Dataset Information

Body Mass Index and Rural Status on Self-Reported Health in Older Adults: 2004-2013 Medicare Expenditure Panel Survey.

ABSTRACT: PURPOSE:To ascertain whether rural status impacts self-reported health and whether the effect of rural status on self-reported health differs by obesity status. METHODS:We identified 22,307 subjects aged ?60 from the Medical Expenditure Panel Survey 2004-2013. Body mass index (BMI) was categorized as underweight, normal, overweight, or obese. Physical and mental component scores of the Short Form-12 assessed self-reported health status. Rural/urban status was defined using metropolitan statistical area. Weighted regression models ascertained the relative contribution of predictors (including rural and BMI) on each subscale. FINDINGS:Mean age was 70.7 years. Rural settings had higher proportions classified as obese (30.7 vs 27.6%; P < .001), and rural residents had lower physical health status (41.7 ± 0.3) than urban (43.4 ± 0.1; P < .001). Obese or underweight persons had lower physical health status (39.5 ± 0.20 and 37.0 ± 0.82, respectively) than normal (44.7 ± 0.18) or overweight (44.6 ± 0.16) persons (P < .001). BMI category stratification was associated with differences in physical health between rural/urban by BMI. Individuals classified as underweight or obese had lower physical health compared to normal, while the differences were less pronounced for mental health. No differences in mental health existed between rural/urban status. A BMI * rural interaction was significant for physical but not mental health. CONCLUSIONS:Rural residents report lower self-reported physical health status compared to urban residents, particularly older adults who are obese or underweight. No interaction was observed between BMI and rural status.

SUBMITTER: Batsis JA

PROVIDER: S-EPMC6454924 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Body Mass Index and Rural Status on Self-Reported Health in Older Adults: 2004-2013 Medicare Expenditure Panel Survey.

Batsis John A JA Whiteman Karen L KL Lohman Matthew C MC Scherer Emily A EA Bartels Stephen J SJ

The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association 20170315

<h4>Purpose</h4>To ascertain whether rural status impacts self-reported health and whether the effect of rural status on self-reported health differs by obesity status.<h4>Methods</h4>We identified 22,307 subjects aged ≥60 from the Medical Expenditure Panel Survey 2004-2013. Body mass index (BMI) was categorized as underweight, normal, overweight, or obese. Physical and mental component scores of the Short Form-12 assessed self-reported health status. Rural/urban status was defined using metropo ...[more]

PMID: 28295614

Similar Datasets

Project description:There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status.This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure.There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals' explanation was understandable.It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.

Project description:ObjectiveTo test for correlation between the growth in adoption of ambulatory electronic health records (EHRs) in the United States during 2010-2013 and hospital admissions and readmissions for elderly Medicare beneficiaries with at least one of four common ambulatory care-sensitive conditions (ACSCs).Data sourcesSK&A Information Services Survey of Physicians, American Hospital Association General Survey and Information Technology Supplement; and the Centers for Medicare & Medicaid Services Chronic Conditions Data Warehouse Geographic Variation Database for 2010 through 2013.Study designFixed effects model estimated the relationship between hospital referral region (HRR) level measures of physician EHR adoption and ACSC admissions and readmissions. Analyzed rates of admissions and 30-day readmissions per beneficiary at the HRR level (restricting the denominator to beneficiaries in our sample), adjusted for differences across HRRs in Medicare beneficiary age, gender, and race. Calculated physician EHR adoption rates as the percentage of physicians in each HRR who report using EHR in ambulatory care settings.Principal findingsEach percentage point increase in market-level EHR adoption by physicians is correlated with a statistically significant decline of 1.06 ACSC admissions per 10,000 beneficiaries over the study period, controlling for the overall time trend as well as market fixed effects and characteristics that changed over time. This finding implies 26,689 fewer ACSC admissions in our study population during 2010 to 2013 that were related to physician ambulatory EHR adoption. This represents 3.2 percent fewer ACSC admissions relative to the total number of such admissions in our study population in 2010. We found no evidence of a correlation between EHR use, by either physicians or hospitals, and hospital readmissions at either the market level or hospital level.ConclusionsThis study extends knowledge about EHRs' relationship with quality of care and utilization. The results suggest a significant association between EHR use in ambulatory care settings and ACSC admissions that is consistent with policy goals to improve the quality of ambulatory care for patients with chronic conditions. The null findings for readmissions support the need for improved interoperability between ambulatory care EHRs and hospital EHRs to realize improvements in readmissions.

