The Role of Individual-Level Factors in Rural Mortality Disparities
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ABSTRACT:
SUBMITTER: Rees-Punia E
PROVIDER: S-EPMC10546552 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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Project description:BackgroundUrban-rural disparity in mortality at older ages is well documented in China. However, surprisingly few studies have systemically investigated factors that contribute to such disparity. This study examined the extent to which individual-level socioeconomic conditions, family/social support, health behaviors, and baseline health status contributed to the urban-rural difference in mortality among older adults in China.MethodsThis research used the five waves of the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2014, a nationally representative sample of older adults aged 65 years or older in China (n = 28,235). A series of hazard regression models by gender and age group examined the association between urban-rural residence and mortality and how this association was modified by a wide range of individual-level factors.ResultsOlder adults in urban areas had 11% (relative hazard ratio (HR) = 0.89, p < 0.01) lower risks of mortality than their rural counterparts when only demographic factors were taken into account. Further adjustments for family/social support, health behaviors, and health-related factors individually or jointly had a limited influence on the mortality differential between urban and rural older adults (HRs = 0.89-0.92, p < 0.05 to p < 0.01). However, we found no urban-rural difference in mortality (HR = 0.97, p > 0.10) after adjusting for individual socioeconomic factors. Similar results were found in women and men, and among the young-old and the oldest-old populations.ConclusionsThe urban-rural disparity in mortality among older adults in China was largely attributable to differences in individual socioeconomic resources (i.e., education, income, and access to healthcare) regardless of gender and age group.
| S-EPMC7526413 | biostudies-literature
Project description:BackgroundRacial disparities in childhood asthma outcomes result from a complex interplay of individual- and neighborhood-level factors.ObjectivesWe sought to examine racial disparities in asthma-related emergency department (ED) visits between African American (AA) and European American (EA) children.MethodsThis is a retrospective study of patients younger than 18 years who visited the ED at Cincinnati Children's for asthma from 2009 to 2018. The outcome was number of ED visits during a year. We assessed 11 social, economic, and environmental variables. Mediation and mixed-effects analyses were used to assess relationships between race, mediators, and number of ED visits.ResultsA total of 31,114 children (46.1% AA, 53.9% EA) had 186,779 asthma-related ED visits. AA children had more visits per year than EA children (2.23 vs 2.15; P < .001). Medicaid insurance was associated with a 7% increase in rate of ED visits compared with commercial insurance (1.07; 95% CI, 1.03-1.1). Neighborhood socioeconomic deprivation was associated with an increased rate of ED visits in AA but not in EA children. Area-level particulate matter with diameter less than 2.5 μm, pollen, and outdoor mold were associated with an increased rate of ED visits for both AA and EA children (all P < .001). Associations between race and number of ED visits were mediated by insurance, area-level deprivation, particulate matter with diameter less than 2.5 μm, and outdoor mold (all P < .001), altogether accounting for 55% of the effect of race on ED visits. Race was not associated with number of ED visits (P = .796) after accounting for mediators.ConclusionsRacial disparities in asthma-related ED visits are mediated by social, economic, and environmental factors, which may be amenable to interventions aimed at improving outcomes and eliminating inequities.
