Do Patients Bypass Rural Hospitals? Determinants of Inpatient Hospital Choice in Rural California

Rural hospitals play a crucial role in providing healthcare to rural Americans, a vulnerable and underserved population; however, rural hospitals have faced threats to their financial viability and many have closed as a result. This paper examines the hospital characteristics that are associated with patients choosing rural hospitals, and sheds light on the types of patients who depend on rural hospitals for care and, hence, may be the most impaired by the closure of rural hospitals. Using data from California hospitals, the paper shows that patients were more likely to choose nearby hospitals, larger hospitals, and hospitals that offered more services and technologies. However, even after adjusting for these factors, patients had a propensity to bypass rural hospitals in favor of large urban hospitals. Offering additional services and technologies would increase the share of rural residents choosing rural hospitals only slightly.Rural hospitals, hospital choice, rural health

Individuals' Use of Care While Uninsured: Effects of Time Since Episode Inception and Episode Length

Few studies have addressed how use of care may vary over the course of an episode of being uninsured or across uninsured episodes of varying duration. This research models the probability that an uninsured individual has (a) any medical expenditures or charges, and (b) any office-based visit during each month of an uninsured episode. We find that the ultimate length of an individual's episode of being uninsured bears relatively little on individuals' use of healthcare in any particular month and that the probability of health care utilization rises during the first year of the episode, with more use in the second six months of the year compared to the first six months.

Racial residential segregation, socioeconomic disparities, and the White-Black survival gap.

ObjectiveTo evaluate the association between racial residential segregation, a prominent manifestation of systemic racism, and the White-Black survival gap in a contemporary cohort of adults, and to assess the extent to which socioeconomic inequality explains this association.DesignThis was a cross sectional study of White and Black men and women aged 35-75 living in 102 large US Core Based Statistical Areas. The main outcome was the White-Black survival gap. We used 2009-2013 CDC mortality data for Black and White men and women to calculate age-, sex- and race adjusted White and Black mortality rates. We measured segregation using the Dissimilarity index, obtained from the Manhattan Institute. We used the 2009-2013 American Community Survey to define indicators of socioeconomic inequality. We estimated the CBSA-level White-Black gap in probability of survival using sequential linear regression models accounting for the CBSA dissimilarity index and race-specific socioeconomic indicators.ResultsBlack men and women had a 14% and 9% lower probability of survival from age 35 to 75 than their white counterparts. Residential segregation was strongly associated with the survival gap, and this relationship was partly, but not fully, explained by socioeconomic inequality. At the lowest observed level of segregation, and with the Black socioeconomic status (SES) assumed to be at the White SES level scenario, the survival gap is essentially eliminated.ConclusionWhite-Black differences in survival remain wide notwithstanding public health efforts to improve life expectancy and initiatives to reduce health disparities. Eliminating racial residential segregation and bringing Black socioeconomic status (SES) to White SES levels would eliminate the White-Black survival gap

Family Structure and Childhood Obesity, Early Childhood Longitudinal Study — Kindergarten Cohort

IntroductionLittle is known about the effect of family structure on childhood obesity among US children. This study examines the effect of number of parents and number of siblings on children's body mass index and risk of obesity.MethodsWe conducted a secondary data analysis of the Early Childhood Longitudinal Study - Kindergarten Cohort (ECLS-K), which consists of a nationally representative cohort of children who entered kindergarten during 1998-1999. Our analyses included 2 cross-sectional outcomes and 1 longitudinal outcome: body mass index (BMI) calculated from measured height and weight, obesity defined as BMI in the 95th percentile or higher for age and sex, and change in BMI from kindergarten through fifth grade.ResultsOther things being equal, children living with single mothers were more likely to be obese by fifth grade than were children living with 2 parents (26% vs 22%, P = .05). Children with siblings had lower BMI and were less likely to be obese than children without siblings. We also found that living with a single mother or no siblings was associated with larger increases in BMI from kindergarten through fifth grade.ConclusionChildren from single-mother families and, especially, children with no siblings are at higher risk for obesity than children living with 2 parents and children with siblings. These findings highlight the influential role that families play in childhood obesity. Additionally, they suggest that health care providers should consider the structure of children's families in discussions with families regarding childhood obesity

