Early Results of an Initiative to Assess Exposure to Firearm Violence in Ambulatory Care: Descriptive Analysis of Electronic Health Record Data.

BACKGROUND:  Current research on firearm violence is largely limited to patients who received care in emergency departments or inpatient acute care settings or who died. This is because standardized disease classification codes for firearm injury only represent bodily trauma. As a result, research on pathways and health impacts of firearm violence is largely limited to people who experienced acute bodily trauma and does not include the estimated millions of individuals who were exposed to firearm violence but did not sustain acute injury. Assessing and collecting data on exposure to firearm violence in ambulatory care settings can expand research and more fully frame the public health issue. OBJECTIVE: The aim of the study is to evaluate the demographic and clinical characteristics of patients who self-reported exposure to firearm violence during a behavioral health visit. METHODS: This study assessed early data from an initiative implemented in 2022 across a national network of ambulatory behavioral health centers to support trauma-informed care by integrating structured data fields on trauma exposure into an electronic health record behavioral health patient assessment form (SmartForm), as such variables are generally not included in standard outpatient medical records. We calculated descriptive statistics on clinic characteristics, patient demographics, and select clinical conditions among clinics that chose to implement the SmartForm and among patients who reported an exposure to firearm violence. Data on patient counts are limited to positive reports of exposure to firearm violence, and the representativeness of firearm exposure among all patients could not be calculated due to unknown variability in the implementation of the SmartForm. RESULTS: There were 323 of 629 (51%) clinics that implemented the SmartForm and reported at least 1 patient exposed to firearm violence. In the first 11 months of implementation, 3165 patients reported a recent or past exposure to firearm violence across the 323 clinics. Among patients reporting exposure, 52.7% (n=1669) were male, 38.8% (n=1229) were Black, 45.7% (n=1445) had posttraumatic stress disorder, 37.5% (n=1186) had a substance abuse disorder (other than nicotine), and 11.7% (n=371) had hypertension. CONCLUSIONS: Current research on firearm violence using standardized data is limited to acute care settings and death data. Early results from an initiative across a large network of behavioral health clinics demonstrate that a high number of clinics chose to implement the SmartForm, resulting in thousands of patients reporting exposure to firearm violence. This study demonstrates that collecting standardized data on firearm violence exposure in ambulatory care settings is feasible. This study further demonstrates that resultant data from ambulatory settings can be used for meaningful analysis in describing populations affected by firearm violence. The results of this study hold promise for further collection of structured data on exposure to firearm violence in ambulatory settings.

Landscape of Pregnancy Care in US Community Health Centers.

PURPOSE: Community health centers (CHCs) provide critical health care access for people who experience high risks during and after pregnancy, however it is unclear to what extent they provide prenatal care. This study seeks to describe clinic and patient characteristics associated with longitudinal prenatal care delivery in CHC settings. METHODS: This retrospective cohort study utilized electronic health record (EHR) data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) between 2018 to 2019 to describe prenatal care provision among CHCs (n = 408), and pregnant CHC patients (n = 28,578) and compared characteristics of patients who received longitudinal prenatal care at CHCs versus those who did not. RESULTS: 41% of CHCs provided longitudinal prenatal care; these CHCs were more likely to be larger, have multidisciplinary teams, and serve higher proportions of nonwhite or non-English speaking patients. Patients who received longitudinal prenatal care at CHCs were racially and ethnically diverse and many had comorbidities. Patients who received longitudinal prenatal care at CHCs (compared with pregnant patients who did not) were more likely to be white or Latinx and more likely to have non-English language preference. CONCLUSIONS: Many CHCs in this national network provide prenatal care and serve pregnant patients at high risk of pregnancy-related complications, including people of color, those with low income, and those with comorbidities. CHCs provide critical access to care for vulnerable populations and will be an important partner in work addressing inequities in maternal morbidity and mortality.

Implementation Support for a Social Risk Screening and Referral Process in Community Health Centers.

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.

Social Risk Screening and Response Equity: Assessment by Race, Ethnicity, and Language in Community Health Centers.

