Intervention to Improve Posthospital Outcomes
Original Investigation Research
interventions. Evid Rep Technol Assess (Full Rep). 2009;(181):1-144, A141-142, B141-B114, passim.
ENRICHD Social Support Inventory. J Cardiopulm Rehabil. 2003;23(6):398-403.
28. Piantadosi S. Block Stratified Randomization: Windows Version 6.0. West Hollywood, CA: Cedars-Sinai Medical Center, 2010.
39. Morris NS, MacLean CD, Chew LD, Littenberg B. The Single Item Literacy Screener: evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006;7:21. doi:10.1186/1471-2296 -7-21.
29. Kangovi S, Barg FK, Carter T, Long JA, Shannon R, Grande D. Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood). 2013;32(7):1196-1203. 30. Kangovi S, Leri D, Clayton C, et al. Penn Center for community health workers. 2013. http://chw.upenn.edu. 2013. Accessed December 18, 2013. 31. Berthold TA, Miller J, Avila-Esparza A. Foundations for Community Health Workers. San Francisco, CA: John Wiley & Sons, Inc; 2009. 32. MacGregor K, Wong S, Sharifi C, Handley M, Bodenheimer T. The action plan project: discussing behavior change in the primary care visit. Ann Fam Med. 2005;3(suppl 2):S39-S40. 33. Redding M, Redding S. Community Health Access Project: pathways building a community outcome production model, 2010. http://chap-ohio.net/. Accessed August 2, 2012. 34. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. http: //www.cdc.gov/Brfss/. Accessed January 3, 2011. 35. Health Tracking Household Survey, 2007 Users Guide for Public-Use Data. Ann Arbor, MI: Inter-University Consortium for Political and Social Research; 2007. http://www.icpsr.umich.edu /cgi-bin/file?comp=none&study=26001&ds=1&file _id=1047080. Accessed December 18, 2013. 36. Maruish ME, Kosinski M. A Guide to the Development of Certified Short Form Interpretation and Reporting Capabilities. Lincoln, RI: Quality Metric Inc; 2009. 37. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4, pt 1):1005-1026. 38. Mitchell PH, Powell L, Blumenthal J, et al. A short social support measure for patients recovering from myocardial infarction: the
40. Smith PC, Schmidt SM, Allensworth-Davies D, Saitz R. A single-question screening test for drug use in primary care. Arch Intern Med. 2010;170(13):1155-1160. 41. Smith PC, Schmidt SM, Allensworth-Davies D, Saitz R. Primary care validation of a single-question alcohol screening test. J Gen Intern Med. 2009;24(7):783-788. 42. Gelberg L, Gallagher TC, Andersen RM, Koegel P. Competing priorities as a barrier to medical care among homeless adults in Los Angeles. Am J Public Health. 1997;87(2):217-220. 43. Gilboy N, Travers D, Wuerz R. Re-evaluating triage in the new millennium: a comprehensive look at the need for standardization and quality. J Emerg Nurs. 1999;25(6):468-473. 44. Wuerz RC, Milne LW, Eitel DR, Travers D, Gilboy N. Reliability and validity of a new five-level triage instrument. Acad Emerg Med. 2000;7(3):236-242. 45. Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27(3):759-769. 46. Marhall GN, Hays RD. The Patient Satisfaction Questionnaire Short Form (PSQ-18). Santa Monica, CA: RAND; 1994. 47. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67-74. 48. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. 49. Martin J. Pennsylvania Health Care Cost Containment Council. http://www.phc4.org/. Published 2012. Accessed December 18, 2013.