Take-Up of Public Insurance and Crowd-out of Private Insurance Under Recent CHIP Expansions to Higher Income Children

We analyze the effects of states’ expansions of CHIP eligibility to children in higher income families during 2002-2009 on take-up of public coverage, crowd-out of private coverage, and rates of uninsurance. Our results indicate these expansions were associated with limited uptake of public coverage and only a two percentage point reduction in the uninsurance rate among these children. Because not all of the take-up of public insurance among eligible children is accounted for by children who transfer from being uninsured to having public insurance, our results suggest that there may be some crowd-out of private insurance coverage; the upper bound crowd-out rate we calculate is 46 percent.

Where Do the Sick Go? Health Insurance and Employment in Small and Large Firms

Small firms that offer health insurance to their employees may face variable premiums if the firm hires an employee with high-expected health costs. To avoid expensive premium variability, a small firm may attempt to maintain a workforce with low-expected health costs. In addition, workers with high-expected health costs may prefer employment in larger firms with health insurance rather than in smaller firms. This results in employment distortions. We examine the magnitude of these employment distortions in hiring, employment, and separations, using the Medical Expenditure Panel Survey from 1996 to 2001. Furthermore, we examine the effect of state small group health insurance reforms that restrict insurers’ ability to deny coverage and restrict premium variability on employment distortions in small firms relative to large firms. We find that workers with high-expected health cost are less likely to be new hires in small firms that offer health insurance, and are less likely to be employed in insured small firms. However, we find no evidence that state small group health insurance reforms have reduced the extent of these distortions. Estimating the magnitude of employment distortions in insured small firms is essential in refining reforms to the small group health insurance market. The difficulties that small firms face in obtaining and maintaining health insurance for their employees have been widely documented (Brown, Hamilton and Medoff, 1990; McLaughlin,1992; Fronstin and Helman, 2000). Only 45% of firms with fewer than 50 employees offer health insurance compared to 97% of firms with 50 or more employees (AHRQ, 2002). This low proportion has been attributed, in part, to the high administrative cost of health insurance for small firms, the low demand for insurance among workers in these firms, and the unwillingness of insurers to take on small firm risks (McLaughlin, 1992, Fronstin and Helman, 2000, Monheit and Vistnes, 1999). In recent decades, small firms that provide health insurance to their employees were in a precarious position. Their premiums were calculated yearly, based on the expected value of their health care utilization. Hence, a single high cost employee could lead to a substantial surcharge on the premiums for the firm (Zellers, McLaughlin, and Frick, 1992). In a survey of small employers that did not offer health insurance, 75 percent claimed that an important reason for not offering insurance was high premium variability (Morrisey, Jensen and Morlock, 1994). Concerns about these problems fueled the passage of numerous state small group health insurance reforms in the 1990s that implemented premium rating reforms and restrictions on pre-existing condition exclusions. While a few states have implemented premium rating reform that has severely restricted small group insurers’ ability to use health status to set premiums, in most states, these reforms have been moderate. Assuming that firms are unable to perfectly tailor individual wages to individual health insurance costs, unexpectedly high premiums may impose a large burden on small firms. Paying high premiums, possibly financed by borrowing at high interest rates, may increase the risk of bankruptcy. If small firms choose not to pay high premiums, and instead drop insurance coverage, they renege on the implicit compensation contract with workers. Employers may opt to raise employee contributions to cover higher costs but large increases may lead to healthier employees dropping coverage. Faced with this predicament, small firms may choose to prevent expensive premium variability by maintaining a work force that has a low-expected utilization of health care services. Thus, the link between employment and health insurance in small firms may result in a welfare loss if it prevents individuals with high-expected health costs from being employed in small firm jobs in which they may have high match specific productivity. Employers may obtain information about employees’ medical conditions in several ways. Before the passage of the 1990 Americans with Disabilities Act (ADA), half of all employers conducted pre-employment medical examinations (U.S. Congress, 1988). Most small group employers