Objective To compare measures of spatial access to care commonly used by policy makers and researchers with the more comprehensive enhanced two‐step floating catchment area (E2SFCA) method. Study Setting Fourteen southwestern Pennsylvania counties. Study Design We estimated spatial access to buprenorphine‐waivered prescribers using three commonly used measures—Euclidean travel distance to the closest prescriber, travel time to the closest provider, and provider‐to‐population ratios—and the E2SFCA. Unlike other measures, the E2SFCA captures provider capacity, potential patient volume, and travel time to prescribers. Data Collection/Extraction Methods We measured provider capacity as the number of buprenorphine prescribers listed at a given address in the Drug Enforcement Agency's 2020 Controlled Substances Act Registrants Database, and we measured potential patient volume as the number of nonelderly adults in a given census tract as reported by the 2018 American Community Survey. We estimated travel times between potential patients and prescribers with Bing Maps and Mapbox application programming interfaces. We then calculated each spatial access measure using the R programming language. We used each measure of spatial access to identify census tracts in the lowest quintile of spatial access to prescribers. Principal Findings The Euclidean distance, travel time, and provider‐to‐population ratio measures identified 48.3%, 47.2%, and 69.9% of the census tracts that the E2SFCA measure identified as being in the lowest quintile of spatial access to care, meaning that these measures misclassify 30%–52% of study area census tracts as having sufficient spatial access to buprenorphine prescribers. Conclusions Measures of spatial access commonly used by policy makers do not sufficiently accurately identify geographic areas with relatively low access to prescribers of buprenorphine. Using the E2SFCA in addition to the commonly used measures would allow policy makers to precisely target interventions to increase spatial access to opioid use disorder treatment and other types of health care services.

Objective: Urban Medicaid enrollees with opioid use disorder often rely on public transit to reach buprenorphine prescribers. Research has not shown whether public transit provides this population with adequate geographic access to buprenorphine prescribers. We examined travel times to buprenorphine prescribers by car and public transit in urban areas, and determined whether car-based Medicaid regulatory standards produce their intended geographic coverage.

Objective: To significantly fit a statistical distribution to the proportion of positive Legionella samples in a series of water samples from multiple facility-premise water systems. Design: Statistical fit test. Setting: A hospital and associated long-term care facility (LTCF) in New York State, as well as temporal and culture data from a deidentified hospital site supplied by one of the vendor laboratories. Methods: Culture samples (n = 1,393) were segmented into 139 test cycles with roughly 10 samples in each. The proportion of positive samples was standardized to 25 total samples per test to give a distribution of discrete values. These values were analyzed for fit with the following discrete distributions: Poisson, negative binomial, geometric, and zero-inflated Poisson. Results: The zero-inflated Poisson distribution fitted to the copper–silver ionization (CSI)-treated and untreated test cycles indicates that 88% of the expected positive proportions should occur by the 30% cutoff (rounded up to 8 positive samples among 25 total samples), similar to the 93% expectation for just CSI-treated test cycles. The other treatment in these data (chlorine dioxide) was not effective in treating Legionella in the sampled buildings, and if there is an underlying distribution to these specific test cycles, it is not the zero-inflated Poisson distribution. Conclusions: In a well-maintained or well-treated premise water distribution system, ~30% or lower proportion of positive Legionella samples should occur. Anything above that cutoff is either very unlikely or not expected at all and indicates a problem in the water system.

Objectives:Limited information is available regarding provider- and patient panel-level factors associated with primary care provider (PCP) adoption/prescribing of medication for opioid use disorder (MOUD).Methods:We assessed a retrospective cohort from 2015 to 2018 within the Pennsylvania Medicaid Program. Participants included PCPs who were Medicaid providers, with no history of MOUD provision, and who treated ≥10 Medicaid enrollees annually. We assessed initial MOUD adoption, defined as an index buprenorphine/buprenorphine-naloxone or oral/extended release naltrexone fill and sustained prescribing, defined as ≥1 MOUD prescription(s) for 3 consecutive quarters from the PCP. Independent variables included provider- and patient panel-level characteristics.Results:We identified 113 rural and 782 urban PCPs who engaged in initial adoption and 36 rural and 288 urban PCPs who engaged in sustained prescribing. Rural/urban PCPs who issued increasingly larger numbers of antidepressant and antipsychotic medication prescriptions had greater odds of initial adoption and sustained prescribing (P < 0.05) compared to those that did not prescribe these medications. Further, each additional patient out of 100 with opioid use disorder diagnosed before MOUD adoption increased the adjusted odds for initial adoption 2% to 4% (95% confidence interval [CI] = 1.01–1.08) and sustained prescribing by 4% to 7% (95% CI = 1.01–1.08). New Medicaid providers in rural areas were 2.52 (95% CI = 1.04–6.11) and in urban areas were 2.66 (95% CI = 1.94, 3.64) more likely to engage in initial MOUD adoption compared to established PCPs.Conclusions:MOUD prescribing adoption was concentrated among PCPs prescribing mental health medications, caring for those with OUD, and new Medicaid providers. These results should be leveraged to test/implement interventions targeting MOUD adoption among PCPs.