50. Weinberger M, Oddone EZ, Henderson WG; Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. Does increased access to primary care reduce hospital readmissions? N Engl J Med. 1996;334(22):14411447. 51. Agresti A. Categorical Data Analysis. 3rd ed. Hoboken, NJ: Wiley; 2013. 52. Little RJA, Rubin DB. Statistical Analysis With Missing Data. 2nd ed. Hoboken, NJ: Wiley; 2002. 53. Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087-1091. 54. Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin NK, Lincoln YS, eds. Handbook of Qualitative Research. Thousand Oaks, CA: Sage; 2000:1-14. 55. Guizzo BS, Krziminski CdeO, de Oliveira DL. The software QSR NVivo 2.0 in qualitative data analysis: a tool for health and human sciences researches [in Portuguese]. Rev Gaucha Enferm. 2003;24(1):53-60. 56. McWilliams JM, Zaslavsky AM, Meara E, Ayanian JZ. Impact of Medicare coverage on basic clinical services for previously uninsured adults. JAMA. 2003;290(6):757-764. 57. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):16881698. 58. Tang N, Stein J, Hsia RY, Maselli JH, Gonzales R. Trends and characteristics of US emergency department visits, 1997-2007. JAMA. 2010;304(6):664-670. 59. Patel A, Rendu A, Moran P, Leese M, Mann A, Knapp M. A comparison of two methods of collecting economic data in primary care. Fam Pract. 2005;22(3):323-327. 60. Kennedy AD, Leigh-Brown AP, Torgerson DJ, Campbell J, Grant A. Resource use data by patient report or hospital records: do they agree? BMC Health Serv Res. 2002;2:2. doi:10.1186/1472 -6963-2-2.
Invited Commentary
Social Determinants of Health From Bench to Bedside Harrison J. Alter, MD, MS
Poverty is misery. It saps nutrients, because the poor may trade sustenance for cheap calories to stave off hunger. It precludes restorative sleep, given the demands of staying alive in the elements of the streets, the noisy crowded quarters, Related article page 535 or the grueling hours of a second job. Poverty challenges the most basic levels of safety, security, hygiene, mental health, and the overall well-being of the lives of the almost 50 million Americans and billions worldwide in its grasp. jamainternalmedicine.com
The socioeconomic gradient is one of the most pervasive and enduring trends in health. Found in nearly every disease entity, from cardiovascular to autoimmune disease, the gradient exists across and within nations.1 The gradient has confounded generations of physicians, whose training and turn of mind typically stop at the clinic door. After all, how far can physicians go? We cannot ensure that our patients keep the home stocked with nutritious foods or can escape nasty pollutants, much less prevent their future homelessness, can we? JAMA Internal Medicine April 2014 Volume 174, Number 4
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a Ndsu Library Periodicals User on 06/07/2015
543
Research Original Investigation
Intervention to Improve Posthospital Outcomes
Well, it turns out we can. Kangovi and colleagues2 report on an innovative community health worker (CHW) program in which members of an urban Philadelphia low-income community, after brief training, are deployed to help guide patients from their community to a more healthful life. With the CHW’s help, intervention patients, compared with randomly allocated usual-care controls, were more likely to make it to their follow-up appointments and had better communication with the medical teams as well as improved mental health function and agency. Perhaps most important in this era of accountable care, although the 30-day readmission rate was similar between groups, among readmitted patients, the intervention group had less than half the rate of multiple events within that window (15.2% vs 40.0%; P = .03). Although the evidence for disease-specific effects of CHWs is fairly robust, data on their general place within health systems are rare, and the cost-savings piece is even harder to establish.3 In part, this is because cost savings are generally harder to demonstrate, particularly when the costs and benefits accrue to different sectors of society or the economy. For example, if a nutrition program supported by a health maintenance organization results only in fewer sick days and improved physical function among its members, but not in decreased use of medical care, its benefits would accrue to the members and their employers while the bill comes to the health maintenance organization. Therefore, despite being a success, the program would have trouble demonstrating its cost-effectiveness to the health maintenance organization. This report is among the best evidence so far in support of what some are now calling upstream medicine, a term based on a common parable about children rushing down a river toward a waterfall. Rather than exhaust all resources to snag the children as they pass, it seems only reasonable to send a party upstream to see who is throwing them in the river in the first place. Upstream medicine is gaining momentum, exemplified by the online community HealthBegins (http://healthbegins.ning.com/), now home to more than 650 “upstreamists” from medicine, public health, community organizing, urban planning, and dozens of other fields. In truth, most CHW efforts in the country