Project description:Excess weight is a public health challenge affecting millions worldwide, including younger age groups. The human exposome concept presents a novel opportunity to comprehensively characterize all non-genetic disease determinants at susceptible time windows. This study aimed to describe the association between multiple lifestyle and clinical exposures and body mass index (BMI) in adolescents using the exposome framework. We conducted an exposome-wide association (ExWAS) study using U.S. National Health and Nutrition Examination Survey (NHANES) 2003-2004 wave for discovery of associations between study population characteristics and zBMI, and used the 2013-2014 wave to replicate analysis. We included non-diabetic and non-pregnant adolescents aged 12-18 years. We performed univariable and multivariable linear regression analysis adjusted for age, sex, race/ethnicity, household smoking, and income to poverty ratio, and corrected for false-discovery rate (FDR). A total of 1899 and 1224 participants were eligible from 2003-2004 and 2013-2014 survey waves. Weighted proportions of overweight were 18.4% and 18.5% whereas those for obese were 18.1% and 20.6% in 2003-2004 and 2013-2014, respectively. Retained exposure agents included 75 laboratory (clinical and biomarkers of environmental chemical exposures) and 64 lifestyle (63 dietary and 1 physical activity) variables. After FDR correction, univariable regression identified 27 and 12 predictors in discovery and replication datasets, respectively, while multivariable regression identified 22 and 9 predictors in discovery and replication datasets, respectively. Six were significant in both datasets: alanine aminotransferase, gamma glutamyl transferase, segmented neutrophils number, triglycerides; uric acid and white blood cell count. In this ExWAS study using NHANES data, we described associations between zBMI, nutritional, clinical and environmental factors in adolescents. Future studies are warranted to investigate the role of the identified predictors as early-stage biomarkers of increased BMI and associated pathologies among adolescents and to replicate findings to other populations.

Project description:BackgroundCatastrophic health expenditure (CHE) is an important indicator of measuring health inequality. Previous studies mainly focused on specific vulnerable populations rather than a wider range of vulnerable areas through panel data. Rural China is often associated with an underdeveloped economy and insufficient health resources. This study aims to update the information on the extent of and trends in the incidence and inequality of CHE among the households of rural China through longitudinal survey data.MethodsData were obtained from three waves of the China Health and Retirement Longitudinal Study (CHARLS): 2013, 2015, and 2018. In total, 2,575 households were included in the analysis. CHE was defined as household health expenditures exceeding 40% of non-food expenditures. Inequality in CHE was measured using the concentration curve and concentration index. The contribution to CHE inequality was decomposed using the concentration index decomposition method.ResultsThe incidence of CHE was 0.2341 (95% CI: 0.22, 0.25) in 2013, 0.2136 (95% CI: 0.20, 0.23) in 2015, and 0.2897 (95% CI: 0.27, 0.31) in 2018 in rural China. The concentration curve lay above the equality line, and the concentration index was negative: -0.1528 (95% CI: -0.1941, -0.1115) in 2013, -0.1010 (95% CI: -0.1442, -0. 0577) in 2015, and -0.0819 (95% CI: -0.1170, -0.0467) in 2018. Economic status, age, and chronic diseases were the main contributors to inequality in CHE.ConclusionsThe incidence of CHE in rural China displayed an upward trend from 2013 to 2018, although it was not continuous. Furthermore, a strong pro-low-economic inequality in CHE existed in rural China. Mainly economic status, age, and chronic diseases contributed to this pro-low-economic inequality. Health policies to allocate resources and services are needed to satisfy the needs of rural households and provide more accessible and affordable health services. More concern needs to be directed toward households with chronic diseases and older persons to reduce the incidence of CHE and promote health equality.

Dataset Information

Body Mass Index and Rural Status on Self-Reported Health in Older Adults: 2004-2013 Medicare Expenditure Panel Survey.

Publications

Body Mass Index and Rural Status on Self-Reported Health in Older Adults: 2004-2013 Medicare Expenditure Panel Survey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets