Craig Evan Pollack

Continuity and the Costs of Care for Chronic Disease

IMPORTANCE Better continuity of care is expected to improve patient outcomes and reduce health care costs, but patterns of use, costs, and clinical complications associated with the current patterns of care continuity have not been quantified. OBJECTIVE To measure the association between care continuity, costs, and rates of hospitalizations, emergency department visits, and complications for Medicare beneficiaries with chronic disease. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of insurance claims data for a 5% sample of Medicare beneficiaries experiencing a 12-month episode of care for congestive heart failure (CHF, n = 53,488), chronic obstructive pulmonary disease (COPD, n = 76,520), or type 2 diabetes mellitus (DM, n = 166,654) in 2008 and 2009. MAIN OUTCOMES AND MEASURES Hospitalizations, emergency department visits, complications, and costs of care associated with the Bice-Boxerman continuity of care (COC) index, a measure of the outpatient COC related to conditions of interest. RESULTS The mean (SD) COC index was 0.55 (0.31) for CHF, 0.60 (0.34) for COPD, and 0.50 (0.32) for DM. After multivariable adjustment, higher levels of continuity were associated with lower odds of inpatient hospitalization (odds ratios for a 0.1-unit increase in COC were 0.94 [95% CI, 0.93-0.95] for CHF, 0.95 [0.94-0.96] for COPD, and 0.95 [0.95-0.96] for DM), lower odds of emergency department visits (0.92 [0.91-0.92] for CHF, 0.93 [0.92-0.93] for COPD, and 0.94 [0.93-0.94] for DM), and lower odds of complications (odds ratio range, 0.92-0.96 across the 3 complication types and 3 conditions; all P < .001). For every 0.1-unit increase in the COC index, episode costs of care were 4.7% lower for CHF (95% CI, 4.4%-5.0%), 6.3% lower for COPD (6.0%-6.5%), and 5.1% lower for DM (5.0%-5.2%) in adjusted analyses. CONCLUSIONS AND RELEVANCE Modest differences in care continuity for Medicare beneficiaries are associated with sizable differences in costs, use, and complications.

Housing affordability and health among homeowners and renters.

BACKGROUND Although lack of affordable housing is common in the U.S., few studies have examined the association between housing affordability and health. PURPOSE Using quasi-experimental methods, the aim of this study was to examine whether housing affordability is linked to a number of important health outcomes, controlling for perceptions of neighborhood quality, and determining whether this association differs by housing tenure (renting versus owning). METHODS Data from the 2008 Southeastern Pennsylvania Household Health Survey, a telephone-based survey of 10,004 residents of Philadelphia and its four surrounding counties, were analyzed. The association between housing affordability and health outcomes was assessed using propensity score methods to compare individuals who reported living in unaffordable housing situations to similar individuals living in affordable ones. RESULTS Overall, 48.4% reported difficulty paying housing costs. People living in unaffordable housing had increased odds of poor self-rated health (AOR=1.75, 95% CI=1.33, 2.29); hypertension (AOR=1.34, 95% CI=1.07, 1.69); arthritis (AOR=1.92, 95% CI=1.56, 2.35); cost-related healthcare nonadherence (AOR=2.94, 95% CI=2.04, 4.25); and cost-related prescription nonadherence (AOR=2.68, 95% CI=1.95, 3.70). There were no significant associations between housing affordability and heart disease, diabetes, asthma, psychiatric conditions, being uninsured, emergency department visits in the past year, obesity, and being a current smoker. Renting rather than owning a home heightened the association between unaffordable housing and self-rated health (AOR=2.55, 95% CI=1.93, 3.37 for renters and not significant among homeowners) and cost-related healthcare nonadherence (AOR=4.74, 95% CI=3.05, 7.35 for renters and AOR=1.99, 95% CI=1.15, 3.46 for homeowners). CONCLUSIONS The financial strain of unaffordable housing is associated with trade-offs that may harm health. Programs that target housing affordability for both renters and homeowners may be an important means for improving health.

Neighborhood poverty, urban residence, race/ethnicity, and asthma: Rethinking the inner-city asthma epidemic.

