In this article, we studied geographic variation in the use of personalized genetic testing for advanced non-small cell lung cancer (NSCLC) and we evaluated the relationship between genetic testing rates and local socioeconomic and ecological variables. We used data on all advanced NSCLC patients who had a genetic test between April 2012 and April 2013 in France in the frame of the IFCT Biomarqueurs-France study (n = 15814). We computed four established measures of geographic variation of the sex-adjusted rates of genetic testing utilization at the “départment” (the French territory is divided into 94 administrative units called ‘départements’) level. We also performed a spatial regression model to determine the relationship between département-level sex-adjusted rates of genetic testing utilization and economic and ecological variables. Our results are the following: (i) Overall, 46.87% lung cancer admission patients obtained genetic testing for NSCLC; département-level utilization rates varied over 3.2-fold. Measures of geographic variation indicated a relatively high degree of geographic variation. (ii) there was a statistically significant relationship between genetic testing rates and per capita supply of general practitioners, radiotherapists and surgeons (negative correlation for the latter); lower genetic testing rates were also associated with higher local poverty rates. French policymakers should pursue effort toward deprived areas to obtain equal access to personalized medicine for advanced NSCLC patients.

-No abstract available-

Background:
In France, Health Regional Agencies (HRA) have to elaborate a Public Health Plan for the 5 coming years. For estimating future population health needs and associated costs to adapt the health services on the regional territory, the HRA in southeastern France requested a prospective analysis, based on demographic and epidemiologic scenarios about major chronic diseases, to evaluate future trends.

Methods:
Six chronic diseases were selected: diabetes (1 or 2), cardiovascular diseases, respiratory diseases, cancers, neurological diseases and dementia. We used medico-administrative data from the National health insurance fund, and adapted algorithms to identify people with these diseases. We calculated prevalence rates according to gender and age and used two alternative scenarios (a constant one, and a trend-based one) to estimate the number of people with chronic diseases in 2023 and 2028, starting in 2016. We also estimated future healthcare costs according a constant and a trend-based scenario.

Results:
The algorithms detect reasonable rates of disease compared to official rates available for 2016. Due to demographic (ageing) and/or epidemiologic trends, the number of people with chronic diseases will highly increase during the next ten years in the South of France region. For instance, between 2016 and 2028, there will be from 15% to 20% more people with diabetes. Associated costs will also be higher (+33% between 2016 and 2028), especially those granted to nursing care (+40%).

Conclusions:
Burden of diseases and health expenditures are going to increase in the future. Projections are needed to help policymakers anticipating the required health services adaptation. Medico-administrative database are an invaluable source of data to do so. The next step of this project will consist in estimating those trends for smaller geographical areas.

This paper aims at quantifying the effect of healthcare programs on economic outcomes in the context of developing countries experiencing epidemiological transitions. It is widely accepted in the literature that treatment programs result in production gains among ill-health workers. However, these programs have the additional effect of modifying both the size and the composition of the working population by increasing the proportion of chronically-ill individuals. First, we define the theoretical conditions under which this macro-epidemiological phenomenon outweighs the positive effect of an increase in production. Second, we decompose the economic consequences of access to antiretroviral treatments against HIV in three sub-Saharan African countries. Forecasts of an individual’s health status, depending on whether he or she has access to medication, are generated using a microsimulation model. We use the model to generate a counterfactual (as if the adverse epidemiological effect did not exist), which allows decomposing the total impact of the HIV-medicines program into two different effects: positive and negative. We find that the positive effect of treatment procurement outweighs the negative epidemiological effect. Of course, this approach is only an indicator of economic performance and should in no way constitute a decision-making criterion about the ethical necessity of access to health care.