RESUMO
BACKGROUND: There is a great need for physicians in Tanzania. In 2012, there were approximately 0.31 physicians per 10,000 individuals nationwide, with a lower ratio in the rural areas, where the majority of the population resides. In response, universities across Tanzania have greatly increased the enrollment of medical students. Yet evidence suggests high attrition of medical graduates to other professions and emigration from rural areas where they are most needed. OBJECTIVE: To estimate the future number of physicians practicing in Tanzania and the potential impact of interventions to improve retention, we built a model that tracks medical students from enrollment through clinical practice, from 1990 to 2025. DESIGN: We designed a Markov process with 92 potential states capturing the movement of 25,000 medical students and physicians from medical training through employment. Work possibilities included clinical practice (divided into rural or urban, public or private), non-clinical work, and emigration. We populated and calibrated the model using a national 2005/2006 physician mapping survey, as well as graduation records, graduate tracking surveys, and other available data. RESULTS: The model projects massive losses to clinical practice between 2016 and 2025, especially in rural areas. Approximately 56% of all medical school students enrolled between 2011 and 2020 will not be practicing medicine in Tanzania in 2025. Even with these losses, the model forecasts an increase in the physician-to-population ratio to 1.4 per 10,000 by 2025. Increasing the absorption of recent graduates into the public sector and/or developing a rural training track would ameliorate physician attrition in the most underserved areas. CONCLUSIONS: Tanzania is making significant investments in the training of physicians. Without linking these doctors to employment and ensuring their retention, the majority of this investment in medical education will be jeopardized.
RESUMO
An early requirement of the UK's Diabetes National Service Framework is enumeration of the total affected population. Existing estimates tend to be based on incomplete lists. In a study conducted over one year in North Liverpool, we compared crude prevalence rates for type 1 and type 2 diabetes with estimates obtained by capture-recapture (CR) analysis of multiple incomplete patient lists, to assess the extent of unascertained but diagnosed cases. Patient databases were constructed from six sources-a hospital diabetes centre; general practitioner registers; hospital admissions with a diagnosis of diabetes; a hospital diabetic retinal clinic; a research list of patients with diabetes admitted with stroke; and a local children's hospital. Log linear modelling was used to estimate missing cases, hence total prevalence. The crude prevalence of diabetes was 1.5% (95% confidence interval [CI] 1.41, 1.52), compared with a CR-adjusted rate of 3.1% (CI 3.03, 3.19). Age-banded CR-adjusted prevalence was always higher in males than in females and the difference became more pronounced with increasing age. Among males, CR-adjusted prevalence rose from 0.4% at age 10-19 years to 18.3% at 80+ years; in females the corresponding figures were 0.4% and 9.3%. The gap between crude and CR-estimated prevalence points to a rate of 'hidden diabetes' that has substantial implications for future diabetes care.
