The implementation of opioid prescribing report cards in Medicaid managed care: a community quality collaborative.

Philadelphia, Pennsylvania, is an urban epicenter of the opioid epidemic, and inappropriate opioid prescribing remains a top concern. To help address this issue, the Philadelphia Medicaid Opioid Prescribing Initiative, a type of community quality collaborative, mailed thousands of local Medicaid providers an individualized prescribing report card in 2017 and 2018. The report card featured details of providers' opioid prescribing, including peer comparison measures and inappropriate prescribing measures like concomitant opioid and benzodiazepine prescribing. This case study describes the unique process of developing and distributing the opioid prescribing report cards, with a particular focus on the role of Medicaid managed care organizations. Using Medicaid pharmacy claims, the extensive variation in prescribing measures within and across specialties is also illustrated. The report card's implementation points to the potential value of collaborations between public health departments and Medicaid managed care organizations and can provide insight for other locally grown policies.

Constructing Within-City Neighborhood Health Rankings in Philadelphia by Using Data From the 500 Cities Project.

INTRODUCTION: Profound geographic disparities in health exist in many US cities. Most reporting on these disparities is based on predetermined administrative districts that may not reflect true neighborhoods. We undertook a ranking project to describe health at the neighborhood level and used Philadelphia, Pennsylvania, as our case study. METHODS: To create neighborhood health rankings, we first divided the city into neighborhoods according to groups of contiguous census tracts. Modeling our ranking methods and indicators on the Robert Wood Johnson Foundation County Health Rankings, we gathered census tract-level data from the Centers for Disease Control and Prevention's 500 Cities Project and local sources and aggregated these data, as needed, to each neighborhood. We assigned composite scores and rankings for both health outcomes and health factors to each neighborhood. RESULTS: Scores for health outcomes and health factors were highly correlated. We found clusters of neighborhoods with low rankings in Philadelphia's northern, lower northeastern, western, and southwestern regions. We disseminated information on rankings throughout the city, including through a comprehensive webpage, public communication, and a museum exhibit. CONCLUSION: The Philadelphia neighborhood health rankings were designed to be accessible to people unfamiliar with public health, facilitating education on drivers of health in communities. Our methods can be used as a model for other cities to create and communicate data on within-city geographic health disparities.

Chronic Disease Surveillance Using Electronic Health Records From Health Centers in a Large Urban Setting.

OBJECTIVES: To assess the validity of electronic health records (EHRs) from a network of health centers for chronic disease surveillance among an underserved population in an urban setting. DESIGN: EHRs from a network of health centers were used to calculate the prevalence of chronic disease among adult and child patient populations during 2016. Two population-based surveys with local estimates of chronic disease prevalence were compared with the EHR prevalences. SETTING: A network of health centers that provides health care services to an underserved population in a large urban setting. PARTICIPANTS: A total of 187 292 patients who had at least 1 health care visit recorded in the Philadelphia health center network. MAIN OUTCOME MEASURE: Chronic disease indicator (CDI) prevalence of adult obesity, adult smoking, adult diabetes, adult hypertension, child obesity, and child asthma. Health center CDI proportions were compared with survey estimates. RESULTS: Overall consistency between the health center estimates and surveys varied by CDI. With the exception of childhood obesity, all health center CDI proportions fell within the 95% CI for at least 1 comparison survey estimate. Statistically significant differences were observed and varied by CDI. CONCLUSIONS: This analysis presents a novel use of existing EHR data to estimate chronic disease prevalence among underserved populations. With the increased use of EHRs in health centers, data from health center networks may supplement chronic disease surveillance efforts, if used appropriately.

COVID-19 Contact Tracing in Two Counties - North Carolina, June-July 2020.

Contact tracing is a strategy implemented to minimize the spread of communicable diseases (1,2). Prompt contact tracing, testing, and self-quarantine can reduce the transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (3,4). Community engagement is important to encourage participation in and cooperation with SARS-CoV-2 contact tracing (5). Substantial investments have been made to scale up contact tracing for COVID-19 in the United States. During June 1-July 12, 2020, the incidence of COVID-19 cases in North Carolina increased 183%, from seven to 19 per 100,000 persons per day* (6). To assess local COVID-19 contact tracing implementation, data from two counties in North Carolina were analyzed during a period of high incidence. Health department staff members investigated 5,514 (77%) persons with COVID-19 in Mecklenburg County and 584 (99%) in Randolph Counties. No contacts were reported for 48% of cases in Mecklenburg and for 35% in Randolph. Among contacts provided, 25% in Mecklenburg and 48% in Randolph could not be reached by telephone and were classified as nonresponsive after at least one attempt on 3 consecutive days of failed attempts. The median interval from specimen collection from the index patient to notification of identified contacts was 6 days in both counties. Despite aggressive efforts by health department staff members to perform case investigations and contact tracing, many persons with COVID-19 did not report contacts, and many contacts were not reached. These findings indicate that improved timeliness of contact tracing, community engagement, and increased use of community-wide mitigation are needed to interrupt SARS-CoV-2 transmission.

Early Impact of the Affordable Care Act Coverage Expansion on Safety-Net Hospital Inpatient Payer Mix and Market Shares.

OBJECTIVE: To examine the impact of the Affordable Care Act's coverage expansion on safety-net hospitals (SNHs). STUDY SETTING: Nine Medicaid expansion states. STUDY DESIGN: Differences-in-differences (DID) models compare payer-specific pre-post changes in inpatient stays of adults aged 19-64 years at SNHs and non-SNHs. DATA COLLECTION METHODS: 2013-2014 Healthcare Cost and Utilization Project State Inpatient Databases. PRINCIPAL FINDINGS: On average per quarter postexpansion, SNHs and non-SNHs experienced similar relative decreases in uninsured stays (DID = -2.2 percent, p = .916). Non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs (DID = 13.8 percent, p = .041). For SNHs, the average decrease in uninsured stays (-146) was similar to the increase in Medicaid stays (153); privately insured stays were stable. For non-SNHs, the decrease in uninsured (-63) plus privately insured (-33) stays was similar to the increase in Medicaid stays (105). SNHs and non-SNHs experienced a similar absolute increase in Medicaid, uninsured, and privately insured stays combined (DID = -16, p = .162). CONCLUSIONS: Postexpansion, non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs, which may reflect patients choosing non-SNHs over SNHs or a crowd-out of private insurance. More research is needed to understand these trends.

Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

OBJECTIVE: We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness. DATA SOURCES: We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012. METHODS: The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics. RESULTS: Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776-0.778) for the mortality index and 0.634 (95% confidence interval, 0.633-0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly. CONCLUSIONS: These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.

Differences in Use of High-quality and Low-quality Hospitals Among Working-age Individuals by Insurance Type.

BACKGROUND: Research suggests that individuals with Medicaid or no insurance receive fewer evidence-based treatments and have worse outcomes than those with private insurance for a broad range of conditions. These differences may be due to patients' receiving care in hospitals of different quality. RESEARCH DESIGN: We used the Healthcare Cost and Utilization Project State Inpatient Databases 2009-2010 data to identify patients aged 18-64 years with private insurance, Medicaid, or no insurance who were hospitalized with acute myocardial infarction, heart failure, pneumonia, stroke, or gastrointestinal hemorrhage. Multinomial logit regressions estimated the probability of admissions to hospitals classified as high, medium, or low quality on the basis of risk-adjusted, in-hospital mortality. RESULTS: Compared with patients who have private insurance, those with Medicaid or no insurance were more likely to be minorities and to reside in areas with low-socioeconomic status. The probability of admission to high-quality hospitals was similar for patients with Medicaid (23.3%) and private insurance (23.0%) but was significantly lower for patients without insurance (19.8%, P<0.01) compared with the other 2 insurance groups. Accounting for demographic, socioeconomic, and clinical characteristics did not influence the results. CONCLUSIONS: Previously noted disparities in hospital quality of care for Medicaid recipients are not explained by differences in the quality of hospitals they use. Patients without insurance have lower use of high-quality hospitals, a finding that needs exploration with data after 2013 in light of the Affordable Care Act, which is designed to improve access to medical care for patients without insurance.

