Objective: The objective was to estimate the rural-urban differences in the receipt of prepregnancy, prenatal, and postpartum services.
Methods: The authors conducted a cross-sectional data analysis using data from the Pregnancy Risk Assessment and Monitoring System from 2016 to 2018 to analyze rural-urban differences in the receipt of medical visits and care content delivery during the prepregnancy year, as well as the prenatal and postpartum periods among birthing people in the US, using survey-weighted multivariable logistic regression models.
Results: Rural-dwelling birthing people were significantly less likely to attend a medical visit in the prepregnancy year or postpartum period, even when controlled for sociodemographic and clinical characteristics. Compared to their urban counterparts, they were also less likely to receive comprehensive screening and counseling in the prepregnancy and postpartum maternity phases.
Conclusion: Efforts to ameliorate rural-urban differences in maternal care access and quality should explicitly adopt multilevel, systemic approaches to policy and program implementation and evaluation. Policymakers and practitioners should consider telehealth as a potential complementary tool to minimize gaps in quality of care which disproportionately impact rural-dwelling birthing people.
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http://dx.doi.org/10.7812/TPP/23.067 | DOI Listing |
Sci Total Environ
December 2024
Département des Sciences en Santé Environnementale, École des Hautes Études en Santé Publique (EHESP), 15, avenue du Professeur-Léon-Bernard, CS 74312, 35043 Rennes Cedex, France; Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), UMR_S 1085, Rennes, France; École supérieure d'aménagement du territoire et de développement régional (ESAD), Université Laval, 2325, rue des Bibliothèques, Québec, QC G1V 0A6, Canada. Electronic address:
Previous studies have documented ethnic and sociodemographic disparities in exposure to drinking water (DW) contaminants. A majority were conducted in the U.S.
View Article and Find Full Text PDFBMC Public Health
December 2024
Department of Population and Development, International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai, Maharashtra, 400088, India.
Background: In Low and Middle Income Countries (LMICs), reduction of multidimensional poverty and the increase in longevity are concomitant. Although a number of studies have estimated multidimensional poverty, studies on estimates of life expectancy and pre-mature mortality by multidimensional poverty are limited. We estimated life expectancy and premature mortality among multidimensionally poor and multidimensionally non-poor in India.
View Article and Find Full Text PDFBMC Public Health
December 2024
Sechenov First Moscow State Medical University, Moscow, Russia.
Introduction: Globally, there has been a decline in the age of menarche; the decline is higher in poorer countries than in richer ones. The measurement of the decline was based on the reported mean age at menarche (MAM) across the countries. There is a significant knowledge gap in investigating the generational decline in MAM in low- and median-income countries (LMC).
View Article and Find Full Text PDFRes Aging
December 2024
Faculty of Psychology, Southwest University, Chongqing, China.
Migrating to urban areas profoundly shapes later-life cognitive function. However, it is unclear whether the acquisition of urban citizenship matters in old age and how the rural-urban differences in cognition change over time. In particular, the existing literature has overlooked the need to differentiate between the effects of health selection and urban attainment.
View Article and Find Full Text PDFJ Environ Manage
December 2024
Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 300072, China; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.
Vegetation changes and human activities in both natural and urban environments have played a crucial role in carbon cycling and sustainable development globally. However, there is an insufficient comparison in national vegetation changes across regions with varying intensities of human activities to those natural areas. Based on urban boundary and night-time light datasets, we have identified and extracted rural, urban-low activity, and urban-high activity areas within China.
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