Differences in up-to-date colorectal and cervical cancer screening rates by ethnicity and preferred language: An analysis across patient-, clinic-, and area-level data sources.

Research objective: There is interest in using clinic- and area-level data to inform cancer control, but it is unclear what value these sources may add in combination with patient-level data sources. This study aimed to investigate associations of up-to-date colorectal and cervical cancer screenings at community health centers (CHCs) with ethnicity and language variables at patient-, clinic-, and area-levels, while exploring whether patient-level associations differed based on clinic-level patient language and ethnicity distributions. Study design: This was a cross-sectional study using data from multiple sources, including electronic health records, clinic patient panel data, and area-level demographic data. The study sample included English-preferring Hispanic, Spanish-preferring Hispanic, English-preferring non-Hispanic, and non-English-preferring non-Hispanic patients eligible for either colorectal cancer (N = 98,985) or cervical cancer (N = 129,611) screenings in 2019 from 130 CHCs in the OCHIN network in CA, OR, and WA. Population studied: The study population consisted of adults aged 45+ eligible for colorectal cancer screening and adults with a cervix aged 25-65 eligible for cervical cancer screening. Principal findings: Spanish-preferring Hispanic patients were significantly more likely to be up-to-date with colorectal and cervical cancer screenings than other groups. Patients seen at clinics with higher concentrations of Spanish-preferring Hispanics were significantly more likely to be up-to-date, as were individuals residing in areas with higher percentages of Spanish-speaking residents. Differential associations between patient ethnicity and language and up-to-date colorectal cancer screenings were greater among patients seen at clinics with higher concentrations of Spanish-preferring Hispanics. Conclusions: The findings highlight that Spanish-speaking Hispanics seen in CHCs have higher rates of up-to-date cervical and colorectal cancer screenings than other groups and that this relationship is stronger at clinics with higher percentages of Spanish-preferring Hispanic patients. Our findings suggest area-level variables are not good substitutions for patient-level data, but variables at the clinic patient panel-level are more informative.

Analgesic methadone prescribing in community health centers among patients with chronic pain.

OBJECTIVE: To examine analgesic methadone prescriptions among community health center (CHC) patients with chronic pain. DESIGN: Observational; two cross-sectional periods. SETTING: Oregon and California CHCs. PATIENTS: Chronic pain patients with ≥1 visit in 2012-2013 or 2017-2018 (N = 158,239). OUTCOMES: Changes in adjusted relative rates (aRRs) of receiving no opioids, short-acting only, long-acting only other than methadone, and methadone; characteristics associated with ≥1 methadone prescription. RESULTS: Opioid prescribing declined over time, with the largest decrease in methadone (aRR = 0.19, 95 percent confidence interval: 0.14-0.27). Among patients receiving ≥1 long-acting opioid, variables associated with methadone prescribing included being aged <65 years, having nonprivate insurance, and an opioid use disorder (OUD) diagnosis. From 2012-2013 to 2017-2018, aRR increased among patients with OUD and decreased for those aged 18-30 (vs ≥65), uninsured and Medicaid-insured (vs private), and race/ethnicity other than non-Hispanic Black (vs non-Hispanic White). CONCLUSIONS: Methadone prescribing decreased in CHCs but remained elevated for several high-risk demographic groups.

Impact of COVID-19 Pandemic on Assessing Tobacco Status in Community Health Centers.

Few have studied the COVID-19 pandemic's impact on tobacco use status assessment and cessation counseling. Electronic health record data from 217 primary care clinics were examined from January 1, 2019 to July 31, 2021. Data included telehealth and in-person visits for 759,138 adult patients (aged ≥18 years). Monthly rates of tobacco assessment per 1,000 patients were calculated. From March 2020 to May 2020, tobacco assessment monthly rates declined by 50% and increased from June 2020 to May 2021 but remained 33.5% lower than pre-pandemic levels. Rates of tobacco cessation assistance changed less, but remain low. These findings are significant given the relevance of tobacco use to increased severity of COVID-19.

The Impact of Social and Clinical Complexity on Diabetes Control Measures.

