3. Impacts of cold and heat waves on mortality

see the decline in the mortality rate, especially for children under 5 and people aged 60 and older [ Figure 3.4 ].

Like other countries, chronic diseases are the main cause of mortality in Viet Nam [ WHO, 2014 ]. The leading cause of mortality in Viet Nam is stroke, which killed 112.6 thousand people, 21.7% of all deaths in Viet Nam [ WHO, 2015 ]. The second cause of mortality is ischemic heart disease, accounting for 7% of deaths in Viet Nam. Other causes of mortality include Chronic Pulmonary Obstructive Disorder (accounting for 4.9% of deaths), lower respiratory infections caused by bacteria, viruses, or fungi (accounting for 4.8% of all deaths), accidents, cancers and other diseases [ WHO, 2015 ].

There are 63 provinces in Viet Nam, and these provinces are grouped into 6 geographic regions: Red River Delta, Northern Midlands and Mountains, and Central Coast in the North and Central Highlands, Southeast and Mekong River Delta in the South. Figure 3.5 presents the geographic maps of the mortality rate and temperature (averaged over the 2000–2018 period). The poverty rate as well as the average temperature is fairly similar between provinces within a region. The correlation between mortality and temperature is not clear from these maps. However, it shows that the mortality rate is higher in the North, which has a lower average temperature, than in the South.

There are different ways to examine the relationship between temperature and mortality. A population-focused way is to estimate the temperature bins on mortality [ e.g., Deschênes and Moretti, 2009; Deschenes and Greenstone, 2011; Burgess et al., 2017; Carleton et al., 2020 ]. The WHO (2018) suggests that the range of minimal-risk high temperatures is about 15–30°C. Low and high temperature can cause health problems and result in risk of death. Temperatures lower than 16°C (61°F) with humidity above 65% were associated with respiratory hazards. The temperature-humidity index can be used to explore the effect of temperature and humidity on health [ e.g., McGregor and Vanos, 2018 ]. In this study, we have tried to estimate the number of days within the temperature bins, but did not find significant effects for the number of days with low or high temperatures on mortality. Possibly, people in cold or hot areas have adapted to their local temperature. However, temperature shocks can cause health problems for people.

The effect of weather extremes, which are assessed by cold and heat waves occurring within a month, is the emphasis of this chapter. A heat wave is a term used to describe a period of very high temperatures. There are two issues in defining a heat wave: selection of a temperature threshold, and definition of the number of consecutive days of prolonged heat. Most studies use a threshold of a given percentile, such as the 90th, 95th or 98th percentile of the temperature distribution in a specific location [ e.g., see review Perkins and Alexander, 2013; and Perkins, 2015 ]. The second issue in defining a heat wave is the duration of consecutive days equal to or above the temperature thresholds. Exposure to hot weather for a number of consecutive days has a more detrimental effect on health than exposure to few hot days that are spaced apart. A heat wave is often defined as at least 3 or 5 consecutive days with daily temperature above a given threshold [ e.g., see review Perkins and Alexander, 2013; and Perkins, 2015 ]. Unlike the number of days in temperature bins, cold and heat waves can have harmful effects on health, since they are more extreme and last for several consecutive days.

[ Figure 3.5 ] Mortality rate and temperature

Panel A. Mortality rate (per 1,000 people) Panel B. Average daily temperature (degree Celsius)

Panel A presents the crude death rate of province, measured as the number of resident deaths per 1000 population in a year. The map shows the mortality averaged over the 2000-2018 period. Panel B presents the average of the daily mean temperature of the province over the same period.

During the 2000–2018 period, we defined a heat wave as three or more consecutive days with daily mean temperatures at or above the 95th percentile of the year-round province -specific temperature distribution. Similarly, we define a cold wave as when the dail mean temperature is equal to or below the 5th percentile of the year-round province-specific temperature distribution for at least three consecutive days. We also define heat waves (and cold waves) as at least 5, 7, and 9 consecutive days with daily mean temperatures at or above the 95 percentile (and at or below the 5 percentile) of the province-specific temperature distribution for robustness analysis.

