| Authors (Study period) | Study population | Exposure metrics | Confounders that have been controlled | Outcome variables relevant to this review | Statistical analysis | Main results |
| Rey et al. (1971 to 2003) | Whole French territory | Heat wave (periods of at least 3 consecutive days when the maximum and the minimum temperature, averaged over whole France, were simultaneously greater than their respective 95th percentile) | Age and gender | Injury and poisoning (V01 - X29, X31 - Y89) and other causes (e.g. ×30) | The excess mortality (observed-expected) and mortality ratio (observed/expected) were calculated for each cause of death | The greatest excess mortality (O - E) in heat waves were observed for cardiovascular diseases, neoplasms, respiratory system diseases, HRC (heat related causes), ill-defined conditions, and injury and poisoning |
| Hajat et al. (1993-2003) | All regions of England and Wales | Mean temperature | Influenza epidemics, ambient levels of PM10 (particulate matter) and ozone (mg/m3) | External cause of death (ICD10 codes: S, T, V, W, X, Y, Z and ICD9 codes: 800.0 - 999.9 or other) | Poisson generalized linear models | The greatest risk of heat mortality was observed for respiratory and external causes |
| Basagana et al. (1983-2006) | Catalonia region of Spain | Heat waves (days with maximum temperature above the 95th percentile) | Humidity and air pollution (PM) | All external cause ICD10 codes (V01 - Y89) and ICD9 codes (E800 - E999) | Conditional logistic regression | Association between extremely hot days and mortality was seen in lag 0 - 2 but was not seen in lag 3 - 6 for the external causes category |
| Ingole et al. (January 2003 to December 2012) | Western India | Heat days were defined as days with maximum temperatures above the 98th percentile (>39˚C), and cold days as days with maximum temperatures below the 2nd percentile (<25˚C) | Day of week; secular trends and other time-varying confounding factors | External cause of death (ICD S00 - T98, V01 - Y98) | Quasi-poisson regression model | External causes of death were not associated with heat or cold days |
| Ishigami et al. (Budapest: 1993-2001 London: 1993-2003 Milan: 1999-2004) | Three cities in Europe (Budapest, London, and Milan) | Daily mean temperature (˚C) | Daily ambient levels of PM10 (μg/m3) (total suspended particles (TSP) in Budapest), and ozone (μg/m3) were obtained for each city | External disease (ICD9 900.0 - 999.9; ICD10 S, T, V, W, X, Y, Z) | Poisson generalised linear models allowing for over-dispersion | Effects of high temperature on death from external causes were apparent in all cities, although not statistically significant in Budapest |