PURPOSE: In an age of value-based payment, primary care providers are increasingly scrutinized on performance metrics that assess quality of care, including the outcomes of their patient population in key areas such as diabetes control. Although such measures often adjust for patient clinical risk factors or clinical complexity, most do not account for the social complexity of patient populations, despite research demonstrating the strong association between social factors and health. METHODS: Using patient electronic health record data from 2 large community health center networks serving safety net patients, we assessed the effect of both clinical and social risk factors on poor glucose control among diabetics. Logistic regression results were used to estimate the impact of adjusting for both clinical and social complexity on provider performance metrics. Clinical complexity was measured at the patient-level using the Charlson Comorbidity Index. Social complexity was measured at the community-level using the Social Deprivation Index. RESULTS: Clinical complexity alone was not consistently associated with poor diabetes control (ie, HbA1c > 9%) in diabetic patients with HbA1c testing during the study period. However, increasing social complexity was significantly associated with higher rates of poor diabetic control in both cohorts. After adding adjustment for social complexity down to the national median score, our models suggest that approximately 25% of providers would have 1 to 2% improvement in the assessment of their diabetes control measures, with 45% showing a 2 to 5% improvement, and 5% showing more than a 5% improvement. CONCLUSIONS: Providers caring for patients with greater social risk factors may benefit from having their performance metrics adjusted for the social complexity of their patient populations.

Prescription opioid use patterns, use disorder diagnoses and addiction treatment receipt after the 2014 Medicaid expansion in Oregon.

BACKGROUND/AIMS: Evidence suggests that Medicaid beneficiaries in the United States are prescribed opioids more frequently than are people who are privately insured, but little is known about opioid prescribing patterns among Medicaid enrollees who gained coverage via the Affordable Care Act Medicaid expansions. This study compared the prevalence of receipt of opioid prescriptions and opioid use disorder (OUD), along with time from OUD diagnosis to medication-assisted treatment (MAT) receipt between Oregon residents who had been continuously insured by Medicaid, were newly insured after Medicaid expansion in 2014 or returned to Medicaid coverage after expansion. DESIGN: Cross-sectional study using inverse-propensity weights to adjust for differences among insurance groups. SETTING: Oregon. PARTICIPANTS: A total of 225 295 Oregon Medicaid adult beneficiaries insured during 2014-15 and either: (1) newly enrolled, (2) returning in 2014 after a > 12-month gap or (3) continuously insured between 2013 and 2015. We excluded patients in hospice care or with cancer diagnoses. MEASUREMENTS: Any opioid-dispensed, chronic (> 90-days) and high-dose (> 90 daily morphine milligram equivalence) opioid use, documented OUD diagnosis and MAT receipt. FINDINGS: Compared with the continuously insured, newly and returning insured enrollees were less likely to be dispensed opioids [newly: 42.3%, 95% confidence interval (CI) = 42.0-42.7%; returning: 49.3%, 95% CI = 48.8-49.7%; continuously: 52.5%, 95% CI = 52.0-53.0%], use opioids chronically (newly: 12.8%, 95% CI = 12.4-13.1%; returning: 11.9%, 95% CI = 11.5-12.3%, continuously: 15.8%, 95% CI = 15.4-16.2%), have OUD diagnoses (newly: 3.6%, 95% CI = 3.4-3.7%; returning: 3.9%, 95% CI = 3.8-4.1%, continuously: 4.7%, 95% CI = 4.5-4.9%) and receive MAT after OUD diagnosis [hazard ratio newly: 0.57, 95% CI = 0.53-0.61; hazard ratio returning: 0.60, 95% CI = 0.56-0.65 (ref: continuously)]. CONCLUSIONS: Residents of Oregon, United States who enrolled or re-enrolled in Medicaid health insurance after expansion of coverage in 2014 as a result of the Affordable Care Act were less likely than those already covered to receive opioids, use them chronically or receive medication-assisted treatment for opioid use disorder.

Not so implausible: impact of longitudinal assessment of implausible anthropometric measures on obesity prevalence and weight change in children and adolescents.

