Identifying virtual care modality in electronic health record data.

Background: Virtual care increased dramatically during the COVID-19 pandemic. The specific modality of virtual care (video, audio, eVisits, eConsults, and remote patient monitoring) has important implications for the accessibility and quality of care, but rates of use are relatively unknown. Methods for identifying virtual care modalities, especially in electronic health records (EHR) are inconsistent. This study (a) developed a method to identify virtual care modalities using EHR data and (b) described the distribution of these modalities over a 3-year study period. Methods: EHR data from 316 primary care safety net clinics throughout the study period (4/1/2020-3/31/2023) were included. Visit type (in-person vs virtual) by adults >18 years old were classified. Expert consultation informed the development of two algorithms to classify virtual care visit modalities; these algorithms prioritized different EHR data elements. We conducted descriptive analyses comparing algorithms and the frequency of virtual care modalities. Results: Agreement between the algorithms was 96.5% for all visits and 89.3% for virtual care visits. The majority of disagreement between the algorithms was among encounters scheduled as audio-only but billed as a video visit. Restricting to visits where the algorithms agreed on visit modality, there were 2-fold more audio-only than video visits. Conclusion: Visit modality classification varies depending upon which data in the EHR are prioritized. Regardless of which algorithm is utilized, safety net clinics rely on audio-only and video visits to provide care in virtual visits. Elimination of reimbursement for audio visits may exacerbate existing inequities in care for low-income patients.

Cardiovascular disease risk management during COVID-19: in-person vs virtual visits.

OBJECTIVES: Limited research has assessed how virtual care (VC) affects cardiovascular disease (CVD) risk management, especially in community clinic settings. This study assessed change in community clinic patients' CVD risk management during the COVID-19 pandemic and CVD risk factor control among patients who had primarily in-person or primarily VC visits. STUDY DESIGN: Retrospective interrupted time-series analysis. METHODS: Data came from an electronic health record shared by 52 community clinics for index (March 1, 2019, to February 29, 2020) and follow-up (July 1, 2020, to February 28, 2022) periods. Analyses compared follow-up period changes in slope and level of population monthly means of 10-year reversible CVD risk score, blood pressure (BP), and hemoglobin A1c (HbA1c) among patients whose completed follow-up period visits were primarily in person vs primarily VC. Propensity score weighting minimized confounding. RESULTS: There were 10,028 in-person and 6593 VC patients in CVD risk analyses, 9874 in-person and 5390 VC patients in BP analyses, and 8221 in-person and 4937 VC patients in HbA1c analyses. The VC group was more commonly younger, female, White, and urban. Mean reversible CVD risk, mean systolic BP, and percentage of BP measurements that were 140/90 mm Hg or higher increased significantly from index to follow-up periods in both groups. Rate of change between these periods was the same for all outcomes in both groups, regardless of care modality. CONCLUSIONS: Among community clinic patients with CVD risk, receiving a majority of care in person vs a majority of care via VC was not significantly associated with longitudinal trends in reversible CVD risk score or key CVD risk factors.

Pandemic-related practice changes and CVD risk management in community clinics.

OBJECTIVES: Understanding how the COVID-19 pandemic affected cardiovascular disease (CVD) risk monitoring in primary care may inform new approaches for addressing modifiable CVD risks. This study examined how pandemic-driven changes in primary care delivery affected CVD risk management processes. STUDY DESIGN: This retrospective study used electronic health record data from patients at 70 primary care community clinics with scheduled appointments from September 1, 2018, to September 30, 2021. METHODS: Analyses examined associations between appointment type and select care process measures: appointment completion rates, time to appointment, and up-to-date documentation for blood pressure (BP) and hemoglobin A1c (HbA1c). RESULTS: Of 1,179,542 eligible scheduled primary care appointments, completion rates were higher for virtual care (VC) vs in-person appointments (10.7 percentage points [PP]; 95% CI, 10.5-11.0; P < .001). Time to appointment was shorter for VC vs in-person appointments (-3.9 days; 95% CI, -4.1 to -3.7; P < .001). BP documentation was higher for appointments completed pre- vs post pandemic onset (16.2 PP; 95% CI, 16.0-16.5; P < .001) and for appointments completed in person vs VC (54.9 PP; 95% CI, 54.6-55.2; P < .001). HbA1c documentation was higher for completed appointments after pandemic onset vs before (5.9 PP; 95% CI, 5.1-6.7; P < .001) and for completed VC appointments vs in-person appointments (3.9 PP; 95% CI, 3.0-4.7; P < .001). CONCLUSIONS: After pandemic onset, appointment completion rates were higher, time to appointment was shorter, HbA1c documentation increased, and BP documentation decreased. Future research should explore the advantages of using VC for CVD risk management while continuing to monitor for unintended consequences.

Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction?

PURPOSE: To assess the impact of a clinical decision support (CDS) system's recommendations on prescribing patterns targeting cardiovascular disease (CVD) when the recommendations are prioritized in order from greatest to least benefit toward overall CVD risk reduction. METHODS: Secondary analysis of trial data from September 20, 2018, to March 15, 2020, where 70 community health center clinics were cluster-randomized to the CDS intervention (42 clinics; 8 organizations) or control group (28 clinics; 7 organizations). Included patients were medication-naïve and aged 40 to 75 years with ≥1 uncontrolled cardiovascular disease risk factor, with known diabetes or cardiovascular disease, or ≥10% 10-year reversible CVD risk. RESULTS: Among eligible encounters with 29,771 patients, the probability of prescribing a medication targeting hypertension was greater at intervention clinic encounters when CDS was used (34.9% [95% CI, 31.5 to 38.3]) versus dismissed (29.6% [95% CI, 26.7 to 32.6]; P < .001), but not when compared with control clinic encounters (34.9% [95% CI, 31.1 to 38.7]; P = .998). Prescribing for dyslipidemia was significantly higher at intervention encounters where the CDS system was used (11.3% [95% CI, 9.3 to 13.3]) compared with dismissed (7.7% [95% CI, 6.1 to 9.3]; P = .003) and to control encounters (8.7% [95% CI, 7.0 to 10.4]; P = .044); smoking cessation medication showed a similar pattern. Except for dyslipidemia, prescribing rates increased according to their prioritization. CONCLUSIONS: Use of this CDS system was associated with significantly higher prescribing targeting most cardiovascular risk factors. These results highlight how displaying prioritized actions to reduce reversible CVD risk could improve risk management. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Diabetes and Hypertension Prevention and Control in Community Health Centers: Impact of the Affordable Care Act.

Access to care significantly improved following the implementation of the Patient Protection and Affordable Care Act. Since its implementation, the number of uninsured Americans has significantly decreased. Medicaid expansion played an important role in community health centers, who serve historically marginalized populations, leading to increased clinic revenue, and improved access to care. As the continuous Medicaid enrollment provision established during the pandemic ended, and states have to make decisions about their program eligibility, exploring the impact of Medicaid expansion on the detection, and management of hypertension and diabetes could inform these decisions. We summarized the effect of Medicaid expansion on community health centers and their patients specific to hypertension and diabetes from existing literature. These studies suggest the beneficial impact of the Affordable Care Act and acquiring insurance on diabetes and hypertension disease detection, treatment, and control for patients receiving care in community health centers. Overall, these studies suggest the clear importance of health insurance coverage, and notably insurance stability, on diabetes and hypertension control.

The Impact of the Affordable Care Act Medicaid Expansion on Acute Diabetes Complications Among Community Health Center Patients.