remain firmly rooted within the medical model, such as chronic disease management, injury prevention, and immunization. But engaging with people in the community is bound to lead us to the parts of their lives that are well upstream of, for example, their medication adherence. Part of the appeal of the approach in the study by Kangovi et al is that patients and CHWs worked together to find aspects of the patients’ lives that, with help, could improve their sense of wellbeing. Such efforts will naturally lead upstream. Finding a comfortable social activity, identifying a food pantry, creating a budget for food—these interventions are not typically identified with medical care, but they lead to a measureable improvement in medical care. Of course, public health efforts are, by definition, upstream from the medical encounter, but these are typically focused at the population level and have been historically at arm’s 544
length from the health care that transpires in a physician’s office or hospital room. As a widely cited commentary4(p131) on social epidemiology observed, in the public health community, “the dominant approach to health care has been to ignore or dismiss it.” Nonetheless, public health has made substantial progress in understanding the role of social factors in health, and the upstream medicine movement is poised to apply many of the findings to patient care. During the past 5 years, in an effort to find patientcentered applications for innovations from the laboratory, the National Institutes of Health5 has spent more than $2 billion funding its Clinical and Translational Science Awards. The collaborative publishes thousands of scientific papers annually: in 2010, more than 1000 each on genetics, metabolism, and physiology. Meanwhile, a quieter revolution in translational science is rolling out. For more than 50 years, epidemiologists have chipped away at the opaque relationships between how we live—our behaviors, our social milieu—and how we feel—our health. Known sometimes as social determinants of health, this constellation of social phenomena can embrace housing, diet (including food insecurity), employment, income, race and ethnicity, social networks, neighborhood context, and many other features of our patients’ lives. Social isolation, for example, has a clear independent association with early mortality.6 For as much detail as epidemiologists and social scientists have provided about these relationships, there remains a wide gulf between what we know about social determinants and what we have been able to achieve with our patients given that knowledge. The CHW movement is a central part of the effort to apply the evidence generated by epidemiologists to patient-centered outcomes. However, in many other quiet corners of the medical care system, further efforts are found. For example, Health Leads (https://healthleadsusa .org/), a help desk to guide patients toward resolving healthrelated social needs, launched in Boston in 1996, now operates in more than 15 hospitals nationally. Another iteration, medical-legal partnerships, provides legal services for poor patients facing eviction, termination of benefits, and many other threats to their well-being. According to the National Center for Medical Legal Partnership, 250 hospitals and clinics now feature formal articulations with legal services or law schools. These collaborations sometimes begin as revenue-generating operations for hospitals by appealing Medicaid and Medicare denials, but typically evolve to cover a wide range of services meant to “improve health outcomes by alleviating legal stressors.”7(p3) Hybrids of these 2 models are emerging. At Highland Hospital in Oakland, California, patients visiting our emergency department can consult with a Highland Health Advocate, a volunteer college student who has access to lawyers, social workers, and specialized web-based tools to help patients navigate their care at Highland, their legal needs, and many of the obstacles facing low- and moderate-income patients. It is plausible that, by mitigating selected hardships of poverty and disadvantage, the pervasive socioeconomic gradient can be bent.
JAMA Internal Medicine April 2014 Volume 174, Number 4
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a Ndsu Library Periodicals User on 06/07/2015
jamainternalmedicine.com
Intervention to Improve Posthospital Outcomes
Original Investigation Research
A secure place to socialize with other elders, a nearby food pantr y, and a ride to the office of the medic al home: with hospital-based medical care as the central cost driver in American medicine, these are opportunities for keeping patients out of the hospital, which is a central focus
of cost control and health reform. If CHWs helping patients with these basic demands can prevent a cascade of hospital admissions and can improve patients’ health and well-being at the same time, we should all start looking up the river.
ARTICLE INFORMATION
REFERENCES
Author Affiliations: Department of Emergency Medicine, Alameda Health System, Highland Hospital, Oakland, California; Department of Emergency Medicine, University of California, San Francisco; Andrew Levitt Center for Social Emergency Medicine, Berkeley, California.
1. Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P; Consortium for the European Review of Social Determinants of Health and the Health Divide. WHO European review of social determinants of health and the health divide. Lancet. 2012;380(9846):1011-1029.
Corresponding Author: Harrison J. Alter, MD, MS, Department of Emergency Medicine, Alameda Health System, Highland Hospital, 1411 E 31st St, Oakland, CA 94602 (halter@alamedahealthsystem .org).
2. Kangovi S, Mitra N, Grande D, et al. Patient-centered community health worker intervention to improve posthospital outcomes: a randomized clinical trial [published online February 10, 2014]. JAMA Intern Med. doi:10.1001/jamainternmed.2013.14327.
Published Online: February 10, 2014. doi:10.1001/jamainternmed.2013.13302. Conflict of Interest Disclosures: None reported.
jamainternalmedicine.com
3. Viswanathan M, Kraschnewski JL, Nishikawa B, et al. Outcomes and costs of community health worker interventions: a systematic review. Med Care. 2010;48(9):792-808.
4. Kaplan GA. What’s wrong with social epidemiology, and how can we make it better? Epidemiol Rev. 2004;26:124-135. 5. The CTSA Program at NIH: opportunities for advancing clinical and translational research. National Institutes of Health website. http://www.ncbi.nlm.nih.gov/books/NBK169203/. Accessed November 27, 2013. 6. Pantell M, Rehkopf D, Jutte D, Syme SL, Balmes J, Adler N. Social isolation: a predictor of mortality comparable to traditional clinical risk factors. Am J Public Health. 2013;103(11):2056-2062. 7. Knight R. Health Care Recovery Dollars: A Sustainable Strategy for Medical-Legal Partnerships. Washington, DC: National Center for Medical Legal Partnership; 2008.
JAMA Internal Medicine April 2014 Volume 174, Number 4
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a Ndsu Library Periodicals User on 06/07/2015
545
Original Investigation Research
interventions. Evid Rep Technol Assess (Full Rep). 2009;(181):1-144, A141-142, B141-B114, passim.
ENRICHD Social Support Inventory. J Cardiopulm Rehabil. 2003;23(6):398-403.
28. Piantadosi S. Block Stratified Randomization: Windows Version 6.0. West Hollywood, CA: Cedars-Sinai Medical Center, 2010.
39. Morris NS, MacLean CD, Chew LD, Littenberg B. The Single Item Literacy Screener: evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006;7:21. doi:10.1186/1471-2296 -7-21.
29. Kangovi S, Barg FK, Carter T, Long JA, Shannon R, Grande D. Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood). 2013;32(7):1196-1203. 30. Kangovi S, Leri D, Clayton C, et al. Penn Center for community health workers. 2013. http://chw.upenn.edu. 2013. Accessed December 18, 2013. 31. Berthold TA, Miller J, Avila-Esparza A. Foundations for Community Health Workers. San Francisco, CA: John Wiley & Sons, Inc; 2009. 32. MacGregor K, Wong S, Sharifi C, Handley M, Bodenheimer T. The action plan project: discussing behavior change in the primary care visit. Ann Fam Med. 2005;3(suppl 2):S39-S40. 33. Redding M, Redding S. Community Health Access Project: pathways building a community outcome production model, 2010. http://chap-ohio.net/. Accessed August 2, 2012. 34. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. http: //www.cdc.gov/Brfss/. Accessed January 3, 2011. 