Gabriela Vazquez-Benitez

Background: Accurate small-area estimates of vaccination rates and disease burden can inform public health interventions. Objective: This study aimed to compare population denominators derived from census data and electronic health record (EHR) data from a statewide collaboration in Minnesota and examine concordance between Centers for Disease Control and Prevention and EHR-based estimates of diabetes and hypertension prevalence at the census tract level. Methods: A retrospective study was conducted using EHR data from 2018 to 2022 from the Minnesota EHR Consortium (MNEHRC), population estimates from the 2020 census, and disease prevalence estimates among adults from the Centers for Disease Control and Prevention Population Level Analysis and Community Estimates (PLACES) project. Patients were included if they had a Minnesota address and a clinic visit in the last 3 years. Patients with hypertension and diabetes were identified based on the presence of at least 1 diagnosis code in the Observational Medical Outcomes Partnership condition occurrence table in the last 5 years or an elevated outpatient blood pressure (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg) on 2 or more days in the last 3 years for hypertension or at least 1 hemoglobin A1c value of ≥6.5 in the last 3 years for diabetes. Results: The 2020 census estimate for the population of Minnesota was 5,707,254. A total of 5,271,191 (92.4% of the census estimate) unique individuals visited 1 of the 11 MNEHRC health care systems in 3 years (2018-2020). The ratio of MNEHRC patients to the Minnesota statewide 2020 census estimate was higher for female individuals (0.97) than for male individuals (0.88) and higher for older age groups (individuals aged 65 years and older: 1.05) than for younger age groups (individuals aged 0-17 years: 0.83). The MNEHRC patient-to-census ratio also differed by race—the ratio was the highest for Black Minnesotans (1.17) and the lowest for American Indian and Alaska Native Minnesotans (0.68). According to MNEHRC data, the percentage of adults in Minnesota with diabetes in 2022 was 9.5% (415,914/4,376,805), and the percentage of adults in Minnesota with hypertension in 2021 was 32.2% (1,365,413/4,234,000). Estimates from PLACES for diabetes were 9.9% (435,481/4,389,028) and for hypertension were 29.9% (1,311,459/4,389,028). The percentage of census tracts where the MNEHRC estimate was within 10% of the PLACES estimate was 40.3% (605/1500) for diabetes and 42.3% (635/1500) for hypertension; 77.9% (1168/1500) of census tracts for diabetes and 79.7% (1195/1500) for hypertension were within 25% agreement. Conclusions: Our analysis suggests that there are both similarities and important differences between small-area estimates derived from EHR and survey data. Such differences suggest that further research is needed to determine the optimal collection method for local estimates of health conditions.

BACKGROUND Accurate small-area estimates of vaccination rates and disease burden can inform public health interventions. OBJECTIVE This study aimed to compare population denominators derived from census data and electronic health record (EHR) data from a statewide collaboration in Minnesota and examine concordance between Centers for Disease Control and Prevention and EHR-based estimates of diabetes and hypertension prevalence at the census tract level. METHODS A retrospective study was conducted using EHR data from 2018 to 2022 from the Minnesota EHR Consortium (MNEHRC), population estimates from the 2020 census, and disease prevalence estimates among adults from the Centers for Disease Control and Prevention Population Level Analysis and Community Estimates (PLACES) project. Patients were included if they had a Minnesota address and a clinic visit in the last 3 years. Patients with hypertension and diabetes were identified based on the presence of at least 1 diagnosis code in the Observational Medical Outcomes Partnership condition occurrence table in the last 5 years or an elevated outpatient blood pressure (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg) on 2 or more days in the last 3 years for hypertension or at least 1 hemoglobin A1c value of ≥6.5 in the last 3 years for diabetes. RESULTS The 2020 census estimate for the population of Minnesota was 5,707,254. A total of 5,271,191 (92.4% of the census estimate) unique individuals visited 1 of the 11 MNEHRC health care systems in 3 years (2018-2020). The ratio of MNEHRC patients to the Minnesota statewide 2020 census estimate was higher for female individuals (0.97) than for male individuals (0.88) and higher for older age groups (individuals aged 65 years and older: 1.05) than for younger age groups (individuals aged 0-17 years: 0.83). The MNEHRC patient-to-census ratio also differed by race—the ratio was the highest for Black Minnesotans (1.17) and the lowest for American Indian and Alaska Native Minnesotans (0.68). According to MNEHRC data, the percentage of adults in Minnesota with diabetes in 2022 was 9.5% (415,914/4,376,805), and the percentage of adults in Minnesota with hypertension in 2021 was 32.2% (1,365,413/4,234,000). Estimates from PLACES for diabetes were 9.9% (435,481/4,389,028) and for hypertension were 29.9% (1,311,459/4,389,028). The percentage of census tracts where the MNEHRC estimate was within 10% of the PLACES estimate was 40.3% (605/1500) for diabetes and 42.3% (635/1500) for hypertension; 77.9% (1168/1500) of census tracts for diabetes and 79.7% (1195/1500) for hypertension were within 25% agreement. CONCLUSIONS Our analysis suggests that there are both similarities and important differences between small-area estimates derived from EHR and survey data. Such differences suggest that further research is needed to determine the optimal collection method for local estimates of health conditions.

