Publications by authors named "Megan J Hoopes"

18 Publications

  • Page 1 of 1

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

J Health Care Poor Underserved 2021 ;32(2):783-798

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.
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http://dx.doi.org/10.1353/hpu.2021.0105DOI Listing
January 2021

Prevalence of Pre-existing Conditions Among Community Health Center Patients Before and After the Affordable Care Act.

J Am Board Fam Med 2019 Nov-Dec;32(6):883-889

From the Department of Family Medicine, Oregon Health & Science University, Portland, OR (NH, HA, MM, JH, JED); OCHIN Inc., Portland, OR (MJH, TS); Biostatistics Group, Oregon Health and Science University-Portland State University School of Public Health, Portland, OR (MM).

Objective: To assess the prevalence of pre-existing conditions for community health center (CHC) patients who gained insurance coverage post-Affordable Care Act (ACA).

Methods: We analyzed electronic health record data from 78,059 patients aged 19 to 64 uninsured at their last visit pre-ACA from 386 CHCs in 19 states. We compared the prevalence and types of pre-existing conditions pre-ACA (2012 to 2013) and post-ACA (2014 to 2015), by insurance status and race/ethnicity.

Results: Pre-ACA, >50% of patients in the cohort had ≥1 Pre-existing condition. Post-ACA, >70% of those who gained insurance coverage had ≥1 condition. Post-ACA, all racial/ethnic subgroups showed an increase in the number of pre-existing conditions, with non-Hispanic Black and Hispanic patients experiencing the largest increases (adjusted prevalence difference, 18.9; 95% CI, 18.2 to 19.6 and 18.3; 95% CI, 17.8 to 18.7, respectively). The most common conditions post-ACA were mental health disorders with the highest prevalence among patients who gained Medicaid (45.6%) and lowest among those who gained private coverage (30.5%).

Conclusions: This study emphasizes the high prevalence of pre-existing conditions among CHC patients and the large increase in the proportion of patients with at least 1 of these diagnoses post-ACA. Given how common these conditions are, repealing pre-existing condition protections could be extremely harmful to millions of patients and would likely exacerbate health care and health disparities.
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http://dx.doi.org/10.3122/jabfm.2019.06.190087DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001872PMC
September 2020

Use of a Preventive Index to Examine Clinic-Level Factors Associated With Delivery of Preventive Care.

Am J Prev Med 2019 08;57(2):241-249

Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; OCHIN, Inc., Portland, Oregon.

Introduction: There is an increasing need for the development of new methods to understand factors affecting delivery of preventive care. This study applies a new measurement approach and assesses clinic-level factors associated with preventive care delivery.

Methods: This retrospective longitudinal cohort study of 94 community health centers used electronic health record data from the OCHIN community health information network, 2014-2015. Clinic-level preventive ratios (time covered by a preventive service/time eligible for a preventive service) were calculated in 2017 for 12 preventive services with A or B recommendations from the U.S. Preventive Services Task Force along with an aggregate preventive index for all services combined. For each service, multivariable negative binomial regression modeling and calculated rate ratios assessed the association between clinic-level variables and delivery of care.

Results: Of ambulatory community health center visits, 59.8% were Medicaid-insured and 10.4% were uninsured. Ambulatory community health centers served 16.9% patients who were Hispanic, 13.1% who were nonwhite, and 68.7% who had household incomes <138% of the federal poverty line. Clinic-level preventive ratios ranged from 3% (hepatitis C screening) to 93% (blood pressure screening). The aggregate preventive index including all screening measures was 47% (IQR, 42%-50%). At the clinic level, having a higher percentage of uninsured visits was associated with lower preventive ratios for most (7 of 12) preventive services.

Conclusions: Approaches that use individual preventive ratios and aggregate prevention indices are promising for understanding and improving preventive service delivery over time. Health insurance remains strongly associated with access to needed preventive care, even for safety net clinic populations.
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http://dx.doi.org/10.1016/j.amepre.2019.03.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6684138PMC
August 2019

Tobacco Cessation in Affordable Care Act Medicaid Expansion States Versus Non-expansion States.

Nicotine Tob Res 2020 05;22(6):1016-1022

Department of Family Medicine, Oregon Health & Science University, Portland, OR.

Introduction: Community health centers (CHCs) care for vulnerable patients who use tobacco at higher than national rates. States that expanded Medicaid eligibility under the Affordable Care Act (ACA) provided insurance coverage to tobacco users not previously Medicaid-eligible, thereby potentially increasing their odds of receiving cessation assistance. We examined if tobacco users in Medicaid expansion states had increased quit rates, cessation medications ordered, and greater health care utilization compared to patients in non-expansion states.