35. Health Tracking Household Survey, 2007 Users Guide for Public-Use Data. Ann Arbor, MI: Inter-University Consortium for Political and Social Research; 2007. http://www.icpsr.umich.edu /cgi-bin/file?comp=none&study=26001&ds=1&file _id=1047080. Accessed December 18, 2013. 36. Maruish ME, Kosinski M. A Guide to the Development of Certified Short Form Interpretation and Reporting Capabilities. Lincoln, RI: Quality Metric Inc; 2009. 37. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4, pt 1):1005-1026. 38. Mitchell PH, Powell L, Blumenthal J, et al. A short social support measure for patients recovering from myocardial infarction: the
40. Smith PC, Schmidt SM, Allensworth-Davies D, Saitz R. A single-question screening test for drug use in primary care. Arch Intern Med. 2010;170(13):1155-1160. 41. Smith PC, Schmidt SM, Allensworth-Davies D, Saitz R. Primary care validation of a single-question alcohol screening test. J Gen Intern Med. 2009;24(7):783-788. 42. Gelberg L, Gallagher TC, Andersen RM, Koegel P. Competing priorities as a barrier to medical care among homeless adults in Los Angeles. Am J Public Health. 1997;87(2):217-220. 43. Gilboy N, Travers D, Wuerz R. Re-evaluating triage in the new millennium: a comprehensive look at the need for standardization and quality. J Emerg Nurs. 1999;25(6):468-473. 44. Wuerz RC, Milne LW, Eitel DR, Travers D, Gilboy N. Reliability and validity of a new five-level triage instrument. Acad Emerg Med. 2000;7(3):236-242. 45. Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27(3):759-769. 46. Marhall GN, Hays RD. The Patient Satisfaction Questionnaire Short Form (PSQ-18). Santa Monica, CA: RAND; 1994. 47. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67-74. 48. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. 49. Martin J. Pennsylvania Health Care Cost Containment Council. http://www.phc4.org/. Published 2012. Accessed December 18, 2013.
50. Weinberger M, Oddone EZ, Henderson WG; Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. Does increased access to primary care reduce hospital readmissions? N Engl J Med. 1996;334(22):14411447. 51. Agresti A. Categorical Data Analysis. 3rd ed. Hoboken, NJ: Wiley; 2013. 52. Little RJA, Rubin DB. Statistical Analysis With Missing Data. 2nd ed. Hoboken, NJ: Wiley; 2002. 53. Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087-1091. 54. Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin NK, Lincoln YS, eds. Handbook of Qualitative Research. Thousand Oaks, CA: Sage; 2000:1-14. 55. Guizzo BS, Krziminski CdeO, de Oliveira DL. The software QSR NVivo 2.0 in qualitative data analysis: a tool for health and human sciences researches [in Portuguese]. Rev Gaucha Enferm. 2003;24(1):53-60. 56. McWilliams JM, Zaslavsky AM, Meara E, Ayanian JZ. Impact of Medicare coverage on basic clinical services for previously uninsured adults. JAMA. 2003;290(6):757-764. 57. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):16881698. 58. Tang N, Stein J, Hsia RY, Maselli JH, Gonzales R. Trends and characteristics of US emergency department visits, 1997-2007. JAMA. 2010;304(6):664-670. 59. Patel A, Rendu A, Moran P, Leese M, Mann A, Knapp M. A comparison of two methods of collecting economic data in primary care. Fam Pract. 2005;22(3):323-327. 60. Kennedy AD, Leigh-Brown AP, Torgerson DJ, Campbell J, Grant A. Resource use data by patient report or hospital records: do they agree? BMC Health Serv Res. 2002;2:2. doi:10.1186/1472 -6963-2-2.
