is that in retirement you should plan to spend about 70% of what you are spending now. In my opinion, that’s nonsense. While a few significant expenses, such as disability and malpractice insurance premiums, will be eliminated, other expenses, such as travel, recreation, and medical care (including long-term care insurance, which no one should be without), will increase. My wife and I are assuming we will spend about the same in retirement as we spend now, and I suggest you do too.

Once you know how much money you will spend per year, you can calculate how much money – in interest- and dividend-producing assets – will be needed to generate that amount.

Ideally, you will want to spend only the interest and dividends; by leaving the principal untouched you will never run short, even if you retire at an unusually young age, or longevity runs in your family (or both). Most financial advisers use the 5% rule: You can safely assume a minimum average of 5% annual return on your nest egg. So if you want to spend $100,000 per year, you will need $2 million in assets; for $200,000, you’ll need $4 million.

This is where you may discover – if your present savings are a long way from your target figure – that early retirement is not a realistic option. Better, though, to make that unpleasant discovery now, rather than face the frightening prospect of running out of money at an advanced age. Don’t be tempted to close a wide gap in a hurry with high-return/high-risk investments, which often backfire, leaving you further than ever from retirement.

Of course, it goes without saying that debt can destroy the best-laid retirement plans. If you carry significant debt, pay it off as soon as possible, and certainly before you retire.

Even if you have no plans to retire in the immediate future, it is never too soon to think about retirement. Young physicians often defer contributing to their retirement plans because they want to save for a new house, or college for their children. But there are tangible tax benefits that you get now, because your contributions usually reduce your taxable income, and your investment grows tax-free until you take it out.

For long-term planning, the most foolproof strategy – seldom employed, because it’s boring – is to sock away a fixed amount per month (after your retirement plan has been funded) in a mutual fund. For example, $1,000 per month for 25 years with the market earning 10% overall comes to almost $2 million, with the power of compounded interest working for you.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.

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Managing symptoms of depression

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Diana looked at her pill bottles and wondered why she was on all these medications when she did not feel any better. She looked at the five bottles: bupropion, paroxetine, diazepam, alprazolam, and zolpidem. She thought about the side effects she was experiencing.

She had been taking this cocktail, in various dosages, for the best part of a year now. Her depression remained unchanged. She made a decision that she would tell her psychiatrist that she wanted off the medications at her next visit. She would then ask for other treatments. She had found many therapies offered on the Internet for treatment of depression, and she hoped her psychiatrist would be able to help her decide which therapies might be best suited for her. Perhaps she would agree to stay on one medication as a compromise as she knew her psychiatrist thought treatment of depression with medication to be important.

Up to 30% of patients with depression do not respond to multiple treatment trials and are considered to have treatment-resistant depression. Most treatment trials for these patients focus on symptom reduction as a goal. This emphasis on symptom reduction often leads to tunnel vision, where other evidence-based treatments become marginalized by psychiatrists. Thus, patients like Diana end up on multiple medications, without an integrated approach to assessment or discussion of combined treatments (medications and psychotherapy).

Dr. Gabor Keitner, who practices in Providence, R.I., and is a member of the Association of Family Psychiatrists, offers a new program aimed at helping patients manage their depression. His philosophical stance is that depression is a chronic illness and that expecting symptoms to be cured with medications is, for most patients, a false hope perpetuated by a consumer society, where the pharmaceutical industry has dominated the education of patients, their families, and the psychiatric profession. He conceptualizes depression, like other chronic medical illnesses, such as diabetes or hypertension, with a similar range of severity. Therefore, the assessment and treatment of depression requires a more nuanced approach.

He is scheduled to present his Management of Depression (MOD) program at this year’s American Psychiatric Association meeting in San Francisco. His MOD program focuses on how a patient such as Diana can build a satisfying life with meaningful goals and relationships – even if her depressive symptoms persist.

In his pilot study, 30 patients with treatment-resistant depression were randomized to treatment as usual (TAU, n = 13) or the MOD program (n = 17) for 12 weeks. The patients in the MOD group had significant improvement in perception of social support (P < .034) and purpose in life (P < .038) scores, in contrast to the TAU group. The MOD group participated in nine adjunctive sessions of disease management focused therapy. The Scales of Psychological Well-Being measured purpose in life, life goals, and meaning. Social support was measured with the Multidimensional Scale of Perceived Social Support. Depression severity was measured by the Montgomery-Åsberg Depression Rating Scale. Patients were assessed at baseline and week 12. Both groups of patients had significant improvements in their depressive symptoms (TAU 35.46 to 25.9 P < .010; MOD 31.88 to 22.41 P < .001) but continued to experience moderate levels of depression. Adjunctive treatment focusing on functioning, life meaning, and relationships, as opposed to symptom reduction, will help Diana to have a more satisfying life, despite her symptoms of depression.

Measuring relational functioning briefly

In another session, Dr. Keitner is slated to present "The Brief Multidimensional Assessment Scale (BMAS): A Mental Health Check Up," coauthored with Abigail K. Mansfield Maraccio, Ph.D., and Joan Kelley. This scale evaluates global mental health outcomes, including quality of life, symptoms, functioning, and relationships. This measure can be used to assess the clinical status of patients at every health encounter and over the course of an illness. Most available scales are either too long for routine clinical use, focus on a narrow range of symptoms, or focus on specific diagnostic groups. Best of all, this new scale takes less than a minute to complete.

The BMAS was tested against The Outcome Questionnaire–45 (OQ45) with 248 psychiatric outpatients as part of their standard ongoing care. Internal consistency was evaluated with Cronbach’s alpha, which was .75 for the four items. Test-retest reliability was assessed using Pearson’s r and ranged from .45 (symptom severity, which can fluctuate daily) to .79 (quality of life) for each of the BMAS items. Concurrent and convergent validity was analyzed with Pearson product moment correlations between BMAS and OQ45 scales. All correlations were significant for the relevant dimensions.

 

 

The BMAS demonstrated acceptable reliability, especially for such a brief measure. It also demonstrated concurrent and convergent validity with a much longer commonly used clinical outcome scale. The BMAS is a useful assessment tool for patients with any clinical condition for which it is desirable to track how the patient is experiencing his or her life situation at a given point in time and when there is a desire to monitor change over time. Notably, BMAS includes health relationships as a measure of good clinical outcome.

A daughter’s documentary about her father

One media workshop slated for the APA meeting will be offered by three members of the Association of Family Psychiatrists: Dr. Michael S. Ascher, Dr. Ira Glick, and Dr. Igor Galynker. They will present a film, "Unlisted: A Story of Schizophrenia." This is a soul-searching examination of responsibility – of parents and children, physicians and patients, and of society and citizens – toward those afflicted with severe mental illness. The film was made by Dr. Delaney Ruston, a Seattle general physician who documents the rebuilding of her relationship with her father. "Unlisted" examines the challenging family dynamics that are present when schizophrenia occurs. Dr. Ruston works hard to overcome the obstacles in accessing appropriate treatment for her father, and her documentary exposes the many failings of the American mental health system as experienced by the families. Dr. Ruston traces the progression of her father’s illness. She studies his medical files and narrates from his autobiographical surrealist novel. In beautifully portrayed scenes, "Unlisted" enters the inner life of Richard Ruston with a clarity and affection missing from many films about people with mental illness.

In summary, family-oriented patient care can be delivered in many ways, from focusing on relational improvement in individual work, to being aware of how to assess and measure relational functioning briefly at each visit, to being able to listen to the accounts of family members and invite them into the treatment room.

Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professions" (New York: Routledge, March 2013), and has been a member of the Association of Family Psychiatrists since 2002.

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Diana looked at her pill bottles and wondered why she was on all these medications when she did not feel any better. She looked at the five bottles: bupropion, paroxetine, diazepam, alprazolam, and zolpidem. She thought about the side effects she was experiencing.

She had been taking this cocktail, in various dosages, for the best part of a year now. Her depression remained unchanged. She made a decision that she would tell her psychiatrist that she wanted off the medications at her next visit. She would then ask for other treatments. She had found many therapies offered on the Internet for treatment of depression, and she hoped her psychiatrist would be able to help her decide which therapies might be best suited for her. Perhaps she would agree to stay on one medication as a compromise as she knew her psychiatrist thought treatment of depression with medication to be important.

Up to 30% of patients with depression do not respond to multiple treatment trials and are considered to have treatment-resistant depression. Most treatment trials for these patients focus on symptom reduction as a goal. This emphasis on symptom reduction often leads to tunnel vision, where other evidence-based treatments become marginalized by psychiatrists. Thus, patients like Diana end up on multiple medications, without an integrated approach to assessment or discussion of combined treatments (medications and psychotherapy).

Dr. Gabor Keitner, who practices in Providence, R.I., and is a member of the Association of Family Psychiatrists, offers a new program aimed at helping patients manage their depression. His philosophical stance is that depression is a chronic illness and that expecting symptoms to be cured with medications is, for most patients, a false hope perpetuated by a consumer society, where the pharmaceutical industry has dominated the education of patients, their families, and the psychiatric profession. He conceptualizes depression, like other chronic medical illnesses, such as diabetes or hypertension, with a similar range of severity. Therefore, the assessment and treatment of depression requires a more nuanced approach.

