The effect of differential privacy on Medicaid participation among racial and ethnic minority groups.

OBJECTIVE: To investigate how county and state-level estimates of Medicaid enrollment among the total, non-Hispanic White, non-Hispanic Black or African American, and Hispanic or Latino/a population are affected by Differential Privacy (DP), where statistical noise is added to the public decennial US census data to protect individual privacy. DATA SOURCES: We obtained population counts from the final version of the US Census Bureau Differential Privacy Demonstration Products from 2010 and combined them with Medicaid enrollment data. STUDY DESIGN: We compared 2010 county and state-level population counts released under the traditional disclosure avoidance techniques and the ones produced with the proposed DP procedures. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: We find the DP method introduces errors up to 10% into counts and proportions of Medicaid participation rate accuracy at the county level, especially for small subpopulations and racial and ethnic minority groups. The effect of DP on Medicaid participation rate accuracy is only small and negligible at the state level. CONCLUSIONS: The implementation of DP in the 2020 census can affect the analyses of health disparities and health care access and use among different subpopulations in the United States. The planned implementation of DP in other census-related surveys such as the American Community Survey can misrepresent Medicaid participation rates for small racial and ethnic minority groups. This can affect Medicaid funding decisions.

The causal impact of sugar taxes on soft drink sales: evidence from France and Hungary.

Sugar-sweetened beverages (SSBs) are associated with increased body weight and obesity, which induce a wide array of health impairments such as diabetes or cardiovascular disorders. Excise taxes have been introduced to counteract SSB consumption. We investigated the effect of sugar taxes on SSB sales in Hungary and France using a synthetic control approach. For France, we found a slight decrease in SSB sales after tax implementation while overall soft drink sales increased. For Hungary, there was only a short-term decrease in SSB sales which disappeared after 2 years, leading to an overall increase in SSB sales. However, both effects are characterized by great uncertainty.

Effects of adherence to pharmacological secondary prevention after acute myocardial infarction on health care costs - an analysis of real-world data.

BACKGROUND: Acute myocardial infarction (AMI), a major source of morbidity and mortality, is also associated with excess costs. Findings from previous studies were divergent regarding the effect on health care expenditure of adherence to guideline-recommended medication. However, gender-specific medication effectiveness, correlating the effectiveness of concomitant medication and variation in adherence over time, has not yet been considered. METHODS: We aim to measure the effect of adherence on health care expenditures stratified by gender from a third-party payer's perspective in a sample of statutory insured Disease Management Program participants over a follow-up period of 3-years. In 3627 AMI patients, the proportion of days covered (PDC) for four guideline-recommended medications was calculated. A generalized additive mixed model was used, taking into account inter-individual effects (mean PDC rate) and intra-individual effects (deviation from the mean PDC rate). RESULTS: Regarding inter-individual effects, for both sexes only anti-platelet agents had a significant negative influence indicating that higher mean PDC rates lead to higher costs. With respect to intra-individual effects, for females higher deviations from the mean PDC rate for angiotensin-converting enzyme (ACE) inhibitors, anti-platelet agents, and statins were associated with higher costs. Furthermore, for males, an increasing positive deviation from the PDC mean increases costs for ß-blockers and a negative deviation decreases costs. For anti-platelet agents, an increasing deviation from the PDC-mean slightly increases costs. CONCLUSION: Positive and negative deviation from the mean PDC rate, independent of how high the mean was, usually negatively affect health care expenditures. Therefore, continuity in intake of guideline-recommended medication is important to save costs.

Effect of BMI on health care expenditures stratified by COPD GOLD severity grades: Results from the LQ-DMP study.

Chronic Obstructive Pulmonary Disease (COPD) is characterized by persistent respiratory symptoms and airflow limitation, which is progressive and not fully reversible. In patients with COPD, body mass index (BMI) is an important parameter associated with health outcomes, e.g. mortality and health-related quality of life. However, so far no study evaluated the association of BMI and health care expenditures across different COPD severity grades. We used claims data and documentation data of a Disease Management Program (DMP) from a statutory health insurance fund (AOK Bayern). Patients were excluded if they had less than 4 observations in the 8 years observational period. Generalized additive mixed models with smooth functions were used to evaluate the association between BMI and health care expenditures, stratified by severity of COPD, indicated by GOLD grades 1-4. We included 30,682 patients with overall 188,725 observations. In GOLD grades 1-3 we found an u-shaped relation of BMI and expenditures, where patients with a BMI of 30 or slightly above had the lowest and underweight and obese patients had the highest health care expenditures. Contrarily, in GOLD grade 4 we found an almost linear decline of health care expenditures with increasing BMI. In terms of expenditures, the often reported obesity paradox in patients with COPD was clearly reflected in GOLD grade 4, while in all other severity grades underweight as well as severely obese patients caused the highest health care expenditures. Reduction of obesity may thus reduce health care expenditures in GOLD grades 1-3.

Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods.

Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications.

Application of Mendelian Randomization to Investigate the Association of Body Mass Index with Health Care Costs.

Causal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations, a method that is often called Mendelian randomization (MR). We describe the assumptions, available methods, and potential pitfalls of using genetic information and how to address them. We estimate the effect of body mass index (BMI) on total health care costs using data from a German observational study and from published large-scale data. In a meta-analysis of several MR approaches, we find that models using genetic instruments identify additional annual costs of €280 for a 1-unit increase in BMI. This is more than 3 times higher than estimates from linear regression without instrumental variables (€75). We found little evidence of a nonlinear relationship between BMI and health care costs. Our results suggest that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.

