A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records.

Introduction: Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates. Methods: We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates. Results: We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute. Conclusions: We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.

Change in Weight Status and Development of Hypertension.

OBJECTIVE: To examine the association of BMI percentile and change in BMI percentile to change in blood pressure (BP) percentile and development of hypertension (HTN). METHODS: This retrospective cohort included 101 606 subjects age 3 to 17 years from 3 health systems across the United States. Height, weight, and BPs were extracted from electronic health records, and BMI and BP percentiles were computed with the appropriate age, gender, and height charts. Mixed linear regression estimated change in BP percentile, and proportional hazards regression was used to estimate risk of incident HTN associated with BMI percentile and change in BMI percentile. RESULTS: The largest increases in BP percentile were observed among children and adolescents who became obese or maintained obesity. Over a median 3.1 years of follow-up, 0.3% of subjects developed HTN. Obese children ages 3 to 11 had twofold increased risk of developing HTN compared with healthy weight children. Obese children and adolescents had a twofold increased risk of developing HTN, and severely obese children had a more than fourfold increased risk. Compared with those who maintained a healthy weight, children and adolescents who became obese or maintained obesity had a more than threefold increased risk of incident HTN. CONCLUSIONS: We observed a strong, statistically significant association between increasing BMI percentile and increases in BP percentile, with risk of incident HTN associated primarily with obesity. The adverse impact of weight gain and obesity in this cohort over a short period underscores the early need for effective strategies for prevention of overweight and obesity.

Factors Associated With Adherence to Blood Pressure Measurement Recommendations at Pediatric Primary Care Visits, Minnesota and Colorado, 2007-2010.

INTRODUCTION: Elevated blood pressure in childhood may predict increased cardiovascular risk in young adulthood. The Task Force on the Diagnosis, Evaluation and Treatment of High Blood pressure in Children and Adolescents recommends that blood pressure be measured in children aged 3 years or older at all health care visits. Guidelines from both Bright Futures and the Expert Panel of Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents recommend annual blood pressure screening. Adherence to these guidelines is unknown. METHODS: We conducted a cross-sectional study to assess compliance with blood pressure screening recommendations in 2 integrated health care delivery systems. We analyzed electronic health records of 103,693 subjects aged 3 to 17 years. Probability of blood pressure measurement documented in the electronic health record was modeled as a function of visit type (well-child vs nonwell-child); patient age, sex, race/ethnicity, and body mass index; health care use; insurance type; and type of office practice or clinic department (family practice or pediatrics). RESULTS: Blood pressure was measured at 95% of well-child visits and 69% of nonwell-child outpatient visits. After adjusting for potential confounders, the percentage of nonwell-child visits with measurements increased linearly with patient age (P < .001). Overall, the proportion of children with annual blood pressure measurements was high and increased with age. Family practice clinics were more likely to adhere to blood pressure measurement guidelines compared with pediatric clinics (P < .001). CONCLUSION: These results show good compliance with recommendations for routine blood pressure measurement in children and adolescents. Findings can inform the development of EHR-based clinical decision support tools to augment blood pressure screening and recognition of prehypertension and hypertension in pediatric patients.