PubMed · 8347737
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A Ahlbom. 1993. Pooling epidemiologic studies.. https://pubmed.ncbi.nlm.nih.gov/8347737/
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Meta-analysis is a quantitative method available to epidemiologists, psychologists, social scientists and others who wish to produce a summary measure of the effect of exposure on disease, based on results from published studies along with a summary measure of uncertainty. The magnitude of the effect vary from study to study because of differences in the features of these studies (design, population, control of confounding variables etc.). From the various studies an estimator is formed by pooling the results found in each study in one summary measure. This summary (or pooled) measure is meaningful only if the magnitude of heterogeneity between study effects is small and can be explained by sampling variation. In this paper, we present HEpiMA, a new comprehensive and user-friendly software program for epidemiologic meta-analysis. HEpiMA has new features that are not available in other programs. The program carries out a complete study of heterogeneity of study effects with 11 hypothesis test results. In addition to model-based methods, the program also implements bootstrap methodology. New useful estimators of heterogeneity, Ri and CV(B), developed by the authors are given in the output. In addition to these unique features, the major advantage of this software is the option for direct entry of adjusted relative risk estimates of individual studies, the most common form of presentation of results in the epidemiologic literature. This program may also be useful for meta-analysts of clinical trials, in which the relative risk is the parameter of interest as it also allows the entry of crude data under the form of 2x2 tables.
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Malaria is a common infectious disease in many tropical countries, including Thailand. The country is located geographically in a tropical zone and the transmission of malaria is particularly common in some regions, for instance in Tak province. The objective of this study is to identify risk factors causing malaria in Tak province in the rainy season by using log-linear models. Tests of independence are used (chi-square and Cramer's V-value tests) to find out the relationships between any two variables. In addition two- and three-dimensional log-linear models are used to obtain estimated parameters and expected frequencies for these models. Amongst the models fitted, the best are chosen based on the analysis of deviance. The results of this study show that most observed variables are significantly related with p-values<0.05. Causes of migration and reasons for staying overnight are highly related to personal variables. Thus, it can be concluded that two of the risk factors for malaria are causes of migration and reasons for staying overnight. Knowledge of prevention is also related to personal variables. Therefore, knowledge of prevention was concluded to be a risk factor affecting prevalence of malaria. For each set of three variables, the best model shows interaction terms of variables that have a relationship but there are no interactions of three effects in these best models.