Browsing by Author "Haft, Howard"
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ItemAssessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events(Elsevier, 2023-06) Goetschius, Leigh G.; Henderson, Morgan; Han, Fei; Mahmoudi, Dillon; Perman, Chad; Haft, Howard; Stockwell, IanPredictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries’ ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care. ItemEvaluating the Impact of Geographic Granularity for Area-Based Social Determinants of Health When Predicting Avoidable Hospital Events(The Hilltop Institute, 2022-06-06) Goetschius, Leigh; Henderson, Morgan; Han, Fei; Perman, Chad; Haft, Howard; Stockwell, Ian; The Hilltop InstituteData Scientist Leigh Goetschius, PhD, presented her research in this poster at the 2022 AcademyHealth Annual Research Meeting (ARM) held June 4-7, 2022, in Washington DC. ItemPredicting avoidable hospital events in Maryland(Wiley, 2021-10-14) Henderson, Morgan; Han, Fei; Perman, Chad; Haft, Howard; Stockwell, IanObjective To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. Data sources Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. Study design Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. Data collection/extraction methods One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. Principal findings We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. Conclusions A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.