Invited Commentary
Social Determinants of Health From Bench to Bedside Harrison J. Alter, MD, MS
Poverty is misery. It saps nutrients, because the poor may trade sustenance for cheap calories to stave off hunger. It precludes restorative sleep, given the demands of staying alive in the elements of the streets, the noisy crowded quarters, Related article page 535 or the grueling hours of a second job. Poverty challenges the most basic levels of safety, security, hygiene, mental health, and the overall well-being of the lives of the almost 50 million Americans and billions worldwide in its grasp. jamainternalmedicine.com
The socioeconomic gradient is one of the most pervasive and enduring trends in health. Found in nearly every disease entity, from cardiovascular to autoimmune disease, the gradient exists across and within nations.1 The gradient has confounded generations of physicians, whose training and turn of mind typically stop at the clinic door. After all, how far can physicians go? We cannot ensure that our patients keep the home stocked with nutritious foods or can escape nasty pollutants, much less prevent their future homelessness, can we? JAMA Internal Medicine April 2014 Volume 174, Number 4
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a Ndsu Library Periodicals User on 06/07/2015
543
Research Original Investigation
Intervention to Improve Posthospital Outcomes
Well, it turns out we can. Kangovi and colleagues2 report on an innovative community health worker (CHW) program in which members of an urban Philadelphia low-income community, after brief training, are deployed to help guide patients from their community to a more healthful life. With the CHW’s help, intervention patients, compared with randomly allocated usual-care controls, were more likely to make it to their follow-up appointments and had better communication with the medical teams as well as improved mental health function and agency. Perhaps most important in this era of accountable care, although the 30-day readmission rate was similar between groups, among readmitted patients, the intervention group had less than half the rate of multiple events within that window (15.2% vs 40.0%; P = .03). Although the evidence for disease-specific effects of CHWs is fairly robust, data on their general place within health systems are rare, and the cost-savings piece is even harder to establish.3 In part, this is because cost savings are generally harder to demonstrate, particularly when the costs and benefits accrue to different sectors of society or the economy. For example, if a nutrition program supported by a health maintenance organization results only in fewer sick days and improved physical function among its members, but not in decreased use of medical care, its benefits would accrue to the members and their employers while the bill comes to the health maintenance organization. Therefore, despite being a success, the program would have trouble demonstrating its cost-effectiveness to the health maintenance organization. This report is among the best evidence so far in support of what some are now calling upstream medicine, a term based on a common parable about children rushing down a river toward a waterfall. Rather than exhaust all resources to snag the children as they pass, it seems only reasonable to send a party upstream to see who is throwing them in the river in the first place. Upstream medicine is gaining momentum, exemplified by the online community HealthBegins (http://healthbegins.ning.com/), now home to more than 650 “upstreamists” from medicine, public health, community organizing, urban planning, and dozens of other fields. In truth, most CHW efforts in the country remain firmly rooted within the medical model, such as chronic disease management, injury prevention, and immunization. But engaging with people in the community is bound to lead us to the parts of their lives that are well upstream of, for example, their medication adherence. Part of the appeal of the approach in the study by Kangovi et al is that patients and CHWs worked together to find aspects of the patients’ lives that, with help, could improve their sense of wellbeing. Such efforts will naturally lead upstream. Finding a comfortable social activity, identifying a food pantry, creating a budget for food—these interventions are not typically identified with medical care, but they lead to a measureable improvement in medical care. Of course, public health efforts are, by definition, upstream from the medical encounter, but these are typically focused at the population level and have been historically at arm’s 544