PURPOSE: Implausible anthropometric measures are typically identified using population outlier definitions, conflating implausible and extreme measures. We determined the impact of a longitudinal outlier approach on prevalence of body mass index (BMI) categories and mean change in anthropometric measures in pediatric electronic health record data. METHODS: We examined 996,131 observations from 147,375 children (10-18 years) in the ADVANCE Clinical Data Research Network, a national network of community health centers. Sex-stratified, mixed effects, linear spline regression modeled weight, height, and BMI as a function of age. Longitudinal outliers were defined as observations with studentized residual greater than |6|; population outliers were defined by Centers for Disease Control-defined z-score thresholds. RESULTS: At least 99.7% of anthropometric measures were not extreme by longitudinal or population definitions (agreement ≥ 0.995). BMI category prevalence after excluding longitudinal or population outliers differed by less than 0.1%. Among children greater than 85th percentile at baseline, annual mean changes in anthropometric measures were larger in data that excluded longitudinal (girls: 1.24 inches, 12.39 pounds, 1.53 kg/m2; boys: 2.34, 14.08, 1.07) versus population outliers (girls: 0.61 inches, 8.22 pounds, 0.75 kg/m2; boys: 1.53, 11.61, 0.48). CONCLUSIONS: Longitudinal outlier methods may reduce underestimation of anthropometric change in children with elevated baseline values.

Medicaid coverage accuracy in electronic health records.

Health insurance coverage facilitates access to preventive screenings and other essential health care services, and is linked to improved health outcomes; therefore, it is critical to understand how well coverage information is documented in the electronic health record (EHR) and which characteristics are associated with accurate documentation. Our objective was to evaluate the validity of EHR data for monitoring longitudinal Medicaid coverage and assess variation by patient demographics, visit types, and clinic characteristics. We conducted a retrospective, observational study comparing Medicaid status agreement between Oregon community health center EHR data linked at the patient-level to Medicaid enrollment data (gold standard). We included adult patients with a Medicaid identification number and ≥1 clinic visit between 1/1/2013-12/31/2014 [>1 million visits (n = 135,514 patients)]. We estimated statistical correspondence between EHR and Medicaid data at each visit (visit-level) and for different insurance cohorts over time (patient-level). Data were collected in 2016 and analyzed 2017-2018. We observed excellent agreement between EHR and Medicaid data for health insurance information: kappa (>0.80), sensitivity (>0.80), and specificity (>0.85). Several characteristics were associated with agreement; at the visit-level, agreement was lower for patients who preferred a non-English language and for visits missing income information. At the patient-level, agreement was lower for black patients and higher for older patients seen in primary care community health centers. Community health center EHR data are a valid source of Medicaid coverage information. Agreement varied with several characteristics, something researchers and clinic staff should consider when using health insurance information from EHR data.

Electronic health record tools to assist with children's insurance coverage: a mixed methods study.

BACKGROUND: Children with health insurance have increased access to healthcare and receive higher quality care. However, despite recent initiatives expanding children's coverage, many remain uninsured. New technologies present opportunities for helping clinics provide enrollment support for patients. We developed and tested electronic health record (EHR)-based tools to help clinics provide children's insurance assistance. METHODS: We used mixed methods to understand tool adoption, and to assess impact of tool use on insurance coverage, healthcare utilization, and receipt of recommended care. We conducted intent-to-treat (ITT) analyses comparing pediatric patients in 4 intervention clinics (n = 15,024) to those at 4 matched control clinics (n = 12,227). We conducted effect-of-treatment-on-the-treated (ETOT) analyses comparing intervention clinic patients with tool use (n = 2240) to intervention clinic patients without tool use (n = 12,784). RESULTS: Tools were used for only 15% of eligible patients. Qualitative data indicated that tool adoption was limited by: (1) concurrent initiatives that duplicated the work associated with the tools, and (2) inability to obtain accurate insurance coverage data and end dates. The ITT analyses showed that intervention clinic patients had higher odds of gaining insurance coverage (adjusted odds ratio [aOR] = 1.32, 95% confidence interval [95%CI] 1.14-1.51) and lower odds of losing coverage (aOR = 0.77, 95%CI 0.68-0.88), compared to control clinic patients. Similarly, ETOT findings showed that intervention clinic patients with tool use had higher odds of gaining insurance (aOR = 1.83, 95%CI 1.64-2.04) and lower odds of losing coverage (aOR = 0.70, 95%CI 0.53-0.91), compared to patients without tool use. The ETOT analyses also showed higher rates of receipt of return visits, well-child visits, and several immunizations among patients for whom the tools were used. CONCLUSIONS: This pragmatic trial, the first to evaluate EHR-based insurance assistance tools, suggests that it is feasible to create and implement tools that help clinics provide insurance enrollment support to pediatric patients. While ITT findings were limited by low rates of tool use, ITT and ETOT findings suggest tool use was associated with better odds of gaining and keeping coverage. Further, ETOT findings suggest that use of such tools may positively impact healthcare utilization and quality of pediatric care. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02298361 ; retrospectively registered on November 5, 2014.