Methods: Using electronic health record (EHR) data from 219 CHCs in 10 states that expanded Medicaid as of January 1, 2014, we identified patients aged 19-64 with tobacco use status documented in the EHR within 6 months prior to ACA Medicaid expansion and ≥1 visit with tobacco use status assessed within 24 months post-expansion (January 1, 2014 to December 31, 2015). We propensity score matched these patients to tobacco users from 108 CHCs in six non-expansion states (n = 27 670 matched pairs; 55 340 patients). Using a retrospective observational cohort study design, we compared odds of having a quit status, cessation medication ordered, and ≥6 visits within the post-expansion period among patients in expansion versus non-expansion states.

Results: Patients in expansion states had increased adjusted odds of quitting (adjusted odds ratio [aOR] = 1.35, 95% confidence interval [CI]: 1.28-1.43), having a medication ordered (aOR = 1.53, 95% CI: 1.44-1.62), and having ≥6 follow-up visits (aOR = 1.34, 95% CI: 1.28-1.41) compared to patients from non-expansion states.

Conclusions: Increased access to insurance via the ACA Medicaid expansion likely led to increased quit rates within this vulnerable population.

Implications: CHCs care for vulnerable patients at higher risk of tobacco use than the general population. Medicaid expansion via the ACA provided insurance coverage to a large number of tobacco users not previously Medicaid-eligible. We found that expanded insurance coverage was associated with increased cessation assistance and higher odds of tobacco cessation. Continued provision of insurance coverage could lead to increased quit rates among high-risk populations, resulting in improvements in population health outcomes and reduced total health care costs.
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http://dx.doi.org/10.1093/ntr/ntz087DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249916PMC
May 2020

Medicaid Expansion Produces Long-Term Impact on Insurance Coverage Rates in Community Health Centers.

J Prim Care Community Health 2017 Oct 17;8(4):206-212. Epub 2017 May 17.

1 Oregon Health & Science University, Portland, OR, USA.

Background: It is crucial to understand the impact of the Affordable Care Act (ACA). This study assesses changes in insurance status of patients visiting community health centers (CHCs) comparing states that expanded Medicaid to those that did not.

Methods: Electronic health record data on 875,571 patients aged 19 to 64 years with ≥ 1 visit between 2012 and 2015 in 412 primary care CHCs in 9 expansion and 4 nonexpansion states. We assessed changes in rates of total, uninsured, Medicaid-insured, and privately insured primary care and preventive care visits; immunizations administered, and medications ordered.

Results: Rates of uninsured visits decreased pre- to post-ACA, with greater drops in expansion (-57%) versus nonexpansion (-20%) states. Medicaid-insured visits increased 60% in expansion states while remaining unchanged in nonexpansion states. Privately insured visits were 2.7 times higher post-ACA in nonexpansion states with no increase in expansion states. Comparing 2015 with 2014: Uninsured visit rates continued to decrease in expansion (-28%) and nonexpansion states (-19%), Medicaid-insured rates did not significantly increase, and privately insured visits increased in nonexpansion states but did not change in expansion states.

Conclusions: Medicaid expansion and subsidies to purchase private coverage likely increased the accessibility of health insurance for patients who had previously not been able to access coverage.
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http://dx.doi.org/10.1177/2150131917709403DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5665709PMC
October 2017

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

Implement Sci 2017 02 10;12(1):14. Epub 2017 Feb 10.

Department of Family Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.

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.
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http://dx.doi.org/10.1186/s13012-017-0543-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5301438PMC
February 2017

Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

Am J Prev Med 2016 11 10;51(5):752-761. Epub 2016 Aug 10.

Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; OCHIN, Inc., Portland, Oregon.

Introduction: Preventive care delivery is an important quality outcome, and electronic data reports are being used increasingly to track these services. It is highly informative when electronic data sources are compared to information manually extracted from medical charts to assess validity and completeness.

Methods: This cross-sectional study used a random sample of Medicaid-insured patients seen at 43 community health centers in 2011 to calculate standard measures of correspondence between manual chart review and two automated sources (electronic health records [EHRs] and Medicaid claims), comparing documentation of orders for and receipt of ten preventive services (n=150 patients/service). Data were analyzed in 2015.

