Peripartum Blood Transfusion Among Rural Women in the United States.

OBJECTIVE: To evaluate the peripartum transfusion rates for rural women compared with urban women in the United States. METHODS: In this population-based retrospective cohort study, geocoded birth records from 2014 to 2016 from the National Center for Health Statistics were used to examine the rural-urban differences in blood transfusion among nulliparous women delivering singleton, vertex pregnancies at term. We compared transfusion rates across the counties on a continuum from urban to rural. We generated a multivariable logistic regression model controlling for age, race, nativity, education, insurance, prenatal care, maternal health, gestational age, intrapartum care, mode of delivery, peripartum factors, and county of delivery. RESULTS: Among 3,346,816 births, the transfusion rates based on maternal county of residence increased as the counties became more rural: large metropolitan-center (1.9/1,000 live births); large metropolitan-fringe (2.4); medium metropolitan (2.6); small metropolitan (2.6); micropolitan (4.5); and noncore rural (5.3). Rural women living and delivering in a rural county had more transfusions (8.5/1,000 live births) than women in more urban counties (2.5/1,000). After adjusting for key covariates, the odds of transfusion were higher among women living in micropolitan (adjusted odds ratio [aOR] 2.25, 95% CI 2.09-2.43) and noncore rural (aOR 2.59, 95% CI 2.38-2.81) counties when compared with women living in large metropolitan counties. County of delivery had a higher association with transfusion than resident county. After adding delivery county to the regression model, the association of transfusion and living in a micropolitan (aOR 1.39, 95% CI 1.19-1.63) or noncore rural (aOR 1.32, 95% CI 1.12-1.55) county diminished. CONCLUSION: The odds of blood transfusion were higher for women in rural areas. The results indicate that the rurality of the county where the birth occurred was associated with more transfusion. This may reflect differences in maternity and blood banking services in rural hospitals and warrants further study to identify opportunities for intervention.

Local Income Inequality, Individual Socioeconomic Status, and Unmet Healthcare Needs in Ohio, USA.

Purpose: Income inequality has been implicated as a potential risk to population health due to lower provision of healthcare services in deeply unequal countries or communities. We tested whether county economic inequality was associated with individual self-report of unmet healthcare needs using a state health survey data set. Methods: Adults residents of Ohio responding to the 2015 Ohio Medicaid Assessment Survey were included in the analysis. Ohio's 88 counties were classified into quartiles according to the Gini coefficient of income inequality. The primary outcome was a composite of self-reported unmet dental care, vision care, mental healthcare, prescription medication, or other healthcare needs within the past year. Unmet healthcare needs were compared according to county inequality quartile using weighted logistic regression. Results: The analytic sample included 37,140 adults. The weighted proportion of adults with unmet healthcare needs was 28%. In multivariable logistic regression, residents of counties in the highest (odds ratio [OR]=1.13, 95% confidence interval [CI]: 1.01-1.26; p=0.030) and second-highest (OR=1.16, 95% CI: 1.04-1.30; p=0.010) quartiles of income inequality experienced more unmet healthcare needs than residents of the most equal counties. Conclusion: Higher county-level income inequality was associated with individual unmet healthcare needs in a large state survey. This finding represents novel evidence for an individual-level association that may explain aggregate-level associations between community economic inequality and population health outcomes.

County socioeconomic characteristics and heart transplant outcomes in the United States.

BACKGROUND: Geographic disparities in survival after heart transplantation have received mixed support in prior studies, and specific geographic characteristics that might be responsible for these differences are unclear. We tested for differences in heart transplant outcomes across United States (US) counties after adjustment for individual-level covariates. Our secondary aim was to evaluate whether specific county-level socioeconomic characteristics explained geographic disparities in survival. METHODS: Data on patients aged ≥18 years undergoing a first-time heart transplant between July 2006 and December 2014 were obtained from the United Network for Organ Sharing. Residents of counties represented by <5 patients were excluded. Patient survival (censored in March 2016) was analyzed using multivariable Cox regression. Shared frailty models were used to test for residual differences in overall all-cause mortality across counties after adjusting for recipient and donor characteristics. Measures of county economic disadvantage, inequality, and racial segregation were obtained from US Census data and coded into quintiles. A likelihood ratio test determined whether adjusting for each county measure improved the fit of the Cox model. RESULTS: Multivariable analysis of 10,879 heart transplant recipients found that, adjusting for individual-level characteristics, there remained statistically significant variation in mortality hazard across US counties (P=.004). Adjusting for quintiles of community disadvantage, economic inequality, or racial segregation did not significantly improve model fit (likelihood ratio test P=.092, P=.273, and P=.107, respectively) and did not explain residual differences in patient survival across counties. CONCLUSIONS: Heart transplantation outcomes vary by county, but this difference is not attributable to county-level socioeconomic disadvantage.

High local unemployment rates limit work after lung transplantation.

BACKGROUND: Most lung transplant (LTx) recipients recover sufficient functional status to resume working, yet unemployment is common after LTx. Weak local labor markets may limit employment opportunities for LTx recipients. METHODS: United Network for Organ Sharing data on first-time LTx recipients 18-60 years old who underwent transplant between 2010 and 2014 were linked to American Community Survey data on unemployment rates at the ZIP Code level. Multivariable competing-risks regression modeled the influence of dichotomous (≥8%) and continuous local unemployment rates on employment after LTx, accounting for the competing risk of mortality. For comparison, analyses were duplicated in a cohort of heart transplant (HTx) recipients who underwent transplant during the same period. RESULTS: The analysis included 3,897 LTx and 5,577 HTx recipients. Work after LTx was reported by 300 (16.3%) residents of low-unemployment areas and 244 (11.9%) residents of high-unemployment areas (p < 0.001). Multivariable analysis of 3,626 LTx recipients with complete covariate data found that high local unemployment rates limited employment after LTx (sub-hazard ratio = 0.605; 95% confidence interval = 0.477, 0.768; p < 0.001), conditional on not working before transplant. Employment after HTx was higher compared with employment after LTx, and not associated with local unemployment rates in multivariable analyses. CONCLUSIONS: LTx recipients of working age exhibit exceptionally low employment rates. High local unemployment rates exacerbate low work participation after LTx, and may discourage job search in this population.