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Household air pollution as a silent killer: women’s status and solid fuel use in developing nations

Abstract

Household air pollution is a leading cause of death globally, as 4.3 million people die prematurely each year from illness attributable to use of solid fuels (WHO 2016a). Many studies contend that gender inequalities are likely to greatly shape the global distribution of solid fuel use and its negative health consequences. We conduct an analysis of 91 developing nations using structural equation models on the prevalence of female indoor air pollution deaths among women and the ratio of female to male indoor air pollution deaths. The results illustrate that women’s status is a robust predictor of solid fuel use, and that improved women’s status also correlates directly with lower female to male indoor air pollution deaths ratios and indirectly with reduced female death prevalence through lower solid fuel dependence. Women’s status additionally mediates the effects of some other notable predictors, such as economic development. Overall, the results bring attention to a “silent killer” in less-developed nations and illustrate that greater female empowerment is an important avenue in addressing this global pandemic.

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Fig. 1
Fig. 2

Notes

  1. 1.

    In this article, we use the phrases “household air pollution” and “indoor air pollution” interchangeably.

  2. 2.

    As previously mentioned, these data do not fully capture within-nation differences in exposure, such as those that are likely experienced by men and women in developing societies. Also, there could be concerns over the quality of the data for additional reasons, such as the use of household surveys from limited regions to estimate national levels of solid fuel use. Despite these concerns, these data represent the only available estimates of indoor air pollution and thus are a necessary starting point for attempting to gain understanding on the causes and consequences of household air pollution across nations. As a robustness check, all analyses were also tested with data on chronic respiratory deaths, as these represent some of the key causes of death from exposure to solid fuels, and the results were entirely consistent with those presented here on indoor air pollution deaths. These results are available from the authors upon request.

  3. 3.

    In addition to the measures listed, we also tested for the influence of a variety of other indicators, such as levels of democracy, foreign debt, international trade measures, etc. None of these were found to have a significant relationship to solid fuel use or deaths from indoor air pollution and compromised model fit; thus, they were excluded from the analysis.

  4. 4.

    Although there were some missing data points among the control variables, the level of missing data was generally low and there appeared to be no pattern to the missing values that would bias the results. The maximum likelihood (ML) missing value estimation used here creates a likelihood for the entire sample by summing the likelihoods for each case, using whatever information each case has available. This means that each country contributes the maximum amount of information possible to the estimation (Arbuckle 1996; Enders and Bandalos 2001). The estimates are consistent and efficient under the condition that the data are missing at random (MAR). This is an easier condition to meet than missing completely at random (MCAR), which is required for methods of listwise deletion. Nonetheless, we also performed the analyses using methods of listwise deletion, on a sample of 60 nations to confirm the reliability of the results. The results were consistent across the two samples, thus demonstrating that the substantive findings are not driven by sample size or the cases under investigation.

  5. 5.

    These are available from the author upon request.

  6. 6.

    These results are available from the authors upon request.

  7. 7.

    These results are also available for the saturated models presented in the Appendix from the authors upon request.

  8. 8.

    The indirect, direct, and total effects for solid fuel use are only modeled based on the results in Fig. 1 to avoid redundancies as the results are nearly identical to those presented in Fig.2.

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Correspondence to Kelly F. Austin.

Appendix

Appendix

Fig. 3
figure3

SEM predicting solid fuel use and prevalence of female indoor air pollution deaths, saturated model notes: *** p < .001; ** p < .01; * p < .05, + <.10 (two-tailed tests); standardized regression coefficient reported. Model fit statistics: chi-square test statistic: χ2 = 11.950 with df = 16 where p = .747 and is non-significant; Incremental Fit Index = 1.006, Tucker-Lewis Index = 1.019, Confirmatory Fit Index = 1.000; root mean squared error of approximation (RMSEA) value = 0.00

Fig. 4
figure4

SEM predicting solid fuel use and ratio of female to male indoor air pollution deaths, saturated model notes: ***p < .001, **p < .01, *p < .05, +<.10 (two-tailed tests); standardized regression coefficient reported. Model fit statistics: chi-square test statistic: χ2 = 10.304 with df = 16 where p = .850 and is non-significant; Incremental Fit Index = 1.010, Tucker-Lewis Index = 1.029, Confirmatory Fit Index = 1.000; root mean squared error of approximation (RMSEA) value = 0.00

Table 6 Regression results for SEM depicted in Fig. 1 predicting the prevalence of female indoor air pollution deaths per 100,000
Table 7 Regression results for SEM depicted in Fig. 2 predicting the ratio of female to male indoor air pollution deaths

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Austin, K.F., Mejia, M.T. Household air pollution as a silent killer: women’s status and solid fuel use in developing nations. Popul Environ 39, 1–25 (2017). https://doi.org/10.1007/s11111-017-0269-z

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Keywords

  • Gender
  • Global health
  • Indoor air pollution
  • Solid fuels
  • Women’s health