Oregon Medicaid Expenditures After the 2014 Affordable Care Act Medicaid Expansion: Over-time Differences Among New, Returning, and Continuously Insured Enrollees.

BACKGROUND: There is interest in assessing health care utilization and expenditures among new Medicaid enrollees after the 2014 Medicaid expansion. Recent studies have not differentiated between newly enrolled individuals and those returning after coverage gaps. OBJECTIVES: To assess health care expenditures among Medicaid enrollees in the 24 months after Oregon's 2014 Medicaid expansions and examine whether expenditure patterns were different among the newly, returning, and continuously insured (CI). RESEARCH DESIGN: Retrospective cohort study using inverse-propensity weights to adjust for differences between groups. SUBJECTS: Oregon adult Medicaid beneficiaries insured continuously from 2014 to 2015 who were either newly, returning, or CI. MEASURES: Monthly expenditures for inpatient care, prescription drugs, total outpatient care, and subdivisions of outpatient care: emergency department, dental, mental and behavioral health, primary care, and specialist care. RESULTS: After initial increases, newly and returning insured (RI) outpatient expenditures dropped below CI. Expenditures for emergency department and dental services among the RI remained higher than among the newly insured. Newly insured mental and behavioral health, primary care, and specialist expenditures plateaued higher than RI. Prescription drug expenditures increased over time for all groups, with CI highest and RI lowest. All groups had similar inpatient expenditures over 24 months post-Medicaid expansion. CONCLUSIONS: Our findings reveal that outpatient expenditures for new nonpregnant, non-dual-eligible Oregon Medicaid recipients stabilized over time after meeting pent-up demand, and prior insurance history affected the mix of services that individuals received. Policy evaluations should consider expenditures over at least 24 months and should account for enrollees' prior insurance histories.

Medicaid's Impact on Chronic Disease Biomarkers: A Cohort Study of Community Health Center Patients.

BACKGROUND: Understanding the impact of health insurance is critical, particularly in the era of Affordable Care Act Medicaid expansion. The electronic health record (EHR) provides new opportunities to quantify health outcomes. OBJECTIVE: To assess changes in biomarkers of chronic disease among community health center (CHC) patients who gained Medicaid coverage with the Oregon Medicaid expansion (2008-2011). DESIGN: Prospective cohort. Patients were followed for 24 months, and rate of mean biomarker change was calculated. Time to a controlled follow-up measurement was compared using Cox regression models. SETTING/PATIENTS: Using EHR data from OCHIN (a non-profit network of CHCs) linked to state Medicaid data, we identified three cohorts of patients with uncontrolled chronic conditions (diabetes, hypertension, and hyperlipidemia). Within these cohorts, we included patients who gained Medicaid coverage along with a propensity score-matched comparison group who remained uninsured (diabetes n = 608; hypertension n = 1244; hyperlipidemia n = 546). MAIN MEASURES: Hemoglobin A1c (HbA1c) for the diabetes cohort, systolic and diastolic blood pressure (SBP and DBP, respectively) for the hypertension cohort, and low-density lipoprotein (LDL) for the hyperlipidemia cohort. KEY RESULTS: All cohorts improved over time. Compared to matched uninsured patients, adults in the diabetes and hypertension cohorts who gained Medicaid coverage were significantly more likely to have a follow-up controlled measurement (hazard ratio [HR] =1.26, p = 0.020; HR = 1.35, p < 0.001, respectively). No significant difference was observed in the hyperlipidemia cohort (HR = 1.09, p = 0.392). CONCLUSIONS: OCHIN patients with uncontrolled chronic conditions experienced objective health improvements over time. In two of three chronic disease cohorts, those who gained Medicaid coverage were more likely to achieve a controlled measurement than those who remained uninsured. These findings demonstrate the effective care provided by CHCs and the importance of health insurance coverage within a usual source of care setting. CLINICAL TRIALS REGISTRATION: NCT02355132 [ https://clinicaltrials.gov/ct2/show/NCT02355132 ].