Understanding Racial and Ethnic Disparities in Postsurgical Complications Occurring in U.S. Hospitals.

OBJECTIVE: To examine the role of patient, hospital, and community characteristics on racial and ethnic disparities in in-hospital postsurgical complications. DATA SOURCES: Healthcare Cost and Utilization Project, 2011 State Inpatient Databases; American Hospital Association Annual Survey of Hospitals; Area Health Resources Files; Centers for Medicare & Medicaid Services Hospital Compare database. METHODS: Nonlinear hierarchical modeling was conducted to examine the odds of patients experiencing any in-hospital postsurgical complication, as defined by Agency for Healthcare Research and Quality Patient Safety Indicators. PRINCIPAL FINDINGS: A total of 5,474,067 inpatient surgical discharges were assessed using multivariable logistic regression. Clinical risk, payer coverage, and community-level characteristics (especially income) completely attenuated the effect of race on the odds of postsurgical complications. Patients without private insurance were 30 to 50 percent more likely to have a complication; patients from low-income communities were nearly 12 percent more likely to experience a complication. Private, not-for-profit hospitals in small metropolitan or micropolitan areas and higher nurse-to-patient ratios led to fewer postsurgical complications. CONCLUSIONS: Race does not appear to be an important determinant of in-hospital postsurgical complications, but insurance and community characteristics have an effect. A population-based approach that includes improving the socioeconomic context may help reduce disparities in these outcomes.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty.

BACKGROUND: Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospital's control, may not accurately reflect a hospital's performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA. METHODS: We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods. RESULTS: Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals. CONCLUSIONS: Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting. LEVEL OF EVIDENCE: Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits.

BACKGROUND: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. OBJECTIVE: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. METHODS: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. RESULTS: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. CONCLUSIONS: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness.

Prevalence of Total Hip and Knee Replacement in the United States.

BACKGROUND: Descriptive epidemiology of total joint replacement procedures is limited to annual procedure volumes (incidence). The prevalence of the growing number of individuals living with a total hip or total knee replacement is currently unknown. Our objective was to estimate the prevalence of total hip and total knee replacement in the United States. METHODS: Prevalence was estimated using the counting method by combining historical incidence data from the National Hospital Discharge Survey and the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases from 1969 to 2010 with general population census and mortality counts. We accounted for relative differences in mortality rates between those who have had total hip or knee replacement and the general population. RESULTS: The 2010 prevalence of total hip and total knee replacement in the total U.S. population was 0.83% and 1.52%, respectively. Prevalence was higher among women than among men and increased with age, reaching 5.26% for total hip replacement and 10.38% for total knee replacement at eighty years. These estimates corresponded to 2.5 million individuals (1.4 million women and 1.1 million men) with total hip replacement and 4.7 million individuals (3.0 million women and 1.7 million men) with total knee replacement in 2010. Secular trends indicated a substantial rise in prevalence over time and a shift to younger ages. CONCLUSIONS: Around 7 million Americans are living with a hip or knee replacement, and consequently, in most cases, are mobile, despite advanced arthritis. These numbers underscore the substantial public health impact of total hip and knee arthroplasties.

All-cause mortality in a population-based type 1 diabetes cohort in the U.S. Virgin Islands.

OBJECTIVE: Type 1 diabetes remains a significant source of premature mortality; however, its burden has not been assessed in the U.S. Virgin Islands (USVI). As such, the objective of this study was to estimate type 1 diabetes mortality in a population-based registry sample in the USVI. RESEARCH DESIGN AND METHODS: We report overall and 20-year mortality in the USVI Childhood (<19 years old) Diabetes Registry Cohort diagnosed 1979-2005. Recent data for non-Hispanic blacks from the Allegheny County, PA population-based type 1 diabetes registry were used to compare mortality in the USVI to the contiguous U.S. RESULTS: As of December 31, 2010, the vital status of 94 of 103 total cases was confirmed (91.3%) with mean diabetes duration 16.8 ± 7.0 years. No deaths were observed in the 2000-2005 cohort. The overall mortality rates for those diagnosed 1979-1989 and 1990-1999 were 1852 and 782 per 100,000 person-years, respectively. Overall cumulative survival for USVI was 98% (95% CI: 97-99) at 10 years, 92% (95% CI: 89-95) at 15 years and 73% (95% CI: 66-80) at 20 years. The overall SMR for non-Hispanic blacks in the USVI was 5.8 (95% CI: 2.7-8.8). Overall mortality and cumulative survival for non-Hispanic blacks did not differ between the USVI and Allegheny County, PA. CONCLUSIONS: This study, as the first type 1 diabetes mortality follow-up in the USVI, confirmed previous findings of poor disease outcomes in racial/ethnic minorities with type 1 diabetes.

An assessment of the current US radiation oncology workforce: methodology and global results of the American Society for Radiation Oncology 2012 Workforce Study.

PURPOSE: To determine the characteristics, needs, and concerns of the current radiation oncology workforce, evaluate best practices and opportunities for improving quality and safety, and assess what we can predict about the future workforce. METHODS AND MATERIALS: An online survey was distributed to 35,204 respondents from all segments of the radiation oncology workforce, including radiation oncologists, residents, medical dosimetrists, radiation therapists, medical physicists, nurse practitioners, nurses, physician assistants, and practice managers/administrators. The survey was disseminated by the American Society for Radiation Oncology (ASTRO) together with specialty societies representing other workforce segments. An overview of the methods and global results is presented in this paper. RESULTS: A total of 6765 completed surveys were received, a response rate of 19%, and the final analysis included 5257 respondents. Three-quarters of the radiation oncologists, residents, and physicists who responded were male, in contrast to the other segments in which two-thirds or more were female. The majority of respondents (58%) indicated they were hospital-based, whereas 40% practiced in a free-standing/satellite clinic and 2% in another setting. Among the practices represented in the survey, 21.5% were academic, 25.2% were hospital, and 53.3% were private. A perceived oversupply of professionals relative to demand was reported by the physicist, dosimetrist, and radiation therapist segments. An undersupply was perceived by physician's assistants, nurse practitioners, and nurses. The supply of radiation oncologists and residents was considered balanced. CONCLUSIONS: This survey was unique as it attempted to comprehensively assess the radiation oncology workforce by directly surveying each segment. The results suggest there is potential to improve the diversity of the workforce and optimize the supply of the workforce segments. The survey also provides a benchmark for future studies, as many changes in the healthcare field exert pressure on the workforce.

Incidence of type 1 and type 2 diabetes in youth in the U.S. Virgin Islands, 2001-2010.

OBJECTIVE: To report the annual incidence of type 1 and type 2 diabetes among youth and to describe characteristics of youth diagnosed with diabetes in the U.S. Virgin Islands (USVI). RESEARCH DESIGN AND METHODS: All residents ≤19 years of age diagnosed with diabetes between January 2001 and December 2010 were identified from review of medical records of all hospitals and confirmed by physician query. RESULTS: A total of 82 eligible patients were identified and the registry ascertainment was estimated to be 98.7% complete. The overall age-adjusted annual incidence rates (per 100, 000) of type 1 and type 2 diabetes for the study period were 15.3 (95% CI: 11.3-20.1) and 9.6 (95% CI: 6.8-13.5), respectively. The incidence of type 1 diabetes increased significantly over the study period, with an epidemic-like threefold increase occurring from 2005 (8.7/100, 000) to 2006 (26.4/100, 000; p = 0.05). The incidence of type 1 diabetes was highest in the 10-19 age group in girls (25.6/100, 000), but no age difference was seen in boys, resulting from the lack of a pubertal peak in non-Hispanic Black boys. The incidence of type 2 diabetes rose significantly between 2001 (5.3/100, 000) and 2010 (12.5/100, 000; p = 0.03). CONCLUSIONS: The incidence of type 1 and type 2 diabetes in youth is increasing in the USVI, similar to global patterns. Further studies are needed to explore the missing pubertal rise in type 1 diabetes incidence in non-Hispanic Black boys and factors associated with the epidemic-like increases observed over the decade.