[Impact of the estimation of the medically insured population on the pharmacological prevalence of diabetes at the regional and small area level].
ABSTRACT Data derived from Health Insurance databases are very useful for health observation. These data are however still underused, particularly for small local areas. This may be partly explained by the lack of reliable data on the number of insured people. Recent simplification of the Répertoire national interrégimes de l'assurance-maladie (RNIAM) indicator (French register of health insurance) gives the opportunity to improve the usefulness of these databases. This indicator specifies the beneficiaries' status towards the General Health Insurance Fund. This study aimed to select the population of beneficiaries, which could be most adequately used to calculate health indicators based on these data.
Data were collected from the outpatient database of the Southeastern France General Health Fund. We compared beneficiaries' characteristics according to the RNIAM indicator, calculated the annual unadjusted and age-adjusted regional and local prevalence of diabetes mellitus in two different populations: the whole initial beneficiaries database, and the population of "effective" beneficiaries (persons whose reimbursements were effectively managed by the General Health Insurance).
The initial database included 4,817,871 beneficiaries. Almost 80% were in the "effective" population, 14% had left the General Health Insurance, or Southeastern France, and 4% were doubles. The annual unadjusted prevalence of diabetes mellitus was 3.31% in the initial database, and more than 20% higher when calculated among "effective" beneficiaries. Impact on aged-adjusted prevalence was less important (+9% at regional level), but the increase varied from 6 to 42% for the small local areas. Impact was much higher on age and gender specific rates.
When Health Insurance databases are used to calculate health indicators at various geographical levels, only "effective" beneficiaries should be selected. The methodology for determining health indicators might be improved by updating databases (e.g. the date of the RNIAM indicator last update should be added).
Article: French national health insurance information system and the permanent beneficiaries sample.[show abstract] [hide abstract]
ABSTRACT: In France, in the early 2000s, legislators ordered that the National Health Insurance regime develop an inter-regime information system (SNIIR-AM) aimed at better understanding and evaluating beneficiaries' health care consumption and associated expenditures. In 2009, it contained data from the general health insurance regime that covers 86% of the French population; approximately 53 million people. Data are only available for a period of two years plus the current year. In addition, a permanent sample of health insurance beneficiaries (EGB) was created from the SNIIR-AM database. This is a permanent, representative cross-sectional sample of the population covered by National Health Insurance which, since 2004, monitors beneficiaries' health care consumption over a period of 20 years. It contains anonymous sociodemographic and medical characteristics and records of health care reimbursements. It was created using a systematic sampling method (1/97) on the two-digit control key of beneficiaries' national identification number and includes both current year reimbursement recipients and non-recipients. In 2009, it grouped together almost 500,000 beneficiaries covered by the National Health Insurance Fund for Salaried Workers; 77% of the population residing in France excluding public service employees and students. The EGB is used to conduct longitudinal studies as it permits tracing back patients' care paths and use of care in both hospital and office-based care environments and to calculate individual expenditures. It also permits the study of certain relatively frequent diseases characterised by a 100% reimbursement rate for certain chronic diseases and the reimbursement of tracer drugs. Eventually, the SNIIR-AM will include beneficiaries covered by all the different Health Insurance regimes in France.Revue d Épidémiologie et de Santé Publique 08/2010; 58(4):286-90. · 0.78 Impact Factor