Protocol for the analysis of a natural experiment on the impact of the Affordable Care Act on diabetes care in community health centers.

BACKGROUND: It is hypothesized that Affordable Care Act (ACA) Medicaid expansions could substantially improve access to health insurance and healthcare services for patients at risk for diabetes mellitus (DM), with pre-DM, or already diagnosed with DM. The ACA called for every state to expand Medicaid coverage by 2014. In a 2012 legal challenge, the US Supreme Court ruled that states were not required to implement Medicaid expansions. This 'natural experiment' presents a unique opportunity to learn whether and to what extent Medicaid expansion can affect healthcare access and services for patients with DM risk, pre-DM, or DM. METHODS/DESIGN: Data from electronic health records (EHRs) from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) clinical data research network, which has data from >700 community health centers (CHCs), was included in the study. EHR data will be linked to Oregon Medicaid claims data. Data collection will include information on changes in health insurance, service receipt, and health outcomes, spanning 9 years (pre- and post-expansion), comparing states that expanded Medicaid, and those that did not. Patients included in this study will be diagnosed with DM, be at risk for DM, or have pre-DM, between the ages of 19 and 64, with ≥1 ambulatory visit. Sample size is estimated to be roughly 275,000 patients. Biostatistical analyses will include the difference-in-differences (DID) methodology and a generalized linear mixed model. Econometric analyses will include a DID two-part method to calculate the difference in Medicaid expenditures in Oregon among newly insured CHC patients. DISCUSSION: Findings will have national relevance on DM health services and outcomes and will be shared through national conferences and publications. The findings will provide information needed to impact the policy as it is related to access to health insurance and receipt of healthcare among a vulnerable population. TRIAL REGISTRATION: This project is registered with ClinicalTrials.gov ( NCT02685384 ). Registered 18 May 2016.

Health Care Utilization Rates After Oregon's 2008 Medicaid Expansion: Within-Group and Between-Group Differences Over Time Among New, Returning, and Continuously Insured Enrollees.

BACKGROUND: Although past research demonstrated that Medicaid expansions were associated with increased emergency department (ED) and primary care (PC) utilization, little is known about how long this increased utilization persists or whether postcoverage utilization is affected by prior insurance status. OBJECTIVES: (1) To assess changes in ED, PC, mental and behavioral health care, and specialist care visit rates among individuals gaining Medicaid over 24 months postinsurance gain; and (2) to evaluate the association of previous insurance with utilization. METHODS: Using claims data, we conducted a retrospective cohort analysis of adults insured for 24 months following Oregon's 2008 Medicaid expansion. Utilization rates among 1124 new and 1587 returning enrollees were compared with those among 5126 enrollees with continuous Medicaid coverage (≥1 y preexpansion). Visit rates were adjusted for propensity score classes and geographic region. RESULTS: PC visit rates in both newly and returning insured individuals significantly exceeded those in the continuously insured in months 4 through 12, but were not significantly elevated in the second year. In contrast, ED utilization rates were significantly higher in returning insured compared with newly or continuously insured individuals and remained elevated over time. New visits to PC and specialist care were higher among those who gained Medicaid compared with the continuously insured throughout the study period. CONCLUSIONS: Predicting the effect of insurance expansion on health care utilization should account for the prior coverage history of new enrollees. In addition, utilization of outpatient services changes with time after insurance, so expansion evaluations should allow for rate stabilization.

Effect of Gaining Insurance Coverage on Smoking Cessation in Community Health Centers: A Cohort Study.

BACKGROUND: Community health center (CHC) patients have high rates of smoking. Insurance coverage for smoking cessation assistance, such as that mandated by the Affordable Care Act, may aid in smoking cessation in this vulnerable population. OBJECTIVE: We aimed to determine if uninsured CHC patients who gain Medicaid coverage experience greater primary care utilization, receive more cessation medication orders, and achieve higher quit rates, compared to continuously uninsured smokers. DESIGN: Longitudinal observational cohort study using electronic health record data from a network of Oregon CHCs linked to Oregon Medicaid enrollment data. PATIENTS: Cohort of patients who smoke and who gained Medicaid coverage in 2008-2011 after ≥ 6 months of being uninsured and with ≥ 1 smoking assessment in the 24-month follow-up period from the baseline smoking status date. This group was propensity score matched to a cohort of continuously uninsured CHC patients who smoke (n = 4140 matched pairs; 8280 patients). INTERVENTION: Gaining Medicaid after being uninsured for ≥ 6 months. MAIN MEASURES: 'Quit' smoking status (baseline smoking status was 'current every day' or 'some day' and status change to 'former smoker' at a subsequent visit), smoking cessation medication order, and ≥ 6 documented visits (yes/no variables) at ≥ 1 smoking status assessment within the 24-month follow-up period. KEY RESULTS: The newly insured had 40 % increased odds of quitting smoking (aOR = 1.40, 95 % CI:1.24, 1.58), nearly triple the odds of having a medication ordered (aOR = 2.94, 95 % CI:2.61, 3.32), and over twice the odds of having ≥ 6 follow-up visits (aOR = 2.12, 95 % CI:1.94, 2.32) compared to their uninsured counterparts. CONCLUSIONS: Newly insured patients had increased odds of quit smoking status over 24 months of follow-up than those who remained uninsured. Providing insurance coverage to vulnerable populations may have a significant impact on smoking cessation.

Healthcare Utilization After a Children's Health Insurance Program Expansion in Oregon.

OBJECTIVE: The future of the Children's Health Insurance Program (CHIP) is uncertain after 2017. Survey-based research shows positive associations between CHIP expansions and children's healthcare utilization. To build on this prior work, we used electronic health record (EHR) data to assess temporal patterns of healthcare utilization after Oregon's 2009-2010 CHIP expansion. We hypothesized increased post-expansion utilization among children who gained public insurance. METHODS: Using EHR data from 154 Oregon community health centers, we conducted a retrospective cohort study of pediatric patients (2-18 years old) who gained public insurance coverage during the Oregon expansion (n = 3054), compared to those who were continuously publicly insured (n = 10,946) or continuously uninsured (n = 10,307) during the 2-year study period. We compared pre-post rates of primary care visits, well-child visits, and dental visits within- and between-groups. We also conducted longitudinal analysis of monthly visit rates, comparing the three insurance groups. RESULTS: After Oregon's 2009-2010 CHIP expansions, newly insured patients' utilization rates were more than double their pre-expansion rates [adjusted rate ratios (95 % confidence intervals); increases ranged from 2.10 (1.94-2.26) for primary care visits to 2.77 (2.56-2.99) for dental visits]. Utilization among the newly insured spiked shortly after coverage began, then leveled off, but remained higher than the uninsured group. CONCLUSIONS: This study used EHR data to confirm that CHIP expansions are associated with increased utilization of essential pediatric primary and preventive care. These findings are timely to pending policy decisions that could impact children's access to public health insurance in the United States.

Receipt of Preventive Services After Oregon's Randomized Medicaid Experiment.

INTRODUCTION: It is predicted that gaining health insurance via the Affordable Care Act will result in increased rates of preventive health services receipt in the U.S., primarily based on self-reported findings from previous health insurance expansion studies. This study examined the long-term (36-month) impact of Oregon's 2008 randomized Medicaid expansion ("Oregon Experiment") on receipt of 12 preventive care services in community health centers using electronic health record data. METHODS: Demographic data from adult (aged 19-64 years) Oregon Experiment participants were probabilistically matched to electronic health record data from 49 Oregon community health centers within the OCHIN community health information network (N=10,643). Intent-to-treat analyses compared receipt of preventive services over a 36-month (2008-2011) period among those randomly assigned to apply for Medicaid versus not assigned, and instrumental variable analyses estimated the effect of actually gaining Medicaid coverage on preventive services receipt (data collected in 2012-2014; analysis performed in 2014-2015). RESULTS: Intent-to-treat analyses revealed statistically significant differences between patients randomly assigned to apply for Medicaid (versus not assigned) for 8 of 12 assessed preventive services. In intent-to-treat analyses, Medicaid coverage significantly increased the odds of receipt of most preventive services (ORs ranging from 1.04 [95% CI=1.02, 1.06] for smoking assessment to 1.27 [95% CI=1.02, 1.57] for mammography). CONCLUSIONS: Rates of preventive services receipt will likely increase as community health center patients gain insurance through Affordable Care Act expansions. Continued effort is needed to increase health insurance coverage in an effort to decrease health disparities in vulnerable populations.