INTRODUCTION: Little has previously been reported about the implementation of social risk screening across racial/ethnic/language groups. To address this knowledge gap, the associations between race/ethnicity/language, social risk screening, and patient-reported social risks were examined among adult patients at community health centers. METHODS: Patient- and encounter-level data from 2016 to 2020 from 651 community health centers in 21 U.S. states were used; data were extracted from a shared Epic electronic health record and analyzed between December 2020 and February 2022. In adjusted logistic regression analyses stratified by language, robust sandwich variance SE estimators were applied with clustering on patient's primary care facility. RESULTS: Social risk screening occurred at 30% of health centers; 11% of eligible adult patients were screened. Screening and reported needs varied significantly by race/ethnicity/language. Black Hispanic and Black non-Hispanic patients were approximately twice as likely to be screened, and Hispanic White patients were 28% less likely to be screened than non-Hispanic White patients. Hispanic Black patients were 87% less likely to report social risks than non-Hispanic White patients. Among patients who preferred a language other than English or Spanish, Black Hispanic patients were 90% less likely to report social needs than non-Hispanic White patients. CONCLUSIONS: Social risk screening documentation and patient reports of social risks differed by race/ethnicity/language in community health centers. Although social care initiatives are intended to promote health equity, inequitable screening practices could inadvertently undermine this goal. Future implementation research should explore strategies for equitable screening and related interventions.

Food Insecurity Screening in Primary Care: Patterns During the COVID-19 Pandemic by Encounter Modality.

INTRODUCTION: Screening for food insecurity in clinical settings is recommended, but implementation varies widely. This study evaluated the prevalence of screening for food insecurity and other social risks in telehealth versus in-person encounters during the COVID-19 pandemic and changes in screening before versus after widespread COVID-19 vaccine availability. METHODS: These cross-sectional analyses used electronic health record and ancillary clinic data from a national network of 400+ community health centers with a shared electronic health record. Food insecurity screening was characterized in 2022 in a sample of 275,465 first encounters for routine primary care at any network clinic during March 11, 2020-December 31, 2021. An adjusted multivariate multilevel probit model estimated screening prevalence on the basis of encounter mode (in-person versus telehealth) and time period (initial pandemic versus after vaccine availability) in a random subsample of 11,000 encounters. RESULTS: Encounter mode was related to food insecurity screening (p<0.0001), with an estimated 9.2% screening rate during in-person encounters, compared with 5.1% at telehealth encounters. There was an interaction between time period and encounter mode (p<0.0001), with higher screening prevalence at in-person versus telehealth encounters after COVID-19 vaccines were available (11.7% vs 4.9%) than before vaccines were available (7.8% vs 5.2%). CONCLUSIONS: Food insecurity screening in first primary care encounters is low overall, with lower rates during telehealth visits and the earlier phase of the COVID-19 pandemic. Future research should explore the methods for enhancing social risk screening in telehealth encounters.

Patient-Reported Social Risks and Clinician Decision Making: Results of a Clinician Survey in Primary Care Community Health Centers.

PURPOSE: To assess the extent that patients' social determinants of health (SDOH) influence safety-net primary care clinicians' decisions at the point of care; examine how that information comes to the clinician's attention; and analyze clinician, patient, and encounter characteristics associated with the use of SDOH data in clinical decision making. METHODS: Thirty-eight clinicians working in 21 clinics were prompted to complete 2 short card surveys embedded in the electronic health record (EHR) daily for 3 weeks. Survey data were matched with clinician-, encounter-, and patient-level variables from the EHR. Descriptive statistics and generalized estimating equation models were used to assess relationships between the variables and the clinician reported use of SDOH data to inform care. RESULTS: Social determinants of health were reported to influence care in 35% of surveyed encounters. The most common sources of information on patients' SDOH were conversations with patients (76%), prior knowledge (64%), and the EHR (46%). Social determinants of health were significantly more likely to influence care among male and non-English-speaking patients, and those with discrete SDOH screening data documented in the EHR. CONCLUSIONS: Electronic health records present an opportunity to support clinicians integrating information about patients' social and economic circumstances into care planning. Study findings suggest that SDOH information from standardized screening documented in the EHR, combined with patient-clinician conversations, may enable social risk-adjusted care. Electronic health record tools and clinic workflows could be used to support both documentation and conversations. Study results also identified factors that may cue clinicians to include SDOH information in point-of-care decision-making. Future research should explore this topic further.

Designing and Implementing an Electronic Health Record-Embedded Card Study in Primary Care: Methods and Considerations.