BACKGROUND Although it is thought that inner-city areas have a high burden of asthma, the prevalence of asthma in inner cities across the United States is not known. OBJECTIVE We sought to estimate the prevalence of current asthma in US children living in inner-city and non-inner-city areas and to examine whether urban residence, poverty, or race/ethnicity are the main drivers of asthma disparities. METHODS The National Health Interview Survey 2009-2011 was linked by census tract to data from the US Census and the National Center for Health Statistics. Multivariate logistic regression models adjusted for sex; age; race/ethnicity; residence in an urban, suburban, medium metro, or small metro/rural area; poverty; and birth outside the United States, with current asthma and asthma morbidity as outcome variables. Inner-city areas were defined as urban areas with 20% or more of households at below the poverty line. RESULTS We included 23,065 children living in 5,853 census tracts. The prevalence of current asthma was 12.9% in inner-city and 10.6% in non-inner-city areas, but this difference was not significant after adjusting for race/ethnicity, region, age, and sex. In fully adjusted models black race, Puerto Rican ethnicity, and lower household income but not residence in poor or urban areas were independent risk factors for current asthma. Household poverty increased the risk of asthma among non-Hispanics and Puerto Ricans but not among other Hispanics. Associations with asthma morbidity were very similar to those with prevalent asthma. CONCLUSIONS Although the prevalence of asthma is high in some inner-city areas, this is largely explained by demographic factors and not by living in an urban neighborhood.

Mortgage Delinquency and Changes in Access to Health Resources and Depressive Symptoms in a Nationally Representative Cohort of Americans Older Than 50 Years

OBJECTIVES We evaluated associations between mortgage delinquency and changes in health and health-relevant resources over 2 years, with data from the Health and Retirement Study, a longitudinal survey representative of US adults older than 50 years. METHODS In 2008, participants reported whether they had fallen behind on mortgage payments since 2006 (n = 2474). We used logistic regression to compare changes in health (incidence of elevated depressive symptoms, major declines in self-rated health) and access to health-relevant resources (food, prescription medications) between participants who fell behind on their mortgage payments and those who did not. RESULTS Compared with nondelinquent participants, the mortgage-delinquent group had worse health status and less access to health-relevant resources at baseline. They were also significantly more likely to develop incident depressive symptoms (odds ratio [OR] = 8.60; 95% confidence interval [CI] = 3.38, 21.85), food insecurity (OR = 7.53; 95% CI = 3.01, 18.84), and cost-related medication nonadherence (OR = 8.66; 95% CI = 3.72, 20.16) during follow-up. CONCLUSIONS Mortgage delinquency was associated with significant elevations in the incidence of mental health impairments and health-relevant material disadvantage. Widespread mortgage default may have important public health implications.