The average number of days per year in cold and heat waves is shown in Panel A of Figure 3.6, which is calculated using the 5th and 95th percentiles of the province-specific tempera-

ture distribution. The figures are calculated as an average across province-by-year. The first bar in Panel A, for example, shows that the province-by-year average number of consecutive days in cold waves (defined as at least 3 cold days) is 14. In cold waves defined as at least 9 consecutive cold days, the average number of consecutive days is only 5.2. It should be noted that this average is below 9, as several provinces have not experienced a cold wave in several years. In heat waves, the average number of consecutive days is lower. For example, in heat waves defined as at least 3 and 9 consecutive hot days, the average number of consecutive days is 10.4 and 2.4, respectively.

During the 2000–2018 period, panel B of Figure 3.6 shows the average percentage of months with at least one cold or heat wave. The percentage is calculated by adding together the percentages from all provinces. It shows that between 2000 and 2018, 16.7% of months had at least one cold wave (which is defined as at least 3 consecutive cold days). The average percentage of months with at least three consecutive hot days experienced by heat waves is 13.6 percent. The percentage of months with at least one cold or heat wave is lower when cold or heat waves are defined for a longer period of time (at least 5, 7, or 9 days).

3.2 Impact of weather extremes on mortality

In this section, we explore the effect of cold and heat waves on the monthly mortality rate using a regression model. The regression model is presented in Box 3.2. We use both the mortality rate as well as log of the mortality rate as dependent variables. We regress these dependent variables on cold and heat waves and control variables. The control include average yearly temperature, total annual precipitation, province-by-month fixed-effects, year-month fixed-effects, and province-specific time trend. The effects of cold and heat waves on mortality are measured by the coefficients of cold and heat waves in the regressions. The effects are summarized in Figure 3.7. In this figure, we graph the point estimate and the 95% confidence interval of the effects of cold and heat waves on the monthly mortality rate (Panel A and B of the figure) and the log of the monthly mortality rate (Panel C and D of the figure). The log of the death rate and the estimated effects of cold and heat waves are remarkably comparable. The results of the effects on the log of the mortality rate are used to interpret the results.

Overall, we find that both cold and heat waves increase the chance of death, and that the effect of both cold and heat waves increases as the cold and heat waves stay longer. The predicted coefficients of ocurrence of at least one cold or heat wave within a month are plotted in Panel C of Figure 3.7. A cold wave lasting at least 5 days raises the monthly mortality rate by roughly 4.9 percent (significant at the 10% level), whereas a cold wave lasting at least 9 days increases the mortality rate by 6.5 percent (significant at the 5 percent level). When compared to a cold wave, a heat wave has a higher and more significant impact on the death rate. If there is a heat wave that lasts for at least 9 days in a month, the death rate rises by 13.5 percent. This shows how a protracted severe heat wave can have a significant impact. However, a heat wave lasting at least 9 days is uncommon. The average percentage of months with at least 9 consecutive hot days occurred only 2.1 percent of the time between 2000 and 2018.

[ Figure 3.6 ] Cold and heat waves

Panel A. The average number of consecutive days in cold and heat waves

Panel B. The percentage of months with cold and heat waves

Panel A of this figure presents the average number of days per year in cold and heat waves, which are defined based on the 5th and 95th percentiles of the province-specific temperature distribution and different durations of consecutive days. Panel B of the figure presents the average percentage of months in which at least a cold or heat wave (defined based on different durations of consecutive days) occurs during the 2000–2018 period. Source: Nguyen et al. (2021).

Panel D depicts the estimated coefficients of the number of days in cold and heat waves on log of mortality rates. All the estimates are positive and statistically significant at the 5% level. An additional day in a cold wave of at least 3 consecutive days (relative to a day within the 5th–95th percentile temperature range) leads to an increase in the mortality rate of 0.6 percent. The corresponding figure for a day in a heat wave is 0.7 percent. For a cold or heat wave defined as at least 9 consecutive days, an additional day in the cold and heat wave increases the mortality rate by 0.6 and 1.2 percent, respectively.

