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Heterogeneous treatment effects of safe water on infectious disease: Do meteorological factors matter?

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Abstract

Mortality from waterborne infectious diseases remains a serious issue globally. Investigating the efficient laying plan of waterworks to mitigate the risk factors for such diseases has been an important research avenue for industrializing countries. While a growing body of the literature has revealed the mitigating effects of water-purification facilities on diseases, the heterogeneous treatment effects of clean water have been understudied. The present study thus focuses on the treatment effect heterogeneity of piped water with respect to the external meteorological environment of cities in industrializing Japan. To estimate the varying effects, we implement fixed-effects semivarying coefficient models to deal with the unobservable confounding factors, using a nationwide city-level panel dataset between 1922 and 1940. We find evidence that the magnitude of safe water on the reduction in the typhoid death rate is larger in cities with a higher temperature, which is consistent with recent epidemiological evidence. These findings underscore the importance of the variations in the external meteorological conditions of the municipalities that install water-purification facilities in developing countries.

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Fig. 1

Sources: Created by authors from the Bureau of the Home Department (1924–1938); Sanitary Bureau of the Department of Welfare (1939–1940); Population Bureau of the Department of Welfare (1942–1943); Statistics and Information Department, Minister’s Secretariat, Ministry of Health and Welfare (1999, p. 84). The sample cities are the cities that were recorded both in the Sanitary Bureau of the Home Department (1877–1938) and in the Sanitary Bureau of the Department of Welfare (1939–1940)

Fig. 2

Sources: Created by authors from the Sanitary Bureau of the Home Department (1929, pp. 56–57); JMA database (see Appendix B in ESM)

Fig. 3

Source: Created by authors from the Sanitary Bureau of the Home Department (1929, pp. 56–57)

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Notes

  1. See also Jalan and Ravallion (2003), Gamper-Rabindran et al. (2010), and Devoto et al. (2012) for the cases of India, Brazil, and Morocco, respectively. Daley et al. (2015) provide an interesting evidence on the importance of residents’ perceptions of the functionality of current water and water sanitation systems in a remote Arctic Aboriginal community.

  2. For example, Jalan and Ravallion (2003) found a variation in the effects of piped water on the prevalence and duration of diarrhea across mothers’ education levels. Ogasawara et al. (2016) also found varying effects of piped water with respect to poverty levels in prewar Tokyo. However, neither study directly modeled the nonlinearity of these effects. A few exceptions include Gamper-Rabindran et al. (2010), employed a panel quantile regression approach, and Ogasawara and Matsushita (2017), employed a semiparametric fixed-effects approach. However, we are the first to use the fixed-effects semivarying coefficient panel-data model to bridge the gaps in the body of the literature.

  3. See also Guzman Herrador et al. (2015), Wang et al. (2012), Dewan et al. (2013), and Listorti and Doumani (2001) for this epidemiological evidence.

  4. The total city population is derived from the population in all cities reported in the vital statistics for each year (Appendix B in ESM). While Noheji and Katō (1954) and Ogasawara et al. (2016) investigated the impacts of water supply on mortality in Gifu and Tokyo, respectively, the present study aims to offer a more thorough discussion on this topic by using a comprehensive city-level dataset.

  5. See Appendix B (ESM) for the data source. Annual precipitation in Japan is also similar to those observed in the Southeast Asian countries.

  6. See also Appendix A.1 (ESM) for details of older water-supply systems. Although old waterworks were installed in a few cities in the nineteenth century, those did not introduce any purification technology. Ogasawara et al. (2016) describe finer details of the waterworks in prewar Tokyo city.

  7. A recent study by Ogasawara and Matsushita (2017) revealed that filtration technology significantly improved water quality. On average, the number of bacterial colonies decreased from roughly 470 CFU/ml in source water to 14 CFU/ml in taps in Japanese cities, well below the criterion value of 100 CFU/ml.

  8. See World Health Organization (2011) and Parry et al. (2002) for the finer details of the symptoms of typhoid fever.

  9. See also an important study by Tseng et al. (2009), which finds the significant relationship between climate change and dengue fever infection.

  10. Since data on the monthly incidences of typhoid fever are not available after 1928, we present the data between 1922 and 1927.

  11. The gestation period of Typhi and onset of symptoms of typhoid fever are usually 7–14 days and more than two weeks, respectively (Parry et al. 2002, p. 1774). Given this relatively long infection period (i.e., roughly one month), the lag between infection and deaths would be valid.

  12. Therefore, we used six observations between 1922 and 1927 to calculate the coefficient for each typhoid measure.

  13. In Appendix A.2 (ESM), we confirm a similar relationship by using maps that illustrate the spatial distribution of the typhoid death rates in the 1920s and 1930s across the Japanese archipelago. We find that the cities in the southwestern part of Japan had relatively high initial typhoid death rates and experienced larger improvements in these rates between the 1920s and 1930s. By contrast, the cities in the northeastern part had relatively low initial rates and experienced smaller improvements.