Background: Real-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial. Objective: This simulation study aimed to investigate alternative methods to adjust for confounding in VE studies that have a test-negative design. Methods: Adjustment for a disease risk score (DRS) is compared with multivariable logistic regression. Both stratification on the DRS and direct covariate adjustment of the DRS are examined. Multivariable logistic regression with all the covariates and with a limited subset of key covariates is considered. The performance of VE estimators is evaluated across a multinomial vaccination exposure in simulated datasets. Results: Bias in VE estimates from multivariable models ranged from –5.3% to 6.1% across 4 levels of vaccination. Standard errors of VE estimates were unbiased, and 95% coverage probabilities were attained in most scenarios. The lowest coverage in the multivariable scenarios was 93.7% (95% CI 92.2%-95.2%) and occurred in the multivariable model with key covariates, while the highest coverage in the multivariable scenarios was 95.3% (95% CI 94.0%-96.6%) and occurred in the multivariable model with all covariates. Bias in VE estimates from DRS-adjusted models was low, ranging from –2.2% to 4.2%. However, the DRS-adjusted models underestimated the standard errors of VE estimates, with coverage sometimes below the 95% level. The lowest coverage in the DRS scenarios was 87.8% (95% CI 85.8%-89.8%) and occurred in the direct adjustment for the DRS model. The highest coverage in the DRS scenarios was 94.8% (95% CI 93.4%-96.2%) and occurred in the model that stratified on DRS. Although variation in the performance of VE estimates occurred across modeling strategies, variation in performance was also present across exposure groups. Conclusions: Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.

BACKGROUND Real-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial. OBJECTIVE This simulation study aimed to investigate alternative methods to adjust for confounding in VE studies that have a test-negative design. METHODS Adjustment for a disease risk score (DRS) is compared with multivariable logistic regression. Both stratification on the DRS and direct covariate adjustment of the DRS are examined. Multivariable logistic regression with all the covariates and with a limited subset of key covariates is considered. The performance of VE estimators is evaluated across a multinomial vaccination exposure in simulated datasets. RESULTS Bias in VE estimates from multivariable models ranged from –5.3% to 6.1% across 4 levels of vaccination. Standard errors of VE estimates were unbiased, and 95% coverage probabilities were attained in most scenarios. The lowest coverage in the multivariable scenarios was 93.7% (95% CI 92.2%-95.2%) and occurred in the multivariable model with key covariates, while the highest coverage in the multivariable scenarios was 95.3% (95% CI 94.0%-96.6%) and occurred in the multivariable model with all covariates. Bias in VE estimates from DRS-adjusted models was low, ranging from –2.2% to 4.2%. However, the DRS-adjusted models underestimated the standard errors of VE estimates, with coverage sometimes below the 95% level. The lowest coverage in the DRS scenarios was 87.8% (95% CI 85.8%-89.8%) and occurred in the direct adjustment for the DRS model. The highest coverage in the DRS scenarios was 94.8% (95% CI 93.4%-96.2%) and occurred in the model that stratified on DRS. Although variation in the performance of VE estimates occurred across modeling strategies, variation in performance was also present across exposure groups. CONCLUSIONS Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.

In the Vaccine Safety Datalink (VSD), we previously reported no association between coronavirus disease 2019 (COVID-19) vaccination in early pregnancy and spontaneous abortion (SAB). The present study aims to understand how time since vaccine rollout or other methodological factors could affect results. Using a case-control design and generalized estimating equations, we estimated the odds ratios (ORs) of COVID-19 vaccination in the 28 days before a SAB or last date of the surveillance period (index date) in ongoing pregnancies and occurrence of SAB, across cumulative 4-week periods from December 2020 through June 2021. Using data from a single site, we evaluated alternative methodological approaches: increasing the exposure window to 42 days, modifying the index date from the last day to the midpoint of the surveillance period, and constructing a cohort design with a time-dependent exposure model. A protective effect (OR = 0.78, 95% confidence interval: 0.69, 0.89), observed with 3-cumulative periods ending March 8, 2021, was attenuated when surveillance extended to June 28, 2021 (OR = 1.02, 95% confidence interval: 0.96, 1.08). We observed a lower OR for a 42-day window compared with a 28-day window. The time-dependent model showed no association. Timing of the surveillance appears to be an important factor affecting the observed vaccine-SAB association.

Real-world evidence is a valuable source of information in healthcare. This study describes the challenges and successes during algorithm development to identify cancer cohorts and multi-agent chem...