Card studies-short surveys about the circumstances within which patients receive care-are traditionally completed on physical cards. We report on the development of an electronic health record (EHR)-embedded card study intended to decrease logistical challenges inherent to paper-based approaches, including distributing, tracking, and transferring the physical cards, as well as data entry and respondent prompts, while simultaneously decreasing the complexity for participants and facilitating rich analyses by linking to clinical and demographic data found in the EHR. Developing the EHR-based programming and data extraction was time consuming, required specialized expertise, and necessitated iteration to rectify issues encountered during implementation. Nonetheless, future EHR-embedded card studies will be able to replicate many of the same processes as informed by these results. Once built, the EHR-embedded card study simplified survey implementation for both the research team and clinic staff, resulting in research-quality data, the ability to link survey responses to relevant EHR data, and a 79% response rate. This detailed accounting of the development and implementation process, including issues encountered and addressed, might guide others in conducting EHR-embedded card studies.

Implications of missingness in self-reported data for estimating racial and ethnic disparities in Medicaid quality measures.

OBJECTIVE: To assess the feasibility and implications of imputing race and ethnicity for quality and utilization measurement in Medicaid. DATA SOURCES AND STUDY SETTING: 2017 Oregon Medicaid claims from the Oregon Health Authority and electronic health records (EHR) from OCHIN, a clinical data research network, were used. STUDY DESIGN: We cross-sectionally assessed Hispanic-White, Black-White, and Asian-White disparities in 22 quality and utilization measures, comparing self-reported race and ethnicity to imputed values from the Bayesian Improved Surname Geocoding (BISG) algorithm. DATA COLLECTION: Race and ethnicity were obtained from self-reported data and imputed using BISG. PRINCIPAL FINDINGS: 42.5%/4.9% of claims/EHR were missing self-reported data; BISG estimates were available for >99% of each and had good concordance (0.87-0.95) with Asian, Black, Hispanic, and White self-report. All estimated racial and ethnic disparities were statistically similar in self-reported and imputed EHR-based measures. However, within claims, BISG estimates and incomplete self-reported data yielded substantially different disparities in almost half of the measures, with BISG-based Black-White disparities generally larger than self-reported race and ethnicity data. CONCLUSIONS: BISG imputation methods are feasible for Medicaid claims data and reduced missingness to <1%. Disparities may be larger than what is estimated using self-reported data with high rates of missingness.

Adjusting for Patient Economic/Access Issues in a Hypertension Quality Measure.

INTRODUCTION: The American Heart Association and American College of Cardiology have proposed adjusting hypertension-related care quality measures by excluding patients with economic/access issues from the denominator of rate calculations. No research to date has explored the methods to operationalize this recommendation or how to measure economic/access issues. This study applied and compared different approaches to populating these denominator exceptions. METHODS: Electronic health record data from 2019 were used in 2021 to calculate hypertension control rates in 84 community health centers. A total of 10 different indicators of patient economic/access barriers to care were used as denominator exclusions to calculate and then compare adjusted quality measure performance. Data came from a nonprofit health center‒controlled network that hosts a shared electronic health record for community health centers located in 22 states. RESULTS: A total of 5 of 10 measures yielded an increase in adjusted hypertension control rates in ≥50% of clinics (average rate increases of 0.7-3.71 percentage points). A total of 3 of 10 measures yielded a decrease in adjusted hypertension control rates in >50% of clinics (average rate decreases of 1.33-13.82 percentage points). A total of 5 measures resulted in excluding >50% of the clinic's patient population from quality measure assessments. CONCLUSIONS: Changes in clinic-level hypertension control rates after adjustment differed depending on the measure of economic/access issue. Regardless of the exclusion method, changes between baseline and adjusted rates were small. Removing community health center patients experiencing economic/access barriers from a hypertension control quality measure resulted in excluding a large proportion of patients, raising concerns about whether this calculation can be a meaningful measure of clinical performance.

Cross-Sectional Associations: Social Risks and Diabetes Care Quality, Outcomes.

