Dataset Information

Association of Neighborhood Socioeconomic Status With Risk of Infection and Sepsis.

ABSTRACT: Background:Prior studies suggest disparities in sepsis risk and outcomes based on place of residence. We sought to examine the association between neighborhood socioeconomic status (nSES) and hospitalization for infection and sepsis. Methods:We conducted a prospective cohort study using data from 30239 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. nSES was defined using a score derived from census data and classified into quartiles. Infection and sepsis hospitalizations were identified over the period 2003-2012. We fit Cox proportional hazards models, reporting hazard ratios (HRs) with 95% confidence intervals (CIs) and examining mediation by participant characteristics. Results:Over a median follow-up of 6.5 years, there were 3054 hospitalizations for serious infection. Infection incidence was lower for participants in the highest nSES quartile compared with the lowest quartile (11.7 vs 15.6 per 1000 person-years). After adjustment for demographics, comorbidities, and functional status, infection hazards were also lower for the highest quartile (HR, 0.84 [95% CI, .73-.97]), with a linear trend (P = .011). However, there was no association between nSES and sepsis at presentation among those hospitalized with infection. Physical weakness, income, and diabetes had modest mediating effects on the association of nSES with infection. Conclusions:Our study shows that differential infection risk may explain nSES disparities in sepsis incidence, as higher nSES is associated with lower infection hospitalization rates, but there is no association with sepsis among those hospitalized. Mediation analysis showed that nSES may influence infection hospitalization risk at least partially through physical weakness, individual income, and comorbid diabetes.

SUBMITTER: Donnelly JP

PROVIDER: S-EPMC6248765 | biostudies-other | 2018 Jun

REPOSITORIES: biostudies-other

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Association of Neighborhood Socioeconomic Status With Risk of Infection and Sepsis.

Donnelly John P JP Lakkur Sindhu S Judd Suzanne E SE Levitan Emily B EB Griffin Russell R Howard George G Safford Monika M MM Wang Henry E HE

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20180601 12

<h4>Background</h4>Prior studies suggest disparities in sepsis risk and outcomes based on place of residence. We sought to examine the association between neighborhood socioeconomic status (nSES) and hospitalization for infection and sepsis.<h4>Methods</h4>We conducted a prospective cohort study using data from 30239 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. nSES was defined using a score derived from census data and classified into quartiles. I ...[more]

PMID: 29444225

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Project description:BackgroundLong-term fine particulate matter (PM2.5) exposure is linked with cardiovascular disease, and disadvantaged status may increase susceptibility to air pollution-related health effects. In addition, there are concerns that this association may be partially explained by confounding by socioeconomic status (SES).ObjectivesWe examined the roles that individual- and neighborhood-level SES (NSES) play in the association between PM2.5 exposure and cardiovascular disease.MethodsThe study population comprised 51,754 postmenopausal women from the Women's Health Initiative Observational Study. PM2.5 concentrations were predicted at participant residences using fine-scale regionalized universal kriging models. We assessed individual-level SES and NSES (Census-tract level) across several SES domains including education, occupation, and income/wealth, as well as through an NSES score, which captures several important dimensions of SES. Cox proportional-hazards regression adjusted for SES factors and other covariates to determine the risk of a first cardiovascular event.ResultsA 5 ?g/m3 higher exposure to PM2.5 was associated with a 13% increased risk of cardiovascular event [hazard ratio (HR) 1.13; 95% confidence interval (CI): 1.02, 1.26]. Adjustment for SES factors did not meaningfully affect the risk estimate. Higher risk estimates were observed among participants living in low-SES neighborhoods. The most and least disadvantaged quartiles of the NSES score had HRs of 1.39 (95% CI: 1.21, 1.61) and 0.90 (95% CI: 0.72, 1.07), respectively.ConclusionsWomen with lower NSES may be more susceptible to air pollution-related health effects. The association between air pollution and cardiovascular disease was not explained by confounding from individual-level SES or NSES. Citation: Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. 2016. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environ Health Perspect 124:1840-1847;?http://dx.doi.org/10.1289/EHP199.

