Effect of daily temperature range on respiratory health in Argentina and its modification by impaired socio-economic conditions and PM10 exposures

Abstract

Epidemiological investigations regarding temperature influence on human health have focused on mortality rather than morbidity. In addition, most information comes from developed countries despite the increasing evidence that climate change will have devastating impacts on disadvantaged populations living in developing countries. In the present study, we assessed the impact of daily temperature range on upper and lower respiratory infections in Cordoba, Argentina, and explored the effect modification of socio-economic factors and influence of airborne particles We found that temperature range is a strong risk factor for admissions due to both upper and lower respiratory infections, particularly in elderly individuals, and that these effects are more pronounced in sub-populations with low education level or in poor living conditions. These results indicate that socio-economic factors are strong modifiers of the association between temperature variability and respiratory morbidity, thus they should be considered in risk assessments.

Keywords: Daily temperature range; Education; Morbidity; Respiratory infections; Socio-economic conditions.

Figure 1

Plots of the smoothing function of daily temperature range with 95% CIs for upper respiratory diseases (left panel) and lower respiratory diseases (right panel).

Figure 2

Percent increase in risk of morbidity due to upper respiratory infections (a) and lower respiratory infections (b), for individual day lags (lag0–lag10) from an unconstrained distributed lag model and for moving averages (2dMA–10dMA). The squares represent the central estimate and the vertical lines the 95 % CI. (Model adjusted for same day RH and PM)

Figure 2

Percent increase in risk of morbidity due to upper respiratory infections (a) and lower respiratory infections (b), for individual day lags (lag0–lag10) from an unconstrained distributed lag model and for moving averages (2dMA–10dMA). The squares represent the central estimate and the vertical lines the 95 % CI. (Model adjusted for same day RH and PM)

Figure 3

Percent increase in admissions for upper and lower respiratory infections for a 10°C increase in DTR by seasons. (Model adjusted for same day RH and PM, for all age groups)

Figure 4

Percent increase in admissions for upper respiratory infections (left) and lower respiratory infections (right) for a 10°C increase in same day DTR by age groups. (Model adjusted for same day RH and PM)