INTRODUCTION: Social risks (e.g., food/transportation insecurity) can hamper type 2 diabetes mellitus (T2DM) self-management, leading to poor outcomes. To determine the extent to which high-quality care can overcome social risks' health impacts, this study assessed the associations between reported social risks, receipt of guideline-based T2DM care, and T2DM outcomes when care is up to date among community health center patients. METHODS: A cross-sectional study of adults aged ≥18 years (N=73,484) seen at 186 community health centers, with T2DM and ≥1 year of observation between July 2016 and February 2020. Measures of T2DM care included up-to-date HbA1c, microalbuminuria, low-density lipoprotein screening, and foot examination, and active statin prescription when indicated. Measures of T2DM outcomes among patients with up-to-date care included blood pressure, HbA1c, and low-density lipoprotein control on or within 6‒12 months of an index encounter. Analyses were conducted in 2021. RESULTS: Individuals reporting transportation or housing insecurity were less likely to have up-to-date low-density lipoprotein screening; no other associations were seen between social risks and clinical care quality. Among individuals with up-to-date care, food insecurity was associated with lower adjusted rates of controlled HbA1c (79% vs 75%, p<0.001), and transportation insecurity was associated with lower rates of controlled HbA1c (79% vs 74%, p=0.005), blood pressure (74% vs 72%, p=0.025), and low-density lipoprotein (61% vs 57%, p=0.009) than among those with no reported need. CONCLUSIONS: Community health center patients received similar care regardless of the presence of social risks. However, even among those up to date on care, social risks were associated with worse T2DM control. Future research should identify strategies for improving HbA1c control for individuals with social risks. TRIAL REGISTRATION: This study is registered at www. CLINICALTRIALS: gov NCT03607617.

Reduction of Long-Term Opioid Prescribing for Back Pain in Community Health Centers After a Medicaid Policy Change.

INTRODUCTION: Beginning around 2011, innumerable policies have aimed to improve pain treatment while minimizing harms from excessive use of opioids. It is not known whether changing insurance coverage for specific conditions is an effective strategy. We describe and assess the effect of an innovative Oregon Medicaid back/neck pain coverage policy on opioid prescribing patterns. METHODS: This retrospective cohort study uses electronic health record data from a network of community health centers (CHCs) in Oregon to analyze prescription opioid dose changes among patients on long-term opioid treatment (LOT) affected by the policy. RESULTS: Of the 1,789 patients on LOT at baseline, 41.6% had an average daily dose of <20 morphine milligram equivalents (MME), 32.3% had ≥20 to <50 MME, 14.5% had ≥50 to <90 MME, and 11.6% ≥90 MME. Around half of each group discontinued opioids within the 18-month policy period. Those who discontinued did so gradually (average of 11 months) regardless of starting dosage. Predictors of discontinuation included: diagnosis of opioid use disorder, older, non-Hispanic white, and less medical complexity. CONCLUSIONS: Regardless of starting opioid dose, nearly half of patients affected by the 2016 Oregon Medicaid back/neck pain treatment policy no longer received opioid prescriptions by the end of the 18-month study period; another 30% decreased their dose. Gradual dose reduction was typical. These outcomes suggest that the policy impacted opioid prescribing. Understanding patient experiences resulting from such policies could help clinicians and policy makers navigate the complex balance between potential harms and benefits of LOT.

Title X Improved Access To Most Effective And Moderately Effective Contraception In US Safety-Net Clinics, 2016-18.

Community health centers are a crucial source of health care for reproductive-age women. Some community health centers receive funding from the federal Title X program, which provides funding for family planning services for low-income women. We describe the provision of the most effective (intrauterine devices and implants) and moderately effective (short-acting hormonal methods) contraceptive methods in a large network of 384 community health center clinics across twenty states in 2016-18. Title X clinics provided more most and moderately effective contraception at all time points and for all age groups (adolescent, young adult, and adult). They provided 52 percent more of the most effective contraceptives to women at risk for pregnancy than clinics not funded by Title X. This finding was especially notable for adolescents (58 percent more). Title X clinics play a key role in access to effective contraception across the US safety net. Strengthening the Title X program should continue to be a policy priority for public health for the Biden-Harris administration.

Patient-level factors associated with receipt of preventive care in the safety net.

Prevention is critical to optimizing health, yet most people do not receive all recommended preventive services. As the complexity of preventive recommendations increases, there is a need for new measurements to capture the degree to which a person is up to date, and identify individual-level barriers and facilitators to receiving needed preventive care. We used electronic health record data from a national network of community health centers (CHCs) in the United States (US) during 2014-2017 to measure patient-level up-to-date status with preventive ratios (measuring up-to-date person-time denoted as a percent) for 12 preventive services and an aggregate preventive index. We use negative binomial regression to identify factors associated with up-to-date preventive care. We assessed 267,767 patients across 165 primary care clinics. Mean preventive ratios ranged from 8.7% for Hepatitis C screening to 83.3% for blood pressure screening. The mean aggregate preventive index was 43%. Lack of health insurance, smoking, and homelessness were associated with lower preventive ratios for most cancer and cardiovascular screenings (p < 0.05). Having more ambulatory visits, better continuity of care, and enrollment in the patient portal were positively associated with the aggregate preventive index (p < 0.05) and higher preventive ratios for all services (p < 0.05) except chlamydia and HIV screening. Overall, receipt of preventive services was low. CHC patients experience many barriers to receiving needed preventive care, but certain healthcare behaviors - regular visits, usual provider continuity, and patient portal enrollment - were consistently associated with more up-to-date preventive care. These associations should inform future efforts to improve preventive care delivery.

