The Impact of Temperature on Mortality …
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tracts, particularly among the elder population, and in low-income households facing
housing deprivation (Amarillo and Carreras 2012; Carreras et al. 2015).
Although very informative, these studies lack national coverage. Moreover, the use
of different health metrics and weather parameters makes it impossible to assess any
geographic heterogeneity of impacts. In a context of climatic change, where resource
to promote adaptation to the new climate norms must be allocated, comparable
countrywide estimates of health costs are most needed to develop a rational adaptation
policy.
This paper makes two contributions to the literature. First, it extends on a very thin
literature on climatic determinants of mortality in Argentina. Second, it provides the
first countrywide and comparable regional estimates of the incidence of temperature
on mortality, breaking it up by gender and age group.
Econometric Modeling
The causal effect of temperature on mortality rates in Argentinean municipalities is
analyzed using nonparametric models for panel data, i.e., repeated cross-sectional
data. Nonparametric models estimated by ordinary least squares have been widely
applied in the epidemiological literature (Barreca and Shimshack 2012; Curriero
et al. 2002; Doyon et al. 2008; Kaiser et al. 2007) because the relationship between
weather and mortality rates is likely to be nonlinear (Basu 2009). Thus, the estimated
benchmark model is described by the following equation:
TM cpmy = f
temp cpmy
+ f
prep cpmy
+ μ m + ω cm + cpmy
(1)
where TM cpmy is the mortality rate per 100,000 inhabitants in municipality c within
province p in month m in year y. The final unit of analysis is a “municipality by
month.” The exposure functions f
temp cpmy
and f
prep cpmy
are additive functions containing parameters to be estimated related to exposure to monthly average
temperature and precipitation, respectively. μ m stands for time effects that allow controlling for potentially confounding effects common to all municipalities that vary
overtime, for example technological advances or macroeconomic shocks affecting
the countrywide economic performance, which might affect health outcomes. Finally,
ω cm is an interaction term which captures temporal variations between municipalities
due to differences in air quality, income levels, educational attainment, and population density. Thus, these fixed effects could be interpreted as a baseline estimate of
mortality rates in each municipality (Barreca and Shimshack 2012).
The exposure functions are estimated by means of restricted piecewise cubic
splines, using the mkspline package in the statistical software Stata, version 14.0.
Restricted cubic spline performs better than a linear spline when working with very
curved functions (Rosenberg et al. 2003), thus allowing both mortality and weather
to vary more flexibly without needing to choose a priori any functional form. A total
of six knots were chosen for both the average monthly temperatures. The number
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tracts, particularly among the elder population, and in low-income households facing
housing deprivation (Amarillo and Carreras 2012; Carreras et al. 2015).
Although very informative, these studies lack national coverage. Moreover, the use
of different health metrics and weather parameters makes it impossible to assess any
geographic heterogeneity of impacts. In a context of climatic change, where resource
to promote adaptation to the new climate norms must be allocated, comparable
countrywide estimates of health costs are most needed to develop a rational adaptation
policy.
This paper makes two contributions to the literature. First, it extends on a very thin
literature on climatic determinants of mortality in Argentina. Second, it provides the
first countrywide and comparable regional estimates of the incidence of temperature
on mortality, breaking it up by gender and age group.
Econometric Modeling
The causal effect of temperature on mortality rates in Argentinean municipalities is
analyzed using nonparametric models for panel data, i.e., repeated cross-sectional
data. Nonparametric models estimated by ordinary least squares have been widely
applied in the epidemiological literature (Barreca and Shimshack 2012; Curriero
et al. 2002; Doyon et al. 2008; Kaiser et al. 2007) because the relationship between
weather and mortality rates is likely to be nonlinear (Basu 2009). Thus, the estimated
benchmark model is described by the following equation:
TM cpmy = f
temp cpmy
+ f
prep cpmy
+ μ m + ω cm + cpmy
(1)
where TM cpmy is the mortality rate per 100,000 inhabitants in municipality c within
province p in month m in year y. The final unit of analysis is a “municipality by
month.” The exposure functions f
temp cpmy
and f
prep cpmy
are additive functions containing parameters to be estimated related to exposure to monthly average
temperature and precipitation, respectively. μ m stands for time effects that allow controlling for potentially confounding effects common to all municipalities that vary
overtime, for example technological advances or macroeconomic shocks affecting
the countrywide economic performance, which might affect health outcomes. Finally,
ω cm is an interaction term which captures temporal variations between municipalities
due to differences in air quality, income levels, educational attainment, and population density. Thus, these fixed effects could be interpreted as a baseline estimate of
mortality rates in each municipality (Barreca and Shimshack 2012).
The exposure functions are estimated by means of restricted piecewise cubic
splines, using the mkspline package in the statistical software Stata, version 14.0.
Restricted cubic spline performs better than a linear spline when working with very
curved functions (Rosenberg et al. 2003), thus allowing both mortality and weather
to vary more flexibly without needing to choose a priori any functional form. A total
of six knots were chosen for both the average monthly temperatures. The number