OBJECTIVE: This study evaluates whether patients residing in expansion states have a greater increase in outpatient diagnoses of acute diabetes complications than those living in non-expansion states following the implementation of the Affordable Care Act (ACA). METHODS: This retrospective cohort study uses electronic health records (EHR) from 10,665 non-pregnant patients, aged 19 to 64 years old who were diagnosed with diabetes in 2012 or 2013 from 347 community health centers (CHCs) across 16 states (11 expansion and 5 non-expansion states). Patients included had ≥1 outpatient ambulatory visit in each of these periods: pre-ACA: 2012 to 2013, post-ACA: 2014 to 2016, and post-ACA: 2017 to 2019. Acute diabetes-related complications were identified using International Classification Diseases (ICD-9-CM and ICD-10-CM) codes classification and could occur on or after diagnosis of diabetes. We performed difference-in-differences (DID) analysis using a generalized estimating equation to compare the change in rates of acute diabetes complications by year and by Medicaid expansion status. RESULTS: There was a greater increase after year 2015 in visits related to abnormal blood glucose among patient living in Medicaid expansion states than in non-expansion states (2017 DID = 0.041, 95% CI = 0.027-0.056). Although both visits due to any acute diabetes complications and infection-related diabetes complications were higher among patients living in Medicaid expansion states, there was no difference in the trend overtime between expansion and non-expansion states. CONCLUSION: We found a significantly greater rate of visits for abnormal blood glucose in patients receiving care in expansion states relative to patients in CHCs in non-expansion states starting in 2015. Additional resources for these clinics, such as the ability to provide blood glucose monitoring devices or mailed/delivered medications, could substantially benefit patients with diabetes.

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers.

Objective: Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use. Materials and Methods: Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data. Results: Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider. Discussion: CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications. Conclusion: Research is needed on improving SDM and CDS adoption in CHCs. Trial Registration: ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Factors Associated With Blood Pressure Control Among Patients in Community Health Centers.

INTRODUCTION: 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. METHODS: 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 pressure <140/90), 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. RESULTS: 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. CONCLUSIONS: 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.

Impact of the Affordable Care Act Medicaid Expansion on Weight Loss among Community Health Center Patients with Obesity.

Context: Obesity affects over 40% of the US population and is linked to multiple preventable health conditions which can cause premature morbidity and mortality. Weight loss of at least 5% in patients with obesity reduces their risk of comorbid conditions and leads to improvement in some conditions, such as diabetes. Patients with obesity from underserved populations are less likely to access primary and preventive care services. The Affordable Care Act (ACA) improved access to care, which may in turn improve access to obesity prevention and treatment and assist with weight loss. Objective: Assess whether the proportion of patients with a weight loss ≥5% was higher in states that expanded Medicaid relative to non-expansion states among patients with obesity receiving care in community health centers (CHCs) which provide health care to underserved patients. Study Design: Retrospective observational cohort study. Setting: Electronic health record data from the ADVANCE (Advancing Data Value Across a National Community Health Center) clinical research network, during years 2012-2017. Population Studied: Patients from 346 CHCs age 19-64 with a body mass index of ≥ 30 kg/m2 during the pre-ACA period (n=34,027). Outcome measures: Proportion of patients with weight loss >5% (WL5+) from pre- to post-ACA. Medicaid expansion status (expansion vs. non-expansion states) stratified by pattern of insurance (uninsured, continuously insured, newly insured, discontinuously insured) and race and ethnicity. Results: The proportion of patients with WL5+ for newly insured patients was greater in expansion (26%) than non-expansion states (20%) (χ2=9.75, p=0.002). Among newly insured patients, Hispanic (22%) and Black (29%) patients residing in expansion states, had larger proportion of patients with WL5+ than those in non-expansion states (20% and 18%, respectively). No differences were observed among non-Hispanic White patients (expansion 28% vs non-expansion 27%). Conclusions: The findings suggest greater improvement in weight management among patients residing in expansion states than those in non-expansion states, especially among racial and ethnic minorities receiving care in CHCs.

Before and During Pandemic Telemedicine Use: An Analysis of Rural and Urban Safety-Net Clinics.

INTRODUCTION: Differences in face-to-face and telemedicine visits before and during the COVID-19 pandemic among rural and urban safety-net clinic patients were evaluated. In addition, this study investigated whether rural patients were as likely to utilize telemedicine for primary care during the pandemic as urban patients. METHODS: Using electronic health record data from safety-net clinics, patients aged ≥18 years with ≥1 visit before or during the COVID-19 pandemic, March 1, 2019-March 31, 2021, were identified, and trends in face-to-face and telemedicine (phone and video) visits for patients by rurality using Rural‒Urban Commuting Area codes were characterized. Multilevel mixed-effects regression models compared service delivery method during the pandemic by rurality. RESULTS: Included patients (N=1,015,722) were seen in 446 safety-net clinics: 83% urban, 10.3% large rural, 4.1% small rural, and 2.6% isolated rural. Before COVID-19, little difference in the percentage of encounters conducted face-to-face versus through telemedicine by rurality was found. Telemedicine visits significantly increased during the pandemic by 27.2 percentage points among patients in isolated rural areas to 52.3 percentage points among patients in urban areas. Rural patients overall had significantly lower odds of using telemedicine for a visit during the pandemic than urban patients. CONCLUSIONS: Despite the increased use of telemedicine in response to the pandemic, rural patients had significantly fewer telemedicine visits than those in more urban areas. Equitable access to telemedicine will depend on continued reimbursement for telemedicine services, but additional efforts are warranted to improve access to and use of health care among rural patients.