He is scheduled to present his Management of Depression (MOD) program at this year’s American Psychiatric Association meeting in San Francisco. His MOD program focuses on how a patient such as Diana can build a satisfying life with meaningful goals and relationships – even if her depressive symptoms persist.

In his pilot study, 30 patients with treatment-resistant depression were randomized to treatment as usual (TAU, n = 13) or the MOD program (n = 17) for 12 weeks. The patients in the MOD group had significant improvement in perception of social support (P < .034) and purpose in life (P < .038) scores, in contrast to the TAU group. The MOD group participated in nine adjunctive sessions of disease management focused therapy. The Scales of Psychological Well-Being measured purpose in life, life goals, and meaning. Social support was measured with the Multidimensional Scale of Perceived Social Support. Depression severity was measured by the Montgomery-Åsberg Depression Rating Scale. Patients were assessed at baseline and week 12. Both groups of patients had significant improvements in their depressive symptoms (TAU 35.46 to 25.9 P < .010; MOD 31.88 to 22.41 P < .001) but continued to experience moderate levels of depression. Adjunctive treatment focusing on functioning, life meaning, and relationships, as opposed to symptom reduction, will help Diana to have a more satisfying life, despite her symptoms of depression.

Measuring relational functioning briefly

In another session, Dr. Keitner is slated to present "The Brief Multidimensional Assessment Scale (BMAS): A Mental Health Check Up," coauthored with Abigail K. Mansfield Maraccio, Ph.D., and Joan Kelley. This scale evaluates global mental health outcomes, including quality of life, symptoms, functioning, and relationships. This measure can be used to assess the clinical status of patients at every health encounter and over the course of an illness. Most available scales are either too long for routine clinical use, focus on a narrow range of symptoms, or focus on specific diagnostic groups. Best of all, this new scale takes less than a minute to complete.

The BMAS was tested against The Outcome Questionnaire–45 (OQ45) with 248 psychiatric outpatients as part of their standard ongoing care. Internal consistency was evaluated with Cronbach’s alpha, which was .75 for the four items. Test-retest reliability was assessed using Pearson’s r and ranged from .45 (symptom severity, which can fluctuate daily) to .79 (quality of life) for each of the BMAS items. Concurrent and convergent validity was analyzed with Pearson product moment correlations between BMAS and OQ45 scales. All correlations were significant for the relevant dimensions.

 

 

The BMAS demonstrated acceptable reliability, especially for such a brief measure. It also demonstrated concurrent and convergent validity with a much longer commonly used clinical outcome scale. The BMAS is a useful assessment tool for patients with any clinical condition for which it is desirable to track how the patient is experiencing his or her life situation at a given point in time and when there is a desire to monitor change over time. Notably, BMAS includes health relationships as a measure of good clinical outcome.

A daughter’s documentary about her father

One media workshop slated for the APA meeting will be offered by three members of the Association of Family Psychiatrists: Dr. Michael S. Ascher, Dr. Ira Glick, and Dr. Igor Galynker. They will present a film, "Unlisted: A Story of Schizophrenia." This is a soul-searching examination of responsibility – of parents and children, physicians and patients, and of society and citizens – toward those afflicted with severe mental illness. The film was made by Dr. Delaney Ruston, a Seattle general physician who documents the rebuilding of her relationship with her father. "Unlisted" examines the challenging family dynamics that are present when schizophrenia occurs. Dr. Ruston works hard to overcome the obstacles in accessing appropriate treatment for her father, and her documentary exposes the many failings of the American mental health system as experienced by the families. Dr. Ruston traces the progression of her father’s illness. She studies his medical files and narrates from his autobiographical surrealist novel. In beautifully portrayed scenes, "Unlisted" enters the inner life of Richard Ruston with a clarity and affection missing from many films about people with mental illness.

In summary, family-oriented patient care can be delivered in many ways, from focusing on relational improvement in individual work, to being aware of how to assess and measure relational functioning briefly at each visit, to being able to listen to the accounts of family members and invite them into the treatment room.

Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professions" (New York: Routledge, March 2013), and has been a member of the Association of Family Psychiatrists since 2002.

Diana looked at her pill bottles and wondered why she was on all these medications when she did not feel any better. She looked at the five bottles: bupropion, paroxetine, diazepam, alprazolam, and zolpidem. She thought about the side effects she was experiencing.

She had been taking this cocktail, in various dosages, for the best part of a year now. Her depression remained unchanged. She made a decision that she would tell her psychiatrist that she wanted off the medications at her next visit. She would then ask for other treatments. She had found many therapies offered on the Internet for treatment of depression, and she hoped her psychiatrist would be able to help her decide which therapies might be best suited for her. Perhaps she would agree to stay on one medication as a compromise as she knew her psychiatrist thought treatment of depression with medication to be important.

Up to 30% of patients with depression do not respond to multiple treatment trials and are considered to have treatment-resistant depression. Most treatment trials for these patients focus on symptom reduction as a goal. This emphasis on symptom reduction often leads to tunnel vision, where other evidence-based treatments become marginalized by psychiatrists. Thus, patients like Diana end up on multiple medications, without an integrated approach to assessment or discussion of combined treatments (medications and psychotherapy).

Dr. Gabor Keitner, who practices in Providence, R.I., and is a member of the Association of Family Psychiatrists, offers a new program aimed at helping patients manage their depression. His philosophical stance is that depression is a chronic illness and that expecting symptoms to be cured with medications is, for most patients, a false hope perpetuated by a consumer society, where the pharmaceutical industry has dominated the education of patients, their families, and the psychiatric profession. He conceptualizes depression, like other chronic medical illnesses, such as diabetes or hypertension, with a similar range of severity. Therefore, the assessment and treatment of depression requires a more nuanced approach.

He is scheduled to present his Management of Depression (MOD) program at this year’s American Psychiatric Association meeting in San Francisco. His MOD program focuses on how a patient such as Diana can build a satisfying life with meaningful goals and relationships – even if her depressive symptoms persist.

In his pilot study, 30 patients with treatment-resistant depression were randomized to treatment as usual (TAU, n = 13) or the MOD program (n = 17) for 12 weeks. The patients in the MOD group had significant improvement in perception of social support (P < .034) and purpose in life (P < .038) scores, in contrast to the TAU group. The MOD group participated in nine adjunctive sessions of disease management focused therapy. The Scales of Psychological Well-Being measured purpose in life, life goals, and meaning. Social support was measured with the Multidimensional Scale of Perceived Social Support. Depression severity was measured by the Montgomery-Åsberg Depression Rating Scale. Patients were assessed at baseline and week 12. Both groups of patients had significant improvements in their depressive symptoms (TAU 35.46 to 25.9 P < .010; MOD 31.88 to 22.41 P < .001) but continued to experience moderate levels of depression. Adjunctive treatment focusing on functioning, life meaning, and relationships, as opposed to symptom reduction, will help Diana to have a more satisfying life, despite her symptoms of depression.

Measuring relational functioning briefly

In another session, Dr. Keitner is slated to present "The Brief Multidimensional Assessment Scale (BMAS): A Mental Health Check Up," coauthored with Abigail K. Mansfield Maraccio, Ph.D., and Joan Kelley. This scale evaluates global mental health outcomes, including quality of life, symptoms, functioning, and relationships. This measure can be used to assess the clinical status of patients at every health encounter and over the course of an illness. Most available scales are either too long for routine clinical use, focus on a narrow range of symptoms, or focus on specific diagnostic groups. Best of all, this new scale takes less than a minute to complete.

The BMAS was tested against The Outcome Questionnaire–45 (OQ45) with 248 psychiatric outpatients as part of their standard ongoing care. Internal consistency was evaluated with Cronbach’s alpha, which was .75 for the four items. Test-retest reliability was assessed using Pearson’s r and ranged from .45 (symptom severity, which can fluctuate daily) to .79 (quality of life) for each of the BMAS items. Concurrent and convergent validity was analyzed with Pearson product moment correlations between BMAS and OQ45 scales. All correlations were significant for the relevant dimensions.

 

 

The BMAS demonstrated acceptable reliability, especially for such a brief measure. It also demonstrated concurrent and convergent validity with a much longer commonly used clinical outcome scale. The BMAS is a useful assessment tool for patients with any clinical condition for which it is desirable to track how the patient is experiencing his or her life situation at a given point in time and when there is a desire to monitor change over time. Notably, BMAS includes health relationships as a measure of good clinical outcome.

A daughter’s documentary about her father

One media workshop slated for the APA meeting will be offered by three members of the Association of Family Psychiatrists: Dr. Michael S. Ascher, Dr. Ira Glick, and Dr. Igor Galynker. They will present a film, "Unlisted: A Story of Schizophrenia." This is a soul-searching examination of responsibility – of parents and children, physicians and patients, and of society and citizens – toward those afflicted with severe mental illness. The film was made by Dr. Delaney Ruston, a Seattle general physician who documents the rebuilding of her relationship with her father. "Unlisted" examines the challenging family dynamics that are present when schizophrenia occurs. Dr. Ruston works hard to overcome the obstacles in accessing appropriate treatment for her father, and her documentary exposes the many failings of the American mental health system as experienced by the families. Dr. Ruston traces the progression of her father’s illness. She studies his medical files and narrates from his autobiographical surrealist novel. In beautifully portrayed scenes, "Unlisted" enters the inner life of Richard Ruston with a clarity and affection missing from many films about people with mental illness.

