Showing papers by "José J. Escarce published in 2013"

Neighborhood socioeconomic disadvantage and mortality after stroke

Abstract: Objective: Residence in a socioeconomically disadvantaged community is associated with mortality, but the mechanisms are not well understood. We examined whether socioeconomic features of the residential neighborhood contribute to poststroke mortality and whether neighborhood influences are mediated by traditional behavioral and biologic risk factors. Methods: We used data from the Cardiovascular Health Study, a multicenter, population-based, longitudinal study of adults ≥65 years. Residential neighborhood disadvantage was measured using neighborhood socioeconomic status (NSES), a composite of 6 census tract variables representing income, education, employment, and wealth. Multilevel Cox proportional hazard models were constructed to determine the association of NSES to mortality after an incident stroke, adjusted for sociodemographic characteristics, stroke type, and behavioral and biologic risk factors. Results: Among the 3,834 participants with no prior stroke at baseline, 806 had a stroke over a mean 11.5 years of follow-up, with 168 (20%) deaths 30 days after stroke and 276 (34%) deaths at 1 year. In models adjusted for demographic characteristics, stroke type, and behavioral and biologic risk factors, mortality hazard 1 year after stroke was significantly higher among residents of neighborhoods with the lowest NSES than those in the highest NSES neighborhoods (hazard ratio 1.77, 95% confidence interval 1.17–2.68). Conclusion: Living in a socioeconomically disadvantaged neighborhood is associated with higher mortality hazard at 1 year following an incident stroke. Further work is needed to understand the structural and social characteristics of neighborhoods that may contribute to mortality in the year after a stroke and the pathways through which these characteristics operate.

Using Insights From Behavioral Economics and Social Psychology to Help Patients Manage Chronic Diseases

Abstract: Despite a revolution in therapeutics, the ability to control chronic diseases remains elusive. We present here a conceptual model of the potential role of behavioral tools in chronic disease control. Clinicians implicitly accept the assumption that patients will act rationally to maximize their self-interest. However, patients may not always be the rational actors that we imagine. Major behavioral barriers to optimal health behavior include patients’ fear of threats to health, unwillingness to think about problems when risks are known or data are ambiguous, the discounting of risks that are far in the future, failure to act due to lack of motivation, insufficient confidence in the ability to overcome a health problem, and inattention due to pressures of everyday life. Financial incentives can stimulate initiation of health-promoting behaviors by reducing or eliminating financial barriers, but may not produce long-term behavior change without additional interventions. Strategies have been developed by behavioral economists and social psychologists to address each of these barriers to better decision-making. These include: labeling positive behaviors in ways consistent with patient life goals and priorities; greater focus on more immediate risks of chronic diseases; intermediate subgoals as steps to a large health goal; and implementation of specific plans as to when, where, and how an action will be taken. Such strategies hold promise for improving health behaviors and disease control, but most have not been studied in medical settings. The effectiveness of these approaches should be evaluated for their potential as tools for the clinician.

