Prevalence and County-Level Distribution of Births in Catholic Hospitals in the US in 2020.
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ABSTRACT:
SUBMITTER: Cartwright AF
PROVIDER: S-EPMC10031385 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
Publications
Prevalence and County-Level Distribution of Births in Catholic Hospitals in the US in 2020.
Cartwright Alice F AF Bullington Brooke W BW Arora Kavita Shah KS Swartz Jonas J JJ
JAMA 20230301 11
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Project description:A shift in focus towards healthy reproductive outcomes may reveal opportunities for novel interventions and strategies to promote optimal health. Using variables from the National Center for Health Statistics restricted use natality files, we calculated Empirical Bayes smoothed (EBS) rates of optimal birth for the all live births-both overall and by maternal race/ethnicity-by applying the smoothing tool in GeoDa version 1.18.0.10 We defined counties achieving greater racial birth equity as those where the overall EBS optimal birth rate was greater than the national 75th percentile and the absolute difference between maternal racial/ethnic categories was smaller than the national 25th percentile difference. During the study period, 49.80% of overall births could be classified as an optimal birth according to the study definition. Of the 3140 US counties, only 282 (8.98%) appeared to advance White-Black equity in optimal births, and 205 (6.53%) appeared to advance White-Hispanic equity in optimal births. In the effort improve maternal health, we should focus not only on the absence of negative outcomes, but also the occurrence of positive outcomes. Our analytic results suggest that optimal births can be measured and that geographic inequities by race occur.
| S-EPMC9529881 | biostudies-literature
Project description:BackgroundCataract surgery is very important to prevent blindness, but its productivity and efficiency in China are unknown. Our study aimed to evaluate the geographical distribution of cataract surgeons and prospectively identify the factors associated with the increased productivity in cataract surgery and efficiency in outpatient ophthalmic services in rural Chinese hospitals.MethodsData were prospectively collated from various hospital datasets and the census registered by the geographical unit county. Prior to mapping, the geographical location data of counties were cross-linked with the equivalent ophthalmologist and service output data to create categories and map multiple data attributes. Descriptive statistical analyses were performed to characterise the data stratified by county. Linear regression analyses were used to explore the factors associated with the increased productivity/efficiency.ResultsThe ophthalmologists, surgical productivity of ophthalmologists and outpatient efficacy of ophthalmologists significantly varied across counties. During the period between 2016 and 2018, the median (IQR) change in surgical productivity of and outpatient efficacy of ophthalmologists were 31.627 (-3.33 to 29.94) and 118.08 (-132.30 to 740.89). In the simple regression analysis for predictors of a high productivity change, only the increased number of phaco machine had statistical significance (p=0.003). In addition, only the gross domestic product per capita in 2016 was associated with an increased improvement in efficiency of outpatient services (p=0.008).ConclusionsThis study demonstrated that the ophthalmologist productivity and the efficiency of outpatient services were unequally geographically distributed, and their predictors were identified. Further studies to elucidate the extent of the problem and improve the health service delivery models are required.
| S-EPMC8256740 | biostudies-literature
Project description:BackgroundTrends in the prevalence of hepatitis C virus (HCV) infection among women delivering live births may differ in rural vs. urban areas of the United States, but estimation of trends based on observed counts may lead to unstable estimates in rural counties due to small numbers.ObjectivesThe objective of the study was to use small area estimation methods to provide updated county-level prevalence estimates and, for the first time, trends in maternal HCV infection among live births by county-level rurality.MethodsCross-sectional natality data from 2016 to 2020 were used to estimate maternal hepatitis C prevalence using hierarchical Bayesian models with spatiotemporal random effects to produce annual county-level estimates of maternal HCV infection and trends over time. Models included a 6-level rural-urban county classification, year, maternal characteristics and county-specific covariates. Data were analysed in 2022.ResultsThere were 90,764/18,905,314 live births (4.8 per 1000) with HCV infection reported on the birth certificate. Hepatitis C prevalence was higher among rural counties as compared to urban counties. Rural counties had the largest annual increases in maternal hepatitis C prevalence (per 1000 births) from 2016 to 2020 (micropolitan: 0.39; noncore: 0.40), with smaller increases among less densely populated urban counties (medium metro: 0.28; small metro: 0.28) and urban counties (large central metro:0.11; large fringe metro: 0.14).ConclusionsThe prevalence of maternal HCV infection was the highest in rural counties, and rural counties saw the greatest average prevalence increase during 2016-2020. County-level data can help in monitoring rural-urban trends in maternal HCV infection to reduce geographic disparities.