In summary, family-oriented patient care can be delivered in many ways, from focusing on relational improvement in individual work, to being aware of how to assess and measure relational functioning briefly at each visit, to being able to listen to the accounts of family members and invite them into the treatment room.

Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professions" (New York: Routledge, March 2013), and has been a member of the Association of Family Psychiatrists since 2002.

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Only doctors can save America

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Dr. Ezekiel J. Emanuel, one of the brains behind Obamacare, has a blunt message for his fellow physicians:

Only you can save America.

He's not just talking about medicine. As might befit someone who holds a faculty title at the business-oriented Wharton School at the University of Pennsylvania, Dr. Emanuel spent much of his keynote address here at the American College of Physicians' annual meeting in San Francisco talking about the U.S. economy. The enormous impact of runaway spending on U.S. health care threatens "everything we care about," including access to health care, state funds available for education, corporate wages for the middle class, and the fiscal health of the nation, he said.

"More than any other group in America, doctors have the power to solve our long-term economic challenges to ensure a prosperous future," Dr. Emanuel said.

Dr. Ezekiel J. Emanuel

If the U.S. health care system were a country, its nearly $3 trillion economy in 2012 would be the fifth largest in the world, behind only the U.S. as a whole, China, Japan, and Germany. "We spend more on health care in this country than the 66 million French spend on everything in their society," he said. "It is an astounding number how much we spend on health care."

Take just the federal portions of Medicare and Medicaid, excluding state spending, and you've still got the 16th largest economy in the world, bigger than the economies of Switzerland, Turkey, or the Netherlands, for example. The impact of any other fiscal variable on the U.S. economy, including Social Security, is swamped by the impact of health care costs, said Dr. Emanuel, who is also chair of medical ethics and health policy at the University of Pennsylvania, Philadelphia.

Per person, the United States far outspends other countries when it comes to health care, and the proportion of the gross domestic product consumed by health care keeps getting larger and larger.

Dr. Emanuel served as a special adviser for health policy to the director of the federal Office of Management and Budget in 2009-2011 - during the design, passage, and first steps to implementation of the Patient Protection and Affordable Care Act (commonly known as Obamacare) - and he seemed to address some critics in absentia who have claimed that health care reform will lead to unwanted rationing of care. There's no need to ration, Dr. Emanuel said. Switzerland doesn't ration care, and it spends far less per capita for what is considered quality health care. "We can do a better job in this country of controlling costs without the need to ration care," he said.

The only way to really control costs is to transform the way U.S. health care is delivered, he said. Ten percent of U.S. patients account for 63% of dollars spent on health care. "You know who they are - people with congestive heart failure, COPD, diabetes, adult asthma, coronary artery disease, cancer. People with chronic multiple chronic illnesses. That's where the money's going. That's where the uneven quality is," and that's where health care delivery needs to improve, he said.

Dr. Emanuel proposed six essential components to transforming the health care system. Among them: The focus needs to be on cost according to value, and getting rid of services with no value. The system must focus on patients' needs, not on physicians' schedules or other concerns. And the system must evolve toward clinicians working as teams including allied health professionals, not as individuals. "We are not going to be, going forward, one-sies and two-sies in practice" anymore, he said.

Greater emphasis on delivering health care via organizations and systems, standardization of processes, and transparency around price and quality will be essential, he added.

Transparency in pricing and quality isn't just something consumers will want. Physicians will want it in order to refer patients to quality care and set prices appropriately, Dr. Emanuel argued. "I think this is inevitable, and I think it's going to happen faster than you think," he said.

Most U.S. physicians are stuck in fee-for-service payment systems, which don't provide the incentives needed for change, he said. Doctors "as a group" should push for changes to the payment system, which will increase physician autonomy but also will assign more financial risk to physicians. "I see no way of getting out of that," Dr. Emanuel said.

In his eyes, if doctors don't push for changes in how health care is delivered, we basically can kiss the U.S. economy and future prosperity good-bye. "Doctors are the only people who can re-engineer the delivery system," he said. "If you don't do it, it ain't gonna happen. It's that simple," he said. All previous reform efforts that did not have physician leadership have failed.

 

 

"You have to lead this," he explained.

No one should expect that reforming the fifth-largest economy in the world could be accomplished in just a few years, however. "It's going to take this decade," Dr. Emanuel predicted.

Dr. Emanuel reported having no financial disclosures.

[email protected]

Twitter: @sherryboschert

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Dr. Ezekiel J. Emanuel, one of the brains behind Obamacare, has a blunt message for his fellow physicians:

Only you can save America.

He's not just talking about medicine. As might befit someone who holds a faculty title at the business-oriented Wharton School at the University of Pennsylvania, Dr. Emanuel spent much of his keynote address here at the American College of Physicians' annual meeting in San Francisco talking about the U.S. economy. The enormous impact of runaway spending on U.S. health care threatens "everything we care about," including access to health care, state funds available for education, corporate wages for the middle class, and the fiscal health of the nation, he said.

"More than any other group in America, doctors have the power to solve our long-term economic challenges to ensure a prosperous future," Dr. Emanuel said.

Dr. Ezekiel J. Emanuel

If the U.S. health care system were a country, its nearly $3 trillion economy in 2012 would be the fifth largest in the world, behind only the U.S. as a whole, China, Japan, and Germany. "We spend more on health care in this country than the 66 million French spend on everything in their society," he said. "It is an astounding number how much we spend on health care."

Take just the federal portions of Medicare and Medicaid, excluding state spending, and you've still got the 16th largest economy in the world, bigger than the economies of Switzerland, Turkey, or the Netherlands, for example. The impact of any other fiscal variable on the U.S. economy, including Social Security, is swamped by the impact of health care costs, said Dr. Emanuel, who is also chair of medical ethics and health policy at the University of Pennsylvania, Philadelphia.

Per person, the United States far outspends other countries when it comes to health care, and the proportion of the gross domestic product consumed by health care keeps getting larger and larger.

Dr. Emanuel served as a special adviser for health policy to the director of the federal Office of Management and Budget in 2009-2011 - during the design, passage, and first steps to implementation of the Patient Protection and Affordable Care Act (commonly known as Obamacare) - and he seemed to address some critics in absentia who have claimed that health care reform will lead to unwanted rationing of care. There's no need to ration, Dr. Emanuel said. Switzerland doesn't ration care, and it spends far less per capita for what is considered quality health care. "We can do a better job in this country of controlling costs without the need to ration care," he said.

The only way to really control costs is to transform the way U.S. health care is delivered, he said. Ten percent of U.S. patients account for 63% of dollars spent on health care. "You know who they are - people with congestive heart failure, COPD, diabetes, adult asthma, coronary artery disease, cancer. People with chronic multiple chronic illnesses. That's where the money's going. That's where the uneven quality is," and that's where health care delivery needs to improve, he said.

Dr. Emanuel proposed six essential components to transforming the health care system. Among them: The focus needs to be on cost according to value, and getting rid of services with no value. The system must focus on patients' needs, not on physicians' schedules or other concerns. And the system must evolve toward clinicians working as teams including allied health professionals, not as individuals. "We are not going to be, going forward, one-sies and two-sies in practice" anymore, he said.

Greater emphasis on delivering health care via organizations and systems, standardization of processes, and transparency around price and quality will be essential, he added.

Transparency in pricing and quality isn't just something consumers will want. Physicians will want it in order to refer patients to quality care and set prices appropriately, Dr. Emanuel argued. "I think this is inevitable, and I think it's going to happen faster than you think," he said.

Most U.S. physicians are stuck in fee-for-service payment systems, which don't provide the incentives needed for change, he said. Doctors "as a group" should push for changes to the payment system, which will increase physician autonomy but also will assign more financial risk to physicians. "I see no way of getting out of that," Dr. Emanuel said.

In his eyes, if doctors don't push for changes in how health care is delivered, we basically can kiss the U.S. economy and future prosperity good-bye. "Doctors are the only people who can re-engineer the delivery system," he said. "If you don't do it, it ain't gonna happen. It's that simple," he said. All previous reform efforts that did not have physician leadership have failed.

 

 

"You have to lead this," he explained.

No one should expect that reforming the fifth-largest economy in the world could be accomplished in just a few years, however. "It's going to take this decade," Dr. Emanuel predicted.

Dr. Emanuel reported having no financial disclosures.

[email protected]

Twitter: @sherryboschert

Dr. Ezekiel J. Emanuel, one of the brains behind Obamacare, has a blunt message for his fellow physicians:

Only you can save America.

