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Abstract
To describe how access to primary and specialty care differs for Medicaid patients
relative to commercially insured patients, and how these differences vary across rural
and urban counties, using comprehensive claims data from Oregon. Cross-sectional study
of risk-adjusted access rates for two types of primary care providers (physicians;
nurse practitioners (NPs) and physician assistants (PAs)); four types of mental health
providers (psychiatrists, psychologists, advanced practice NPs or PAs specializing
in mental health care, behavioral specialists); and four physician specialties (obstetrics
and gynecology, general surgery, gastroenterology, dermatology). 420,947 Medicaid
and 638,980 commercially insured adults in Oregon, October 2014–September 2015. Presence
of any visit with each provider type, risk-adjusted for sex, age, and health conditions.
Relative to commercially insured individuals, Medicaid enrollees had lower rates of
access to primary care physicians (− 11.82%; CI − 12.01 to − 11.63%) and to some specialists
(e.g., obstetrics and gynecology, dermatology), but had equivalent or higher rates
of access to NPs and PAs providing primary care (4.33%; CI 4.15 to 4.52%) and a variety
of mental health providers (including psychiatrists, NPs and PAs, and other behavioral
specialists). Across all providers, the largest gaps in Medicaid-commercial access
rates were observed in rural counties. The Medicaid-commercial patient mix was evenly
distributed across primary care physicians, suggesting that access for Medicaid patients
was not limited to a small subset of primary care providers. This cross-sectional
study found lower rates of access to primary care physicians for Medicaid enrollees,
but Medicaid-commercial differences in access rates were not present across all provider
types and displayed substantial variability across counties. Policies that address
rural-urban differences as well as Medicaid-commercial differences—such as expansions
of telemedicine or changes in the workforce mix—may have the largest impact on improving
access to care across a wide range of populations. The online version of this article
(10.1007/s11606-019-05439-z) contains supplementary material, which is available to
authorized users.
In this commentary, I place the maturing field of rural health research and policy in the context of the rural health disparities documented in Health United States, 2001, Urban and Rural Health Chartbook. Because of recent advances in our understanding of the determinants of health, the field must branch out from its traditional focus on access to health care services toward initiatives that are based on models of population health. In addition to presenting distinct regional differences, the chartbook shows a pattern of risky health behaviors among rural populations that suggest a "rural culture" health determinant. This pattern suggests that there may be environmental and cultural factors unique to towns, regions, or United States Department of Agriculture (USDA) economic types that affect health behavior and health.
There is limited research on rural-urban disparities in U.S. life expectancy. This study examined trends in rural-urban disparities in life expectancy at birth in the U.S. between 1969 and 2009. The 1969-2009 U.S. county-level mortality data linked to a rural-urban continuum measure were analyzed. Life expectancies were calculated by age, gender, and race for 3-year time periods between 1969 and 2004 and for 2005-2009 using standard life-table methodology. Differences in life expectancy were decomposed by age and cause of death. Life expectancy was inversely related to levels of rurality. In 2005-2009, those in large metropolitan areas had a life expectancy of 79.1 years, compared with 76.9 years in small urban towns and 76.7 years in rural areas. When stratified by gender, race, and income, life expectancy ranged from 67.7 years among poor black men in nonmetropolitan areas to 89.6 among poor Asian/Pacific Islander women in metropolitan areas. Rural-urban disparities widened over time. In 1969-1971, life expectancy was 0.4 years longer in metropolitan than in nonmetropolitan areas (70.9 vs 70.5 years). By 2005-2009, the life expectancy difference had increased to 2.0 years (78.8 vs 76.8 years). The rural poor and rural blacks currently experience survival probabilities that urban rich and urban whites enjoyed 4 decades earlier. Causes of death contributing most to the increasing rural-urban disparity and lower life expectancy in rural areas include heart disease, unintentional injuries, COPD, lung cancer, stroke, suicide, and diabetes. Between 1969 and 2009, residents in metropolitan areas experienced larger gains in life expectancy than those in nonmetropolitan areas, contributing to the widening gap. Published by American Journal of Preventive Medicine on behalf of American Journal of Preventive Medicine.
We use insurance claims data covering 28% of individuals with employer-sponsored health insurance in the United States to study the variation in health spending on the privately insured, examine the structure of insurer-hospital contracts, and analyze the variation in hospital prices across the nation. Health spending per privately insured beneficiary differs by a factor of three across geographic areas and has a very low correlation with Medicare spending. For the privately insured, half of the spending variation is driven by price variation across regions, and half is driven by quantity variation. Prices vary substantially across regions, across hospitals within regions, and even within hospitals. For example, even for a nearly homogeneous service such as lower-limb magnetic resonance imaging, about a fifth of the total case-level price variation occurs within a hospital in the cross section. Hospital market structure is strongly associated with price levels and contract structure. Prices at monopoly hospitals are 12% higher than those in markets with four or more rivals. Monopoly hospitals also have contracts that load more risk on insurers (e.g., they have more cases with prices set as a share of their charges). In concentrated insurer markets the opposite occurs-hospitals have lower prices and bear more financial risk. Examining the 366 mergers and acquisitions that occurred between 2007 and 2011, we find that prices increased by over 6% when the merging hospitals were geographically close (e.g., 5 miles or less apart), but not when the hospitals were geographically distant (e.g., over 25 miles apart).
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