Results: Using manual chart review as the gold standard, automated EHR extraction showed near-perfect to perfect agreement (κ=0.96-1.0) for services received within the primary care setting (e.g., BMI, blood pressure). Receipt of breast and colorectal cancer screenings, services commonly referred out, showed moderate (κ=0.42) to substantial (κ=0.62) agreement, respectively. Automated EHR extraction showed near-perfect agreement (κ=0.83-0.97) for documentation of ordered services. Medicaid claims showed near-perfect agreement (κ=0.87) for hyperlipidemia and diabetes screening, and substantial agreement (κ=0.67-0.80) for receipt of breast, cervical, and colorectal cancer screenings, and influenza vaccination. Claims showed moderate agreement (κ=0.59) for chlamydia screening receipt. Medicaid claims did not capture ordered or unbilled services.

Conclusions: Findings suggest that automated EHR and claims data provide valid sources for measuring receipt of most preventive services; however, ordered and unbilled services were primarily captured via EHR data and completed referrals were more often documented in claims data.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5067199PMC
http://dx.doi.org/10.1016/j.amepre.2016.07.004DOI Listing
November 2016

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

J Gen Intern Med 2016 10 21;31(10):1198-205. Epub 2016 Jun 21.

Department of Family Medicine, Oregon Health & Science University, Portland, OR, USA.

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.
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http://dx.doi.org/10.1007/s11606-016-3781-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5023615PMC
October 2016

Utilization of Community Health Centers in Medicaid Expansion and Nonexpansion States, 2013-2014.

J Ambul Care Manage 2016 Oct-Dec;39(4):290-8

OCHIN, Inc, Portland, Oregon (Ms Hoopes); Oregon Health and Science University, Portland, Oregon (Ms Angier, Dr Bailey, Dr Huguet, Dr Marino, and Dr DeVoe)Kaiser Permanente Northwest Center for Health Research, OCHIN, Inc, Portland, Oregon (Dr Gold).

Using electronic health record data, we examined longitudinal changes in community health center (CHC) visit rates from 2013 through 2014 in Medicaid expansion versus nonexpansion states. Visits from 219 CHCs in 5 expansion states and 4 nonexpansion states were included. Rates were computed using generalized estimating equation Poisson models. Rates increased in expansion state CHCs for new patient, preventive, and limited-service visits (14%, 41%, and 23%, respectively, P < .01 for all), whereas these rates remained unchanged in nonexpansion states. One year after ACA Medicaid expansions, CHCs in expansion states saw an influx of new patients and provided increased preventive services.
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http://dx.doi.org/10.1097/JAC.0000000000000123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942402PMC
July 2017

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

Am J Prev Med 2016 Feb 23;50(2):161-70. Epub 2015 Oct 23.

Department of Family Medicine, Oregon Health and Science University, Portland, Oregon; OCHIN, Inc., Portland, Oregon.

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.
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http://dx.doi.org/10.1016/j.amepre.2015.07.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4718854PMC
February 2016

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

Ann Fam Med 2015 Jul-Aug;13(4):312-20

Oregon Health & Science University, Department of Family Medicine, Portland, Oregon

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.
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http://dx.doi.org/10.1370/afm.1812DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4508170PMC
April 2016

Uncovering a missing demographic in trauma registries: epidemiology of trauma among American Indians and Alaska Natives in Washington State.

Inj Prev 2015 Oct 29;21(5):335-43. Epub 2015 Apr 29.

National Center of Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Background: The objectives of this study were to evaluate racial misclassification in a statewide trauma registry and to describe the epidemiology of trauma among the Washington American Indian and Alaska Native (AI/AN) population.

Methods: We performed probabilistic record linkage between the Washington Trauma Registry (2005-2009) and Northwest Tribal Registry, a dataset of known AI/AN. AI/AN patients were compared with caucasians on demographic, injury and clinical outcome factors. A multivariable model estimated odds of mortality.

Results: Record linkage increased ascertainment of AI/AN cases in the trauma registry 71%, from 1777 to 3039 cases. Compared with caucasians, AI/AN trauma patients were younger (mean age=36 vs 47 years, p<0.001) and more commonly male (66.5% vs 61.2%, p<0.001). AI/AN experienced more intentional injuries (suicide or homicide: 20.1% vs 6.7%, p<0.001), a higher proportion of severe traumatic brain injury (20.7% vs 16.8%, p=0.004) and were less likely than caucasians to use safety equipment such as seat belts/airbags (53.9% vs 76.7%, p<0.001). ISSs were similar (ISS >15: 21.4% vs 20.5%, p=0.63), and no difference was observed in mortality after adjustment for covariates (p=0.58).

Conclusions: Linkage to a state trauma registry improved data quality by correcting racial misclassification, allowing for a comprehensive description of injury patterns for the AI/AN population. AI/AN sustained more severe injuries with similar postinjury outcomes to caucasians. Future efforts should focus on primary prevention for this population, including increased use of seat belts and child safety seats and reduction of interpersonal violence and suicide.
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http://dx.doi.org/10.1136/injuryprev-2014-041419DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603226PMC
October 2015

Disparities in life expectancy of pacific northwest American Indians and Alaska natives: analysis of linkage-corrected life tables.