The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series.

Surgical measures to combat obesity are very effective in terms of weight loss, recovery from diabetes, and improvement in cardiovascular risk factors. However, previous studies found both positive and negative results regarding the effect of bariatric surgery on health care utilization. Using claims data from the largest health insurance provider in Germany, we estimated the causal effect of bariatric surgery on health care costs in a time period ranging from 2 years before to 3 years after bariatric intervention. Owing to the absence of a control group, we employed a Bayesian structural forecasting model to construct a synthetic control. We observed a decrease in medication and physician expenditures after bariatric surgery, whereas hospital expenditures increased in the post-intervention period. Overall, we found a slight increase in total costs after bariatric surgery, but our estimates include a high degree of uncertainty.

Do alternative weighting approaches for an Index of Multiple Deprivation change the association with mortality? A sensitivity analysis from Germany.

OBJECTIVES: This study aimed to assess the impact of using different weighting procedures for the German Index of Multiple Deprivation (GIMD) investigating their link to mortality rates. DESIGN AND SETTING: In addition to the original (normative) weighting of the GIMD domains, four alternative weighting approaches were applied: equal weighting, linear regression, maximization algorithm and factor analysis. Correlation analyses to quantify the association between the differently weighted GIMD versions and mortality based on district-level official data from Germany in 2010 were applied (n=412 districts). OUTCOME MEASURES: Total mortality (all age groups) and premature mortality (<65 years). RESULTS: All correlations of the GIMD versions with both total and premature mortality were highly significant (p<0.001). The comparison of these associations using Williams's t-test for paired correlations showed significant differences, which proved to be small in respect to absolute values of Spearman's rho (total mortality: between 0.535 and 0.615; premature mortality: between 0.699 and 0.832). CONCLUSIONS: The association between area deprivation and mortality proved to be stable, regardless of different weighting of the GIMD domains. The theory-based weighting of the GIMD should be maintained, due to the stability of the GIMD scores and the relationship to mortality.

Performance of the UKPDS Outcomes Model 2 for Predicting Death and Cardiovascular Events in Patients with Type 2 Diabetes Mellitus from a German Population-Based Cohort.

BACKGROUND AND OBJECTIVE: Accurate prediction of relevant outcomes is important for targeting therapies and to support health economic evaluations of healthcare interventions in patients with diabetes. The United Kingdom Prospective Diabetes Study (UKPDS) risk equations are some of the most frequently used risk equations. This study aims to analyze the calibration and discrimination of the updated UKPDS risk equations as implemented in the UKPDS Outcomes Model 2 (UKPDS-OM2) for predicting cardiovascular (CV) events and death in patients with type 2 diabetes mellitus (T2DM) from population-based German samples. METHODS: Analyses are based on data of 456 individuals diagnosed with T2DM who participated in two population-based studies in southern Germany (KORA (Cooperative Health Research in the Region of Augsburg)-A: 1997/1998, n = 178; KORA-S4: 1999-2001, n = 278). We compared the participants' 10-year observed incidence of mortality, CV mortality, myocardial infarction (MI), and stroke with the predicted event rate of the UKPDS-OM2. The model's calibration was evaluated by Greenwood-Nam-D'Agostino tests and discrimination was evaluated by C-statistics. RESULTS: Of the 456 participants with T2DM (mean age 65 years, mean diabetes duration 8 years, 56% male), over the 10-year follow-up time 129 died (61 due to CV events), 64 experienced an MI, and 46 a stroke. The UKPDS-OM2 significantly over-predicted mortality and CV mortality by 25% and 28%, respectively (Greenwood-Nam-D'Agostino tests: p < 0.01), but there was no significant difference between predicted and observed MI and stroke risk. The model poorly discriminated for death (C-statistic [95% confidence interval] = 0.64 [0.60-0.69]), CV death (0.64 [0.58-0.71]), and MI (0.58 [0.52-0.66]), and failed to discriminate for stroke (0.57 [0.47-0.66]). CONCLUSIONS: The study results demonstrate acceptable calibration and poor discrimination of the UKPDS-OM2 for predicting death and CV events in this population-based German sample. Those limitations should be considered when using the UKPDS-OM2 for economic evaluations of healthcare strategies or using the risk equations for clinical decision-making.

Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study.

BACKGROUND: The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments. METHODS: We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany. RESULTS: Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63-89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%). CONCLUSIONS: It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance.

Tweedie distributions for fitting semicontinuous health care utilization cost data.

BACKGROUND: The statistical analysis of health care cost data is often problematic because these data are usually non-negative, right-skewed and have excess zeros for non-users. This prevents the use of linear models based on the Gaussian or Gamma distribution. A common way to counter this is the use of Two-part or Tobit models, which makes interpretation of the results more difficult. In this study, I explore a statistical distribution from the Tweedie family of distributions that can simultaneously model the probability of zero outcome, i.e. of being a non-user of health care utilization and continuous costs for users. METHODS: I assess the usefulness of the Tweedie model in a Monte Carlo simulation study that addresses two common situations of low and high correlation of the users and the non-users of health care utilization. Furthermore, I compare the Tweedie model with several other models using a real data set from the RAND health insurance experiment. RESULTS: I show that the Tweedie distribution fits cost data very well and provides better fit, especially when the number of non-users is low and the correlation between users and non-users is high. CONCLUSION: The Tweedie distribution provides an interesting solution to many statistical problems in health economic analyses.