required the completion of a family health questionnaire for insurance coverage (Zellers et al., 1992, Cutler 1994). While ADA now restricts employer inquiries on employee health, it does not apply to firms with under 15 employees. In addition, employer compliance with the ADA may be hindered because its stipulations about pre-employment health inquiries are vague. Medical inquires are allowed if they pertain to the applicant’s ability to perform the job. In addition, medical information is explicitly allowed in the use of medical underwriting for insurance (Epstein, 1996). The media continues to report cases where employers easily obtain employee medical records (Rubin, 1998), or employees have been laid-off because of high health costs (O’Connor, 1996), or employee premiums have been adjusted to reflect the employee’s claims experience (Kolata, 1992). The Health Insurance Portability and Accountability Act of 1996 (HIPAA) includes a nondiscrimination provision that bars a group health plan or issuer from discriminating in eligibility or contributions on the basis of a health status-related factor. However, HIPAA allows medical underwriting and allows insurers to rate groups of employees based on health status as long as the premium rate for all employees is blended. This stipulation prevents employers from requiring higher cost employees to contribute a higher premium share, but does not shield employers from bearing the costs for a sick worker. Economists have typically believed that health insurance is an attribute of “good jobs” and workers do not choose jobs based on whether or not the job provides health insurance. In fact, this precept is behind the notion that employment is a mechanism for minimizing adverse selection in the market for health insurance (see, for example, Gruber and Levitt, 2000). However, a number of recent studies have suggested that worker demand for health insurance may play an important role in employment decisions. Workers with high-expected family costs may prefer jobs that offer health insurance, and conversely, workers with low preferences for health insurance may sort into jobs that lack health insurance. (Monheit and Vistnes, 1999, Monheit and Vistnes, 2006, Royalty and Abraham, 2005, Bundorf and Pauly, 2004). In this paper, we use the Medical Expenditure Panel Survey (MEPS) from 1996 to 2001 to examine the magnitude of employment distortions for workers with high-expected health costs. Since health insurance and employment are linked, health insurance may be an important determinant of employment outcomes. High-expected health costs may reduce the probability that workers are employed in firms where they have the highest match specific productivity. We estimate the magnitude of distortions in hiring, employment, and separations. Furthermore, we examine the effect of state small group health insurance reforms that restrict insurers’ ability to deny coverage and restrict premium variability on employment distortions in small firms relative to large firms. Estimating the magnitude of employment distortions in insured small firms and understanding the effect of small group regulation on these distortions is essential in deciding optimal public policy towards the small group health insurance market.

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

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

Developing predictive models of health literacy.

IntroductionLow health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy.MethodsWe analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having 'above basic' proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S.ResultsAll variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone.ConclusionsMultivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL

Neighborhood context and ethnicity differences in body mass index: A multilevel analysis using the NHANES III (1988-1994)

A growing body of literature has documented a link between neighborhood context and health outcomes. However, little is known about the relationship between neighborhood context and body mass index (BMI) or whether the association between neighborhood context and BMI differs by ethnicity. This paper investigates several neighborhood characteristics as potential explanatory factors for the variation of BMI across the United States; further, this paper explores to what extent segregation and the concentration of disadvantage across neighborhoods help explain ethnic disparities in BMI. Using data geo-coded at the census tract-level and linked with individual-level data from the Third National Health and Examination Survey in the United States (U.S.), we find significant variation in BMI across U.S. neighborhoods. In addition, neighborhood characteristics have a significant association with body mass and partially explain ethnic disparities in BMI, net of individual-level adjustments. These data also reveal evidence that ethnic enclaves are not in fact advantageous for the body mass index of Hispanics - a relationship counter to what has been documented for other health outcomes