  14. Another possible path for typhoid infection may relate to the fly population, which builds up over several months from the survivors of the winter and early spring. However, the number of flies can be easily reduced during the continuous monsoons in Japan, as they are less likely to carry human fecal particles onto human food during heavy rain. In fact, our analytical result supports the evidence that the improving effects of safe water on typhoid fever are less likely to depend on precipitation (see Sect. 5).

  15. In particular, we have

    $$\begin{aligned} \sqrt{Nh}\left[ {\hat{\alpha }}(u)-\alpha (u)-\frac{h^{2}}{2}\gamma _2\ddot{\alpha }(u)\right] {\mathop {\rightarrow }\limits ^{d}} N(0, \zeta (u)), \quad \text{ as } \ n, T_i \rightarrow \infty , \end{aligned}$$

    where \(\zeta (u)=\frac{\sigma _v^2(c_1^2\delta _0+2c_1c_2\delta _1+c_2^2\delta _2)}{f(u)E[x_{it}^2]}\), \(c_1=\gamma _2/(\gamma _2-\gamma _1^2)\), \(c_2=-\gamma _1/(\gamma _2-\gamma _1^2)\), \(\gamma _i=\int u^i K(u)\hbox {d}u\), and \(\delta _i=\int u^iK^2(u)\hbox {d}u\).

    A consistent estimator of \(\sigma _v^2\) is given by

    $$\begin{aligned} {\hat{\sigma }}_v^2=\frac{1}{N}(Y-M\{X, {\hat{\alpha }(U)\}}-Z{\hat{\beta }-D_{\mu }}{{\hat{\mu }}})'(Y-M\{X, {\hat{\alpha }(U)\}}-Z{\hat{\beta }-D_{\mu }}{{{\hat{\mu }}}}). \end{aligned}$$

  16. These experiments could be regarded as falsification tests. This means that if these non-waterborne deaths did not include a set of deaths from typhoid fever, the estimated effects of safe water on the CSM death rate and scarlet fever death rate should be negligible.

  17. The taps for piped water without filtration technology are not considered here as these waterworks could not provide clean water and were sometimes contaminated (Fukushima et al. 1940).

  18. Since clean water improves overall hygiene levels via clean drinking water itself, handwashing, food washing, bathing, and laundry, the popularization of tap water may decrease the risk of typhoid fever infection (World Health Organization 2011). Therefore, the larger the value of the coverage of tap water, the more citizens are able to access clean water. Note that we cannot use the number of households with water taps or the number of supplied people here; thus, the coverage rate is defined by using the number of water taps. Since the average number of family members in cities at that time was approximately 4.6 people, 10% of the coverage may imply that roughly 40% of households had water taps.

  19. Since children above five years would typically be susceptible to typhoid fever, we singled out this age group. We have confirmed that the main results are unchanged if we use the shares of population aged 0–5, 6–14, 15–24, 25–59, and 60+. See Appendix C.2 (ESM) for the results.

  20. Another possibility is the Mills–Reincke phenomenon, which emphasizes the case in which improvements in waterworks can also improve deaths from water-related diseases, including respiratory infections. See Ferrie and Troesken (2008).

  21. Using the mean value of the coverage of tap water does not change this estimate with approximately 24% of the total decline in the typhoid death rate.

  22. Note that the number of observations in Columns (1) and (2) is lower because of the unbalanced dataset.

  23. Distances are measured between the city offices by using geospatial information. Elevation is measured by using GSI Maps of the Geospatial Information Authority of Japan (see Appendix B in ESM for the data source). The mean distance value between a certain city and its nearest neighboring city is 36 km. The elevation ranges from 1.0 to 457.5 m with a median value of 7.3 m. As shown in Fig. A.1 (ESM), most Japanese cities are coastal cities, whereas several cities are located at higher altitudes.

  24. See Nagashima (2004) for a discussion on the potential adverse effects of the Great Kantō earthquake of 1923 on typhoid fever in Tokyo.

  25. See also Hunter (2014) and Hunter and Ogasawara (2016) for the detailed discussions on the relatively short-run impacts of the earthquake of 1923.

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Acknowledgements

This study was supported by JSPS KAKENHI Grant No. 16K17153. There are no conflicts of interest to declare. The authors wish to thank the editor, two anonymous referees, and Badi Baltagi for helpful comments on the paper. We also thank Tatsuki Inoue for excellent research assistance.

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Authors and Affiliations

  1. Graduate School of Social Sciences, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan

    Kota Ogasawara

  2. Graduate School of Economics, Hitotsubashi University, 2-1, Naka, Kunitachi, Tokyo, 186-8601, Japan

    Yukitoshi Matsushita

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  1. Kota Ogasawara
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Correspondence to Kota Ogasawara.

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Ogasawara, K., Matsushita, Y. Heterogeneous treatment effects of safe water on infectious disease: Do meteorological factors matter?. Cliometrica 13, 55–82 (2019). https://doi.org/10.1007/s11698-017-0169-6

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