He's not just talking about medicine. As might befit someone who holds a faculty title at the business-oriented Wharton School at the University of Pennsylvania, Dr. Emanuel spent much of his keynote address here at the American College of Physicians' annual meeting in San Francisco talking about the U.S. economy. The enormous impact of runaway spending on U.S. health care threatens "everything we care about," including access to health care, state funds available for education, corporate wages for the middle class, and the fiscal health of the nation, he said.

"More than any other group in America, doctors have the power to solve our long-term economic challenges to ensure a prosperous future," Dr. Emanuel said.

Dr. Ezekiel J. Emanuel

If the U.S. health care system were a country, its nearly $3 trillion economy in 2012 would be the fifth largest in the world, behind only the U.S. as a whole, China, Japan, and Germany. "We spend more on health care in this country than the 66 million French spend on everything in their society," he said. "It is an astounding number how much we spend on health care."

Take just the federal portions of Medicare and Medicaid, excluding state spending, and you've still got the 16th largest economy in the world, bigger than the economies of Switzerland, Turkey, or the Netherlands, for example. The impact of any other fiscal variable on the U.S. economy, including Social Security, is swamped by the impact of health care costs, said Dr. Emanuel, who is also chair of medical ethics and health policy at the University of Pennsylvania, Philadelphia.

Per person, the United States far outspends other countries when it comes to health care, and the proportion of the gross domestic product consumed by health care keeps getting larger and larger.

Dr. Emanuel served as a special adviser for health policy to the director of the federal Office of Management and Budget in 2009-2011 - during the design, passage, and first steps to implementation of the Patient Protection and Affordable Care Act (commonly known as Obamacare) - and he seemed to address some critics in absentia who have claimed that health care reform will lead to unwanted rationing of care. There's no need to ration, Dr. Emanuel said. Switzerland doesn't ration care, and it spends far less per capita for what is considered quality health care. "We can do a better job in this country of controlling costs without the need to ration care," he said.

The only way to really control costs is to transform the way U.S. health care is delivered, he said. Ten percent of U.S. patients account for 63% of dollars spent on health care. "You know who they are - people with congestive heart failure, COPD, diabetes, adult asthma, coronary artery disease, cancer. People with chronic multiple chronic illnesses. That's where the money's going. That's where the uneven quality is," and that's where health care delivery needs to improve, he said.

Dr. Emanuel proposed six essential components to transforming the health care system. Among them: The focus needs to be on cost according to value, and getting rid of services with no value. The system must focus on patients' needs, not on physicians' schedules or other concerns. And the system must evolve toward clinicians working as teams including allied health professionals, not as individuals. "We are not going to be, going forward, one-sies and two-sies in practice" anymore, he said.

Greater emphasis on delivering health care via organizations and systems, standardization of processes, and transparency around price and quality will be essential, he added.

Transparency in pricing and quality isn't just something consumers will want. Physicians will want it in order to refer patients to quality care and set prices appropriately, Dr. Emanuel argued. "I think this is inevitable, and I think it's going to happen faster than you think," he said.

Most U.S. physicians are stuck in fee-for-service payment systems, which don't provide the incentives needed for change, he said. Doctors "as a group" should push for changes to the payment system, which will increase physician autonomy but also will assign more financial risk to physicians. "I see no way of getting out of that," Dr. Emanuel said.

In his eyes, if doctors don't push for changes in how health care is delivered, we basically can kiss the U.S. economy and future prosperity good-bye. "Doctors are the only people who can re-engineer the delivery system," he said. "If you don't do it, it ain't gonna happen. It's that simple," he said. All previous reform efforts that did not have physician leadership have failed.

 

 

"You have to lead this," he explained.

No one should expect that reforming the fifth-largest economy in the world could be accomplished in just a few years, however. "It's going to take this decade," Dr. Emanuel predicted.

Dr. Emanuel reported having no financial disclosures.

[email protected]

Twitter: @sherryboschert

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Patient Prediction Model Trims Avoidable Hospital Readmissions

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Patient Prediction Model Trims Avoidable Hospital Readmissions

A new prediction model that uses a familiar phrase can help identify potentially avoidable hospital patient readmissions, according to a report in JAMA Internal Medicine.

The retrospective cohort study, "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients," used a model dubbed HOSPITAL to create a score that targets patients most likely to benefit from pre-discharge interventions. The model is based on seven factors: hemoglobin at discharge, discharge from an oncology service, sodium levels at discharge, procedure during the index admission, index type of admission, number of admissions in the prior 12 months, and length of stay. The HOSPITAL score had fair discriminatory power (C statistic 0.71) and good calibration, the authors noted.

"By definition, these [interventions] are expensive and you really want to reserve them for the patients that are most likely to benefit," says study co-author Jeffrey Schnipper, MD, MPH, FHM, director of clinical research and an associate physician in the general medicine division at Brigham and Women's Hospital in Boston.

The study identified 879 potentially avoidable discharges out of 10,731 eligible discharges, or 8.5%. The estimated potentially avoidable readmission risk was 18%. Dr. Schnipper says that in absolute reduction, the model could cut 2% to 3% of readmissions.

"This is an evolution of sophistication in how we think about this work," Dr. Schnipper adds. "Not all patients have a preventable readmission. Maybe some of those patients are more likely to benefit. The next step is to prove it. That's the gold standard and that’s our next study." TH

Visit our website for more information on 30-day readmissions.


 

 

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A new prediction model that uses a familiar phrase can help identify potentially avoidable hospital patient readmissions, according to a report in JAMA Internal Medicine.

The retrospective cohort study, "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients," used a model dubbed HOSPITAL to create a score that targets patients most likely to benefit from pre-discharge interventions. The model is based on seven factors: hemoglobin at discharge, discharge from an oncology service, sodium levels at discharge, procedure during the index admission, index type of admission, number of admissions in the prior 12 months, and length of stay. The HOSPITAL score had fair discriminatory power (C statistic 0.71) and good calibration, the authors noted.

"By definition, these [interventions] are expensive and you really want to reserve them for the patients that are most likely to benefit," says study co-author Jeffrey Schnipper, MD, MPH, FHM, director of clinical research and an associate physician in the general medicine division at Brigham and Women's Hospital in Boston.

The study identified 879 potentially avoidable discharges out of 10,731 eligible discharges, or 8.5%. The estimated potentially avoidable readmission risk was 18%. Dr. Schnipper says that in absolute reduction, the model could cut 2% to 3% of readmissions.

"This is an evolution of sophistication in how we think about this work," Dr. Schnipper adds. "Not all patients have a preventable readmission. Maybe some of those patients are more likely to benefit. The next step is to prove it. That's the gold standard and that’s our next study." TH

Visit our website for more information on 30-day readmissions.


 

 

A new prediction model that uses a familiar phrase can help identify potentially avoidable hospital patient readmissions, according to a report in JAMA Internal Medicine.

The retrospective cohort study, "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients," used a model dubbed HOSPITAL to create a score that targets patients most likely to benefit from pre-discharge interventions. The model is based on seven factors: hemoglobin at discharge, discharge from an oncology service, sodium levels at discharge, procedure during the index admission, index type of admission, number of admissions in the prior 12 months, and length of stay. The HOSPITAL score had fair discriminatory power (C statistic 0.71) and good calibration, the authors noted.

"By definition, these [interventions] are expensive and you really want to reserve them for the patients that are most likely to benefit," says study co-author Jeffrey Schnipper, MD, MPH, FHM, director of clinical research and an associate physician in the general medicine division at Brigham and Women's Hospital in Boston.

The study identified 879 potentially avoidable discharges out of 10,731 eligible discharges, or 8.5%. The estimated potentially avoidable readmission risk was 18%. Dr. Schnipper says that in absolute reduction, the model could cut 2% to 3% of readmissions.

"This is an evolution of sophistication in how we think about this work," Dr. Schnipper adds. "Not all patients have a preventable readmission. Maybe some of those patients are more likely to benefit. The next step is to prove it. That's the gold standard and that’s our next study." TH

Visit our website for more information on 30-day readmissions.


 

 

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Hospitals Seek Ways to Defuse Angry Doctors

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Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.

A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.

"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.

In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.

The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.

"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH

Visit our website for more information about the impact of workloads on hospitalists.


 

 

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Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.

A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.

"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.

In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.

The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.

"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH

Visit our website for more information about the impact of workloads on hospitalists.


 

 

Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.

A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.

"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.

In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.

The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.

"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH

Visit our website for more information about the impact of workloads on hospitalists.


 

 

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VIDEO: Five Reasons You Should Attend HM13 in Washington, D.C.

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Alstonia scholaris

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Alstonia scholaris

Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).

In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.

Courtesy Wikimedia Commons/Binh Giang/Public Domain
Alstonia scholaris has a long history of use in traditional and homeopathic medicine.

Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).

The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).

In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).

Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).

Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).

 

 

In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.

The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.

In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).

In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.

The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.

The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.

Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).

In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).

Conclusion

Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.

Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].

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Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).

In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.

Courtesy Wikimedia Commons/Binh Giang/Public Domain
Alstonia scholaris has a long history of use in traditional and homeopathic medicine.

Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).

The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).

In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).

Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).

Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).

 

 

In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.

The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.

In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).

In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.

The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.

The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.

Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).

In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).

Conclusion

Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.

Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].

Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).

In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.

