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Objective: To evaluate access to prenatal care for pregnant patients receiving medication for opioid use disorder (MOUD) under Medicaid coverage in Florida., Methods: A cross-sectional, secret shopper study was conducted in which calls were made to randomly selected obstetric clinicians' offices in Florida. Callers posed as a 14-week-pregnant patient with Medicaid insurance who was receiving MOUD from another physician and requested to schedule a first-time prenatal care appointment. Descriptive statistics were used to report our primary outcome, the callers' success in obtaining appointments from Medicaid-enrolled physicians' offices. Wait time for appointments and reasons the physician offices refused appointments to callers were collected., Results: Overall, 2,816 obstetric clinicians are enrolled in Florida Medicaid. Callers made 1,747 attempts to contact 1,023 randomly selected physicians' offices from June to September 2021. Only 48.9% of medical offices (n=500) were successfully reached by phone, of which 39.4% (n=197) offered a prenatal care appointment to the caller. The median wait time until the first appointment was 15 days (quartile 1: 7; quartile 3: 26), with a range of 0-55 days. However, despite offering an appointment, 8.6% of the medical offices stated that they do not accept Medicaid insurance payment or would accept only self-pay. Among the 60.6% of callers unable to secure an appointment, the most common reasons were that the clinician was not accepting patients taking methadone (34.7%) or was not accepting any new patients with Medicaid insurance (23.8%) and that the pregnancy would be too advanced by the time of the first available appointment (7.3%)., Conclusion: This secret shopper study found that the majority of obstetric clinicians' offices enrolled in Florida Medicaid do not accept pregnant patients with Medicaid insurance who are taking MOUD. Policy changes are needed to ensure access to adequate prenatal care for patients with opioid use disorder., Competing Interests: Financial Disclosure The authors did not report any potential conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Cancer data from population-based cancer registries under-report cancer cases, especially for cancers primarily diagnosed and treated in outpatient clinical settings, away from hospital-based cancer registrars. Previously, we developed alternative methods of cancer case capture including a claims-based method, which identified a large proportion of cancer cases missed by traditional population-based cancer registries. In this study, we adapted a claims-based method for statewide implementation of cancer surveillance in Florida. Between 2010 and 2017 the claims-based method identified 143,083 cancer abstracts, of which 42% were new and 58% were previously registered. The claims-based method led to the creation of 53,419 new cancer cases in the state cancer registry, which made up 9.3% of all cancer cases registered between 2010 and 2017. The types of cancers identified by the claims-based method were typical of the kinds primarily diagnosed and treated in outpatient oncology clinic settings, such as hematological malignancies, prostate cancer, melanoma, breast cancer, and bladder cancer. These cases were added to the Florida cancer registry and may produce an artefactual increase in cancer incidence, which is believed to be closer to the actual burden of cancer in the state.

Background : The dichotomization or categorization of rural-urban codes, as nominal variables, is a prevailing paradigm in cancer disparity studies. The paradigm represents continuous rural-urban transition as discrete groups, which results in a loss of ordering information and landscape continuum, and thus may contribute to mixed findings in the literature. Few studies have examined the validity of using rural-urban codes as continuous variables in the same analysis. Methods : We geocoded cancer cases in north central Florida between 2005 and 2010 collected by Florida Cancer Data System. Using a linear hierarchical model, we regressed the occurrence of late stage cancer (including breast, colorectal, hematological, lung, and prostate cancer) on the rural-urban codes as continuous variables. To validate, the results were compared to those from using a truly continuous rurality data of the same study region. Results : In term of associations with late-stage cancer risk, the regression analysis showed that the use of rural-urban codes as continuous variables produces consistent outcomes with those from the truly continuous rurality for all types of cancer. Particularly, the rural-urban codes at the census tract level yield the closest estimation and are recommended to use when the continuous rurality data is not available. Conclusions : Methodologically, it is valid to treat rural-urban codes directly as continuous variables in cancer studies, in addition to converting them into categories. This proposed continuous-variable method offers researchers more flexibility in their choice of analytic methods and preserves the information in the ordering. It can better inform how cancer risk varies, degree by degree, over a finer spectrum of rural-urban landscape.