Children and the elderly are more susceptible to harsh weather than others [ e.g., see Basu, 2009; Arbuthnott and Hajat, 2017; Geruso and Spears, 2018 ]. Table 3.5 shows the coefficients of the number of days spent in cold and heat waves in death rate regressions for various age groups. There are 32 regressions in total, and the table only shows the estimated coefficients for each regression’s number of days in cold and heat waves. The findings imply that only the elderly, particularly those over the age of 80, are harmed by weather extremes. For people under the age of 40, almost all cold and heat wave coefficients are statistically insignificant. All of the predicted coefficients of cold and heat waves are positive for adults over 40, indicating that they have a negative impact on their health. The death rate of adults over the age of 80 is significantly higher than that of younger people. In Table 3.5, we look at how the effects of cold and heat waves on mortality differ for men and women. Overall, there is no discernible difference in the influence on death rates between men and women.

We believe that cold and heat waves cause death primarily through a direct and detrimental influence on health for two reasons. Firstly, cold and heat waves only have shortterm effects on mortality. To examine the medium-term effect of cold and heat waves, we include one-month and two-month lagged variables of cold and heat waves to examine the sensitivity of the effect estimates, as well as the medium-term effect of cold and heat waves. Most lagged variables of cold and heat waves are positive but not statistically significant at conventional levels. This suggests that cold and heat waves do not have medium- or long-term effects on mortality. Second, we conduct regressions of per capita income of provinces with cold and heat waves over the course of a year to see if cold and heat waves have indirect effects on health by reducing income. We find little evidence that cold and heat waves have a major impact on income. As a result, income is not a factor in the increased mortality caused by cold and heat waves.

3.3 Projection

In this section, we use projection of daily data for Viet Nam using different Representative Concentration Pathway (RCP) scenarios to predict cold and heat waves in the 2020–2100 period. In the IPCC’s fifth Assessment Report in 2014, four paths were employed for climate modelling and research. Different climate futures described in the pathways are all considered feasible, depending on the amount of greenhouse gases emitted in the coming years. The RCPs are named after a possible range of radiative forcing values in the year 2100 (2.6, 4.5, 6, and 8.5 W/m2, respectively) [ IPCC, 2019 ].

For the sake of simplicity, we assume that people’s health and economic conditions, as well as their ability to adjust to climate change,

[ Figure 3.7 ] Estimated effects (and 95% confidence interval) of cold and heat waves on the all-age mortality rate

Panel A. Estimated effects of cold and heat waves on the mortality rate

Panel B. Estimated effects of the number days in cold and heat waves on the mortality rate Panel C. Estimated effects of cold and heat waves on log of the mortality rate

Panel D. Estimated effects of the number days in cold and heat waves on log of the mortality rate

Source: Nguyen et al. (2021).

[ Table 3.5 ] Regression of morality rates for different population groups

Dep endent variables tality Mor r ate of f emale

tality Mor r ate of males

tality Mor r ate of people

tality Mor r ate of people

tality Mor r ate of people

tality Mor r ate of people

tality Mor r ate of people

tality Mor r ate of people

per per (deaths (deaths

aged 80+

aged 60-79

aged 40-59

aged 16-39

aged 5-15

aged 04

people) people) 100,000 per 100,000 per (deaths per (deaths per (deaths per (deaths (deaths per (deaths people) people) 100,000 people) 100,000 people) 100,000 people) 100,000 people) 100,000 100,000 (6) (7) (8)

(5)

(4)

(2) (3)

(1) 0.0743 (0.0768)

0.2940** (0.1190)

5.2655*** (1.9113) 0.1647* (0.0832)

0.1193 (0.1044)

4.5024** (2.0700) 0.0752 (0.0706)

0.2737** (0.1127)

4.3609** (2.0221) 0.1924* (0.1090)

0.1525 (0.1035)

4.5884* (2.3812) 0.0972 (0.0729)

0.2349** (0.1161)

4.0651* (2.0609) 0.2851** (0.1290)

