A. Rural-Urban Health Disparities

Although overall life expectancy in the US has improved rapidly over the course of the 20th century, geographical inequalities in health increased in the last 3 decades. During the 1970s and early 1980s, there was virtually no gap in the rural-urban mortality rates, however, since the mid-1980s mortality rate in urban counties declined more rapidly, resulting in the rural-urban mortality gap, which has been widening over the past 3 decades. Growing evidence indicates that rural residents face considerable disparities in disease, morbidity, potentially preventable deaths, longevity, life expectancy, and mortality compared to their urban counterparts. Rural residents are typically poorer, less educated, have limited access to healthcare, and worse health outcomes compared to their urban counterparts. Furthermore, rural areas are disproportionately inhabited by the elderly; although only 17% of the overall US population resides in rural areas, about 22% of adults 65 years and over live in rural areas. With the overall growing population of older adults and the increasing migration of baby boomers to rural communities, accommodating their evolving health services needs becomes more challenging. 

Nurse practitioners' scope of practice and rural-urban disparities in mortality rates

Although nurse practitioners (NPs) might be the main providers of primary care to some communities, different states pursue different regulations for NP’s practice authority. This study compared the trends in mortality rates and supply of physicians in states with different NPs practice authority, using AHRF and CMF data. We categorized states based on the severity of restrictive policy on NPs in three groups: full practice, reduced practice, and restricted practice. We compared the trends in age-adjusted mortality rate and supply of primary care in rural and urban areas separately in each type of state and examined within-state changes in urban-rural differences in physician supply and mortality rate over time. Our results indicate that the rural-urban mortality gap has increased while physicians' supply has declined as the level of restriction increased. Thus, people living in rural areas in the restricted states suffer from not receiving care. Furthermore, the rural-urban disparity in mortality has declined only in full practice states, regardless of the increase or decrease in disparity in physicians' supply over time. Additionally, there was a negative association between a decline in rural-urban disparity in physician supply and a decline in rural-urban disparity in mortality, only in full or reduced practice states, compared to the restricted practice states. 

Supply of Primary Care Professionals and the Rural-Urban Disparities in Prevalence of Dementia and CIND

Moving forward, I am currently studying rural and urban disparity in the prevalence of dementia and CIND and their association with the supply of primary care professionals. This research is being supported by an NIA-funded research scientist position at the Minority Aging Health Economics Research Center at USC (USC-RCMAR). The goal of this research is to provide evidence that can guide the development of efficient, high-quality healthcare and supportive services for vulnerable rural Medicare beneficiaries with cognitive impairment and dementia. Thus, I will study and analyze rural-urban disparities in the prevalence of dementia and CIND, health care utilization, as well as investigating the association between availability of primary care professionals and prevalence of dementia and CIND in the rural and urban markets. Findings from this study lay the groundwork for future studies and will provide preliminary evidence for my NIH R21/R01 proposal on Health Services Research on Minority Health and Health Disparities, which aims to further this line of research, which will be the next step toward reducing health disparities in the rural and urban areas.

B. Health Care Policy

Long-term Effects of Supply Control Policies

One of the earliest efforts to curb healthcare spending in the United States was Certificate-of-need (CON) programs which are based on the idea that a bed built for an insured population is a filled bed and were adopted by states in the early 1970s to limit and manage the expansion of different health care institutions. A large set of empirical studies documented that CON laws have been unsuccessful in limiting the growth of the hospital, nursing home (NH), and home health (HH) spending. Despite this lack of evidence on the effectiveness of CON programs, 34 states have some form of CON programs that remained mostly unchanged over the last three decades. These CON programs mainly targeted NH and HH industry that provides long-term care which is predominantly financed by states through the Medicaid program. Such long-term presence of CON laws raises an important concern that CON imposes a barrier to entry and diminishes the threat of competition for incumbents that may affect the quality of care. This study offers new evidence on the evolvement of NHs in states with different CON laws and provides evidence on how market power has negatively influenced quality of care in NH markets, by comparing the trends in NH’s structural characteristics, staffing and quality, using a state-level longitudinal data from Online Survey, Certification and Reporting file and state Policy Data, encompassing the period 1992-2017. In general, we found that NH quality improved more rapidly in states without CON laws. Furthermore, the constant growth of NH bed supply alongside a decline in the number of NHs in states with home health CON indicates that barriers to entry have provided market power for the existing NHs, resulting in slower NH quality improvement due to their lower competitive structure. 

