(Study period)



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