The Effects of Quality of Care on Costs: A Conceptual Framework

Abstract: When assessing a health care system or a facet of care, the fundamental dimensions of performance should include the quality of the care provided and the economic costs related to receiving that care. The U.S. health care system does not function well on either dimension. The most comprehensive study of quality to date found that for adults, care adhered to basic recommendations about what should or should not be done only 55 percent of the time (McGlynn et al. 2003). Although health outcomes in the United States lag far behind those in Canada, Australia, Germany, France, and the United Kingdom, in 2009, per-capita health care expenditures were about twice as high (Commonwealth Fund Commission 2011). Policymakers and observers have recently become hopeful, however, about the possibility of improving quality while simultaneously saving money (IOM 2010a, 2010b, 2010c). Accordingly, numerous studies have examined the costs attributable to poor quality, the relationship between performance on quality measures and health care expenditures, and the costs and health effects associated with improvement programs. Several new policies have been designed to spur improvements in quality and reduce health care costs in the United States, such as establishing accountable care organizations, incentives for adopting health information technology, and penalties for hospital-acquired complications (Berwick 2011; Blumenthal 2011; Kaushal et al. 2006; Milstein 2009). When policymakers, researchers, and other decision makers work on a policy issue like this one, a shared framework can create a common understanding of the issues and facilitate the design of analyses. We know of no conceptual model or framework, however, that describes how specific dimensions of quality produce variations in health care and other types of costs. Even so, the authors of well-known conceptual models addressing quality have contemplated economic matters. Donabedian excluded costs from his well-known quality-of-care model, choosing to focus on clinical issues rather than value-laden questions of balancing costs against health benefits (Donabedian 1980). Yet during the same time period, he published several articles on health care costs, including one describing a production function for health outcomes at different expenditure levels (Donabedian, Wheeler, and Wyszewianski 1982). In 2001, the Institute of Medicine (IOM) defined six dimensions of quality: effectiveness, safety, efficiency, timeliness, patient-centeredness, and access (IOM 2000, 2010a). Effectiveness and safety relate to increasing the likelihood of favorable health outcomes. Efficiency is an economic construct, which the IOM defines as maximizing performance (i.e., health care outcomes) by producing the best possible outputs from a given set of resources or inputs (IOM 2010a). In the field of health economics, several conceptual frameworks focus on efficiency, but agreement about how to define efficiency and the related concept, value, is limited (AQA 2006; IOM 2009, 2010a; McGlynn 2008; Medicare Payment Advisory Commission 2006; National Quality Forum 2010; Palmer and Torgerson 1999). Yet none of these works describes how quality influences costs or efficiency. The lack of such a conceptual framework creates a confusing situation in which numerous analyses purport to examine quality and costs but each analysis measures something different from the next. Table 1 illustrates this phenomenon using examples of studies addressing glycemic control for type 2 diabetes mellitus over the long term or the treatment of uncomplicated low back pain. Some studies report structural measures of quality, such as implementing an integrated program that helps primary care physicians manage diabetes, along with the costs of conducting the program. Others describe process measures, such as adhering to guidelines that recommend active modalities for physical therapy for low back pain, and estimate associated health care expenditures. Finally, some authors have estimated changes in health outcomes and outcome-related costs, such as reductions in macrovascular complications with improved glycemic control and the associated health care and disability costs. While there are valid reasons for choosing different measures of quality and costs, a conceptual framework would make it easier to understand the selections made. A framework would also remind researchers of variables that may be conceptually relevant, preventing their inadvertent omission from an analysis. TABLE 1 Examples of Studies Addressing the Quality of Glycemic Control among People with Type 2 Diabetes Mellitus or Quality of Care for Low Back Pain and Some Measure of Cost Given these considerations, we offer the Quality-Cost Framework. For examples, we used studies of glycemic control for type 2 diabetes and of the treatment of low back pain. We selected these conditions because they are common; their care affects clinical outcomes as well as associated costs; and some quality problems and interventions have been well studied. Before describing the Quality-Cost Framework, we must define quality of care and costs because the terms’ connotations vary (Donabedian 1980). Our framework emphasizes “health-related quality,” which we define by drawing from Donabedian's 1982 exposition on quality and costs: “The highest quality care is that which yields the greatest expected improvement in health status, health being defined broadly to include physical, physiological, and psychological dimensions” (Donabedian, Wheeler, and Wyszewianski 1982, 976). Our framework distinguishes health-related from non-health-related dimensions of quality (those not expected to affect health) for three reasons: (1) improving health is the fundamental objective of health care; (2) health-related and non-health-related dimensions of quality influence costs differently; and (3) the two types of dimensions can sometimes be negatively associated. Consider, for example, satisfaction, an important dimension of quality from the patient's perspective (Browne et al. 2010). Satisfaction appears to have both health-related and non-health-related components. Some studies show that higher satisfaction is associated with better health outcomes, yet perhaps surprisingly, other investigations have found the reverse, including one nationwide study that found higher patient satisfaction with outpatient care was associated with increased mortality (Browne et al. 2010; Fenton et al. 2012). On the one hand, patient satisfaction promotes adherence to treatment (Browne et al. 2010), which should improve outcomes. On the other hand, providers sometimes face stark choices between promoting satisfaction and optimizing health. One example is deciding whether to discontinue opioids for patients with chronic, noncancer pain who exhibit vague signs of misuse, which may indicate an increased risk for addiction and overdose. In addition to occasionally conflicting with the goal of improving health, striving to improve satisfaction can also promote discretionary health care expenditures. Amenities such as concierge services, luxury waiting areas, and gourmet hospital meals increase discretionary health care expenditures but produce no health benefits. Both patients and society value—and may be willing to pay for—non-health-related dimensions of quality, including services that improve satisfaction but not physical or psychological health. Yet to facilitate distinctions between higher-value and lower-value health care, a framework examining the effect of quality on costs should distinguish non-health-related from health-related expenditures. To define costs, the framework focuses on health care and health status–related costs. Health care costs are expenditures for health care and costs to patients and families, such as the value of time spent in care, self-care, and informal caregiving activities; and the expenditures related to these activities, such as for special diets. Health status–related costs are costs related to declines in the ability to function (which we refer to as “functional-decrement costs”), and the economic value associated with losses of quality and quantity of life. We discuss specific categories later.

Exploring psychosocial pathways between neighbourhood characteristics and stroke in older adults: the cardiovascular health study

Abstract: Objectives: to investigate whether psychosocial pathways mediate the association between neighbourhood socioeconomic disadvantage and stroke. Methods: prospective cohort study with a follow-up of 11.5 years. Setting: the Cardiovascular Health Study, a longitudinal population-based cohort study of older adults ≥65 years. Measurements: the primary outcome was adjudicated incident ischaemic stroke. Neighbourhood socioeconomic status (NSES) was measured using a composite of six census-tract variables. Psychosocial factors were assessed with standard measures for depression, social support and social networks. Results: of the 3,834 white participants with no prior stroke, 548 had an incident ischaemic stroke over the 11.5-year follow-up. Among whites, the incident stroke hazard ratio (HR) associated with living in the lowest relative to highest NSES quartile was 1.32 (95% CI = 1.01–1.73), in models adjusted for individual SES. Additional adjustment for psychosocial factors had a minimal effect on hazard of incident stroke (HR = 1.31, CI = 1.00–1.71). Associations between NSES and stroke incidence were not found among African-Americans (n = 785) in either partially or fully adjusted models. Conclusions: psychosocial factors played a minimal role in mediating the effect of NSES on stroke incidence among white older adults.

Health Status and Behavioral Risk Factors in Older Adult Mexicans and Mexican Immigrants to the United States

Abstract: Objectives: Investigate the “salmon-bias” hypothesis, which posits that Mexicans in the U.S. return to Mexico due to poor health, as an explanation for the Hispanic health paradox in which Hispanics in the United States are healthier than might be expected from their socioeconomic status. Method: Sample includes Mexicans age 50 years or above living in the United States and Mexico from the 2003 Mexican Health and Aging Study and the 2004 Health and Retirement Study. Logistic regressions examine whether nonmigrants or return migrants have different odds than immigrants of reporting a health outcome. Results: The salmon-bias hypothesis holds for select health outcomes. However, nonmigrants and return migrants have better health outcomes than immigrants on a variety of indicators. Discussion: Overall, the results of this study do not support the salmon-bias hypothesis; other explanations for the paradox could be explored.

Medicare Payment Reform and Provider Entry and Exit in the Post-Acute Care Market

Abstract: Objective To understand the impacts of Medicare payment reform on the entry and exit of post-acute providers.