Courtesy Wikimedia Commons/Binh Giang/Public Domain
Alstonia scholaris has a long history of use in traditional and homeopathic medicine.

Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).

The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).

In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).

Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).

Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).

 

 

In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.

The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.

In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).

In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.

The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.

The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.

Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).

In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).

Conclusion

Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.

Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].

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Alstonia scholaris, Apocynaceae family, homeopathic medicine, Ayurvedic medicine in India, alkaloids ditamine, echitamine, echitanines, malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions, asthma, bronchitis, helminthiasis, agalactia, Leslie Baumann
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Alstonia scholaris, Apocynaceae family, homeopathic medicine, Ayurvedic medicine in India, alkaloids ditamine, echitamine, echitanines, malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions, asthma, bronchitis, helminthiasis, agalactia, Leslie Baumann
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Influence of neighborhood household income on early death or urgent hospital readmission

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

Study Setting and Data Sources

This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).

Study Population

This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.

For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.

Study Outcome

The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.

Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile

In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]

We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.

Analysis

We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.

To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Description of Study Patients by 30‐Day Death or Urgent Readmission Status
VariableValueNo Death/Readmission, n=38,189Death/Readmission, n=2,638Overall, N=40,827
  • NOTE: Abbreviations: ALC, alternate level of care (indicating a patient who does not currently require hospitalization but is awaiting alternate living arrangements, such as nursing home); CMG, Case Mix Group; ED, emergency department; IQR, interquartile range; LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score (C), and emergency‐department use (E); LOS, length of stay; SD, standard deviation. The Charlson index measures number and severity of patient comorbidities.[20] Nonteaching hospitals having <100 beds were classified as small. The CMG score[14] quantifies the independent probability that particular admission types are followed by early death or urgent readmission.

Mean age (SD), y 57.39 (18.3)67.17 (17.2)58.02 (18.4)
Female sex 20,04452.5%1,29148.9%21,33552.3%
Charlson index028,90875.7%1,23846.9%30,14673.8%
 145011.7%36213.7%4,81211.8%
 22,6687.0%42716.2%3,0957.6%
 3+2,1635.7%61123.2%2,7746.8%
ED visits in previous 6 moths024,59964.4%1,21045.9%25,80963.2%
 1211,26229.5%1,00838.2%12,27030.1%
 3+2,3286.1%42015.9%2,7486.7%
Urgent hospitalizations, previous year033,72988.3%1,79668.1%35,52587.0%
 13,4259.0%52519.9%3,9509.7%
 1+1,0352.7%31712.0%1,3523.3%
Elective hospitalizations, previous year035,98894.2%2,38990.6%38,37794.0%
 11,9985.2%2138.1%2,2115.4%
 2+2030.5%361.4%2390.6%
Hospital typeNonteaching, large20,55453.8%1,33450.6%21,88853.6%
 Nonteaching, small5,23913.7%48718.5%572614.0%
 Teaching12,39632.5%81731.0%13,21332.4%
Urgent admit 23,76962.2%2,22384.3%25,99263.7%
LOS rounded to nearest day, median (IQR) 3 (26)5 (311)3 (26)
Any hospital days on ALC06461.7%1274.8%7731.9%
CMG score of index admission027,25771.4%1,59460.4%28,85170.7%
 1+5,21813.7%94835.9%6,16615.1%
 <05,71415.0%963.6%5,81014.2%
LACE+ score of index admission, median (IQR) 31 (1848)61 (4175)32 (1951)
Household‐income quintile1 (poorest)7,79820.4%62123.5%8,41920.6%
 27,81220.5%58622.2%8,39820.6%
 37,55719.8%48418.3%8,04119.7%
 47,56119.8%50019.0%8,06119.7%
 5 (richest)7,46119.5%44716.9%7,90819.4%

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).

Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk
 Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) 
 1 (1416) [A]2 (1727)3 (2839)4 (4056)5 (57114) [B]Income Quintile Overall [C]
  • NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).

Income quintile      
1 (poorest)18/1,485 (1.2%)42/1,667 (2.5%)65/1,635 (4.0%)117/1,722 (6.8%)379/1,910 (19.8%)621/8,419 (7.4%)
221/1,627 (1.3%)39/1,665 (2.3%)65/1,598 (4.1%)130/1,808 (5.2%)331/1,700 (19.5%)586/8,398 (7.0%)
318/1,761 (1.0%)33/1,665 (2.0%)63/1,568 (4.0%)96/1,499 (6.4%)274/1,548 (17.7%)484/8,041 (6.0%)
427/1,851 (1.5%)42/1,698 (2.4%)57/1,585 (3.6%)110/1,548 (6.1%)264/1,379 (19.1%)500/8,061 (6.2%)
5 (richest)20/1,864 (1.1%)32/1,736 (1.8%)60/1,468 (4.1%)107/1,525 (7.0%)228/1,315 (17.3%)447/7,908 (5.6%)
Risk quintile overall [D]104/8,588 (1.2%)188/8,431 (2.2%)310/7,854 (4.0%)560/8,102 (6.9%)1476/7,852 (18.8%)2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1
The incremental influence of important factors on the association of neighborhood income quintile with early death or urgent readmission. This figure presents results from a series of logistic‐regression models having death or urgent readmission within 30 days of discharge from hospital as the outcome. Each plot presents the adjusted OR (horizontal axis) relative to the poorest income quintile, 1, for income quintiles 2 through 5 (the wealthiest quintile). Other covariates entered into the model are presented on the left side, with all (except the final model containing LACE alone) being cumulative, so that the model adding patient sex (“ Sex”) also contains patient age (the variable above). Each point estimate is flanked by 95% CIs. The P value for linear trend over the income quintiles is presented on the right. Abbreviations: ALC, alternate level of care; CI, confidence interval; CMG, Case Mix Group; LACE , length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E); OR, odds ratio.

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Acknowledgment

Disclosure: Nothing to report.

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References
  1. Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
  2. Lynch J, Smith GD, Harper S, et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):599.
  3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406414.
  4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
  5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363372.
  6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981988.
  7. Billings J, Mijanovich T. Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):16431654.
  8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389393.
  9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211219.
  10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811817.
  11. Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:164.
  12. Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):8089.
  13. Wilkins RH. PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
  14. Walraven C, Wong J, Forster AJ. Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80e89.
  15. Coleman EA, Williams MV. Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287290.
  16. Walraven C, Wong J, Forster AJ, Hawken S. Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
  17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 24692475.
  18. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111122.
  19. Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067E1072.
  20. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.
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Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

Study Setting and Data Sources

This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).

Study Population

This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.

For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.

Study Outcome

The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.

Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile

In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]

We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.

Analysis

We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.

To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Description of Study Patients by 30‐Day Death or Urgent Readmission Status
VariableValueNo Death/Readmission, n=38,189Death/Readmission, n=2,638Overall, N=40,827
  • NOTE: Abbreviations: ALC, alternate level of care (indicating a patient who does not currently require hospitalization but is awaiting alternate living arrangements, such as nursing home); CMG, Case Mix Group; ED, emergency department; IQR, interquartile range; LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score (C), and emergency‐department use (E); LOS, length of stay; SD, standard deviation. The Charlson index measures number and severity of patient comorbidities.[20] Nonteaching hospitals having <100 beds were classified as small. The CMG score[14] quantifies the independent probability that particular admission types are followed by early death or urgent readmission.

Mean age (SD), y 57.39 (18.3)67.17 (17.2)58.02 (18.4)
Female sex 20,04452.5%1,29148.9%21,33552.3%
Charlson index028,90875.7%1,23846.9%30,14673.8%
 145011.7%36213.7%4,81211.8%
 22,6687.0%42716.2%3,0957.6%
 3+2,1635.7%61123.2%2,7746.8%
ED visits in previous 6 moths024,59964.4%1,21045.9%25,80963.2%
 1211,26229.5%1,00838.2%12,27030.1%
 3+2,3286.1%42015.9%2,7486.7%
Urgent hospitalizations, previous year033,72988.3%1,79668.1%35,52587.0%
 13,4259.0%52519.9%3,9509.7%
 1+1,0352.7%31712.0%1,3523.3%
Elective hospitalizations, previous year035,98894.2%2,38990.6%38,37794.0%
 11,9985.2%2138.1%2,2115.4%
 2+2030.5%361.4%2390.6%
Hospital typeNonteaching, large20,55453.8%1,33450.6%21,88853.6%
 Nonteaching, small5,23913.7%48718.5%572614.0%
 Teaching12,39632.5%81731.0%13,21332.4%
Urgent admit 23,76962.2%2,22384.3%25,99263.7%
LOS rounded to nearest day, median (IQR) 3 (26)5 (311)3 (26)
Any hospital days on ALC06461.7%1274.8%7731.9%
CMG score of index admission027,25771.4%1,59460.4%28,85170.7%
 1+5,21813.7%94835.9%6,16615.1%
 <05,71415.0%963.6%5,81014.2%
LACE+ score of index admission, median (IQR) 31 (1848)61 (4175)32 (1951)
Household‐income quintile1 (poorest)7,79820.4%62123.5%8,41920.6%
 27,81220.5%58622.2%8,39820.6%
 37,55719.8%48418.3%8,04119.7%
 47,56119.8%50019.0%8,06119.7%
 5 (richest)7,46119.5%44716.9%7,90819.4%

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).

Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk
 Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) 
 1 (1416) [A]2 (1727)3 (2839)4 (4056)5 (57114) [B]Income Quintile Overall [C]
  • NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).

Income quintile      
1 (poorest)18/1,485 (1.2%)42/1,667 (2.5%)65/1,635 (4.0%)117/1,722 (6.8%)379/1,910 (19.8%)621/8,419 (7.4%)
221/1,627 (1.3%)39/1,665 (2.3%)65/1,598 (4.1%)130/1,808 (5.2%)331/1,700 (19.5%)586/8,398 (7.0%)
318/1,761 (1.0%)33/1,665 (2.0%)63/1,568 (4.0%)96/1,499 (6.4%)274/1,548 (17.7%)484/8,041 (6.0%)
427/1,851 (1.5%)42/1,698 (2.4%)57/1,585 (3.6%)110/1,548 (6.1%)264/1,379 (19.1%)500/8,061 (6.2%)
5 (richest)20/1,864 (1.1%)32/1,736 (1.8%)60/1,468 (4.1%)107/1,525 (7.0%)228/1,315 (17.3%)447/7,908 (5.6%)
Risk quintile overall [D]104/8,588 (1.2%)188/8,431 (2.2%)310/7,854 (4.0%)560/8,102 (6.9%)1476/7,852 (18.8%)2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1
The incremental influence of important factors on the association of neighborhood income quintile with early death or urgent readmission. This figure presents results from a series of logistic‐regression models having death or urgent readmission within 30 days of discharge from hospital as the outcome. Each plot presents the adjusted OR (horizontal axis) relative to the poorest income quintile, 1, for income quintiles 2 through 5 (the wealthiest quintile). Other covariates entered into the model are presented on the left side, with all (except the final model containing LACE alone) being cumulative, so that the model adding patient sex (“ Sex”) also contains patient age (the variable above). Each point estimate is flanked by 95% CIs. The P value for linear trend over the income quintiles is presented on the right. Abbreviations: ALC, alternate level of care; CI, confidence interval; CMG, Case Mix Group; LACE , length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E); OR, odds ratio.

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Acknowledgment

Disclosure: Nothing to report.

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

Study Setting and Data Sources

This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).

Study Population

This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.

For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.

Study Outcome

The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.

Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile

In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]

We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.

Analysis

We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.

To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Description of Study Patients by 30‐Day Death or Urgent Readmission Status
VariableValueNo Death/Readmission, n=38,189Death/Readmission, n=2,638Overall, N=40,827
  • NOTE: Abbreviations: ALC, alternate level of care (indicating a patient who does not currently require hospitalization but is awaiting alternate living arrangements, such as nursing home); CMG, Case Mix Group; ED, emergency department; IQR, interquartile range; LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score (C), and emergency‐department use (E); LOS, length of stay; SD, standard deviation. The Charlson index measures number and severity of patient comorbidities.[20] Nonteaching hospitals having <100 beds were classified as small. The CMG score[14] quantifies the independent probability that particular admission types are followed by early death or urgent readmission.

Mean age (SD), y 57.39 (18.3)67.17 (17.2)58.02 (18.4)
Female sex 20,04452.5%1,29148.9%21,33552.3%
Charlson index028,90875.7%1,23846.9%30,14673.8%
 145011.7%36213.7%4,81211.8%
 22,6687.0%42716.2%3,0957.6%
 3+2,1635.7%61123.2%2,7746.8%
ED visits in previous 6 moths024,59964.4%1,21045.9%25,80963.2%
 1211,26229.5%1,00838.2%12,27030.1%
 3+2,3286.1%42015.9%2,7486.7%
Urgent hospitalizations, previous year033,72988.3%1,79668.1%35,52587.0%
 13,4259.0%52519.9%3,9509.7%
 1+1,0352.7%31712.0%1,3523.3%
Elective hospitalizations, previous year035,98894.2%2,38990.6%38,37794.0%
 11,9985.2%2138.1%2,2115.4%
 2+2030.5%361.4%2390.6%
Hospital typeNonteaching, large20,55453.8%1,33450.6%21,88853.6%
 Nonteaching, small5,23913.7%48718.5%572614.0%
 Teaching12,39632.5%81731.0%13,21332.4%
Urgent admit 23,76962.2%2,22384.3%25,99263.7%
LOS rounded to nearest day, median (IQR) 3 (26)5 (311)3 (26)
Any hospital days on ALC06461.7%1274.8%7731.9%
CMG score of index admission027,25771.4%1,59460.4%28,85170.7%
 1+5,21813.7%94835.9%6,16615.1%
 <05,71415.0%963.6%5,81014.2%
LACE+ score of index admission, median (IQR) 31 (1848)61 (4175)32 (1951)
Household‐income quintile1 (poorest)7,79820.4%62123.5%8,41920.6%
 27,81220.5%58622.2%8,39820.6%
 37,55719.8%48418.3%8,04119.7%
 47,56119.8%50019.0%8,06119.7%
 5 (richest)7,46119.5%44716.9%7,90819.4%

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).

Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk
 Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) 
 1 (1416) [A]2 (1727)3 (2839)4 (4056)5 (57114) [B]Income Quintile Overall [C]
  • NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).

Income quintile      
1 (poorest)18/1,485 (1.2%)42/1,667 (2.5%)65/1,635 (4.0%)117/1,722 (6.8%)379/1,910 (19.8%)621/8,419 (7.4%)
221/1,627 (1.3%)39/1,665 (2.3%)65/1,598 (4.1%)130/1,808 (5.2%)331/1,700 (19.5%)586/8,398 (7.0%)
318/1,761 (1.0%)33/1,665 (2.0%)63/1,568 (4.0%)96/1,499 (6.4%)274/1,548 (17.7%)484/8,041 (6.0%)
427/1,851 (1.5%)42/1,698 (2.4%)57/1,585 (3.6%)110/1,548 (6.1%)264/1,379 (19.1%)500/8,061 (6.2%)
5 (richest)20/1,864 (1.1%)32/1,736 (1.8%)60/1,468 (4.1%)107/1,525 (7.0%)228/1,315 (17.3%)447/7,908 (5.6%)
Risk quintile overall [D]104/8,588 (1.2%)188/8,431 (2.2%)310/7,854 (4.0%)560/8,102 (6.9%)1476/7,852 (18.8%)2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1
The incremental influence of important factors on the association of neighborhood income quintile with early death or urgent readmission. This figure presents results from a series of logistic‐regression models having death or urgent readmission within 30 days of discharge from hospital as the outcome. Each plot presents the adjusted OR (horizontal axis) relative to the poorest income quintile, 1, for income quintiles 2 through 5 (the wealthiest quintile). Other covariates entered into the model are presented on the left side, with all (except the final model containing LACE alone) being cumulative, so that the model adding patient sex (“ Sex”) also contains patient age (the variable above). Each point estimate is flanked by 95% CIs. The P value for linear trend over the income quintiles is presented on the right. Abbreviations: ALC, alternate level of care; CI, confidence interval; CMG, Case Mix Group; LACE , length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E); OR, odds ratio.

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Acknowledgment

Disclosure: Nothing to report.

References
  1. Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
  2. Lynch J, Smith GD, Harper S, et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):599.
  3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406414.
  4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
  5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363372.
  6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981988.
  7. Billings J, Mijanovich T. Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):16431654.
  8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389393.
  9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211219.
  10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811817.
  11. Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:164.
  12. Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):8089.
  13. Wilkins RH. PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
  14. Walraven C, Wong J, Forster AJ. Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80e89.
  15. Coleman EA, Williams MV. Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287290.
  16. Walraven C, Wong J, Forster AJ, Hawken S. Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
  17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 24692475.
  18. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111122.
  19. Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067E1072.
  20. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.
References
  1. Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
  2. Lynch J, Smith GD, Harper S, et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):599.
  3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406414.
  4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
  5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363372.
  6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981988.
  7. Billings J, Mijanovich T. Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):16431654.
  8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389393.
  9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211219.
  10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811817.
  11. Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:164.
  12. Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):8089.
  13. Wilkins RH. PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
  14. Walraven C, Wong J, Forster AJ. Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80e89.
  15. Coleman EA, Williams MV. Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287290.
  16. Walraven C, Wong J, Forster AJ, Hawken S. Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
  17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 24692475.
  18. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111122.
  19. Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067E1072.
  20. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.
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Address for correspondence and reprint requests: Carl van Walraven, MD, MSc, Ottawa Hospital Research Institute, Administrative Services Building, 1053 Carling Ave, First Floor, Room 1003, Ottawa ON K1Y 4E9; Telephone: 613–761‐4903; Fax: 613–761‐5492; E‐mail: [email protected]
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Hospitalists Can’t Ignore Rise in Carbapenem-Resistant Enterobacteriaceae (CRE) Infections

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Hospitalists Can’t Ignore Rise in Carbapenem-Resistant Enterobacteriaceae (CRE) Infections

Neil Fishman, MD, associate chief medical officer at the University of Pennsylvania Health System in Philadelphia, sounds like a football coach when he says the best way to fight carbapenem-resistant Enterobacteriaceae (CRE) infections is with a good defense. Hospitalists and others should focus on contact precautions, hand hygiene, removing gowns and gloves before entering new rooms, and even suggest better room cleanings when trying to prevent the spread of CRE, he says. In fact, he has worked with SHM leadership for years to engage hospitalists about the “critical necessity of antimicrobial stewardship.”

“They’re all critical to prevent transmission,” says Dr. Fishman, who chairs the CDC’s Health Infection Control Practices Advisory Committee. “That’s part of the things that can be done in the here and now to try to prevent people from getting infected with these organisms. It’s what the CDC calls ‘detect and prevent.’”

Dr. Fishman’s suggestions echo findings in a new CDC report that shows a threefold increase in the proportion of Enterobacteriaceae bugs that proved resistant to carbapenem in the past decade. The data, in the CDC’s Morbidity and Mortality Weekly Report, showed the proportion of reported Enterobacteriacae that were CRE infections jumped to 4.2% in 2011 from 1.2% in

2001, according to data from the National Nosocomial Infection Surveillance system.

“It is a very serious public health threat,” says co-author Alex Kallen, MD, MPH, a medical epidemiologist and outbreak response coordinator in the CDC’s Division of Healthcare Quality Promotion. “Maybe it’s not that common now, but with no action, it has the potential to become much more common—like a lot of the other MDROs [multidrug-resistant organisms] that hospitalists see regularly. [Hospitalists] have a lot of control over some of the things that could potentially lead to increased transmission.”

Part of the problem, Dr. Fishman says, is a lack of antibiotic options. Polymyxins briefly showed success against the bacteria, but performance is waning. Dr. Fishman estimates it will be up to eight years before a new antibiotic to combat the infection is in widespread use.

Listen to Dr. Fishman discuss the history of treating CRE infections and importance of antimicrobial stewardship.

Both he and Dr. Kallen say hospitalists can help reduce the spread of CRE through antibiotic stewardship, review of detailed patient histories to ferret out risk factors, and dedication to contact precautions and hand hygiene.

Dr. Kallen notes hospitalists also can play a leadership role in coordinating efforts for patients transferring between hospitals and other institutions (i.e. skilled nursing or assisted-living facilities). Part of being that leader is refusing to dismiss possible CRE cases.

“If you’re a place that doesn’t see this very often, and you see one, that’s a big deal,” Dr. Kallen says. “It needs to be acted on aggressively. Being proactive is much more effective than waiting until it’s common and then trying to intervene.” TH

Richard Quinn is a freelance writer in New Jersey.

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Neil Fishman, MD, associate chief medical officer at the University of Pennsylvania Health System in Philadelphia, sounds like a football coach when he says the best way to fight carbapenem-resistant Enterobacteriaceae (CRE) infections is with a good defense. Hospitalists and others should focus on contact precautions, hand hygiene, removing gowns and gloves before entering new rooms, and even suggest better room cleanings when trying to prevent the spread of CRE, he says. In fact, he has worked with SHM leadership for years to engage hospitalists about the “critical necessity of antimicrobial stewardship.”

“They’re all critical to prevent transmission,” says Dr. Fishman, who chairs the CDC’s Health Infection Control Practices Advisory Committee. “That’s part of the things that can be done in the here and now to try to prevent people from getting infected with these organisms. It’s what the CDC calls ‘detect and prevent.’”

Dr. Fishman’s suggestions echo findings in a new CDC report that shows a threefold increase in the proportion of Enterobacteriaceae bugs that proved resistant to carbapenem in the past decade. The data, in the CDC’s Morbidity and Mortality Weekly Report, showed the proportion of reported Enterobacteriacae that were CRE infections jumped to 4.2% in 2011 from 1.2% in

2001, according to data from the National Nosocomial Infection Surveillance system.

“It is a very serious public health threat,” says co-author Alex Kallen, MD, MPH, a medical epidemiologist and outbreak response coordinator in the CDC’s Division of Healthcare Quality Promotion. “Maybe it’s not that common now, but with no action, it has the potential to become much more common—like a lot of the other MDROs [multidrug-resistant organisms] that hospitalists see regularly. [Hospitalists] have a lot of control over some of the things that could potentially lead to increased transmission.”

Part of the problem, Dr. Fishman says, is a lack of antibiotic options. Polymyxins briefly showed success against the bacteria, but performance is waning. Dr. Fishman estimates it will be up to eight years before a new antibiotic to combat the infection is in widespread use.

Listen to Dr. Fishman discuss the history of treating CRE infections and importance of antimicrobial stewardship.

Both he and Dr. Kallen say hospitalists can help reduce the spread of CRE through antibiotic stewardship, review of detailed patient histories to ferret out risk factors, and dedication to contact precautions and hand hygiene.

Dr. Kallen notes hospitalists also can play a leadership role in coordinating efforts for patients transferring between hospitals and other institutions (i.e. skilled nursing or assisted-living facilities). Part of being that leader is refusing to dismiss possible CRE cases.

“If you’re a place that doesn’t see this very often, and you see one, that’s a big deal,” Dr. Kallen says. “It needs to be acted on aggressively. Being proactive is much more effective than waiting until it’s common and then trying to intervene.” TH

Richard Quinn is a freelance writer in New Jersey.

Neil Fishman, MD, associate chief medical officer at the University of Pennsylvania Health System in Philadelphia, sounds like a football coach when he says the best way to fight carbapenem-resistant Enterobacteriaceae (CRE) infections is with a good defense. Hospitalists and others should focus on contact precautions, hand hygiene, removing gowns and gloves before entering new rooms, and even suggest better room cleanings when trying to prevent the spread of CRE, he says. In fact, he has worked with SHM leadership for years to engage hospitalists about the “critical necessity of antimicrobial stewardship.”

“They’re all critical to prevent transmission,” says Dr. Fishman, who chairs the CDC’s Health Infection Control Practices Advisory Committee. “That’s part of the things that can be done in the here and now to try to prevent people from getting infected with these organisms. It’s what the CDC calls ‘detect and prevent.’”

Dr. Fishman’s suggestions echo findings in a new CDC report that shows a threefold increase in the proportion of Enterobacteriaceae bugs that proved resistant to carbapenem in the past decade. The data, in the CDC’s Morbidity and Mortality Weekly Report, showed the proportion of reported Enterobacteriacae that were CRE infections jumped to 4.2% in 2011 from 1.2% in

2001, according to data from the National Nosocomial Infection Surveillance system.

“It is a very serious public health threat,” says co-author Alex Kallen, MD, MPH, a medical epidemiologist and outbreak response coordinator in the CDC’s Division of Healthcare Quality Promotion. “Maybe it’s not that common now, but with no action, it has the potential to become much more common—like a lot of the other MDROs [multidrug-resistant organisms] that hospitalists see regularly. [Hospitalists] have a lot of control over some of the things that could potentially lead to increased transmission.”

Part of the problem, Dr. Fishman says, is a lack of antibiotic options. Polymyxins briefly showed success against the bacteria, but performance is waning. Dr. Fishman estimates it will be up to eight years before a new antibiotic to combat the infection is in widespread use.

Listen to Dr. Fishman discuss the history of treating CRE infections and importance of antimicrobial stewardship.

Both he and Dr. Kallen say hospitalists can help reduce the spread of CRE through antibiotic stewardship, review of detailed patient histories to ferret out risk factors, and dedication to contact precautions and hand hygiene.

Dr. Kallen notes hospitalists also can play a leadership role in coordinating efforts for patients transferring between hospitals and other institutions (i.e. skilled nursing or assisted-living facilities). Part of being that leader is refusing to dismiss possible CRE cases.

“If you’re a place that doesn’t see this very often, and you see one, that’s a big deal,” Dr. Kallen says. “It needs to be acted on aggressively. Being proactive is much more effective than waiting until it’s common and then trying to intervene.” TH

Richard Quinn is a freelance writer in New Jersey.

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The wizard of insurance

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Thirty years ago, many college patients I saw were covered by a school health policy written by a company I will call James S. Fred Insurance. Because this happened long before electronic claims submissions, we knew that ours were handled by someone named Lucille.

For reasons I no longer recall, I found myself strolling in downtown Boston one afternoon, when I saw a large office building that listed none other than James S. Fred Insurance as a major tenant. I took the elevator to the 17th floor, went in, and asked for Lucille.

Sure enough, sitting in a quiet cubicle, there she was: a pleasant older woman who did the college accounts, a small cog in a massive wheel. When I introduced myself, Lucille recognized my name and greeted me warmly.

"I never expected to meet you in person," I said, "But since I have, perhaps I can tell you about a problem we’re having with reimbursement. I described the issue. Lucille took out a large manual, listing the terms of the company’s college coverage. "Here it is," she said, showing me the relevant paragraph.

I thanked her and took the book. But when I read the paragraph, I saw that it didn’t say what she said it said. I pointed this out.

"My goodness," said Lucille. "You’re right. We should be reimbursing you for that, shouldn’t we?"