| S-EPMC9887733 | biostudies-literature
Project description:BackgroundComprehensive, individual-level social determinants of health (SDOH) are not collected in national transplant registries, limiting research aimed at understanding the relationship between SDOH and waitlist outcomes among kidney transplant candidates.MethodsWe merged Organ Procurement and Transplantation Network data with individual-level SDOH data from LexisNexis, a commercial data vendor, and conducted a competing risk analysis to determine the association between individual-level SDOH and the cumulative incidence of living donor kidney transplant (LDKT), deceased donor kidney transplant (DDKT), and waitlist mortality. We included adult kidney transplant candidates placed on the waiting list in 2020, followed through December 2023.ResultsIn multivariable analysis, having public insurance (Medicare or Medicaid), less than a college degree, and any type of derogatory record (liens, history of eviction, bankruptcy and/ felonies) were associated with lower likelihood of LDKT. Compared with patients with estimated individual annual incomes ≤ $30,000, patients with incomes ≥ $120,000 were more likely to receive a LDKT (sub distribution hazard ratio (sHR), 2.52; 95% confidence interval (CI), 2.03-3.12). Being on Medicare (sHR, 1.49; 95% CI, 1.42-1.57), having some college or technical school, or at most a high school diploma were associated with a higher likelihood of DDKT. Compared with patients with incomes ≤ $30,000, patients with incomes ≥ $120,000 were less likely to receive a DDKT (sHR, 0.60; 95% CI, 0.51-0.71). Lower individual annual income, having public insurance, at most a high school diploma, and a record of liens or eviction were associated with higher waitlist mortality.ConclusionsPatients with adverse individual-level SDOH were less likely to receive LDKT, more likely to receive DDKT, and had higher risk of waitlist mortality. Differential relationships between SDOH, access to LDKT, DDKT, and waitlist mortality suggest the need for targeted interventions aimed at decreasing waitlist mortality and increasing access to LDKT among patients with adverse SDOH.
| S-EPMC11338441 | biostudies-literature
Project description:ObjectiveTo understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010.Data sourceAge-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research.Study designWe used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade.Principal findingsDecomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide.ConclusionsDifferences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.
| S-EPMC6232410 | biostudies-literature
Project description: Not available
| S-EPMC10932753 | biostudies-literature
Project description:BackgroundTwo strong risk factors for gastroschisis are young maternal age (<20 years) and low/normal pre-pregnancy body mass index (BMI), yet the reasons remain unknown. We explored whether neighborhood-level socioeconomic position (nSEP) during pregnancy modified these associations.MethodsWe analyzed data from 1269 gastroschisis cases and 10,217 controls in the National Birth Defects Prevention Study (1997-2011). To characterize nSEP, we applied the neighborhood deprivation index and used generalized estimating equations to calculate odds ratios and relative excess risk due to interaction.ResultsElevated odds of gastroschisis were consistently associated with young maternal age and low/normal BMI, regardless of nSEP. High-deprivation neighborhoods modified the association with young maternal age. Infants of young mothers in high-deprivation areas had lower odds of gastroschisis (adjusted odds ratio [aOR]: 3.1, 95% confidence interval [CI]: 2.6, 3.8) than young mothers in low-deprivation areas (aOR: 6.6; 95% CI: 4.6, 9.4). Mothers of low/normal BMI had approximately twice the odds of having an infant with gastroschisis compared to mothers with overweight/obese BMI, regardless of nSEP (aOR range: 1.5-2.3).ConclusionOur findings suggest nSEP modified the association between gastroschisis and maternal age, but not BMI. Further research could clarify whether the modification is due to unidentified biologic and/or non-biologic factors.
| S-EPMC10527855 | biostudies-literature
Project description: Not available
| S-EPMC11877160 | biostudies-literature
Project description:Across the U.S, it is a documented fact that rural areas have longer ambulance response times and tend to have lower median income. The objective of this study was to test if the rural-urban emergency medical service (EMS) response time disparity was related to wealth disparity in the state of Connecticut. All mean EMS response times were sourced from the 2016 Office of Emergency Medical Services Data Report. Rural definitions were sourced from the Connecticut Office of Rural Health. Median income data was drawn from the Connecticut Office of Policy and Management. A Mann-Whitney U test determined if the average rural EMS response time was greater than the non-rural EMS response time. Pearson coefficients quantified the relationship between median income and EMS response time. A t-test ascertained if the average median income differed between the two datasets. The mean EMS response time was 12.98 min (SD = 3.36) rural and 8.26 min (SD = 2.12) non-rural. Rural mean response time and median income were not significantly correlated (r = -.148, p=.247); non-rural mean response time and median income were also not significantly related. No significant disparity was detected (t=0.478, p=.633) between the mean rural household income ($98,258) and mean non-rural household income ($95,706). Significant disparities in EMS response times can exist between rural and non-rural towns separate from median income trends, as is the case in Connecticut. These findings may have limited generalizability because of Connecticut's relatively high median income as compared to other states yet may be relevant to states with similar economic metrics.
