Dataset Information

Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011.

ABSTRACT: Producing indices composed of multiple input variables has been embedded in some data processing and analytical methods. We aim to test the feasibility of creating data-driven indices by aggregating input variables according to principal component analysis (PCA) loadings. To validate the significance of both the theory-based and data-driven indices, we propose principles to review innovative indices. We generated weighted indices with the variables obtained in the first years of the two-year panels in the Medical Expenditure Panel Survey initiated between 1996 and 2011. Variables were weighted according to PCA loadings and summed. The statistical significance and residual deviance of each index to predict mortality in the second years was extracted from the results of discrete-time survival analyses. There were 237,832 surviving the first years of panels, represented 4.5 billion civilians in the United States, of which 0.62% (95% CI = 0.58% to 0.66%) died in the second years of the panels. Of all 134,689 weighted indices, there were 40,803 significantly predicting mortality in the second years with or without the adjustment of age, sex and races. The significant indices in the both models could at most lead to 10,200 years of academic tenure for individual researchers publishing four indices per year or 618.2 years of publishing for journals with annual volume of 66 articles. In conclusion, if aggregating information based on PCA loadings, there can be a large number of significant innovative indices composing input variables of various predictive powers. To justify the large quantities of innovative indices, we propose a reporting and review framework for novel indices based on the objectives to create indices, variable weighting, related outcomes and database characteristics. The indices selected by this framework could lead to a new genre of publications focusing on meaningful aggregation of information.

SUBMITTER: Chao YS

PROVIDER: S-EPMC5590867 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011.

Chao Yi-Sheng YS Wu Chao-Jung CJ

PloS one 20170908 9

Producing indices composed of multiple input variables has been embedded in some data processing and analytical methods. We aim to test the feasibility of creating data-driven indices by aggregating input variables according to principal component analysis (PCA) loadings. To validate the significance of both the theory-based and data-driven indices, we propose principles to review innovative indices. We generated weighted indices with the variables obtained in the first years of the two-year pan ...[more]

PMID: 28886057

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:IntroductionThe use of mail-order pharmacies is generally associated with lower healthcare costs and improved medication adherence. To promote the use of mail-order pharmacies, it is important to understand the time trends in their use and whether these trends vary by population subgroups.MethodsThis study used the 1996-2018 Medical Expenditure Panel Survey to determine the annual prevalence of mail-order pharmacy use (defined as purchasing ≥1 prescription from a mail-order or online pharmacy) among U.S. adult prescription users and its variation by population characteristics. Logistic regression was used to determine the correlates of mail-order pharmacy use. Results were presented for medications and therapeutic classes most commonly purchased by mail-order pharmacy exclusive users. Analyses were conducted in December 2020.ResultsThe annual prevalence of mail-order pharmacy use among U.S. adult prescription users increased from 10.2% (95% CI=9.3, 11.1) in 1996 to 17.0% (95% CI=15.9, 18.1) in 2005 and then declined to 15.7% (95% CI=14.9, 16.6) by 2018. Absolute differences in the prevalence of use by race/ethnicity, education, and health insurance coverage widened over time, whereas they remained stable when stratifying by sex, age, marital status, region, limitations in daily activities, pain interference, health status, number of chronic conditions, and access to medical care. Among mail-order pharmacy exclusive users, the 3 most commonly purchased medications were atorvastatin (16.7%), levothyroxine (13.6%), and lisinopril (13.1%); the 3 most commonly purchased therapeutic classes were cardiovascular agents (57.9%), metabolic agents (52.1%), and central nervous system agents (29.6%).ConclusionsThe prevalence of mail-order pharmacy use has declined in recent years and has shown significant variation across population subgroups. Future research should examine whether the declining trends and variation in use may influence the management of chronic conditions and the disparities in health and healthcare costs.

Dataset Information

Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011.

Publications

Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets