Self-reported Health Status Data Research Articles

P78 Low-Income Adults Enrolled in a Cost-Offset, Community-Supported Agriculture Intervention are not Nationally Representative

<h3>Background</h3> Cost-offset, Community-supported Agriculture (CO-CSA) is a model in which low-income households receive subsidized shares of a local farm's harvest. The goal is to address diet and health disparities among a population with historically poor access to and consumption of healthy foods. The appeal of such an intervention, and thus its reach, is limited to a self-selected sub-sample of the low-income population that may be non-representative. <h3>Objective</h3> To compare characteristics of CO-CSA enrolled adults from low-income households with those of a nationally-representative sample of low-income adults. We hypothesized that CO-CSA enrollees would be less demographically diverse, have greater nutrition-related knowledge, and report better health than the national sample. <h3>Study Design, Setting, Participants</h3> CO-CSA enrollee data were drawn from the baseline survey of a multi-state randomized controlled trial testing the CO-CSA model in conjunction with nutrition education among low-income households with children in Vermont, New York, North Carolina, and Washington (n=305). Those data were compared to a nationally-representative sample of low-income adults from households with children evaluated via the National Health and Nutrition Examination Survey (2007-2010). <h3>Measurable Outcome/Analysis</h3> Descriptive statistics for demographic, food security, nutrition knowledge, and self-reported health status data were generated using SPSS for both samples. <h3>Results</h3> Compared to the national sample, a greater proportion of CO-CSA enrollees were female (97% vs 57%), white (76% vs 45%), highly educated (49% vs 7% college graduates), and food insecure (57% vs 49%); a smaller proportion were Hispanic (6% vs 34%). A greater proportion of CO-CSA enrollees knew the daily recommendation for fruits and vegetables (3-5 cups; 61% vs 16%) and reported being in good or excellent health (34% vs 25%). <h3>Conclusions</h3> Differences between the CO-CSA and NHANES samples may be related to the CO-CSA study sampling frame (English-speaking households in certain regions of the U.S.). Results support continued exploration into whether CO-CSA and other local food system interventions have the potential to reach beyond female, white, educated, and nutritionally-knowledgeable segments of the target population.

Improving Prediction of High-Cost Health Care Users with Medical Check-Up Data.

Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.

Predicting cost of care using self-reported health status data

BackgroundWe examined whether self-reported employee health status data can improve the performance of administrative data-based models for predicting future high health costs, and develop a predictive model for predicting new high cost individuals.MethodsThis retrospective cohort study used data from 8,917 Safeway employees self-insured by Safeway during 2008 and 2009. We created models using step-wise multivariable logistic regression starting with health services use data, then socio-demographic data, and finally adding the self-reported health status data to the model.ResultsAdding self-reported health data to the baseline model that included only administrative data (health services use and demographic variables; c-statistic = 0.63) increased the model” predictive power (c-statistic = 0.70). Risk factors associated with being a new high cost individual in 2009 were: 1) had one or more ED visits in 2008 (adjusted OR: 1.87, 95 % CI: 1.52, 2.30), 2) had one or more hospitalizations in 2008 (adjusted OR: 1.95, 95 % CI: 1.38, 2.77), 3) being female (adjusted OR: 1.34, 95 % CI: 1.16, 1.55), 4) increasing age (compared with age 18-35, adjusted OR for 36-49 years: 1.28; 95 % CI: 1.03, 1.60; adjusted OR for 50-64 years: 1.92, 95 % CI: 1.55, 2.39; adjusted OR for 65+ years: 3.75, 95 % CI: 2.67, 2.23), 5) the presence of self-reported depression (adjusted OR: 1.53, 95 % CI: 1.29, 1.81), 6) chronic pain (adjusted OR: 2.22, 95 % CI: 1.81, 2.72), 7) diabetes (adjusted OR: 1.73, 95 % CI: 1.35, 2.23), 8) high blood pressure (adjusted OR: 1.42, 95 % CI: 1.21, 1.67), and 9) above average BMI (adjusted OR: 1.20, 95 % CI: 1.04, 1.38).DiscussionThe comparison of the models between the full sample and the sample without theprevious high cost members indicated significant differences in the predictors. This has importantimplications for models using only the health service use (administrative data) given that the past high costis significantly correlated with future high cost and often drive the predictive models.ConclusionsSelf-reported health data improved the ability of our model to identify individuals at risk for being high cost beyond what was possible with administrative data alone.

Differences in healthy life expectancy for the US population by sex, race/ethnicity and geographic region: 2008.

Healthy life expectancy (HLE) varies among demographic segments of the US population and by geography. To quantify that variation, we estimated the national and regional HLE for the US population by sex, race/ethnicity and geographic region in 2008. National HLEs were calculated using the published 2008 life table and the self-reported health status data from the National Health Interview Survey (NHIS). Regional HLEs were calculated using the combined 2007-09 mortality, population and NHIS health status data. In 2008, HLE in the USA varied significantly by sex, race/ethnicity and geographical regions. At 25 years of age, HLE for females was 47.3 years and ∼2.9 years greater than that for males at 44.4 years. HLE for non-Hispanic white adults was 2.6 years greater than that for Hispanic adults and 7.8 years greater than that for non-Hispanic black adults. By region, the Northeast had the longest HLE and the South had the shortest. The HLE estimates in this report can be used to monitor trends in the health of populations, compare estimates across populations and identify health inequalities that require attention.

Longitudinal Analysis of Self-Reported Health Status by Mixture Latent Auto-Regressive Models

Summary Motivated by an application to a longitudinal data set coming from the Health and Retirement Study about self-reported health status, we propose a model for longitudinal data which is based on a latent process to account for the unobserved heterogeneity between sample units in a dynamic fashion. The latent process is modelled by a mixture of auto-regressive AR(1) processes with different means and correlation coefficients, but with equal variances. We show how to perform maximum likelihood estimation of the proposed model by the joint use of an expectation–maximization algorithm and a Newton–Raphson algorithm, implemented by means of recursions developed in the hidden Markov model literature. We also introduce a simple method to obtain standard errors for the parameter estimates and suggest a strategy to choose the number of mixture components. In the application the response variable is ordinal; however, the approach may also be applied in other settings. Moreover, the application to the self-reported health status data set allows us to show that the model proposed is more flexible than other models for longitudinal data based on a continuous latent process. The model also achieves a goodness of fit that is similar to that of models based on a discrete latent process following a Markov chain, while retaining a reduced number of parameters. The effect of different formulations of the latent structure of the model is evaluated in terms of estimates of the regression parameters for the covariates.

Understanding the Growth in Federal Disability Programs: Who are the Marginal Beneficiaries and How Much Do They Cost?

SSI and SSDI, the two work disability programs administered by the Social Security Administration (SSA), have been marked by concerns about target efficiency since their inception. This study uses SSA administrative data linked with National Health Interview Survey data (NHIS) to examine health status, labor force participation at time of NHIS interview, and linked mortality data to examine mortality during the period following NHIS interview. The self-reported health status data present two strong and consistent patterns: denied applicants report being in considerably worse health than non-applicants, and beneficiaries appear to be sicker yet. In logit models among disability beneficiaries, women are significantly less likely to report excellent/very good health, but race has no significant effect. While being female decreased the probability of good health, it has no significant effect on the probability of reporting no work limitation at time of interview among beneficiaries. Although race was not significant in the model of self-reported health, both Hispanics and non-Hispanic blacks are significantly more likely to report no work limitation at time of interview. This study has important limitations. NHIS respondents who link to the SSA administrative data may not be representative of all individuals with disability application histories. In addition, individuals must live long enough after disability determination to be drawn into an NHIS sample, and these results reflect the experience of that subsample of disability applicants who do not die during the determination process or soon thereafter.

Predictors of Medication Adherence and Associated Health Care Costs in an Older Population With Overactive Bladder Syndrome: A Longitudinal Cohort Study

We examined the relationship between self-reported health status data, subsequent antimuscarinic medication adherence and health care service use in older adults with OAB syndrome in a managed care setting. This was a longitudinal cohort study of older adults in the southeastern United States with OAB who completed a health status assessment, used antimuscarinic medications and were enrolled in an HMO continuously for 1 to 3 years. Demographic, clinical and use related economic variables were also retrieved from the administrative claims data of patient HMOs. Prescription refill patterns were used to measure medication adherence. Associations were examined with a sequential, mixed model regression approach. A total of 275 patients were included. The severity of comorbidity (Charlson index), patient perception of quality of life (Short Form-12 scores) and total number of prescribed medications during the year prior to enrollment in a Medicare HMO were independently associated with decreased antimuscarinic MPRs after enrollment. After controlling for other variables increased antimuscarinic MPR remained the strongest predictor of decreased total annual health care costs (5.6% decrease in annual costs with every 10% increase in MPR, p < 0.001). We found strong associations between decreased antimuscarinic medication adherence and increased health care service use in older adults with OAB in a managed care setting. Health status assessments completed at enrollment had the potential to identify enrollees at higher risk for nonadherent behaviors and poor health related outcomes.

Predictors of medication adherence and associated health care costs in an older population with type 2 diabetes mellitus: A longitudinal cohort study

Background: The cost of treating diabetes mellitus and its complications is high ($91.8 billion in the United States in 2002). It is important to understand predictors of adherence to therapy with different antidiabetic medications and to determine the relationships between adherence and health care service utilization in older adults (aged ≥65 years) with type 2 diabetes mellitus. Objective: The aim of this study was to examine the relationship between self-reported health status data, subsequent antidiabetic medication adherence, and health care service utilization in older adults with type 2 diabetes mellitus in a managed care setting. Methods: This was a longitudinal cohort study of older adults in the south-eastern United States with type 2 diabetes mellitus who completed a health status assessment, used antidiabetic medications, and were enrolled in a health maintenance organization (HMO) continuously for 1 to 5 years. The baseline assessment included questions related to demographics, health care service utilization in the year before enrollment, lifestyle, and quality of life. Demographic, clinical, and utilization-related economic variables were also retrieved from the administrative claims data of the patients' HMO. Prescription refill patterns were used to measure adherence. Associations were examined with a sequential, mixed-model, regression approach. Model appropriateness was tested via sensitivity analyses with logged and unlogged dependent variables. Results: A total of 775 patients were included. Increased comorbidity severity and an emergency room visit during the year prior to enrollment in a Medicare HMO were independently associated with decreased antidiabetic medication possession ratios (MPRs) after enrollment. After controlling for type of medication therapy and other variables, increased antidiabetic MPR remained the strongest predictor of decreased total annual health care costs (8.6% to 28.9% decrease in annual costs with every 10% increase in MPR; P < 0.001). Adherence to anti-diabetic medications was a greater driver of cost reduction than other concurrent medications (eg, statins) in this population. Conclusions: This study found strong associations between decreased anti-diabetic medication adherence and increased health care service utilization in older adults with type 2 diabetes mellitus in a managed care setting. Health status assessments completed at time of enrollment had the potential to identify enrollees with higher risk for both nonadherent behaviors and poor health-related outcomes.