length from the health care that transpires in a physician’s office or hospital room. As a widely cited commentary4(p131) on social epidemiology observed, in the public health community, “the dominant approach to health care has been to ignore or dismiss it.” Nonetheless, public health has made substantial progress in understanding the role of social factors in health, and the upstream medicine movement is poised to apply many of the findings to patient care. During the past 5 years, in an effort to find patientcentered applications for innovations from the laboratory, the National Institutes of Health5 has spent more than $2 billion funding its Clinical and Translational Science Awards. The collaborative publishes thousands of scientific papers annually: in 2010, more than 1000 each on genetics, metabolism, and physiology. Meanwhile, a quieter revolution in translational science is rolling out. For more than 50 years, epidemiologists have chipped away at the opaque relationships between how we live—our behaviors, our social milieu—and how we feel—our health. Known sometimes as social determinants of health, this constellation of social phenomena can embrace housing, diet (including food insecurity), employment, income, race and ethnicity, social networks, neighborhood context, and many other features of our patients’ lives. Social isolation, for example, has a clear independent association with early mortality.6 For as much detail as epidemiologists and social scientists have provided about these relationships, there remains a wide gulf between what we know about social determinants and what we have been able to achieve with our patients given that knowledge. The CHW movement is a central part of the effort to apply the evidence generated by epidemiologists to patient-centered outcomes. However, in many other quiet corners of the medical care system, further efforts are found. For example, Health Leads (https://healthleadsusa .org/), a help desk to guide patients toward resolving healthrelated social needs, launched in Boston in 1996, now operates in more than 15 hospitals nationally. Another iteration, medical-legal partnerships, provides legal services for poor patients facing eviction, termination of benefits, and many other threats to their well-being. According to the National Center for Medical Legal Partnership, 250 hospitals and clinics now feature formal articulations with legal services or law schools. These collaborations sometimes begin as revenue-generating operations for hospitals by appealing Medicaid and Medicare denials, but typically evolve to cover a wide range of services meant to “improve health outcomes by alleviating legal stressors.”7(p3) Hybrids of these 2 models are emerging. At Highland Hospital in Oakland, California, patients visiting our emergency department can consult with a Highland Health Advocate, a volunteer college student who has access to lawyers, social workers, and specialized web-based tools to help patients navigate their care at Highland, their legal needs, and many of the obstacles facing low- and moderate-income patients. It is plausible that, by mitigating selected hardships of poverty and disadvantage, the pervasive socioeconomic gradient can be bent.
JAMA Internal Medicine April 2014 Volume 174, Number 4
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a Ndsu Library Periodicals User on 06/07/2015
jamainternalmedicine.com
Intervention to Improve Posthospital Outcomes
Original Investigation Research
A secure place to socialize with other elders, a nearby food pantr y, and a ride to the office of the medic al home: with hospital-based medical care as the central cost driver in American medicine, these are opportunities for keeping patients out of the hospital, which is a central focus
of cost control and health reform. If CHWs helping patients with these basic demands can prevent a cascade of hospital admissions and can improve patients’ health and well-being at the same time, we should all start looking up the river.
ARTICLE INFORMATION
REFERENCES
Author Affiliations: Department of Emergency Medicine, Alameda Health System, Highland Hospital, Oakland, California; Department of Emergency Medicine, University of California, San Francisco; Andrew Levitt Center for Social Emergency Medicine, Berkeley, California.
1. Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P; Consortium for the European Review of Social Determinants of Health and the Health Divide. WHO European review of social determinants of health and the health divide. Lancet. 2012;380(9846):1011-1029.
Corresponding Author: Harrison J. Alter, MD, MS, Department of Emergency Medicine, Alameda Health System, Highland Hospital, 1411 E 31st St, Oakland, CA 94602 (halter@alamedahealthsystem .org).
2. Kangovi S, Mitra N, Grande D, et al. Patient-centered community health worker intervention to improve posthospital outcomes: a randomized clinical trial [published online February 10, 2014]. JAMA Intern Med. doi:10.1001/jamainternmed.2013.14327.
Published Online: February 10, 2014. doi:10.1001/jamainternmed.2013.13302. Conflict of Interest Disclosures: None reported.
jamainternalmedicine.com
3. Viswanathan M, Kraschnewski JL, Nishikawa B, et al. Outcomes and costs of community health worker interventions: a systematic review. Med Care. 2010;48(9):792-808.
4. Kaplan GA. What’s wrong with social epidemiology, and how can we make it better? Epidemiol Rev. 2004;26:124-135. 5. The CTSA Program at NIH: opportunities for advancing clinical and translational research. National Institutes of Health website. http://www.ncbi.nlm.nih.gov/books/NBK169203/. Accessed November 27, 2013. 6. Pantell M, Rehkopf D, Jutte D, Syme SL, Balmes J, Adler N. Social isolation: a predictor of mortality comparable to traditional clinical risk factors. Am J Public Health. 2013;103(11):2056-2062. 7. Knight R. Health Care Recovery Dollars: A Sustainable Strategy for Medical-Legal Partnerships. Washington, DC: National Center for Medical Legal Partnership; 2008.
JAMA Internal Medicine April 2014 Volume 174, Number 4
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a Ndsu Library Periodicals User on 06/07/2015
545
