Coronavirus disease 2019 has had a substantial impact on racial and ethnic minority populations and essential workers in the United States, but the role of geographic social and economic inequities (i.e., deprivation) in these disparities has not been examined (1,2). As of July 9, 2020, Utah had reported 27,356 confirmed COVID-19 cases. To better understand how area-level deprivation might reinforce ethnic, racial, and workplace-based COVID-19 inequities (3), the Utah Department of Health (UDOH) analyzed confirmed cases of infection with SARS-CoV-2 (the virus that causes COVID-19), COVID-19 hospitalizations, and SARS-CoV-2 testing rates in relation to deprivation as measured by Utah's Health Improvement Index (HII) (4). Age-weighted odds ratios (weighted ORs) were calculated by weighting rates for four age groups (≤24, 25-44, 45-64, and ≥65 years) to a 2000 U.S. Census age-standardized population. Odds of infection increased with level of deprivation and were two times greater in high-deprivation areas (weighted OR = 2.08; 95% confidence interval [CI] = 1.99-2.17) and three times greater (weighted OR = 3.11; 95% CI = 2.98-3.24) in very highdeprivation areas, compared with those in very low-deprivation areas. Odds of hospitalization and testing also increased with deprivation, but to a lesser extent. Local jurisdictions should use measures of deprivation and other social determinants of health to enhance transmission reduction strategies (e.g., increasing availability and accessibility of SARS-CoV-2 testing and distributing prevention guidance) to areas with greatest need. These strategies might include increasing availability and accessibility of SARS-CoV-2 testing, contact tracing, isolation options, preventive care, disease management, and prevention guidance to facilities (e.g., clinics, community centers, and businesses) in areas with high levels of deprivation.Confirmed COVID-19 cases reported by local health departments and UDOH to the Utah National Electronic Disease Surveillance System during March 3-July 9, 2020, were included in the analysis. Addresses were used to assign cases to one of 99 Utah small statistical areas,* each with an HII score ranging from 72 to 160 (4). HII is a composite index calculated using nine indicators from the Utah Behavioral Risk Factor * Utah's small statistical areas are areas delineated by the state to facilitate community-level reporting for the smallest units with enough data to be reliable.

Background Electronic health record (EHR) data, collected primarily for individual patient care and billing purposes, compiled in health information exchanges (HIEs) may have a secondary use for population health surveillance of noncommunicable diseases. However, data compilation across fragmented data sources into HIEs presents potential barriers and quality of data is unknown. Methods We compared 2015 patient data from a mid-size health system (Database A) to data from System A patients in the Utah HIE (Database B). We calculated concordance of structured data (sex and age) and unstructured data (blood pressure reading and A1C). We estimated adjusted hypertension and diabetes prevalence in each database and compared these across age groups. Results Matching resulted in 72,356 unique patients. Concordance between Database A and Database B exceeded 99% for sex and age, but was 89% for A1C results and 54% for blood pressure readings. Sensitivity, using Database A as the standard, was 57% for hypertension and 55% for diabetes. Age and sex adjusted prevalence of diabetes (8.4% vs 5.8%, Database A and B, respectively) and hypertension (14.5% vs 11.6%, respectively) differed, but this difference was consistent with parallel slopes in prevalence over age groups in both databases. Conclusions We identified several gaps in the use of HIE data for surveillance of diabetes and hypertension. High concordance of structured data demonstrate some promise in HIEs capacity to capture patient data. Improving HIE data quality through increased use of structured variables may help make HIE data useful for population health surveillance in places with fragmented EHR systems. Electronic supplementary material The online version of this article (10.1186/s12889-019-7367-z) contains supplementary material, which is available to authorized users.

Objectives Among young people, dual use of marijuana and e-cigarette, or vaping, products (EVPs) is linked with using more inhalant substances and other substances, and poorer mental health. To understand antecedents and potential risks of dual use in adults, we analyzed a representative adult population in Utah. Methods We used data from the 2018 Utah Behavioral Risk Factor Surveillance System (n = 10 380) and multivariable logistic regression to evaluate differences in sociodemographic characteristics, comorbidities, and risk factors among adults aged ≥18 who reported currently using both EVPs (any substance) and marijuana (any intake mode), compared with a referent group of adults who used either or neither. Results Compared with the referent group, adults using EVPs and marijuana had greater odds of being aged 18-29 (adjusted odds ratio [aOR] = 12.44; 95% CI, 6.15-25.14) or 30-39 (aOR = 3.75; 95% CI, 1.73-8.12) versus ≥40, being male (aOR = 3.29; 95% CI, 1.82-5.96) versus female, reporting ≥14 days of poor mental health in previous 30 days (aOR = 2.30; 95% CI, 1.23-4.32) versus <14 days, and reporting asthma (aOR = 2.09; 95% CI, 1.02-4.31), chronic obstructive pulmonary disorder (aOR = 2.94; 95% CI, 1.19-7.93), currently smoking cigarettes (aOR = 4.56; 95% CI, 2.63-7.93), or past-year use of prescribed chronic pain medications (aOR = 2.13; 95% CI, 1.06-4.30), all versus not. Conclusions Clinicians and health promotion specialists working with adults using both EVPs and marijuana should assess risk factors and comorbidities that could contribute to dual use or associated outcomes and tailor prevention messaging accordingly.