Community Health Center Use After Oregon's Randomized Medicaid Experiment.

PURPOSE: There is debate about whether community health centers (CHCs) will experience increased demand from patients gaining coverage through Affordable Care Act Medicaid expansions. To better understand the effect of new Medicaid coverage on CHC use over time, we studied Oregon's 2008 randomized Medicaid expansion (the "Oregon Experiment"). METHODS: We probabilistically matched demographic data from adults (aged 19-64 years) participating in the Oregon Experiment to electronic health record data from 108 Oregon CHCs within the OCHIN community health information network (originally the Oregon Community Health Information Network) (N = 34,849). We performed intent-to-treat analyses using zero-inflated Poisson regression models to compare 36-month (2008-2011) usage rates among those selected to apply for Medicaid vs not selected, and instrumental variable analyses to estimate the effect of gaining Medicaid coverage on use. Use outcomes included primary care visits, behavioral/mental health visits, laboratory tests, referrals, immunizations, and imaging. RESULTS: The intent-to-treat analyses revealed statistically significant differences in rates of behavioral/mental health visits, referrals, and imaging between patients randomly selected to apply for Medicaid vs those not selected. In instrumental variable analyses, gaining Medicaid coverage significantly increased the rate of primary care visits, laboratory tests, referrals, and imaging; rate ratios ranged from 1.27 (95% CI, 1.05-1.55) for laboratory tests to 1.58 (95% CI, 1.10-2.28) for referrals. CONCLUSIONS: Our results suggest that use of many different types of CHC services will increase as patients gain Medicaid through Affordable Care Act expansions. To maximize access to critical health services, it will be important to ensure that the health care system can support increasing demands by providing more resources to CHCs and other primary care settings.

Effect of expanding medicaid for parents on children's health insurance coverage: lessons from the Oregon experiment.

IMPORTANCE: In the United States, health insurance is not universal. Observational studies show an association between uninsured parents and children. This association persisted even after expansions in child-only public health insurance. Oregon's randomized Medicaid expansion for adults, known as the Oregon Experiment, created a rare opportunity to assess causality between parent and child coverage. OBJECTIVE: To estimate the effect on a child's health insurance coverage status when (1) a parent randomly gains access to health insurance and (2) a parent obtains coverage. DESIGN, SETTING, AND PARTICIPANTS: Oregon Experiment randomized natural experiment assessing the results of Oregon's 2008 Medicaid expansion. We used generalized estimating equation models to examine the longitudinal effect of a parent randomly selected to apply for Medicaid on their child's Medicaid or Children's Health Insurance Program (CHIP) coverage (intent-to-treat analyses). We used per-protocol analyses to understand the impact on children's coverage when a parent was randomly selected to apply for and obtained Medicaid. Participants included 14409 children aged 2 to 18 years whose parents participated in the Oregon Experiment. EXPOSURES: For intent-to-treat analyses, the date a parent was selected to apply for Medicaid was considered the date the child was exposed to the intervention. In per-protocol analyses, exposure was defined as whether a selected parent obtained Medicaid. MAIN OUTCOMES AND MEASURES: Children's Medicaid or CHIP coverage, assessed monthly and in 6-month intervals relative to their parent's selection date. RESULTS: In the immediate period after selection, children whose parents were selected to apply significantly increased from 3830 (61.4%) to 4152 (66.6%) compared with a nonsignificant change from 5049 (61.8%) to 5044 (61.7%) for children whose parents were not selected to apply. Children whose parents were randomly selected to apply for Medicaid had 18% higher odds of being covered in the first 6 months after parent's selection compared with children whose parents were not selected (adjusted odds ratio [AOR]=1.18; 95% CI, 1.10-1.27). The effect remained significant during months 7 to 12 (AOR=1.11; 95% CI, 1.03-1.19); months 13 to 18 showed a positive but not significant effect (AOR=1.07; 95% CI, 0.99-1.14). Children whose parents were selected and obtained coverage had more than double the odds of having coverage compared with children whose parents were not selected and did not gain coverage (AOR=2.37; 95% CI, 2.14-2.64). CONCLUSIONS AND RELEVANCE: Children's odds of having Medicaid or CHIP coverage increased when their parents were randomly selected to apply for Medicaid. Children whose parents were selected and subsequently obtained coverage benefited most. This study demonstrates a causal link between parents' access to Medicaid coverage and their children's coverage.

