PubMed · 9437688
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R Bellocco, M Pagano. 1998. Poisson regression.. https://pubmed.ncbi.nlm.nih.gov/9437688/
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OBJECTIVE: The aim of this study was to assess trends in new HIV-positivities identified in the Provence-Alpes-Cote d'Azur area in southern France in 1996 and 1997 and to identify any weaknesses in the screening policy. METHODS: Data were collected from the EVALVIH epidemiological surveillance system initiated in 1995. The data were obtained from both anonymous reports of medical biology laboratories and of prescribing physicians in the area. RESULTS: From January 1996 to December 1997, 825 persons (67% men, 33% women) were found to be HIV-positive. Although the percentage of intravenous drug users (18%) and homo-bisexual individuals (23%) remained high, the proportion of heterosexuals was higher (45%). The number of identified cases of HIV seropositivity fell from 271 in the first semester 1996 to 137 in the second semester 1997, i.e. a 49% decline (p < 0.001). There was no significant difference in this trend for age, gender, transmission mode, or geographical origin of the subjects. CONCLUSION: These results confirm the positive impact of the HIV prevention program in the Provence-Alpes-Cote d'Azur region and the insufficiencies of the screening policy requiring urgent reconsideration.
The identification of heterogeneity in effects between studies is a key issue in meta-analyses of observational studies, since it is critical for determining whether it is appropriate to pool the individual results into one summary measure. The result of a hypothesis test is often used as the decision criterion. In this paper, the authors use a large simulation study patterned from the key features of five published epidemiologic meta-analyses to investigate the type I error and statistical power of five previously proposed asymptotic homogeneity tests, a parametric bootstrap version of each of the tests, and tau2-bootstrap, a test proposed by the authors. The results show that the asymptotic DerSimonian and Laird Q statistic and the bootstrap versions of the other tests give the correct type I error under the null hypothesis but that all of the tests considered have low statistical power, especially when the number of studies included in the meta-analysis is small (<20). From the point of view of validity, power, and computational ease, the Q statistic is clearly the best choice. The authors found that the performance of all of the tests considered did not depend appreciably upon the value of the pooled odds ratio, both for size and for power. Because tests for heterogeneity will often be underpowered, random effects models can be used routinely, and heterogeneity can be quantified by means of R(I), the proportion of the total variance of the pooled effect measure due to between-study variance, and CV(B), the between-study coefficient of variation.
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