Primary and mental health service use in community health center patients before and after cancer diagnosis.

BACKGROUND: Cancer survivors face increased risk for chronic diseases resulting from cancer, preexisting conditions, and cancer treatment. Having an established primary care clinic or health insurance may influence patients' receipt of recommended preventive care necessary to manage, treat, or diagnose new conditions. This study sought to understand receipt of healthcare in community health centers (CHCs) before and after cancer diagnosis among cancer survivors. We also examined the type of care received and assessed whether being established with a CHC or the type of health insurance affected the use of services. METHODS: Using electronic health record data and linked cancer registries from 5,649 CHC patients in three states from 2012 through 2018, we obtained monthly rates of primary care and mental health/behavioral health (MHBH) visits and the probability of receipt of care before and after a cancer diagnosis. RESULTS: Seventy-five percent of CHC patients diagnosed with cancer returned to their primary CHC for care within 2-years of their diagnosis. Among those who returned, there was a sharp increase in primary and MHBH care shortly before their diagnosis. Significantly more primary care (pre: 19.6%, post: 21.9%, p < 0.001) and MHBH care (pre: 1.2%, post: 1.6%, p < 0.001) was received after diagnosis than before. However, uninsured patients had fewer visits after their diagnosis than before. CONCLUSION: Use of preventive care for cancer survivors is particularly important. Having an established primary care clinic may help to ensure survivors receive recommended screening and care.

Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease: A Cluster Randomized Clinical Trial.

Importance: Management of cardiovascular disease (CVD) risk in socioeconomically vulnerable patients 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 Participants: This 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 Relevance: The 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 preliminary evidence that this technology has the potential to improve clinical care in socioeconomically vulnerable patients with high CVD risk. Trial Registration: ClinicalTrials.gov Identifier: NCT03001713.

Characterizing acute and chronic complications among patients with diabetes mellitus in community health centers.

Context: Diabetes mellitus affects about 10% of the world's population and can lead to serious complications, which reduce life quality and expectancy. People with low income have higher risk of diabetes complications than those with high income, but data on the trends of diabetes complications in underserved populations are scarce. Community health centers (CHCs) serve millions of patients in the United States regardless of their health insurance status and are an ideal setting for assessing the rate of diabetes complications in underserved populations. Objectives: To describe diabetes-related acute and chronic complications among patients served by CHCs. Study Design: Retrospective cohort study of electronic health record data from the ADVANCE clinical research network. Population studied: Patients with diabetes between ages 19 and 64 (excluding pregnant women) and ≥1 primary care ambulatory CHC visit in 2017 (N=85,442). Outcome Measures: Rates and type of diabetes-related acute and chronic complications recorded in 2017. Results: The incidence of acute complications among patients with diabetes in 2017 was 14%. Patients experiencing acute diabetes-related complications had on average 1.8 complications (range 1-33). The most common acute complications were infections (58.3%), abnormal blood glucose or related metabolic abnormalities (20.5%), and strokes or transient neurological deficits (6.8%). Patients with acute complications were proportionally more likely to be female, non-Hispanic white, have Medicaid insurance, out-of-control diabetes, a prescription for insulin, a diagnosis of substance use disorder, and co-occurring physical or mental conditions. For chronic complications, the prevalence in 2017 was 77% among patients with diabetes. Patients experiencing chronic diabetes-related complications had on average 2.3 complications (range 1-19). The most common chronic complications were cardiovascular disease (33.9%), endocrine/metabolic symptoms (22.8%), and neurological symptoms (13.0%). Patients with chronic complications were proportionally more likely to be male, non-Hispanic black, have health insurance, obesity, substance use disorder, longer period with diabetes diagnosis, and co-occurring conditions. Conclusions: The vast majority of patients with diabetes receiving care in CHCs had chronic complications. These findings are concerning, as diabetes-related complications are associated with greater healthcare utilization and patient morbidity.