0.1975* (0.1082)

6.9202*** (2.4346) 0.1342* (0.0767)

0.3063** (0.1296)

4.1322** (2.0547) 0.2716** (0.1313)

0.2171** (0.1055)

8.6812*** (2.4793)

0.8443 (0.5369) 0.4004 (0.4869) 0.8126 (0.4943) 0.5119 (0.4683) 0.9184 (0.5677) 1.0251* (0.5177) 1.3919** (0.6492) 0.8034* (0.4470)

0.1513* (0.0760) 0.2518** (0.1224) 0.1558* (0.0785) 0.2554* (0.1342) 0.1288* (0.0749) 0.2302 (0.1649) 0.1810* (0.1007) 0.2076 (0.1813)

-0.0401 (0.0587) -0.0512 (0.0537) -0.0122 (0.0519) -0.0254 (0.0627) -0.0505 (0.0544) -0.0439 (0.0616) -0.0298 (0.0634) -0.0748 (0.0680)

0.0409 (0.0267) -0.0002 (0.0382) 0.0318 (0.0224) 0.0264 (0.0430) 0.0555* (0.0326) -0.0143 (0.0438) 0.0485 (0.0299) -0.0018 (0.0494)

0.0891 (0.1421) -0.0294 (0.1517) 0.1617 (0.1482) -0.1212 (0.1499) 0.1671 (0.1580) -0.0297 (0.1548) 0.1640 (0.1717) 0.0282 (0.1683)

variables Explanatory # days with at least 3 consecutive days percentiles of temperature below the 5 th # da ys with at least 3 consecutive days percentiles of temperature abo ve the 95 th # da ys with at least 5 consecutive days percentiles of temperature below the 5 th # da ys with at least 5 consecutive days percentiles of temperature abo ve the 95 th # da ys with at least 7 consecutive days percentiles of temperature below the 5 th # da ys with at least 7 consecutive days percentiles of temperature abo ve the 95 th # da ys with at least 9 consecutive days percentiles of temperature below the 5 th # da ys with at least 9 consecutive days percentiles of temperature abo ve the 95 th 12,639

12,639

12,639

12,639

12,639

12,639

12,639

12,639

vations Obser Note: This table reports the coefficients of the number of days in cold and heat waves in regressions of the mortality rate of people at different age groups. A cold (heat) wave is defined as at least k (3, 5, 7, and 9) consecutive days which have daily mean temperature at or below the 5 th percentiles (at or above the 95 th percentile) of the temperature distribution of specific province. There are 32 regressions in total. The regression specifications are the same as those in Table A.1 in Appendix. The control variables include annual average temperature (°C), annual precipitation (mm), province-specific time trend, province-by-month fixed-effects, and year-by- month fixed-effects. Robust standard errors in parentheses. Standard errors are clustered at the province and year-by-month levels. *** p<0.01, ** p<0.05, * p<0.1. Source: Nguyen et al. (2021).

[ Box 3.2 ] Estimation of the effect of climate

Model to estimates the effects of climate on infectious disease In the first stage, we estimate this equation

where ycpmt is the number of infected cases in disease-category c of province p in the month m of the year t, Tkpmt is the number of days in temperature bin k in province p in month m of year t, Rainfallpmt Windspeedpmt are average rainfall and windspeed in the month m. We use four variables of fixed effects: vc for category of diseases, δp for provinces, σcm for category x month and θcp for category x province. The heterogeneous marginal effect of climate on infectious disease is captures in the function as:

where MeanTp is the sample-period average temperature of province p and Budgetp is the average of budget that province p allocating on medical heath care over sample period.