Project description:ImportanceStudying long-term changes in neighborhood socioeconomic status (SES) may help to better understand the associations between neighborhood exposure and weight outcomes and provide evidence supporting neighborhood interventions. Little previous research has been done to examine associations between neighborhood SES and weight loss, a risk factor associated with poor health outcomes in the older population.ObjectiveTo determine whether improvements in neighborhood SES are associated with reduced likelihoods of excessive weight gain and excessive weight loss and whether declines are associated with increased likelihoods of these weight outcomes.Design, study, and participantsThis cohort study was conducted using data from the National Institutes of Health-AARP (formerly known as the American Association of Retired Persons) Diet and Health study (1995-2006). The analysis included a cohort of 126 179 adults (aged 50-71 years) whose neighborhoods at baseline (1995-1996) were the same as at follow-up (2004-2006). All analyses were performed from December 2018 through December 2020.ExposuresLiving in a neighborhood that experienced 1 of 8 neighborhood SES trajectories defined based on a national neighborhood SES index created using data from the US Census and American Community Survey. The 8 trajectory groups, in which high, or H, indicated rankings at or above the sample median of a specific year and low, or L, indicated rankings below the median, were HHH (ie, high in 1990 to high in 2000 to high in 2010), or stable high; HLL, or early decline; HHL, or late decline; HLH, or transient decline; LLL, or stable low; LHH, or early improvement; LLH, or late improvement; and LHL, or transient improvement.Main outcomes and measuresExcessive weight gain and loss were defined as gaining or losing 10% or more of baseline weight.ResultsAmong 126 179 adults, 76 225 (60.4%) were men and the mean (SD) age was 62.1 (5.3) years. Improvements in neighborhood SES were associated with lower likelihoods of excessive weight gain and weight loss over follow-up, while declines in neighborhood SES were associated with higher likelihoods of excessive weight gain and weight loss. Compared with the stable low group, the risk was significantly reduced for excessive weight gain in the early improvement group (odds ratio [OR], 0.87; 95% CI, 0.79-0.95) and for excessive weight loss in the late improvement group (OR, 0.89; 95% CI, 0.80-1.00). Compared with the stable high group, the risk of excessive weight gain was significantly increased for the early decline group (OR, 1.19; 95% CI, 1.08-1.31) and late decline group (OR, 1.13; 95% CI, 1.04-1.24) and for excessive weight loss in the early decline group (OR, 1.15; 95% CI, 1.02-1.28). The increases in likelihood were greater when the improvement or decline in neighborhood SES occurred early in the study period (ie, 1990-2000) and was substantiated throughout the follow-up (ie, the early decline and early improvement groups). Overall, we found a linear association between changes in neighborhood SES and weight outcomes, in which every 5 percentile decline in neighborhood SES was associated with a 1.2% to 2.4% increase in the risk of excessive weight gain or loss (excessive weight gain: OR, 1.01; 95% CI, 1.00-1.02 for women; OR, 1.02; 95% CI, 1.01-1.03 for men; excessive weight loss: OR, 1.02; 95% CI, 1.01-1.03 for women; OR, 1.02; 95% CI, 1.01-1.03 for men; P for- trend < .0001).Conclusions and relevanceThese findings suggest that changing neighborhood environment was associated with changes in weight status in older adults.

Project description:BackgroundAir pollution and neighborhood socioeconomic status (nSES) have been shown to affect cognitive decline in older adults. In previous studies, nSES acts as both a confounder and an effect modifier between air pollution and cognitive decline.ObjectivesThis study aims to examine the individual and joint effects of air pollution and nSES on cognitive decline on adults 50 years and older in Metro Atlanta, USA.MethodsPerceived memory and cognitive decline was assessed in 11,897 participants aged 50+ years from the Emory Healthy Aging Study (EHAS) using the cognitive function instrument (CFI). Three-year average air pollution concentrations for 12 pollutants and 16 nSES characteristics were matched to participants using census tracts. Individual exposure linear regression and LASSO models explore individual exposure effects. Environmental mixture modeling methods including, self-organizing maps (SOM), Bayesian kernel machine regression (BKMR), and quantile-based G-computation explore joint effects, and effect modification between air pollutants and nSES characteristics on cognitive decline.ResultsParticipants living in areas with higher air pollution concentrations and lower nSES experienced higher CFI scores (beta: 0.121; 95 % CI: 0.076, 0.167) compared to participants living in areas with low air pollution and high nSES. Additionally, the BKMR model showed a significant overall mixture effect on cognitive decline, suggesting synergy between air pollution and nSES. These joint effects explain protective effects observed in single-pollutant linear regression models, even after adjustment for confounding by nSES (e.g., an IQR increase in CO was associated with a 0.038-point lower (95 % CI: -0.06, -0.01) CFI score).DiscussionObserved protective effects of single air pollutants on cognitive decline can be explained by joint effects and effect modification of air pollutants and nSES. Researchers must consider nSES as an effect modifier if not a co-exposure to better understand the complex relationships between air pollution and nSES in urban settings.

Project description:ObjectiveThis study developed and internally validated a predictive model for preterm birth (PTB) to examine the ability of neighbourhood socioeconomic status (SES) to predict PTB.DesignCohort study using individual-level data from two community-based prospective pregnancy cohort studies (All Our Families (AOF) and Alberta Pregnancy Outcomes and Nutrition (APrON)) and neighbourhood SES data from the 2011 Canadian census.SettingCalgary, Alberta, Canada.ParticipantsPregnant women who were <24 weeks of gestation and >15 years old were enrolled in the cohort studies between 2008 and 2012. Overall, 5297 women participated in at least one of these cohorts: 3341 women participated in the AOF study, 2187 women participated in the APrON study and 231 women participated in both studies. Women who participated in both studies were only counted once.Primary and secondary outcome measuresPTB (delivery prior to 37 weeks of gestation).ResultsThe rates of PTB in the least and most deprived neighbourhoods were 7.54% and 10.64%, respectively. Neighbourhood variation in PTB was 0.20, with an intra-class correlation of 5.72%. Neighbourhood SES, combined with individual-level predictors, predicted PTB with an area under the receiver-operating characteristic curve (AUC) of 0.75. The sensitivity was 91.80% at a low-risk threshold, with a high false-positive rate (71.50%), and the sensitivity was 5.70% at a highest risk threshold, with a low false-positive rate (0.90%). An agreement between the predicted and observed PTB demonstrated modest model calibration. Individual-level predictors alone predicted PTB with an AUC of 0.60.ConclusionAlthough neighbourhood SES combined with individual-level predictors improved the overall prediction of PTB compared with individual-level predictors alone, the detection rate was insufficient for application in clinical or public health practice. A prediction model with better predictive ability is required to effectively find women at high risk of preterm delivery.

Dataset Information

Association of Neighborhood Socioeconomic Status With Risk of Infection and Sepsis.

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Association of Neighborhood Socioeconomic Status With Risk of Infection and Sepsis.

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