Public Health Rep 2015 Jan-Feb;130(1):71-80

Seattle Indian Health Board, Urban Indian Health Institute, Seattle, WA.

Objectives: American Indians and Alaska Natives (AI/ANs) experience a high burden of mortality and other disparities compared with the general population. Life tables are an important population health indicator; however, federal agencies have not produced life tables for AI/ANs, largely due to racial misclassification on death certificates. Our objective was to correct this misclassification and create life tables for AI/ANs who resided in the Pacific Northwest region of the U.S., making comparisons with the general population.

Methods: To correct racial misclassification, we conducted probabilistic record linkages between death certificates from three Northwest states-Idaho, Oregon, and Washington State-issued during 2008-2010, and AI/AN patient registration records. We calculated mortality rates and generated period life tables for AI/ANs and non-Hispanic white (NHW) Americans.

Results: Overall life expectancy at birth for Northwest AI/ANs was 72.8 years, which was 6.9 years lower than that of NHW Americans. Male AI/ANs had a lower life expectancy (70.9 years) than female AI/ANs (74.6 years). The disparity in life expectancy between AI/ANs and their NHW counterparts was higher for females (with AI/ANs living 7.3 years fewer than NHW females) than for males (with AI/ANs living 6.7 years fewer than NHW males). The greatest disparity in mortality rates was seen among young adults.

Conclusion: Data linkage with a registry of known AI/ANs allowed us to generate accurate life tables that had not previously been available for this population and revealed disparities in both life expectancy at birth and survival across the life span. These results represent an important tool to help AI/AN communities as they monitor their health and promote efforts to eliminate health disparities.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4245288PMC
http://dx.doi.org/10.1177/003335491513000109DOI Listing
March 2015

Racial misclassification of American Indians and Alaska Natives by Indian Health Service Contract Health Service Delivery Area.

Am J Public Health 2014 Jun 22;104 Suppl 3:S295-302. Epub 2014 Apr 22.

Melissa A. Jim is with the Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Albuquerque, NM. Elizabeth Arias is with the Division of Vital Statistics, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD. Dean S. Seneca is with the Division of Public Health Capacity Development, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA. Megan J. Hoopes is with the Northwest Tribal Epidemiology Center, Northwest Portland Area Indian Health Board, Portland, OR. Cheyenne C. Jim is with Immunization Services Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Albuquerque, NM. Norman J. Johnson is with the National Longitudinal Mortality Study Branch, US Census Bureau, Suitland, MD. Charles L. Wiggins is with the New Mexico Tumor Registry, University of New Mexico Cancer Center, Albuquerque.

Objectives: We evaluated the racial misclassification of American Indians and Alaska Natives (AI/ANs) in cancer incidence and all-cause mortality data by Indian Health Service (IHS) Contract Health Service Delivery Area (CHSDA).

Methods: We evaluated data from 3 sources: IHS-National Vital Statistics System (NVSS), IHS-National Program of Cancer Registries (NPCR)/Surveillance, Epidemiology and End Results (SEER) program, and National Longitudinal Mortality Study (NLMS). We calculated, within each data source, the sensitivity and classification ratios by sex, IHS region, and urban-rural classification by CHSDA county.

Results: Sensitivity was significantly greater in CHSDA counties (IHS-NVSS: 83.6%; IHS-NPCR/SEER: 77.6%; NLMS: 68.8%) than non-CHSDA counties (IHS-NVSS: 54.8%; IHS-NPCR/SEER: 39.0%; NLMS: 28.3%). Classification ratios indicated less misclassification in CHSDA counties (IHS-NVSS: 1.20%; IHS-NPCR/SEER: 1.29%; NLMS: 1.18%) than non-CHSDA counties (IHS-NVSS: 1.82%; IHS-NPCR/SEER: 2.56%; NLMS: 1.81%). Race misclassification was less in rural counties and in regions with the greatest concentrations of AI/AN persons (Alaska, Southwest, and Northern Plains).

Conclusions: Limiting presentation and analysis to CHSDA counties helped mitigate the effects of race misclassification of AI/AN persons, although a portion of the population was excluded.
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http://dx.doi.org/10.2105/AJPH.2014.301933DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035863PMC
June 2014

Estimating demand for care after a medicaid expansion: lessons from Oregon.