Model to estimates the effects of weather extremes on mortality We estimate the effect of cold and heat waves on the mortality using the following regression:

where yp is the mortality rate of province p in month m of year t (per 100,000). In this study, we use both the mortality rate and log of the mortality rate as the dependent variables. Coldpmt is the number of consecutive days in a cold wave and Heatpmt is the number of consecutive days in heat waves in province p in month m of year t. The cold wave is defined as at least a number of consecutive days (3, 5, 7 and 9 days) with daily mean temperature at or below the 5th percentile of the province-specific temperature distribution, while the heat wave is defined as at least a number of consecutive days (3, 5, 7 and 9 days) with daily mean temperature at or above the 95th percentile of the temperature distribution. X_pmt is a vector of control variables. Our main control variables include the average of yearly temperature, which is calculated by averaging the daily mean temperatures in the province, and the total annual precipitation. We also control for the province-by-month fixed-effects, Ppm, and year-month fixed-effects Mmt. In addition, we include the province-specific time trend, Tp, which allows for province-specific time trends in the mortality rate. We adopt the multiway clustering technique of Cameron et al. (2011), which allows us to deal with the correlation of error within provinces over time and between provinces within a month simultaneously.

will remain unchanged in the future. As a result, we create the cold and heat waves for the 2020–2100 timeframe using the same 5th and 95th percentiles as the temperature distribution 2000–2018. Panels A and B of Figure 3.8 present the projected number of consecutive days in cold and heat waves under different RCP scenarios. In this graph, a cold (heat) wave is defined as at least 3 consecutive days with daily mean temperature at or below the 5th percentiles (at or above the 95th percentile) of the temperature distribution of specific province. Since the average temperature is projected to decrease in the coming year, the number of days in cold waves decreases quickly and is close to 0 in years 2050s. On the other hand, the number of days in heat waves increases significantly and reaches maximum figures in around 2060. The number of heat waves is largest for the RCP6.0 scenario and smallest for the RCP2.6 scenario. To anticipate the number of fatalities per 100,000 people caused by cold and heat waves in the next years, we multiplied the predicted number of days in cold and heat waves by the estimated influence of cold and heat waves on mortality rate (0.18 and 0.14, respectively, as shown in Panel B of Figure 3.7). For the projection, we use the effect estimates of cold and heat waves on the mortality rate rather than the log of the mortality rate. The mortality rate is a more accurate means of estimating the number of people who will die as a result of future temperature extremes. Since we utilize the constant effect estimates of cold or heat waves on mortality, the forms of the number of fatalities caused by cold and heat waves (shown in Panels C and D) are quite similar to the morphologies of cold and heat waves. Heat-related deaths are expected to rise in the future, reaching a peak around 2060. These findings raise severe concerns regarding the health effects of future heat in Viet Nam.

4. Main conclusions and policy implications

Understanding the effect of weather extremes on health has received great attention from scholars and practitioners. This chapter provides new insight into climate change’s impact on infectious disease and mortality in Viet Nam. The diversity of climate in non-identical regions in Viet Nam generates different effects for climatic factors on communicable disease. We found that temperature and wind-speed affect infectious diseases — vector borne, airborne, and waterborne — for all regions. The effects vary across the regions. A stronger effect is found for vector-borne disease than others.

We find evidence for heterogeneous effects across different climatic regions and the role of health-care expenditures. A colder region will be more impacted by hot temperatures than cold temperatures. In contrast, a hot region will be more sensitive to cold temperatures than hot temperatures. There will be direct economic costs through expenditures for controlling and treating disease. Our analysis also highlights the indirect cost of disease impact caused by global climate warming. In our report, the indirect economic costs of infectious disease is estimated through

[ Figure 3.8 ] The projected number of consecutive days in cold and heat waves and the number of deaths under different Representative Concentration Pathway scenarios

Panel A. The projected number of consecutive days in cold waves Panel C. The projected number of deaths per 100,000 people due to cold waves

Panel D. The projected number of deaths per 100,000 people due to heat waves

Panel B. The projected number of consecutive days in heat waves

This figure presents the projected number of consecutive days in cold and heat waves under different Representative Concentration Pathway scenarios and the estimated number of deaths due to these cold and heat waves over the 2020–2100 period. A cold (heat) wave is defined as at least 3 consecutive days which have daily mean temperature at or below the 5th percentiles (at or above the 95th percentile) of the temperature distribution of specific province. Source: Nguyen et al. (2021).