Receipt of diabetes preventive services differs by insurance status at visit.

BACKGROUND: Lack of insurance is associated with suboptimal receipt of diabetes preventive care. One known reason for this is an access barrier to obtaining healthcare visits; however, little is known about whether insurance status is associated with differential rates of receipt of diabetes care during visits. PURPOSE: To examine the association between health insurance and receipt of diabetes preventive care during an office visit. METHODS: This retrospective cohort study used electronic health record and Medicaid data from 38 Oregon community health centers. Logistic regression was used to test the association between insurance and receipt of four diabetes services during an office visit among patients who were continuously uninsured (n=1,117); continuously insured (n=1,466); and discontinuously insured (n=336) in 2006-2007. Generalized estimating equations were used to account for within-patient correlation. Data were analyzed in 2013. RESULTS: Overall, continuously uninsured patients had lower odds of receiving services at visits when due, compared to those who were continuously insured (AOR=0.73, 95% CI=0.66, 0.80). Among the discontinuously insured, being uninsured at a visit was associated with lower odds of receipt of services due at that visit (AOR=0.77, 95% CI=0.64, 0.92) than being insured at a visit. CONCLUSIONS: Lack of insurance is associated with a lower probability of receiving recommended services that are due during a clinic visit. Thus, the association between being uninsured and receiving fewer preventive services may not be completely mediated by access to clinic visits.

Agreement of Medicaid claims and electronic health records for assessing preventive care quality among adults.

To compare the agreement of electronic health record (EHR) data versus Medicaid claims data in documenting adult preventive care. Insurance claims are commonly used to measure care quality. EHR data could serve this purpose, but little information exists about how this source compares in service documentation. For 13 101 Medicaid-insured adult patients attending 43 Oregon community health centers, we compared documentation of 11 preventive services, based on EHR versus Medicaid claims data. Documentation was comparable for most services. Agreement was highest for influenza vaccination (κ = 0.77; 95% CI 0.75 to 0.79), cholesterol screening (κ = 0.80; 95% CI 0.79 to 0.81), and cervical cancer screening (κ = 0.71; 95% CI 0.70 to 0.73), and lowest on services commonly referred out of primary care clinics and those that usually do not generate claims. EHRs show promise for use in quality reporting. Strategies to maximize data capture in EHRs are needed to optimize the use of EHR data for service documentation.

Diabetes care quality is highly correlated with patient panel characteristics.

INTRODUCTION: Health care reimbursement is increasingly based on quality. Little is known about how clinic-level patient characteristics affect quality, particularly in community health centers (CHCs). METHODS: Using data from electronic health records for 4019 diabetic patients from 23 primary care CHCs in the OCHIN practice-based research network, we calculated correlations between a clinic's patient panel characteristics and rates of delivery of diabetes preventive services in 2007. Using regression models, we estimated the proportion of variability in clinics' preventive services rates associated with the variability in the clinics' patient panel characteristics. We also explored whether clinics' performance rates were affected by how patient panel denominators were defined. RESULTS: Clinic rates of hemoglobin testing, influenza immunizations, and lipid screening were positively associated with the percentage of patients with continuous health insurance coverage and negatively associated with the percentage of uninsured patients. Microalbumin screening rates were positively associated with the percentage of racial minorities in a clinic's panel. Associations remained consistent with different panel denominators. CONCLUSIONS: Clinic variability in delivery rates of preventive services correlates with differences in clinics' patient panel characteristics, particularly the percentage of patients with continuous insurance coverage. Quality scores that do not account for these differences could create disincentives to clinics providing diabetes care for vulnerable patients.