Geographic, racial/ethnic, and socioeconomic inequities in broadband access.

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.

High Blood Pressure Reduction, Health Insurance Status, and Social Deprivation Index in U.S. Community Health Centers.

Introduction: Lack of health insurance is a risk factor for uncontrolled hypertension, but it is unknown whether health insurance or neighborhood-level social deprivation is associated with greater reductions in blood pressure over time. Methods: We estimated the association of health insurance and social deprivation index on blood pressure reduction over time using electronic health record data from 2012 to 2017. We included patients aged 19-64 years with an initial systolic blood pressure ≥150 mmHg or diastolic blood pressure ≥100 mmHg and ≥1 additional visit from 93 community health centers in states that expanded Medicaid in 2014. Results: We included 66,207 patients: 20.1% uninsured, 64.8% publicly insured, and 15.1% privately insured. Adjusting for patient characteristics and baseline blood pressure, systolic blood pressure/diastolic blood pressure declined over the study period by 21.3/11.2 mmHg, 22.0/11.4 mmHg, and 21.1/10.7 mmHg among uninsured, publicly insured, and privately insured individuals, respectively. There were small but significantly greater reductions in systolic blood pressure among patients with public insurance than among those who were uninsured (difference= -1.3, 95% CI= -1.6, -1.0) but none associated with social deprivation index. There were no differences in diastolic blood pressure reductions over time by insurance status or social deprivation index. Blood pressure control (systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg) was significantly greater among publicly or commercially insured individuals than among uninsured individuals (51.7%, 51.5%, 44.6% respectively, both comparisons p<0.001), with no associations between blood pressure control and social deprivation index. Conclusions: Reductions in blood pressure were large but mostly not associated with insurance type or social deprivation index. Additional research is needed to understand the factors that lead to blood pressure reduction in community health center settings.

Understanding Health Need and Services Received by Youth in Foster Care in Community Safety-Net Health Centers in Oregon.

Youth in foster care have significant unmet health needs. We assessed health needs and health service use among youth in foster care in Oregon using electronic health record data from 258 community health centers and Medicaid enrollment data from 2014-2016. We identified 2,140 youth in foster care and a matched comparison group of 6,304 youth from the same clinics who were not in foster care, and compared the groups on demographic characteristics, health needs, and health service use. Youth in foster care were significantly more likely to have at least one chronic health condition, at least one mental health condition, and at least one mental health service compared with controls. Youth in foster care were significantly less likely to have a primary care visit. Despite significant mental health needs among youth in foster care, few received mental health care; this lack was greater among African American and Hispanic youth.

Rates of Undiagnosed Hypertension and Diagnosed Hypertension Without Anti-hypertensive Medication Following the Affordable Care Act.

BACKGROUND: The Affordable Care Act (ACA) Medicaid expansion improved access to health insurance and health care 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 to 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. CLINICAL TRIALS REGISTRATION: Trial Number NCT03545763.

Prevalence of Pre-existing Conditions Among Community Health Center Patients With COVID-19: Implications for the Patient Protection and Affordable Care Act.

BACKGROUND: Short- and long-term effects of COVID-19 will likely be designated pre-existing conditions. We describe the prevalence of pre-existing conditions among community health center patients overall and those with COVID-19 by race/ethnicity. MATERIALS AND METHODS: This cross-sectional study used electronic health record data from OCHIN, a network of 396 community health centers across 14 states. RESULTS: Among all patients with COVID-19, 33% did not have a pre-existing condition before the pandemic. Up to half of COVID-19-positive non-Hispanic Asians (51%), Hispanic (36%), and non-Hispanic black (28%) patients did not have a pre-existing condition before the pandemic. CONCLUSIONS: The future of the Patient Protection and Affordable Care Act is uncertain, and the long-term health effects of COVID-19 are largely unknown; therefore, ensuring people with pre-existing conditions can acquire health insurance is essential to achieving health equity.