So that was it. The massive insurance giant in the glass-and-steel skyscraper turned out to be a little old lady in a cubicle who couldn’t read the manual. It was like pulling back the curtain and finding out that the Wizard of Oz was a geezer with a wind machine.

I thought of this last week when I had a talk about my own personal coverage with a Midwest insurer. The issue turned on their responsibility for covering a service provided by a physician who does not participate in Medicare at all. (Yes, I am on Medicare now.)

Last year, I spoke with a human at the company who explained that all I needed to do was confirm that the provider was not Medicare affiliated. This year, after paying a few claims, they apparently changed their mind and sent letters demanding payback and saying they would only pay what Medicare would have, even if Medicare actually didn’t.

I appealed. The appeal was denied. I could not reach a human. I gave up.

Then last week, Jeanette called from Chicago. She described herself as Head of the Appeals Division, in a voice that sounded like Marian, the no-nonsense librarian from "The Music Man."

"Our policy is based on what’s in the manual," she said. "Let me see if I can find it. Oh, here it is." Then she read a passage about doctors who don’t accept Medicare assignments. "We ask them to submit claims anyway," she explained.

"Forgive me," I said, "but a doctor who doesn’t accept assignment is a Medicare provider, just one who won’t accept as full payment what Medicare allows. My doctor is not a Medicare provider at all. He can’t submit a claim, because he doesn’t have a Medicare provider number."

"My goodness," said Jeanette. "I think you may be right. Have you documented this for us?"

"With every claim," I said. "I followed your company’s instructions, and attached to every claim my doctor’s letter saying he doesn’t participate in Medicare. You should have a dozen or so copies of this letter. If you can’t find any, I’ll be happy to send another."

"Oh, here it is!" said Jeanette. "Yes, I see. We need to rectify this."

I danced a mental jig around the room. Lucille must be long retired, but I’d love to invite her and Jeanette for tea.

"I’m really grateful to have the chance to speak to person," I told Jeanette. "Thanks so much for listening."

You could hear Jeanette glow right through the phone. "Why, you’re welcome," she said. "You’ve made my whole day!"

Faceless bureaucracies can seem intimidating, impersonal, malevolent, diabolical, Kafkaesque.

But sometimes, they’re just little old ladies who have trouble reading manuals. To find out, just follow the yellow brick road.

Dr. Rockoff practices dermatology in Brookline, Mass.

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Thirty years ago, many college patients I saw were covered by a school health policy written by a company I will call James S. Fred Insurance. Because this happened long before electronic claims submissions, we knew that ours were handled by someone named Lucille.

For reasons I no longer recall, I found myself strolling in downtown Boston one afternoon, when I saw a large office building that listed none other than James S. Fred Insurance as a major tenant. I took the elevator to the 17th floor, went in, and asked for Lucille.

Sure enough, sitting in a quiet cubicle, there she was: a pleasant older woman who did the college accounts, a small cog in a massive wheel. When I introduced myself, Lucille recognized my name and greeted me warmly.

"I never expected to meet you in person," I said, "But since I have, perhaps I can tell you about a problem we’re having with reimbursement. I described the issue. Lucille took out a large manual, listing the terms of the company’s college coverage. "Here it is," she said, showing me the relevant paragraph.

I thanked her and took the book. But when I read the paragraph, I saw that it didn’t say what she said it said. I pointed this out.

"My goodness," said Lucille. "You’re right. We should be reimbursing you for that, shouldn’t we?"

So that was it. The massive insurance giant in the glass-and-steel skyscraper turned out to be a little old lady in a cubicle who couldn’t read the manual. It was like pulling back the curtain and finding out that the Wizard of Oz was a geezer with a wind machine.

I thought of this last week when I had a talk about my own personal coverage with a Midwest insurer. The issue turned on their responsibility for covering a service provided by a physician who does not participate in Medicare at all. (Yes, I am on Medicare now.)

Last year, I spoke with a human at the company who explained that all I needed to do was confirm that the provider was not Medicare affiliated. This year, after paying a few claims, they apparently changed their mind and sent letters demanding payback and saying they would only pay what Medicare would have, even if Medicare actually didn’t.

I appealed. The appeal was denied. I could not reach a human. I gave up.

Then last week, Jeanette called from Chicago. She described herself as Head of the Appeals Division, in a voice that sounded like Marian, the no-nonsense librarian from "The Music Man."

"Our policy is based on what’s in the manual," she said. "Let me see if I can find it. Oh, here it is." Then she read a passage about doctors who don’t accept Medicare assignments. "We ask them to submit claims anyway," she explained.

"Forgive me," I said, "but a doctor who doesn’t accept assignment is a Medicare provider, just one who won’t accept as full payment what Medicare allows. My doctor is not a Medicare provider at all. He can’t submit a claim, because he doesn’t have a Medicare provider number."

"My goodness," said Jeanette. "I think you may be right. Have you documented this for us?"

"With every claim," I said. "I followed your company’s instructions, and attached to every claim my doctor’s letter saying he doesn’t participate in Medicare. You should have a dozen or so copies of this letter. If you can’t find any, I’ll be happy to send another."

"Oh, here it is!" said Jeanette. "Yes, I see. We need to rectify this."

I danced a mental jig around the room. Lucille must be long retired, but I’d love to invite her and Jeanette for tea.

"I’m really grateful to have the chance to speak to person," I told Jeanette. "Thanks so much for listening."

You could hear Jeanette glow right through the phone. "Why, you’re welcome," she said. "You’ve made my whole day!"

Faceless bureaucracies can seem intimidating, impersonal, malevolent, diabolical, Kafkaesque.

But sometimes, they’re just little old ladies who have trouble reading manuals. To find out, just follow the yellow brick road.

Dr. Rockoff practices dermatology in Brookline, Mass.

Thirty years ago, many college patients I saw were covered by a school health policy written by a company I will call James S. Fred Insurance. Because this happened long before electronic claims submissions, we knew that ours were handled by someone named Lucille.

For reasons I no longer recall, I found myself strolling in downtown Boston one afternoon, when I saw a large office building that listed none other than James S. Fred Insurance as a major tenant. I took the elevator to the 17th floor, went in, and asked for Lucille.

Sure enough, sitting in a quiet cubicle, there she was: a pleasant older woman who did the college accounts, a small cog in a massive wheel. When I introduced myself, Lucille recognized my name and greeted me warmly.

"I never expected to meet you in person," I said, "But since I have, perhaps I can tell you about a problem we’re having with reimbursement. I described the issue. Lucille took out a large manual, listing the terms of the company’s college coverage. "Here it is," she said, showing me the relevant paragraph.

I thanked her and took the book. But when I read the paragraph, I saw that it didn’t say what she said it said. I pointed this out.

"My goodness," said Lucille. "You’re right. We should be reimbursing you for that, shouldn’t we?"

So that was it. The massive insurance giant in the glass-and-steel skyscraper turned out to be a little old lady in a cubicle who couldn’t read the manual. It was like pulling back the curtain and finding out that the Wizard of Oz was a geezer with a wind machine.

I thought of this last week when I had a talk about my own personal coverage with a Midwest insurer. The issue turned on their responsibility for covering a service provided by a physician who does not participate in Medicare at all. (Yes, I am on Medicare now.)

Last year, I spoke with a human at the company who explained that all I needed to do was confirm that the provider was not Medicare affiliated. This year, after paying a few claims, they apparently changed their mind and sent letters demanding payback and saying they would only pay what Medicare would have, even if Medicare actually didn’t.

I appealed. The appeal was denied. I could not reach a human. I gave up.

Then last week, Jeanette called from Chicago. She described herself as Head of the Appeals Division, in a voice that sounded like Marian, the no-nonsense librarian from "The Music Man."

"Our policy is based on what’s in the manual," she said. "Let me see if I can find it. Oh, here it is." Then she read a passage about doctors who don’t accept Medicare assignments. "We ask them to submit claims anyway," she explained.

"Forgive me," I said, "but a doctor who doesn’t accept assignment is a Medicare provider, just one who won’t accept as full payment what Medicare allows. My doctor is not a Medicare provider at all. He can’t submit a claim, because he doesn’t have a Medicare provider number."

"My goodness," said Jeanette. "I think you may be right. Have you documented this for us?"

"With every claim," I said. "I followed your company’s instructions, and attached to every claim my doctor’s letter saying he doesn’t participate in Medicare. You should have a dozen or so copies of this letter. If you can’t find any, I’ll be happy to send another."

"Oh, here it is!" said Jeanette. "Yes, I see. We need to rectify this."

I danced a mental jig around the room. Lucille must be long retired, but I’d love to invite her and Jeanette for tea.

"I’m really grateful to have the chance to speak to person," I told Jeanette. "Thanks so much for listening."

You could hear Jeanette glow right through the phone. "Why, you’re welcome," she said. "You’ve made my whole day!"

Faceless bureaucracies can seem intimidating, impersonal, malevolent, diabolical, Kafkaesque.

But sometimes, they’re just little old ladies who have trouble reading manuals. To find out, just follow the yellow brick road.

Dr. Rockoff practices dermatology in Brookline, Mass.

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