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Understanding the multilevel factors associated with controlled blood pressure is important to determine modifiable factors for future interventions, especially among populations living in poverty. This study identified clinically important factors associated with blood pressure control among patients receiving care in community health centers.This study includes 31,089 patients with diagnosed hypertension by 2015 receiving care from 103 community health centers; aged 19-64 years; and with ≥1 yearly visit with ≥1 recorded blood pressure in 2015, 2016, and 2017. Blood pressure control was operationalized as an average of all blood pressure measurements during all the 3 years and categorized as controlled (blood pressure140/90), partially controlled (mixture of controlled and uncontrolled blood pressure), or never controlled. Multinomial mixed-effects logistic regression models, conducted in 2022, were used to calculate unadjusted ORs and AORs of being in the never- or partially controlled blood pressure groups versus in the always-controlled group.A total of 50.5% had always controlled, 39.7% had partially controlled, and 9.9% never had controlled blood pressure during the study period. The odds of being partially or never in blood pressure control were higher for patients without continuous insurance (AOR=1.09; 95% CI=1.03, 1.16; AOR=1.18; 95% CI=1.07, 1.30, respectively), with low provider continuity (AOR=1.24; 95% CI=1.15, 1.34; AOR=1.28; 95% CI=1.13, 1.45, respectively), with a recent diagnosis of hypertension (AOR=1.34; 95% CI=1.20, 1.49; AOR=1.19; 95% CI=1.00, 1.42), with inconsistent antihypertensive medications (AOR=1.19; 95% CI=1.11, 1.27; AOR=1.26; 95% CI=1.13, 1.41, respectively), and with fewer blood pressure checks (AOR=2.14; 95% CI=1.97, 2.33; AOR=2.17; 95% CI=1.90, 2.48, respectively) than for their counterparts.Efforts targeting continuous and consistent access to care, antihypertensive medications, and regular blood pressure monitoring may improve blood pressure control among populations living in poverty.

Evidence is needed about how to effectively support health care providers in implementing screening for social risks (adverse social determinants of health) and providing related referrals meant to address identified social risks. This need is greatest in underresourced care settings. The authors tested whether an implementation support intervention (6 months of technical assistance and coaching study clinics through a five-step implementation process) improved adoption of social risk activities in community health centers (CHCs). Thirty-one CHC clinics were block-randomized to six wedges that occurred sequentially. Over the 45-month study period from March 2018 to December 2021, data were collected for 6 or more months preintervention, the 6-month intervention period, and 6 or more months postintervention. The authors calculated clinic-level monthly rates of social risk screening results that were entered at in-person encounters and rates of social risk-related referrals. Secondary analyses measured impacts on diabetes-related outcomes. Intervention impact was assessed by comparing clinic performance based on whether they had versus had not yet received the intervention in the preintervention period compared with the intervention and postintervention periods. In assessing the results, the authors note that five clinics withdrew from the study for various bandwidth-related reasons. Of the remaining 26, a total of 19 fully or partially completed all 5 implementation steps, and 7 fully or partially completed at least the first 3 steps. Social risk screening was 2.45 times (95% confidence interval [CI], 1.32–4.39) higher during the intervention period compared with the preintervention period; this impact was not sustained postintervention (rate ratio, 2.16; 95% CI, 0.64–7.27). No significant difference was seen in social risk referral rates during the intervention or postintervention periods. The intervention was associated with greater blood pressure control among patients with diabetes and lower rates of diabetes biomarker screening postintervention. All results must be interpreted considering that the Covid-19 pandemic began midway through the trial, which affected care delivery generally and patients at CHCs particularly. Finally, the study results show that adaptive implementation support was effective at temporarily increasing social risk screening. It is possible that the intervention did not adequately address barriers to sustained implementation or that 6 months was not long enough to cement this change. Underresourced clinics may struggle to participate in support activities over longer periods without adequate resources, even if lengthier support is needed. As policies start requiring documentation of social risk activities, safety-net clinics may be unable to meet these requirements without adequate financial and coaching/technical support.

Additional file 1:Appendix Figure 1. CONSORT diagram. Appendix Table 1. Interview guide for health insurance assistors. Appendix Table 2. Enrollment Tool Field Definitions for Documentation and Tracking. Appendix Figure 2. The enrollment tool.

IMPACCT electronic health record (EHR) tool descriptions. Illustrations of EHR study tools designed for use by community health center staff assisting patients with insurance enrollment. Data from fictitious patients were used when creating the illustrations. (DOCX 10958Â kb)

Guide to social determinants of health screening and referral-making using the electronic health record. (PDF 4124Â kb)

Baseline survey. (DOCX 23Â kb)

Guide to social determinants of health screening and referral-making using the electronic health record. (PDF 4124Â kb)

Baseline survey. (DOCX 23Â kb)