| S-EPMC9918643 | biostudies-literature
Project description: Not available
| S-EPMC8980920 | biostudies-literature
Project description:ImportanceIt is generally accepted that birth rates are negatively associated with income. However, less is known about the nature and evolution of natality differences between high- and low-income counties and how these differences are associated with the recent decline in the birth rate overall in the US.ObjectiveTo quantify the association between county-level income and natality between 2000 and 2020 and explore how natality inequality is associated with recent nationwide natality declines.Design, setting, and participantsThis cross-sectional study included all births to US women aged 15 to 44 years among county-year-level observations with at least 100 women of the same age.ExposuresCounty-level median household income rank.Main outcomes and measuresAssociation between natality and income and between natality inequality and national natality trends. Income ventiles were used to rank counties from 1 to 20, with each ventile including 5% of the female population aged 15 to 44 years. A counterfactual simulation was used to estimate natality rates while holding natality inequality constant at its year 2000 value.ResultsA total of 86 679 356 births were aggregated to 65 554 county-year-level observations from 2000 to 2020. The analysis yielded 2 main results. First was the changing nature of inequality. Estimates of the natality income gradient, which reflect the association between county-level natality and county-level income, changed from -0.061 (95% CI, -0.200 to 0.078) in 2000 to -0.572 (95% CI, -0.678 to -0.466) in 2020, reflecting an increase in natality inequality. Intuitively, a negative gradient reflects that natality is lower in higher-income counties compared with lower-income counties. Second, the counterfactual simulation showed that there would have been an additional 3.5 million births (an increase of 4.1%) over the remainder of the study period had natality inequality remained constant at its year 2000 value. While this counterfactual is based on strong assumptions that are unlikely to hold in practice, it suggests that natality may have been higher during this period absent the rise in natality inequality.Conclusions and relevanceThe findings of this cross-sectional study suggest that natality inequality has increased in recent years and is likely associated with nationwide natality declines. The causes of this inequality remain to be explored in future work.
| S-EPMC9857058 | biostudies-literature
Project description:PurposeApproximately 16% of U.S. births to women aged 15-19 years are repeat (second or higher order) births. Repeat teen mothers are at elevated risk for poor perinatal outcomes. Geographic clustering and correlates of repeat teen birth are unknown.MethodsData from birth certificates on N = 629,939 teen births in N = 3,108 U.S. counties in 2015-2017 were merged with data on county-level demographic, socioeconomic, and health provider characteristics. We identified contiguous clusters of counties with significantly elevated rates of first teen births only, repeat teen births, both, or neither between 2015 and 2017 and compared demographic, socioeconomic, and medical provider characteristics of counties between 2010 and 2016 in each cluster type.ResultsA total of 193 counties (6.21%) had high rates of repeat births only; 504 (16.22%) had high rates of first teen birth only; 991 (31.89%) had high rates of both repeat and first teen births; and 1,420 (45.69%) had neither. Counties with high repeat (vs. first only) birth rates had higher rates of poverty and unemployment, higher levels of income inequality, lower high school graduation rates, a higher share of racial and ethnic minority residents, fewer publicly funded family planning clinics per capita, and more women receiving contraceptive services at publicly funded clinics.ConclusionsFirst and repeat teen births cluster in differentially resourced geographic areas. Counties with high repeat teen birth rates have lower socioeconomic conditions than counties with high rates of first teen births only. These counties are more reliant on publicly funded family planning clinics but have fewer of them per capita.
| S-EPMC6814573 | biostudies-literature
Project description:BackgroundAlthough management is important in healthcare, low- and middle-income countries (LMICs) have little experience measuring the competence of hospital management. While improving hospital management is the main focus of hospital reform in China, few studies have empirically documented the baseline situation to inform policy design.MethodsWe assessed the management practices of county-level hospitals in Guizhou in southwest China during 2015. We used the Development World Management Survey (D-WMS) instrument to interview 273 managers in 139 hospitals. We scored the management practices of the sampled hospitals, overall and in four dimensions (operations, monitoring, targets, personnel management) and three processes (implementation, usage, monitoring). We then converted the scores to the WMS scale and compared these with data from two other LMICs and seven high-income countries (HICs).ResultsOn a scale of 1 ('worst practice') to 5 ('best practice'), the mean (SD) hospital D-WMS scores were 2.57 (0.46) overall; 2.71 (0.48), 2.64 (0.58), 2.40 (0.64), and 2.56 (0.40) for operation, monitoring, target, and personnel, respectively; and 2.43 (0.48), 2.62 (0.48), and 2.66 (0.47) for implementation, usage, and monitoring, respectively. After conversion to WMS scores, China ranked seventh of 10 countries, after six HICs and higher than one HIC and two other LMICs (Brazil and India). China ranked higher than the two LMICs in each of the four dimensional scores.ConclusionsChinese county-level hospitals should improve their low quality of management by prioritizing target-setting and process implementation, particularly in personnel management. Meanwhile, modern management training should be given to most clinical managers.
