Background: As apart of the U.S. Department of Energy (DOE) Former Workers' Medical Surveillance Program, a Needs Assessment was conducted at Los Alamos National Laboratory (LANL). The objective was to identify former LANL employees who may be at significant risk for occupational disease and determine whether a medical examination program could reduce morbidity or mortality. We describe the needs assessment approach used at LANL. Methods: An algorithm was developed to make needs determinations. Information on factors including exposure, health impacts, size of exposed populations, and LANL worker concerns and recommendations were obtained. Each of these factors was scored from 1 to 3. The resulting factor sum was then multiplied by a binary (1 or 0) intervention suitability factor which was 1 if both of the following were available: (1) a screening test with acceptable sensitivity and specificity for the health outcome of concern; and (2) an intervention that decreases morbidity or mortality. This resulted in an Intervention Needs Score that was used to set priorities for the medical examination program for the estimated 35,000 former LANL workers. Results: Analysis of the algorithm output suggested that six exposure categories be recommended for consideration in a medical examination program. Beryllium, asbestos, and noise clearly warranted inclusion. Lead and ionizing radiation required careful consideration regarding availability of screening tests. Solvents were problematic due to the lack of screening tests and suitable intervention in formerly exposed workers. Conclusions: The algorithm approach to the needs assessment at LANL documented that six chemical and physical agents should be considered as candidates for inclusion in a medical examination program for former workers.
|Original language||English (US)|
|Number of pages||12|
|Journal||American Journal of Industrial Medicine|
|State||Published - Nov 1 2002|
- Medical screening
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health