J Ambul Care Manage 2014 Oct-Dec;37(4):282-92

Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Drs Gold and Fortmann); Oregon Health & Science University, Department of Family Medicine, Portland (Drs Bailey, Marino, Heintzman, and DeVoe); Oregon Health & Science University, Department of Public Health & Preventive Medicine, Division of Biostatistics, Portland (Ms O'Malley); and OCHIN, Inc, Portland, Oregon (Messrs Hoopes and Cowburn and Dr Nelson).

To estimate how the Affordable Care Act's Medicaid expansions will affect demand for services, we measured ambulatory care utilization among adult patients who gained insurance during Oregon's 2008 Medicaid expansion. Using electronic health record data from 67 community health centers, we assessed pre- and postcoverage utilization among patients who gained insurance, compared with patients continuously insured or uninsured. In comparisons of the pre- and postcoverage periods, mean annual encounters among persons who gained insurance increased 22% to 35%, but declined in the comparison groups. These findings suggest that providers should expect a significant increase in demand among patients who gain Medicaid coverage through the Affordable Care Act.
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http://dx.doi.org/10.1097/JAC.0000000000000023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4172441PMC
August 2016

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

J Am Med Inform Assoc 2014 Jul-Aug;21(4):720-4. Epub 2014 Feb 7.

Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA OCHIN, Inc, Portland, Oregon, USA.

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.
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http://dx.doi.org/10.1136/amiajnl-2013-002333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078280PMC
August 2014

Regional differences and tribal use of American Indian/Alaska Native cancer data in the Pacific Northwest.

J Cancer Educ 2012 Apr;27(1 Suppl):S73-9

Northwest Tribal Epidemiology Center, Northwest Portland Area Indian Health Board, 2121 SW Broadway Drive, Suite 300, Portland, OR 97201, USA.

In the Pacific Northwest, cancer is a leading cause of morbidity and mortality for American Indians and Alaska Natives (AI/AN). Misclassification of AI/AN race in state cancer registries causes cancer burden to be underestimated. Furthermore, local-level data are rarely available to individual tribes for use in health assessment and program planning. We corrected race coding in the cancer registries of Idaho, Oregon, and Washington using probabilistic record linkage to a file derived from patient registration records from Indian Health Service and a large urban clinic. We calculated cancer incidence and mortality measures by state, comparing AI/AN to non-Hispanic White (NHW) race. Record linkages identified a high prevalence of misclassified race. Differences in AI/AN cancer patterns were identified across the three state region. Compared to NHW, AI/AN experienced disproportionate late stage rates of some screen-detectable cancers. The correct classification of race is a crucial factor in cancer surveillance and can reveal regional differences even within a relatively small area. The availability of local-level cancer data can help inform tribes in appropriate intervention efforts.
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http://dx.doi.org/10.1007/s13187-012-0325-4DOI Listing
April 2012

Including self-reported race to improve cancer surveillance data for American Indians and Alaska Natives in Washington state.

J Registry Manag 2010 ;37(2):43-8

Northwest Tribal Epidemiology Center, Northwest Portland Area Indian Health Board, Portland, OR 97201, USA.

Background: American Indians and Alaska Natives (AI/AN) are frequently misclassified as another race in cancer surveillance systems, resulting in underestimated morbidity and mortality. Linkage methods with administrative records have been used to correct AI/AN misclassification, but AI/AN populations living in urban areas, and those who self-identify as AI/AN race, continue to be under-ascertained. The aim of this study was to evaluate racial misclassification in two cancer registries in Washington State using an urban AI/AN patient roster linked with a list of Indian Health Service (IHS) enrollees.

Methods: We conducted probabilistic record linkages to identify racial misclassification using a combined demographic dataset of self-identified AI/AN patients of a large, urban Indian health center, and administratively-identified AI/AN enrolled with the IHS. Age-adjusted incidence rates were calculated for 3 linkage populations: AI/ AN originally coded in each cancer registry, post-linkage AI/AN identified through the IHS roster alone, and post-linkage AI/AN identified through either the urban or IHS file.

Results: In the state and regional cancer registries, 11% and 18%, respectively, of matched cases were originally coded as a race other than AI/AN; approximately 35% of these were identified by the urban file alone. Incidence rate estimates increased after linkage with the IHS file, and further increased with the addition of urban records. Matches identified by the urban patient file resulted in the largest relative incidence change being demonstrated for King County (which includes Seattle); the all-site invasive cancer rate increased 8.8%, from 443 to 482 per 100,000.

Conclusions: Inclusion of urban and self-identified AI/AN records can increase case ascertainment in cancer surveillance systems beyond linkage methods using only administrative sources.
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December 2010
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