HEALTH NEEDS ASSESSMENT METHODOLOGY APPLIED TO ALBAQA REFUGEE CAMP, JORDAN

Health needs assessment is an essential step before planning for a new program or evaluating an existing program. The methodology applied follows principles that might differ from one country to another. The purpose of this study was to determine if the methodology applied to assess health needs in the developing nations, particularly Albaqa Refugee Camp in Jordan, differed from the methodology used to assess health needs in developed nations.^ In this study, a method for health needs assessment was developed using the developed countries published literature and was applied to a developing country, Jordan. However, the method did not apply exactly as expected for several reasons. Some of the problems were the incompleteness and unavailability of the health data, and its poor quality in terms of validity and reliability. Thus, some adaptations were needed and a new health needs assessment methodology specific for a particular developing country is proposed. This method depends on utilizing the primary, secondary, and tertiary data, as well as conducting surveys to collect all the data that could not be found in those data sources.^ In general, it was concluded from this study that there is a difference between methodology of a developed country's health needs assessment and a developing country's, specifically Jordan's, health needs assessment. ^

A Bayesian analysis for spatial processes with application to mapping

In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^