Novel integration of governmental data sources using machine learning to identify super-utilization among U.S. counties.

Background: Super-utilizers consume the greatest share of resource intensive healthcare (RIHC) and reducing their utilization remains a crucial challenge to healthcare systems in the United States (U.S.). The objective of this study was to predict RIHC among U.S. counties, using routinely collected data from the U.S. government, including information on consumer spending, offering an alternative method for identifying super-utilization among population units rather than individuals. Methods: Cross-sectional data from 5 governmental sources in 2017 were used in a machine learning pipeline, where target-prediction features were selected and used in 4 distinct algorithms. Outcome metrics of RIHC utilization came from the American Hospital Association and included yearly: (1) emergency rooms visit, (2) inpatient days, and (3) hospital expenditures. Target-prediction features included: 149 demographic characteristics from the U.S. Census Bureau, 151 adult and child health characteristics from the Centers for Disease Control and Prevention, 151 community characteristics from the American Community Survey, and 571 consumer expenditures from the Bureau of Labor Statistics. SHAP analysis identified important target-prediction features for 3 RIHC outcome metrics. Results: 2475 counties with emergency rooms and 2491 counties with hospitals were included. The median yearly emergency room visits per capita was 0.450 [IQR:0.318, 0.618], the median inpatient days per capita was 0.368 [IQR: 0.176, 0.826], and the median hospital expenditures per capita was $2104 [IQR: $1299.93, 3362.97]. The coefficient of determination (R2), calculated on the test set, ranged between 0.267 and 0.447. Demographic and community characteristics were among the important predictors for all 3 RIHC outcome metrics. Conclusions: Integrating diverse population characteristics from numerous governmental sources, we predicted 3-outcome metrics of RIHC among U.S. counties with good performance, offering a novel and actionable tool for identifying super-utilizer segments in the population. Wider integration of routinely collected data can be used to develop alternative methods for predicting RIHC among population units.

Quantifying differences in packaged food and drink purchases among households with diet-related cardiometabolic multi-morbidity: a cross-sectional analysis.

BACKGROUND: Diet is important for chronic disease management, with limited research understanding dietary choices among those with multi-morbidity, the state of having 2 or more chronic conditions. The objective of this study was to identify associations between packaged food and drink purchases and diet-related cardiometabolic multi-morbidity (DRCMM). METHODS: Cross-sectional associations between packaged food and drink purchases and household DRCMM were investigated using a national sample of U.S. households participating in a research marketing study. DRCMM households were defined as household head(s) self-reporting 2 or more diet-related chronic conditions. Separate multivariable logistic regression models were used to model the associations between household DRCMM status and total servings of, and total calories and nutrients from, packaged food and drinks purchased per month, as well as the nutrient density (protein, carbohydrates, and fat per serving) of packaged food and drinks purchased per month, adjusted for household size. RESULTS: Among eligible households, 3795 (16.8%) had DRCMM. On average, households with DRCMM versus without purchased 14.8 more servings per capita, per month, from packaged foods and drinks (p < 0.001). DRCMM households were 1.01 times more likely to purchase fat and carbohydrates in lieu of protein across all packaged food and drinks (p = 0.002, p = 0.000, respectively). DRCMM households averaged fewer grams per serving of protein, carbohydrates, and fat per month across all food and drink purchases (all p < 0.001). When carbonated soft drinks and juices were excluded, the same associations for grams of protein and carbohydrates per serving per month were seen (both p < 0.001) but the association for grams of fat per serving per month attenuated. CONCLUSIONS: DRCMM households purchased greater quantities of packaged food and drinks per capita than non-DRCMM households, which contributed to more fat, carbohydrates, and sodium in the home. However, food and drinks in DRCMM homes on average were lower in nutrient-density. Future studies are needed to understand the motivations for packaged food and drink choices among households with DRCMM to inform interventions targeting the home food environment.

Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

BACKGROUND: Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a novel and actionable tool for identifying super-utilizer segments in the population. Consumer expenditures may offer additional value in predicting RIHC beyond typical population characteristics alone. METHODS: Cross-sectional data from 2017 was extracted from 5 unique sources. The outcome was RIHC and included emergency room (ER) visits, inpatient days, and hospital expenditures, all expressed as log per capita. Candidate predictors from 4 broad groups were used, including demographics, adults and child health characteristics, community characteristics, and consumer expenditures. Candidate predictors were expressed as per capita or per capita percent and were aggregated from zip-codes to HSAs using weighed means. Machine learning approaches (Random Forrest, LASSO) selected important features from nearly 1,000 available candidate predictors and used them to generate 4 distinct models, including non-regularized and LASSO regression, random forest, and gradient boosting. Candidate predictors from the best performing models, for each outcome, were used as independent variables in multiple linear regression models. Relative contribution of variables from each candidate predictor group to regression model fit were calculated. RESULTS: The median ER visits per capita was 0.482 [IQR:0.351-0.646], the median inpatient days per capita was 0.395 [IQR:0.214-0.806], and the median hospital expenditures per capita was $2,302 [1$,544.70-$3,469.80]. Using 1,106 variables, the test-set coefficient of determination (R2) from the best performing models ranged between 0.184-0.782. The adjusted R2 values from multiple linear regression models ranged from 0.311-0.8293. Relative contribution of consumer expenditures to model fit ranged from 23.4-33.6%. DISCUSSION: Machine learning models predicted RIHC among HSAs using diverse population data, including novel consumer expenditures and provides an innovative tool to predict population-based healthcare utilization and expenditures. Geographic variation in utilization and spending were identified.