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Background: Although identifying individuals who are at increased risk of dying during heat waves and instituting protective measures represent an established strategy, the evidence supporting the components of this strategy and their strengths has yet to be evaluated. We conducted a meta-analysis of observational studies on risk and protective factors in heat wave-related deaths. Methods: Using the OVID interface, we searched Medline (1966-2006) and CINHAL (1982-2006) databases. The Web sites of the World Health Organization, Institut National de Veille Sanitaire, and Centers for Disease Control and Prevention were also visited. The search terms included heat wave, heat stroke, heatstroke, sunstroke, and heat stress disorders. Eligible studies were case-control or cohort studies. Odds ratios (ORs) and information on study quality were abstracted by 2 investigators independently. Six case-control studies involving 1065 heat wave-related deaths were identified. Results: Being confined to bed (OR, 6.44; 95% confidence interval [CI], 4.5-9.2), not leaving home daily (OR, 3.35; 95% CI, 1.6-6.9), and being unable to care for oneself (OR, 2.97; 95% CI, 1.8-4.8) were associated with the highest risk of death during heat waves. Preexisting psychiatric illness (OR, 3.61; 95% CI, 1.3-9.8) tripled the risk of death, followed by cardiovascular (OR, 2.48; 95% CI, 1.3-4.8) and pulmonary (OR, 1.61; 95% CI, 1.2-2.1) illness. Working home airconditioning (OR, 0.23; 95% CI, 0.1-0.6), visiting cool environments (OR, 0.34; 95% CI, 0.2-0.5), and increasing social contact (OR, 0.40; 95% CI, 0.2-0.8) were strongly associated with better outcomes. Taking extra showers or baths (OR, 0.32; 95% CI, 0.1-1.1) and using fans (OR, 0.60; 95% CI, 0.4-1.1) were associated with a trend toward lower risk of death. Conclusion: The present study identified several prognostic factors that could help to detect those individuals who are at highest risk during heat waves and to provide a basis for potential risk-reducing interventions in the setting of heat waves.

The reliability of continuous or binary outcome measures is usually assessed by estimation of the intraclass correlation coefficient (ICC). A crucial step for this purpose is the determination of the required sample size. In this review, we discuss the contributions made in this regard and derive the optimal allocation for the number of subjects k and the number of repeated measurements n that minimize the variance of the estimated ICC. Cost constraints are discussed for both normally and non-normally distributed responses, with emphasis on the case of dichotomous assessments. Tables showing optimal choices of k and n are given along with the guidelines for the efficient design of reliability studies.

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