| S-EPMC8755900 | biostudies-literature
Project description:BackgroundLocal conditions where people live continue to influence prostate cancer outcomes. By examining local characteristics associated with trends in Black-White differences in prostate cancer-specific mortality over time, we aim to identify factors driving county-level prostate cancer-specific mortality disparities over a 15-year period.MethodsWe linked county-level data (Area Health Resource File) with clinicodemographic data of men with prostate cancer (Surveillance, Epidemiology, and End Results registry) from 2005 to 2020. Generalized linear mixed models evaluated associations between race and county-level age-standardized prostate cancer-specific mortality, adjusting for age; year of death; rurality; county-level education; income; uninsured rates; and densities of urologists, radiologists, primary care practitioners, and hospital beds.ResultsIn 1085 counties, 185 390 patients were identified of which 15.8% were non-Hispanic Black. Racial disparities in prostate cancer-specific mortality narrowed from 2005 to 2020 (25.4 per 100 000 to 19.2 per 100 000 overall, 57.9 per 100 000 to 38 per 100 000 for non-Hispanic Black patients, and 23.4 per 100 000 to 18.3 per 100 000 for non-Hispanic White patients). For non-Hispanic Black and non-Hispanic White patients, county prostate cancer-specific mortality changes varied greatly (-65% to +77% and -61% to +112%, respectively). From 2016 to 2020, non-Hispanic Black patients harbored greater prostate cancer-specific mortality risk (relative risk = 2.09, 95% confidence interval [CI] = 2.01 to 2.18); higher radiation oncologist density was associated with lower mortality risk (relative risk = 0.93, 95% CI = 0.89 to 0.98), while other practitioner densities were not.ConclusionAlthough overall rates improved, specific counties experienced worsening race-based disparities over time. Identifying locations of highest (and lowest) mortality disparities remains critical to development of location-specific solutions to racial disparities in prostate cancer outcomes.
| S-EPMC11631307 | biostudies-literature
Project description:ObjectiveThe objective of this study was to assess the association between United States county-level COVID-19 mortality and changes in presidential voting between 2016 and 2020.Study designThe study design is a county-level ecological study.MethodsWe analysed county-level population-weighted differences in partisan vote change, voter turnout and sociodemographic and health status characteristics across pre-election COVID-19 mortality quartiles. We estimated a population-weighted linear regression of the 2020-2016 Democratic vote change testing the significance of differences between quartiles of COVID-19 mortality, controlling for other county characteristics.ResultsThe overall change in the 2020-2016 Democratic vote was +2.9% but ranged from a +4.3% increase in the lowest mortality quartile counties to +0.9% in the highest mortality quartile counties. Change in turnout ranged from +9.1% in the lowest mortality counties to only +6.2% in highest mortality counties. In regression estimates, the highest mortality quartile was associated with a -1.26% change in the Democratic 2020-2016 vote compared with the lowest quartile (P < 0.001).ConclusionsHigher county-level COVID-19 mortality was associated with smaller increases in Democratic vote share in 2020 compared with 2016. Possible explanations to be explored in future research could include fear of in-person voting in heavily Democratic, high-mortality counties, fear of the economic effects of perceived Democratic support for tighter lockdowns and stay-at-home orders and general exhaustion that lowered political participation in hard-hit counties.
| S-EPMC9451615 | biostudies-literature
Project description:Coal has historically been a primary energy source in the United States. The byproducts of coal combustion, such as fine particulate matter (PM2.5), have increasingly been associated with adverse birth outcomes. The goal of this study was to leverage the current progressive transition away from coal in the United States (U.S.) to assess whether coal PM2.5 is associated with preterm birth rates and whether this association differs by maternal Black/White race/ethnicity. Using a novel dispersion modeling approach, we estimated PM2.5 pollution from coal-fired power plants nationwide at the county-level during the study period (2000-2018). We also obtained county-level preterm birth rates for non-Hispanic White and non-Hispanic Black mothers. We used a generalized additive mixed model to estimate the relationship between coal PM2.5 and preterm birth rates, overall and stratified by maternal race. We included a natural spline to allow for non-linearity in the concentration-response curve. We observed a positive non-linear relationship between coal PM2.5 and preterm birth rate, which plateaued at higher levels of pollution. We also observed differential associations by maternal race; the association was stronger for White women, especially at higher levels of coal PM2.5 (> 2.0 μg/m3). Our findings suggest that the transition away from coal may reduce preterm birth rates in the U.S.
| S-EPMC8443161 | biostudies-literature