Income Eligibility Thresholds, Premium Contributions, and Children's Coverage Outcomes: A Study of CHIP Expansions

Abstract: Movement toward universal coverage of children is a defining feature of Children's Health Insurance Program (CHIP) changes during the last decade. In the years leading up to the passage of the Patient Protection and Affordable Care Act (ACA) in March 2010, many states began implementing far more inclusionary CHIP programs, extending eligibility to children in families with incomes far higher than historical eligibility thresholds. Between 2002 and 2009, 13 states boosted their income eligibility threshold for CHIP to between 200 and 400 percent of the federal poverty line (FPL). Such changes have not occurred without controversy. A touchstone has been concern about the potential for and magnitude of “crowd-out”—a decrease in private insurance coverage in response to the increased availability of public coverage. Both to reduce the potential for crowd-out and as a means to ensure the financial viability of their expanded CHIP programs, most expansion states began requiring newly eligible higher income families to contribute to the premium costs of coverage. Premium contribution requirements in 2008 among states that offered coverage to families with incomes at least three times FPL ranged from $240 to more than $1,000 per year for a family with two children.1 States also implemented other precautionary measures designed to reduce crowd-out, such as waiting periods between loss of private coverage and public eligibility. A number of studies using a variety of analytic approaches, including instrumental variables (IV) estimation as well as difference-in-difference approaches, have explored the effects on coverage outcomes of public health insurance expansions, including the Medicaid expansions of the late 1980s (e.g., Cutler and Gruber 1996; Ham and Shore-Sheppard 2005; Shore-Sheppard 2008), the introduction of CHIP in 1996 (LoSasso and Buchmueller 2004; Hudson, Selden, and Banthin 2005; Bansak and Raphael 2006; Sommers et al. 2007; Gruber and Simon 2008; Dubay and Kenney 2009), and the CHIP expansions of the 2002–2009 period (Gresenz et al. 2012). A few studies have examined the effects of premium contribution requirements for CHIP on coverage outcomes for children. Using data from before 2003, Hadley et al. (2006/2007) and Kenney, Hadley, and Blavin (2006/2007) estimate the effects of premium contributions on coverage outcomes for children in families with incomes less than 300 percent of the FPL who are eligible for public insurance. Both studies find that premium contribution requirements reduce enrollment in public insurance programs, with offsetting increases in uninsurance and private insurance. However, to our knowledge, no study has explored the effects of premium contributions associated with more recent CHIP expansions to higher income children. Moreover, little is known about the interplay between income eligibility thresholds and premium contribution requirements in determining coverage outcomes, especially among higher income children. This study uses simulation techniques to understand the effects of CHIP income eligibility thresholds and premium contribution requirements on health insurance coverage outcomes among children targeted by recent CHIP expansions, which include children in families with incomes up to 400 percent of the FPL. Specifically, we examine the effects of various income eligibility threshold and premium contribution combinations on take-up of public coverage, private coverage, and overall insurance coverage rates. Our analyses are based on data from 2002 to 2009, a period during which 18 states expanded eligibility for CHIP.

Are our actions aligned with our evidence? The skinny on changing the landscape of obesity.

Abstract: Recent debate about the role of food deserts in the United States (i.e., places that lack access to healthy foods) has prompted discussion on policies being enacted, including efforts that encourage the placement of full-service supermarkets into food deserts. Other initiatives to address obesogenic neighborhood features include land use zoning and parks renovations. Yet, there is little evidence to demonstrate that such policies effect change. While we suspect most researchers and policymakers would agree that effective neighborhood change could be a powerful tool in combating obesity, we desperately need strong and sound evidence to guide decisions about where and how to invest.

Food policy research: we need better measurement, better study designs, and reasonable and measured actions based on the available evidence.

