Introduction Broadband access is a “super determinant of health.” Understanding the spatial distribution and predictors of access may help target government programs and telehealth applications. Our aim was to examine broadband access across geography and sociodemographic characteristics using American Community Survey (ACS) data. Methods We used 5‐year ACS estimates from 2014 to 2018 to evaluate broadband access across contiguous US census tracts. Rural‐Urban Commuting Area (RUCA) codes were categorized as metropolitan, micropolitan, small town, and isolated rural. We performed bivariate analyses to determine differences by RUCA categories and meeting the Healthy People 2020 (HP2020) objective (83.2% broadband access) or not. We conducted spatial statistics and spatial regression analyses to identify clusters of broadband access and sociodemographic factors associated with broadband access. Results No RUCA grouping met the HP2020 objective; 80.6% of households had broadband access, including 82.0% of metropolitan, 73.9% of micropolitan, 70.7% of small town, and 70.0% of isolated rural households. Areas with high percentages of Black residents had lower broadband access, particularly in isolated rural tracts (54.9%). Low access was spatially clustered in the Southeast, Southwest, and northern plains. In spatial regression models, poverty and education were most strongly associated with broadband access, while the proportion of American Indian/Alaska Native population was the strongest racial/ethnic factor. Conclusions Rural areas had less broadband access with the greatest disparities experienced among geographically isolated areas with larger Black and American Indian/Alaska Native populations, more poverty, and lower educational attainment, following well‐known social gradients in health. Resources and initiatives should target these areas of greatest need.

Background The Affordable Care Act (ACA) Medicaid expansion improved access to health insurance and healthcare services. This study assessed whether the rate of patients with undiagnosed hypertension and the rate of patients with hypertension without anti-hypertensive medication decreased post-ACA in community health center (CHC). Methods We analyzed electronic health record data from 2012-2017 for 126,699 CHC patients aged 19-64 years with ≥1 visit pre-ACA and ≥1 post-ACA in 14 Medicaid expansion states. We estimated the prevalence of patients with undiagnosed hypertension (high blood pressure reading without a diagnosis for ≥1 day) and the prevalence of patients with hypertension without anti-hypertensive medication by year and health insurance type (continuously uninsured, continuously insured, gained insurance, and discontinuously insured). We compared the time to diagnosis or to anti-hypertensive medication pre- vs post-ACA. Results Overall, 37.3% of patients had undiagnosed hypertension and 27.0% of patients with diagnosed hypertension were without a prescribed anti-hypertensive medication for ≥1 day during the study period. The rate of undiagnosed hypertension decreased from 2012 through 2017. Those who gained insurance had the lowest rates of undiagnosed hypertension (2012: 14.8%; 2017: 6.1%). Patients with hypertension were also more likely to receive anti-hypertension medication during this period, especially uninsured patients who experienced the largest decline (from 47.0% to 8.1%). Post-ACA, among patients with undiagnosed hypertension, time to diagnosis was shorter for those who gained insurance than other insurance types. Conclusions Those who gained health insurance were appropriately diagnosed with hypertension faster and more frequently post-ACA than those with other insurance types.

IMPORTANCE Management of cardiovascular disease (CVD) risk in socioeconomically vulnerablepatients is suboptimal; better risk factor control could improve CVD outcomes. OBJECTIVE To evaluate the impact of a clinical decision support system (CDSS) targeting CVD risk in community health centers (CHCs). DESIGN, SETTING, AND PARTICIPANTSThis cluster randomized clinical trial included 70 CHC clinics randomized to an intervention group (42 clinics; 8 organizations) or a control group that received no intervention (28 clinics; 7 organizations) from September 20, 2018, to March 15, 2020. Randomization was by CHC organization accounting for organization size. Patients aged 40 to 75 years with (1) diabetes or atherosclerotic CVD and at least 1 uncontrolled major risk factor for CVD or (2) total reversible CVD risk of at least 10% were the population targeted by the CDSS intervention. INTERVENTIONS A point-of-care CDSS displaying real-time CVD risk factor control data and personalized, prioritized evidence-based care recommendations. MAIN OUTCOMES AND MEASURES One-year change in total CVD risk and reversible CVD risk (ie, the reduction in 10-year CVD risk that was considered achievable if 6 key risk factors reached evidence-based levels of control). RESULTS Among the 18 578 eligible patients (9490 [51.1%] women; mean [SD] age, 58.7 [8.8] years), patients seen in control clinics (n = 7419) had higher mean (SD) baseline CVD risk (16.6% [12.8%]) than patients seen in intervention clinics (n = 11 159) (15.6% [12.3%]; P < .001); baseline reversible CVD risk was similarly higher among patients seen in control clinics. The CDSS was used at 19.8% of 91 988 eligible intervention clinic encounters. No population-level reduction in CVD risk was seen in patients in control or intervention clinics; mean reversible risk improved significantly more among patients in control (−0.1% [95% CI, −0.3% to −0.02%]) than intervention clinics (0.4%[95% CI, 0.3% to 0.5%]; P < .001). However, when the CDSS was used, both risk measures decreased more among patients with high baseline risk in intervention than control clinics; notably, mean reversible risk decreased by an absolute 4.4% (95% CI, −5.2% to −3.7%) among patients in intervention clinics compared with 2.7% (95% CI, −3.4% to −1.9%) among patients in control clinics (P = .001). CONCLUSIONS AND RELEVANCEThe CDSS had low use rates and failed to improve CVD risk in the overall population but appeared to have a benefit on CVD risk when it was consistently used for patients with high baseline risk treated in CHCs. Despite some limitations, these results provide (continued) Key Points Question Does a clinical decision support system (CDSS) proven to reduce cardiovascular risk in integrated care settings also reduce cardiovascular risk in community health centers? Findings In this cluster randomized clinical trial of 18 578 eligible patients, although CDSS adoption rates were low, CDSS use was associated with significantly improved reversible risk of cardiovascular disease among patients with the hig...

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