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.

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.

Clinic factors associated with utilization of a pregnancy-intention screening tool in community health centers.

OBJECTIVE: Routine pregnancy-intention screening in the primary care setting is a promising practice to help patients achieve their reproductive goals. We aim to describe the utilization of a pregnancy-intention screening tool integrated in the electronic health record (EHR) of a national network of community health centers (CHCs) and identify clinic-level factors associated with tool use. STUDY DESIGN: We conducted a clinic-level retrospective observational study to assess tool utilization during the first 3 years after the tool was made available in the EHR (November 2015 to October 2018). We describe characteristics of clinics with higher tool utilization (≥90th percentile) versus lower utilization (<90th percentile) and the types of providers who used the tool. We then employ negative binomial regression to identify independent clinic-level factors associated with tool utilization. RESULTS: Across 194 clinics in our study sample which served 289,754 eligible female patients, the tool was used for 113,116 (39%). Medical assistants performed 60.3% of screenings and clinicians performed 11.2%. CHCs with higher tool utilization rates were more likely to be located in rural settings (RR 1.75, 95% CI 1.07-2.87) and serve patient populations with higher proportions of women (RR 1.32, 95% CI 1.24-1.41) and lower proportions of patients with non-English language preference (RR 0.92, 95% CI 0.89-0.95). CONCLUSIONS: Many health centers utilized pregnancy-intention screening after an EHR-based tool was made available, though overall screening rates were low. IMPLICATIONS: Additional study of implementation strategies and effectiveness of pregnancy-intention screening tools is needed. Implementation of future pregnancy-intention screening interventions must be tailored to address clinic-level barriers and facilitators to screening.

Assessing Cancer History Accuracy in Primary Care Electronic Health Records Through Cancer Registry Linkage.

BACKGROUND: Many cancer survivors receive primary care in community health centers (CHCs). Cancer history is an important factor to consider in the provision of primary care, yet little is known about the completeness or accuracy of cancer history data contained in CHC electronic health records (EHRs). METHODS: We probabilistically linked EHR data from more than1.5 million adult CHC patients to state cancer registries in California, Oregon, and Washington and estimated measures of agreement (eg, kappa, sensitivity, specificity). We compared demographic and clinical characteristics of cancer patients as estimated by each data source, evaluating distributional differences with absolute standardized mean differences. RESULTS: A total 74 707 cancer patients were identified between the 2 sources (EHR only, n = 22 730; registry only, n = 23 616; both, n = 28 361). Nearly one-half of cancer patients identified in registries were missing cancer documentation in the EHR. Overall agreement of cancer ascertainment in the EHR vs cancer registries (gold standard) was moderate (kappa = 0.535). Cancer site-specific agreement ranged from substantial (eg, prostate and female breast; kappa > 0.60) to fair (melanoma and cervix; kappa < 0.40). Comparing population characteristics of cancer patients as ascertained from each data source, groups were similar for sex, age, and federal poverty level, but EHR-recorded cases showed greater medical complexity than those ascertained from cancer registries. CONCLUSIONS: Agreement between EHR and cancer registry data was moderate and varied by cancer site. These findings suggest the need for strategies to improve capture of cancer history information in CHC EHRs to ensure adequate delivery of care and optimal health outcomes for cancer survivors.

Impacts of the Affordable Care Act on Receipt of Women's Preventive Services in Community Health Centers in Medicaid Expansion and Nonexpansion States.