Abstract: To the Editor: Lucan and Chambers, in their Letter to the Editor, call for better measurement in order to move food-environment research forward. We wholeheartedly agree. In fact, this issue is not new to the obesity policy agenda1. However, attaining validated and detailed food environment data in a large-scale (e.g., national) setting is prohibitively costly. Our study included 68,132 women living in in 18,186 census tracts across the United States2. We excluded women who lived in census tracts with a population count of less than 500 and women living outside metropolitan statistical areas because we believed measures of the food environment would not be comparable in urban and rural areas. As we pointed out in our paper, most studies to date have analyzed a single type of food outlet (e.g., grocery stores or fast-food outlets) at one time. We examined multiple dimensions of the food environment in a national dataset – and believe that these data and analyses bring the state of the literature forward. We concur that detailed ground observations, such as the ones Lucan and Chambers reference, are ideal. However, these can only be executed in confined geographic settings. Such data would be extremely difficult, if not impossible, to attain on a national scale. We also agree with Lucan and Chambers’ concerns with 1) assumptions that establishments categorized as full-service supermarkets are all comparable; 2) potentially relevant food sources such as farmers’ markets and mobile produce stands may not be captured through commercial database listings; and 3) using radial buffers and proximity to a store to capture access without capturing factors such as transportation mode, travel time and social norms around food purchasing. Such detailed evidence can complement and validate studies based on large national data to ensure that policies are based on a solid scientific foundation. Importantly, we are working on that. Members of our team are involved with the largest study to date in the United States that is capitalizing on a natural experiment of the elimination of a food desert (1R01CA149105, Does a New Supermarket Improve Dietary Behaviors of Low-income African Americans?). Examination of a natural experiment of this type (i.e., elimination of a food desert) is allowing our team to overcome many of these limitations, from reliance on unvalidated commercial databases (we are conducting food audits to collect price, quality and availability of food data from all food purchasing venues in residential neighborhoods included in our study as well as the most frequently report venues our enrolled population reports shopping), to a longitudinal quasi-experimental study design with a control or comparison neighborhood, and extensive data on residents’ dietary intake, travel mode, time spent in shopping, and experience of food purchasing. We have just completed our baseline data collection and hope that findings from this study when completed will be replicable to other low-income African American neighborhoods across the United States. However, we are indeed confined to one large natural experiment and unlike our published study, will not be analyzing data based on tens of thousands of individuals and census tracts. We agree that the field faces measurement limitations. Large observational studies, such as the one our paper reported, have imperfect measures. However, given the considerable impact of nutrition on obesity and other health problems, we believe that reasonable and measured actions based on the available evidence need to be considered. Policy makers cannot afford to rely solely on data from detailed studies of a few neighborhoods (one could argue a requirement for grounding approaches) and need to know whether results hold at a national level. Our study does that, and the methods we used are necessary to such a study. Although we agree that the field also needs studies of small areas with rich and detailed measures to complement the national data and help us determine their validity, we still conclude that our findings support restricting the development of fast-food outlets and attracting grocery stores, and are committed to additional research that overcomes the limitations of large studies such as the one we published.

Medicare Payment Reform and Provider Entry and Exit

Abstract: OBJECTIVE To understand the impacts of Medicare payment reform on the entry and exit of post-acute providers. DATA SOURCES Medicare Provider of Services data, Cost Reports, and Census data from 1991 through 2010. STUDY DESIGN We examined market-level changes in entry and exit after payment reforms relative to a preexisting time trend. We also compared changes in high Medicare share markets relative to lower Medicare share markets and for freestanding relative to hospital-based facilities. DATA EXTRACTION METHODS We calculated market-level entry, exit, and total stock of home health agencies, skilled nursing facilities, and inpatient rehabilitation facilities from Provider of Services files between 1992 and 2010. We linked these measures with demographic information from the Census and American Community Survey, information on Certificate of Need laws, and Medicare share of facilities in each market drawn from Cost Report data. PRINCIPAL FINDINGS Payment reforms reducing average and marginal payments reduced entries and increased exits from the market. Entry effects were larger and more persistent than exit effects. Entry and exit rates fluctuated more for home health agencies than skilled nursing facilities. Effects on number of providers were consistent with entry and exit effects. CONCLUSIONS Payment reform affects market entry and exit, which in turn may affect market structure, access to care, quality and cost of care, and patient outcomes. Policy makers should consider potential impacts of payment reforms on post-acute care market structure when implementing these reforms.