Accountable Care Organizations and Health Care Disparities

Under Section 3022 of the Affordable Care Act, the Centers for Medicare & Medicaid Services is tasked with developing and testing accountable care organizations (ACOs). The goal of ACOs is to group hospitals and physician practices together to facilitate and incentivize quality improvement and cost containment—critical steps for US health care. However, careful consideration and monitoring during the programs implementation is needed to ensure that ACOs do not have the unintended consequence of reinforcing health care disparities. Racial/ethnic disparities in health care are well documented in the United States. These disparities arise, in part, because of differences in the site of care. Black and white patients tend to receive care from different clinicians who work at different hospitals and in different health care sys-tems.1-3 Primary care clinicians for white and black patients report varying levels of institutional resources1 and in many settings, hospitals that treat a large proportion of black patients appear to provide lower-quality care than hospitals that treat a larger portion of white patients.2 The de facto segregation of the health care system has important implications for the creation and implementation of ACOs.4 The process of creating ACOs may reinforce racial/ethnic differences in sites of care by further concentrating patients from certain racial/ethnic groups within particular health care organizations. Although many integrated delivery systems and multispecialty group practices may already qualify as ACOs, other hospitals and independent practices must enter into contractual relationships to become an ACO.5 Profitable practices are more desirable partners for these relationships and wealthier hospitals likely have a greater ability to compete for these practices. Although not explicitly selecting patients by race, ethnicity, or socioeconomic status, the current reality is that profitability in health care is strongly correlated with caring for fewer low-income patients and low-income patients are disproportionately not white. To the degree that the creation of an ACO enables wealthy practices to preferentially align with one another, this process has the potential to further concentrate wealth and racial/ethnic groups within certain ACOs. Once established, the successful implementation of an ACO would depend on its ability to influence costs and quality in treating its targeted population of patients.6 Exerting this influence will require ACOs to develop strategies to keep their patients within their own system because patients who travel between ACOs create substantial financial risk. To the degree that these strategies are successful in limiting movement between systems, they are likely to accentuate racial/ethnic differences in where patients receive care. This segregation is problematic on its own but becomes even more concerning if it is associated with inequity in the quality and resources of the different ACOs. Programs and infrastructure to improve value within an ACO require financial investment. Fewer financial resources within health care systems that disproportionately care for lower-income patients may impede the systems ability to meet quality benchmarks, implement programs to reduce costs, and qualify for potential shared savings. Similar concerns have been raised for other pay-for-performance programs in health care.7 Without careful implementation, these programs can make the rich richer and the poor poorer, further widening racial/ethnic disparities in health care and health outcomes. Moreover, because ACOs are a demonstration project, many hospitals that disproportionately care for patients from certain racial/ethnic groups may opt not to participate, either because of limited resources or because care for these populations is too fragmented. These health care systems are rarely early adopters of innovation.8 Although it remains uncertain whether ACOs will produce substantial, if any, benefits for patients, it is clear that patients who receive care at hospitals that do not participate in ACOs will not have the opportunity to experience any potential gains. In a worst-case scenario, the cherry picking of practices in ACO formation and the process of owning patient panels will concentrate white patients within certain hospital systems that will be able to make the greatest investment in improving value and will receive the greatest benefit from the ACO arrangement. Although not intentional, this scenario leaves lower-income patients who are less likely to be white more concentrated in hospital systems that have relatively fewer financial resources and less ability to compete in a new world of accountable care. Of course, the factors influencing racial/ethnic disparities are complex and several are likely to mitigate the chance of this worst-case scenario. The distribution of racial/ethnic groups across hospitals may vary by geography and may not always lead to worse care for patients who are not white. Urban academic medical centers serve a large proportion of city-dwelling populations and could be an important counterweight to these trends if they participate and succeed in the models of accountable care. In addition, because these patients often have fragmented care, initiatives that improve coordination of care may provide them with a greater benefit. Given appropriate risk and severity adjustment, ACOs with more lower-income patients who are not white may experience a higher return on investment if they can successfully address the burden of care fragmentation. However, given the uncertainty about the potential impact of ACOs on racial/ethnic disparities in health care, it is critical to evaluate and address the potential unintended consequences of ACOs during program implementation. The legislation explicitly recognizes the need to risk-adjust for patient characteristics when determining benchmarks and to monitor for the avoidance of at-risk patients. At-risk populations should include not only individuals with high health care needs and expenses but also individuals from medically underserved racial/ethnic groups and individuals with low-socioeconomic status. Several additional steps should be considered. First, the Centers for Medicare & Medicaid Services should mandate the reporting of quality indicators by race/ethnicity within ACOs to determine the impact—both positive and negative—on disparities. Second, it will be important to examine whether the distribution of patients by race/ethnicity between ACOs is associated with the quality of care Medicare beneficiaries receive. Understanding these system-level differences is critical for determining program effectiveness and improving its design. Third, some hospitals and clinicians will choose not to enroll in the demonstration project so the program should monitor which clinician and patient populations are excluded. Incentives may be necessary to ensure adequate representation of diverse patient populations and health care systems. Fourth, it may be necessary to take active steps to avoid patient and practice cherry picking in ACO creation. Monitoring and enforcing such a policy is likely to be the most challenging step, especially because ACOs are designed to reflect existing referral patterns. Recent hospital and practice consolidations have raised concern from an antitrust perspective9 and should be monitored from a disparities perspective. ACOs hold substantial promise to modify existing reimbursement structures to reward high-value health care. Their success will require influencing the pathways through which patients receive specialty and hospital care. This process of ACO formation is playing out against a backdrop of widespread racial/ethnic disparities in health care, driven, in part, by segregation in hospitals and practices. Although any opportunity to influence the sites in which patients receive care could lead to more diverse racial/ethnic access to medical services, ACOs are unlikely to reduce and may even exacerbate disparities in care without active intervention to monitor and incentivize equity within and across ACO populations. The goals of improved care coordination, increased quality, and lower costs are critical for all segments of society.