BACKGROUND: The Affordable Care Act (ACA) increased health insurance coverage throughout the United States and improved care delivery for some services. We assess whether ACA implementation and Medicaid expansion were followed by greater receipt of recommended preventive services among women and girls in a large network of community health centers. METHODS: Using electronic health record data from 354 community health centers in 14 states (10 expansion, 4 nonexpansion), we used generalized estimating equations and difference-in-difference methods to compare receipt of six recommended preventive services (cervical cancer screening, human papilloma virus vaccination, chlamydia screening, influenza vaccination, human immunodeficiency virus screening, and blood pressure screening) among active female patients ages 11 to 65 (N = 711,121) before and after ACA implementation and between states that expanded versus did not expand Medicaid. RESULTS: Except for blood pressure screening, receipt of all examined preventive services increased after ACA implementation in both Medicaid expansion and nonexpansion states. Influenza vaccination and blood pressure screening increased more in expansion states (adjusted absolute prevalence difference-in-difference, 1.55; 95% confidence interval, 0.51-2.60; and 1.98; 95% confidence interval, 0.91-3.05, respectively). Chlamydia screening increased more in nonexpansion states (adjusted absolute prevalence difference-in-difference: -4.21; 95% confidence interval, -6.98 to -1.45). Increases in cervical cancer screening, human immunodeficiency virus screening, and human papilloma virus vaccination did not differ significantly between expansion and nonexpansion states. CONCLUSIONS: Among female patients at community health centers, receipt of recommended preventive care improved after ACA implementation in both Medicaid expansion and nonexpansion states, although the overall rates remained low. Continued support is needed to overcome barriers to preventive care in this population.

Private/marketplace insurance in community health centers 5 years post-affordable care act in medicaid expansion and non-expansion states.

Community health centers (CHCs) play an important role in providing care for the safety net population. After implementation of the Affordable Care Act, many patients gained insurance through state and federal marketplaces. Using electronic health record data from 702,663 patients in 257 clinics across 20 states, we sought to explore the following differences between Medicaid expansion and non-expansion state CHCs: (1) trends in private/marketplace insurance post-expansion, and (2) whether CHC patients retain private/marketplace insurance. We found that patients in non-expansion state CHCs relied more heavily on private/marketplace insurance than patients in expansion states and had increases in private/marketplace-insured visits from 2014 through 2018. Additionally, there appeared to be seasonal variation in private/marketplace-insured visits that were more pronounced in non-expansion states. While a greater percentage of patients in non-expansion states retained private/marketplace insurance than in expansion states, a greater percentage of those who did not retain it became uninsured. In comparison, a greater percentage of patients in expansion states who lost private/marketplace insurance gained other types of health insurance. CHCs' ability to provide adequate care for vulnerable populations relies, in part, on federal grants as well as reimbursement from insurers: decreases in either could result in reduced capacity or quality of care for patients seen in CHCs.

The Affordable Care Act: Effects of Insurance on Diabetes Biomarkers.

OBJECTIVE: We sought to understand how Affordable Care Act (ACA) Medicaid expansion insurance coverage gains are associated with changes in diabetes-related biomarkers. RESEARCH DESIGN AND METHODS: This was a retrospective observational cohort study using electronic health record data from 178 community health centers (CHCs) in the ADVANCE (Accelerating Data Value Across a National Community Health Center Network) network. We assessed changes in diabetes-related biomarkers among adult patients with diabetes in 10 Medicaid expansion states (n = 25,279), comparing newly insured with continuously insured, discontinuously insured, and continuously uninsured patients pre- to post-ACA expansion. Primary outcomes included changes from 24 months pre- to 24 months post-ACA in glycosylated hemoglobin (HbA1c), systolic (SBP) and diastolic (DBP) blood pressure, and LDL cholesterol levels. RESULTS: Newly insured patients exhibited a reduction in adjusted mean HbA1c levels (8.24% [67 mmol/mol] to 8.17% [66 mmol/mol]), which was significantly different from continuously uninsured patients, whose HbA1c levels increased (8.12% [65 mmol/mol] to 8.29% [67 mmol/mol]; difference-in-differences [DID] -0.24%; P < 0.001). Newly insured patients showed greater reductions than continuously uninsured patients in adjusted mean SBP (DID -1.8 mmHg; P < 0.001), DBP (DID -1.0 mmHg; P < 0.001), and LDL (DID -3.3 mg/dL; P < 0.001). Among patients with elevated HbA1c in the 3 months prior to expansion, newly insured patients were more likely than continuously uninsured patients to have a controlled HbA1c measurement by 24 months post-ACA (hazard ratio 1.25; 95% CI 1.02-1.54]. CONCLUSIONS: Post-ACA, newly insured patients had greater improvements in diabetes-related biomarkers than continuously uninsured, discontinuously insured, or continuously insured patients. Findings suggest that health insurance gain via ACA facilitates access to appropriate diabetes care, leading to improvements in diabetes-related biomarkers.