Precision of Provider Licensure Data for Mapping Member Accessibility to Medicaid Managed Care Provider Networks

BACKGROUND: In July 2018, the Centers for Medicare and Medicaid Services (CMS) updated its Medicaid Managed Care (MMC) regulations that govern network and access standards for enrollees. There have been few published studies of whether there is accurate geographic information on primary care providers to monitor network adequacy. METHODS: We analyzed a sample of nurse practitioner (NP) and physician address data registered in the state labor, licensing, and regulation (LLR) boards and the National Provider Index (NPI) using employment location data contained in the patient-centered medical home (PCMH) data file. Our main outcome measures were address discordance (%) at the clinic-level, city, ZIP code, and county spatial extent and the distance, in miles, between employment location and the LLR/NPI address on file. RESULTS: Based on LLR records, address information provided by NPs corresponded to their place of employment in 5% of all cases. NP address information registered in the NPI corresponded to their place of employment in 64% of all cases. Among physicians, the address information provided in the LLR and NPI corresponded to the place of employment in 64 and 72% of all instances. For NPs, the average distance between the PCMH and the LLR address was 21.5 miles. Using the NPI, the distance decreased to 7.4 miles. For physicians, the average distance between the PCMH and the LLR and NPI addresses was 7.2 and 4.3 miles. CONCLUSIONS: Publicly available data to forecast state-wide distributions of the NP workforce for MMC members may not be reliable if done using state licensure board data. Meaningful improvements to correspond with MMC policy changes require collecting and releasing information on place of employment

Community Social Determinants and Health Outcomes Drive Availability of Patient-Centered Medical Homes

The collaborative design of America\u27s patient-centered medical homes places these practices at the forefront of emerging efforts to address longstanding inequities in the quality of primary care experienced among socially and economically marginalized populations. We assessed the geographic distribution of the country\u27s medical homes and assessed whether they are appearing within communities that face greater burdens of disease and social vulnerability. We assessed overlapping spatial clusters of mental and physical health surveys; health behaviors, including alcohol-impaired driving deaths and drug overdose deaths; as well as premature mortality with clusters of medical home saturation and community socioeconomic characteristics. Overlapping spatial clusters were assessed using odds ratios and marginal effects models, producing four different scenarios of resource need and resource availability. All analyses were conducted using county-level data for the contiguous US states. Counties having lower uninsured rates and lower poverty rates were the most consistent indicators of medical home availability. Overall, the analyses indicated that medical homes are more likely to emerge within communities that have more favorable health and socioeconomic conditions to begin with. These findings suggest that intersecting the spatial footprints of medical homes in relation to health and socioeconomic data can provide crucial information for policy makers and payers invested in narrowing the gaps between clinic availability and the communities that experience the brunt of health and social inequalities

Association of Patient-Centered Medical Home Designation and Quality Indicators Within Hrsa-Funded Community Health Center Delivery Sites

BACKGROUND: Patient-Centered Medical Home (PCMH) adoption is an important strategy to help improve primary care quality within Health Resources and Service Administration (HRSA) community health centers (CHC), but evidence of its effect thus far remains mixed. A limitation of previous evaluations has been the inability to account for the proportion of CHC delivery sites that are designated medical homes. METHODS: Retrospective cross-sectional study using HRSA Uniform Data System (UDS) and certification files from the National Committee for Quality Assurance (NCQA) and the Joint Commission (JC). Datasets were linked through geocoding and an approximate string-matching algorithm. Predicted probability scores were regressed onto 11 clinical performance measures using 10% increments in site-level designation using beta logistic regression. RESULTS: The geocoding and approximate string-matching algorithm identified 2615 of the 6851 (41.8%) delivery sites included in the analyses as having been designated through the NCQA and/or JC. In total, 74.7% (n = 777) of the 1039 CHCs that met the inclusion criteria for the analysis managed at least one NCQA- and/or JC-designated site. A proportional increase in site-level designation showed a positive association with adherence scores for the majority of all indicators, but primarily among CHCs that designated at least 50% of its delivery sites. Once this threshold was achieved, there was a stepwise percentage point increase in adherence scores, ranging from 1.9 to 11.8% improvement, depending on the measure. CONCLUSION: Geocoding and approximate string-matching techniques offer a more reliable and nuanced approach for monitoring the association between site-level PCMH designation and clinical performance within HRSA\u27s CHC delivery sites. Our findings suggest that transformation does in fact matter, but that it may not appear until half of the delivery sites become designated. There also appears to be a continued stepwise increase in adherence scores once this threshold is achieved

Diabetes and the socioeconomic and built environment: geovisualization of disease prevalence and potential contextual associations using ring maps