The geographic accessibility of retail clinics for underserved populations.

BACKGROUND The extent to which retail clinics provide access to care for underserved populations remains largely unknown. The purpose of this study was to determine whether retail clinics tend to be located in census tracts with higher medical need. METHODS The locations of retail clinics as of July 1, 2008, were mapped and linked to the 2000 US Census and 2008 Health Resources and Services Administration data. Bivariate analyses and logistic regression models with random effects were used to compare the characteristics of census tracts with and without retail clinics. To determine whether retail clinics followed the underlying distribution of chain stores, the location of clinics conditional on there being a chain store was analyzed in 6 counties. RESULTS Of the 932 retail clinics, 930 were successfully mapped. Eighteen states had no retail clinics, and 17 states had 25 or more clinics. Within counties with at least 1 retail clinic, census tracts with retail clinics had a lower black population percentage, lower poverty rates, and higher median incomes and were less likely to be medically underserved areas/populations compared with census tracts without retail clinics. Similarly, stores with retail clinics were less likely to be located in medically underserved areas compared with stores without retail clinics. CONCLUSION Retail clinics are currently located in more advantaged neighborhoods, which may make them less accessible for those most in need.

Physician Social Networks and Variation in Prostate Cancer Treatment in Three Cities

OBJECTIVE To examine whether physician social networks are associated with variation in treatment for men with localized prostate cancer. DATA SOURCE 2004-2005 Surveillance, Epidemiology and End Results-Medicare data from three cities. STUDY DESIGN We identified the physicians who care for patients with prostate cancer and created physician networks for each city based on shared patients. Subgroups of urologists were defined as physicians with dense connections with one another via shared patients. PRINCIPAL FINDINGS Subgroups varied widely in their unadjusted rates of prostatectomy and the racial/ethnic and socioeconomic composition of their patients. There was an association between urologist subgroup and receipt of prostatectomy. In city A, four subgroups had significantly lower odds of prostatectomy compared with the subgroup with the highest rates of prostatectomy after adjusting for patient clinical and sociodemographic characteristics. Similarly, in cities B and C, subgroups had significantly lower odds of prostatectomy compared with the baseline. CONCLUSIONS Using claims data to identify physician networks may provide an insight into the observed variation in treatment patterns for men with prostate cancer.

Patient Sharing Among Physicians and Costs of Care: A Network Analytic Approach to Care Coordination Using Claims Data

BACKGROUNDImproving care coordination is a national priority and a key focus of health care reforms. However, its measurement and ultimate achievement is challenging.OBJECTIVETo test whether patients whose providers frequently share patients with one another—what we term ‘care density’—tend to have lower costs of care and likelihood of hospitalization.DESIGNCohort studyPARTICIPANTS9,596 patients with congestive heart failure (CHF) and 52,688 with diabetes who received care during 2009. Patients were enrolled in five large, private insurance plans across the US covering employer-sponsored and Medicare Advantage enrolleesMAIN MEASURESCosts of care, rates of hospitalizationsKEY RESULTSThe average total annual health care cost for patients with CHF was

ASSOCIATION OF SELF-REPORTED HOSPITAL DISCHARGE HANDOFFS WITH 30-DAY READMISSIONS

29,456, and

The Growth Of Retail Clinics And The Medical Home: Two Trends In Concert Or In Conflict?

14,921 for those with diabetes. In risk adjusted analyses, patients with the highest tertile of care density, indicating the highest level of overlap among a patient’s providers, had lower total costs compared to patients in the lowest tertile (