| S-EPMC11615172 | biostudies-literature
Project description:IntroductionInfant mortality rates are higher in nonmetropolitan areas versus large metropolitan areas. Variation by race/ethnicity and cause of death has not been assessed. Urban-rural infant mortality rate differences were quantified by race/ethnicity and cause of death.MethodsNational Vital Statistics System linked birth/infant death data (2014-2016) were analyzed in 2019 by 3 urban-rural county classifications: large metropolitan, medium/small metropolitan, and nonmetropolitan. Excess infant mortality rates (rate differences) by urban-rural classification were calculated relative to large metropolitan areas overall and for each racial/ethnic group. The number of excess deaths, population attributable fraction, and proportion of excess deaths attributable to underlying causes of death was calculated.ResultsNonmetropolitan areas had the highest excess infant mortality rate overall. Excess infant mortality rates were substantially lower for Hispanic infants than other races/ethnicities. Overall, 7.4% of infant deaths would be prevented if all areas had the infant mortality rate of large metropolitan areas. With more than half of births occurring outside of large metropolitan areas, the population attributable fraction was highest for American Indian/Alaska Natives (20.3%) and whites, non-Hispanic (14.3%). Excess infant mortality rates in both nonmetropolitan and medium/small metropolitan areas were primarily attributable to sudden unexpected infant deaths (42.3% and 31.9%) and congenital anomalies (30.1% and 26.8%). This pattern was consistent for all racial/ethnic groups except black, non-Hispanic infants, for whom preterm-related and sudden unexpected infant deaths accounted for the largest share of excess infant mortality rates.ConclusionsInfant mortality increases with rurality, and excess infant mortality rates are predominantly attributable to sudden unexpected infant deaths and congenital anomalies, with differences by race/ethnicity regarding magnitude and cause of death.
| S-EPMC6980981 | biostudies-literature
Project description:We examined individual-level and neighborhood-level predictors of mortality in CRC patients diagnosed in Florida to identify high-risk groups for targeted interventions.Demographic and clinical data from the Florida Cancer Data System registry (2007-2011) were linked with Agency for Health Care Administration and US Census data (n = 47,872). Cox hazard regression models were fitted with candidate predictors of CRC survival and stratified by age group (18-49, 50-64, 65+).Stratified by age group, higher mortality risk per comorbidity was found among youngest (21%), followed by middle (19%), and then oldest (14%) age groups. The two younger age groups had higher mortality risk with proximal compared to those with distal cancer. Compared with private insurance, those in the middle age group were at higher death risk if not insured (HR = 1.35), or received healthcare through Medicare (HR = 1.44), Medicaid (HR = 1.53), or the Veteran's Administration (HR = 1.26). Only Medicaid in the youngest (52% higher risk) and those not insured in the oldest group (24% lower risk) were significantly different from their privately insured counterparts. Among 18-49 and 50-64 age groups there was a higher mortality risk among the lowest SES (1.17- and 1.23-fold higher in the middle age and 1.12- and 1.17-fold higher in the older age group, respectively) compared to highest SES. Married patients were significantly better off than divorced/separated (HR = 1.22), single (HR = 1.29), or widowed (HR = 1.19) patients.Factors associated with increased risk for mortality among individuals with CRC included being older, uninsured, unmarried, more comorbidities, living in lower SES neighborhoods, and diagnosed at later disease stage. Higher risk among younger patients was attributed to proximal cancer site, Medicaid, and distant disease; however, lower SES and being unmarried were not risk factors in this age group. Targeted interventions to improve survivorship and greater social support while considering age classification may assist these high-risk groups.
| S-EPMC4149543 | biostudies-other