<p>Abstract</p> <p>Background</p> <p>Efforts to stem the diabetes epidemic in the United States and other countries must take into account a complex array of individual, social, economic, and built environmental factors. Increasingly, scientists use information visualization tools to "make sense" of large multivariate data sets. Recently, ring map visualization has been explored as a means of depicting spatially referenced, multivariate data in a single information graphic. A ring map shows multiple attribute data sets as separate rings of information surrounding a base map of a particular geographic region of interest. In this study, ring maps were used to evaluate diabetes prevalence among adult South Carolina Medicaid recipients. In particular, county-level ring maps were used to evaluate disparities in diabetes prevalence among adult African Americans and Whites and to explore potential county-level associations between diabetes prevalence among adult African Americans and five measures of the socioeconomic and built environment—persistent poverty, unemployment, rurality, number of fast food restaurants per capita, and number of convenience stores per capita. Although Medicaid pays for the health care of approximately 15 percent of all diabetics, few studies have examined diabetes in adult Medicaid recipients at the county level. The present study thus addresses a critical information gap, while illustrating the utility of ring maps in multivariate investigations of population health and environmental context.</p> <p>Results</p> <p>Ring maps showed substantial racial disparity in diabetes prevalence among adult Medicaid recipients and suggested an association between adult African American diabetes prevalence and rurality. Rurality was significantly positively associated with diabetes prevalence among adult African American Medicaid recipients in a multivariate statistical model.</p> <p>Conclusions</p> <p>Efforts to reduce diabetes among adult African American Medicaid recipients must extend to rural African Americans. Ring maps can be used to integrate diverse data sets, explore attribute associations, and achieve insights critical to the promotion of population health.</p

Comparison of Small-Area Deprivation Measures as Predictors of Chronic Disease Burden in a Low-Income Population

Background: Measures of small-area deprivation may be valuable in geographically targeting limited resources to prevent, diagnose, and effectively manage chronic conditions in vulnerable populations. We developed a census-based small-area socioeconomic deprivation index specifically to predict chronic disease burden among publically insured Medicaid recipients in South Carolina, a relatively poor state in the southern United States. We compared the predictive ability of the new index with that of four other small-area deprivation indicators. Methods: To derive the ZIP Code Tabulation Area-Level Palmetto Small-Area Deprivation Index (Palmetto SADI), we evaluated ten census variables across five socioeconomic deprivation domains, identifying the combination of census indicators most highly correlated with a set of five chronic disease conditions among South Carolina Medicaid enrollees. In separate validation studies, we used both logistic and spatial regression methods to assess the ability of Palmetto SADI to predict chronic disease burden among state Medicaid recipients relative to four alternative small-area socioeconomic deprivation measures: the Townsend index of material deprivation; a single-variable poverty indicator; and two small-area designations of health care resource deprivation, Primary Care Health Professional Shortage Area and Medically Underserved Area/Medically Underserved Population. Results: Palmetto SADI was the best predictor of chronic disease burden (presence of at least one condition and presence of two or more conditions) among state Medicaid recipients compared to all alternative deprivation measures tested. Conclusions: A low-cost, regionally optimized socioeconomic deprivation index, Palmetto SADI can be used to identify areas in South Carolina at high risk for chronic disease burden among Medicaid recipients and other low-income Medicaid-eligible populations for targeted prevention, screening, diagnosis, disease self-management, and care coordination activitie

Making sense of uncertainty: why uncertainty is part of science

Scientific uncertainty is prominent in research that has big implications for our society: could the Arctic be ice-free in summer by 2080? Will a new cancer drug be worth its side effects? Is this strain of ‘flu going to be a dangerous epidemic? Uncertainty is normal currency in scientific research. Research goes on because we don’t know everything. Researchers then have to estimate how much of the picture is known and how confident we can all be that their findings tell us what’s happening or what’s going to happen. This is uncertainty

Enhanced interpretation of newborn screening results without analyte cutoff values

A collaboration among 157 newborn screening programs in 47 countries has lead to the creation of a database of 705,333 discrete analyte concentrations from 11,462 cases affected with 57 metabolic disorders, and from 631 heterozygotes for 12 conditions. This evidence was first applied to establish disease ranges for amino acids and acylcarnitines, and clinically validate 114 cutoff target ranges. Objective: To improve quality and performance with an evidence-based approach, multivariate pattern recognition software has been developed to aid in the interpretation of complex analyte profiles. The software generates tools that convert multiple clinically significant results into a single numerical score based on overlap between normal and disease ranges, penetration within the disease range, differences between specific conditions, and weighted correction factors. Design: Eighty-five on-line tools target either a single condition or the differential diagnosis between two or more conditions. Scores are expressed as a numerical value and as the percentile rank among all cases with the condition chosen as primary target, and are compared to interpretation guidelines. Tools are updated automatically after any new data submission (2009- 2011: 5.2 new cases added per day on average). Main outcome measures: Retrospective evaluation of past cases suggest that these tools could have avoided at least half of 277 false positive outcomes caused by carrier status for fatty acid oxidation disorders, and could have prevented 88% of false negative events caused by cutoff 7 values set inappropriately. In Minnesota, their prospective application has been a major contributing factor to the sustained achievement of a false positive rate below 0.1% and a positive predictive value above 60%. Conclusions: Application of this computational approach to raw data could make cutoff values for single analytes effectively obsolete. This paradigm is not limited to newborn screening and is applicable to the interpretation of diverse multi-analyte profiles utilized in laboratory medicine. Abstract wor