Discrete choice model and the discharge pattern of ACO and non-ACO affiliated hospitals

In my work on Accountable Care Organizations (ACOs), I have examined ACO- and non-ACO hospitals’ discharge pattern to skilled nursing facilities (SNFs) based on SNFs CMS five-star quality measures, using a 20 percent random sample of beneficiaries discharged to an SNF following an acute care hospitalization during 2008-2015. Measures of proximity between the patient’s home and each SNF and from SNF to hospital has been used as instrument variable to control for potential bias in choosing SNFs. Following the random utility maximization model, a discharge function was specified using the conditional logit model and assigned each SNF in the choice set an expected star rating based on the predicted probabilities obtained from the choice model. The main outcome variable was defined as an indicator variable, for each individual, as being admitted to an SNF with a higher star rating than the expected rating. Finally, using the difference in difference method the outcome variable for beneficiaries attributed to Shared ACOs and the non-ACOs (control group) before (2008-2011) and after (2012-15) the start of ACO contracts were compared, controlling for beneficiaries’ demographics and clinical characteristics. We found a general increase in the quality of skilled nursing facilities and an overall increase in the percentage of patients being sent to higher-quality SNFs over time. The ACO- affiliated hospitals, however, could send their patients to higher star-rating SNFs compared to non-ACO hospitals. 

Accountable care organizations and racial/ethnic disparities in discharges to SNFs

As an extension to my current work on ACOs, I am working on an R03 grant proposal, to identify racial/ethnic characteristics of beneficiaries admitted to ACO- and non-ACO hospitals, and analyze hospital discharge patterns to SNFs based on race. The central hypothesis of this investigation, which is based on prior studies on ACOs, is that minorities are more likely to be admitted to non-ACO hospitals, thus have lower quality SNF choices, and that these restricted options result in worse health outcomes, contributing to racial/ethnic health disparities.

C. Life Course Approach to Health at Older Age

Aging is a lifelong process and health disparities begin in childhood, as its effects on health persist across the life span. The life-course theory postulates that our ultimate health outcomes are, in part, a response to an accumulation of advantages and disadvantages that begin early in life. The life course perspective describes a dynamic process between social status and health, emphasizing that personal development is a lifelong process, and such development interacts with the social environment to create trajectories of well−being. A life-course perspective calls on policymakers and civil society to invest in the various phases of life, especially at key transition points when risks to well-being and windows of opportunity are greatest. These include critical periods for both biological and social development, including childhood, early and mid-adulthood, and current conditions as an older adult.

Healthy Aging: A life course health production function approach

My Ph.D. research contributed to the life course approach to study healthy aging, using the US nationally representative data from the 2010 and 2012 Health and Retirement Study (HRS) on noninstitutionalized seniors. In my research, I hypothesized that health at an older age is a combination of multiple direct and indirect factors over the course of an individual’s life that determines whether someone ages well or will develop chronic health issues. I used Grossman’s framework of a health production function to model the life course theory of older age health and well-being. I examined determinants of healthy aging and explored the role of mid-life factors as mediators of childhood circumstances. In this study, I explored how various factors determined in different periods of an individual’s life contribute to healthy aging, and then modeled the adulthood pathways that link early-life influences to later life health. In our model, there are three pathways (education, income, and wealth) through which early-life influences indirectly affect healthy aging. 

In the first paper from my Ph.D. work, published on Research on Aging, we proposed a new strategy for operationalizing the concept of healthy aging and then demonstrated its usefulness. Specifically, I defined an index measure of healthy aging as a continuous variable, one that measures the degree to which an individual meets healthy aging criteria, as an alternative to the discrete (0,1) indicator for healthy aging, that earlier studies have used. I defined the index variable as the average standardized score for each individual in the five domains of healthy aging, mostly used in the literature: major disease, disability, physical functioning, cognitive functioning, and social engagement. I adopted a linear multivariate regression model for healthy aging and a linear regression model for the mid-life mediators. In the second paper, we used the discrete measure of healthy aging, as was previously used in the literature. I proposed a new method for calculating the direct and indirect effects in a model with the discrete outcome, using Maddala’s approach in calculating the marginal effects of a discrete choice model. We adopted a multivariate binary regression for healthy aging and linear regression for mid-life factors. 

In both set-ups, since the error terms across these equations may be correlated, we adopted a seemingly unrelated regressions (SUR) model, in order to recognize random shocks in childhood, which were not directly observable in the data and might have influenced downstream life outcomes. After estimating each model, we calculated the direct, indirect, and total effects of childhood characteristics on the probability of healthy aging. To ensure our findings generalize to the population of non-institutionalized Americans, ages 65 and older, all estimates were weighted using sampling weights. We found that favorable childhood conditions significantly improved healthy aging scores, both directly and indirectly, mediated through education, income, and wealth. Our findings complement available research by showing that healthy aging is a function of childhood, adult, and later-life factors. The pathways from childhood factors to healthy aging, however, could be more complex than previously reported, and not considering the mediation role of mid-life factors leads to an underestimation of the effects of the childhood circumstances on healthy aging. Overall we have found that health at older age starts in childhood and that midlife circumstances are transmitters of the imprint of childhood conditions.

Cognitive functioning at older age: A life course health production function approach

Expanding this stream of research, I am studying cognitive aging in the older Mexican population. This study will address the direct and indirect links between childhood and cognitive aging using national-level Mexican population data. Using MHAS enables cross-national comparisons of Mexicans, Mexican-born migrants, in the US and second-generation American-Mexican older adults, using similar datasets in the US (such as HRS).