# **5. Climate change impact #4: Human Health**

This section describes the methodology and presents some estimates of the effects of increases in temperature on labor productivity, due to changes in mortality and morbidity incidence of some diseases.

The approach follows the one in Bosello, Roson and Tol (2006) by considering some vector-borne diseases (malaria, dengue, schistomiasis), heat and cold related diseases, and diarrhea. It does not consider other diseases and impacts mentioned in the IPCC AR5 (2014), like effects of extreme events, heat exposure effects on labor productivity (separately considered), hemorrhagic fever with renal syndrome, plague, chikungunya fever, japanese and tick-borne encephalitis, cholera and other (nondiarrhea) enteric infections, air quality and nutrition related diseases, allergic diseases, mental health.

Because of lack of data, it is not possible to ascertain possible non-linear impacts of temperature, so the results are expressed as changes in average labor productivity for a +1°C increase in temperature (implicitly assuming that the relationship is approximately linear). Also, the focus is on impacts on labor productivity, whereas other impacts, like those on private and public expenditure for health services, or non-market impacts (e.g., value of life for retired persons) are not taken into account.

We consider only the direct effect of temperature on the incidence of the various diseases, despite the fact that other variables (most notably economic development expressed through income levels) are very important (especially for vector-borne and diarrhea illnesses). To this end, the projected income levels at the year 2050 are taken as reference values for determining the degree of vulnerability in each region. This method implies that indirect effects on human health are not taken into account. For instance, climate change could bring about a reduction of income and a worsening of living conditions, making a society more vulnerable to the direct effects on health.

# _5.1 Methodology_

The starting point of the analysis presented in Bosello, Roson and Tol (2006), which is in turn based on Tol (2002), is a survey of the epidemiological, medical and interdisciplinary literature, with the aim of obtaining best estimates for the number of extra cases of mortality and morbidity (for a set of diseases) associated with a given increase in average temperature. These estimates often specify the distribution of cases in the age/sex structure of a population, as well as the length of the illness period (if applicable).

This information can therefore be combined with data on the structure of the working population, to infer the number of lost working days or other variables. For example, Bosello, Roson and Tol (2006), present the following Table 3, expressing the “additional years of life diseased in 2050 by region and disease”.

**Table 3** .  Additional years of life diseased in 2050 by region and disease

||**Malaria**|**Schistom.**|**Dengue**|**Cardio**|**Respiratory**|**Diarrhea**|**TOT**|
|---|---|---|---|---|---|---|---|
|**USA**|0|0|0|-167,357|22,257|83,070|-62,030|
|**Europe Un.**|0|0|0|-171,908|20,936|25,608|-125,364|
|**E.E.F.S.U.**|0|0|0|-259,884|46,884|57,717|-155,283|
|**Japan**|0|0|0|-65,353|33,161|912|-31,280|
|**Rest Ann.I**|0|0|0|-45,232|11,108|1,361|-32,763|
|**Energy Exp.**|7,219|-1,088|29|-66,363|1,706,267|112,633|1,758,697|
|**China India**|632|0|0|-1,119,902|770,340|156,271|-192,659|
|**Rest World**|232,737|-154,375|203|-194,383|3,683,042|834,294|44,01,518|

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