Geospatial analysis of health risks and solid waste management behaviour

ABSTRACT

The lack of proper waste management behaviour creates environmental debasement and induces health risks. This study employs primary survey data collected in 2016 to investigate the association between health risks and improper waste management behaviour by households in three locations of Rupandehi District, Nepal. The health risk is measured by a series of water-related symptoms such as Diarrhea, Jaundice, Typhoid Fever, Worms, and Cholera. This paper’s novel contribution is that we identify the spatial nature of the prevalence of waterborne diseases and related factors such as solid waste management behaviour, hygiene infrastructure and personal cleanliness, and socio-economic status of the households. We use a spatial autoregressive model under the negative binomial family, and the result indicates a significant spatial autocorrelation of waterborne diseases. Moreover, we find the significant effect of improper waste management practices on waterborne diseases. The result is consistent even after various robustness and falsification tests. The findings from this study indicate the acute need to raise awareness concerning the malicious effect of improper waste management and the urge to provide wider access to waste management services.

KEYWORDS:

• Waste management behaviour
• waterborne diseases
• diarrheal incidences
• Spatial Autoregressive Model
• negative binomial model

Acknowledgement

We would like to thank Open Society Foundation for providing financial support through the Civil Society Scholar Award. We are grateful to Pratiman-Neema Foundation (PNMF); Pratiman-Neema Health Institute (PNHI); and Lumbini Center for Sustainability (LCS), Nepal for serving as a host institution and assisting with the logistics during the survey.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 VDC is the Village Development Committee. This is a lower administrative part in Nepal.

2 The full survey consisted of questions to understand the environmental and health concerns as well as the personal cleanliness and hygienic information of households in Nepal. Further details on the survey is available in Kunwar and Bohara (2019).

3 Deep tubewell is considered as one of the safest sources of water in the study area. A deep hole is drilled into the ground (usually more than 150 m) with a hand pump attached to the top (UNHCR, 2012). This source of water is considered safe because of the depth from which the water is extracted. At a level of 150 m, it is very unlikely any contaminants can survive, and the water is also free from any human activity.

4 The percentage of households using Flush Toilet might seem a little high in the context of developing countries. However, our survey includes data from one municipality and 2 VDC’s. The municipality experiences a better standard of living due to higher household income and the overall livelihood status compared to the two VDCs. Due to the proportional weight sampling while undertaking the survey, a large portion of the observation were drawn from the municipality. Hence, we see a high number of households employing the Flush Toilet system for their sanitation facility.

5 Available at https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/modeling-spatial-relationships.htm.

6 To use the K-nearest neighborhood method, it is necessary that each household is surrounded by a minimum (K = 8) number of households, which is available in our data.

7 The negative binomial model falls under the Generalized Linear Model (GLM). GLM is a generalization of the ordinary least square (OLS) where the error distribution of the response variable is not a normal distribution. In GLM, the linear model is related to the response variable by a link function. In this paper, this link function is equation 9.

8 These intervals are $-1.65<\mathrm{Z}<-1.96$, $-1.96<\mathrm{Z}<-2.58$, and $-2.58<\mathrm{Z}$

9 We have found these results by using a k = 8 nearest neighborhoods matrix. For robustness, we also estimated the model with k = 10, and the results were insensitive.

10 To determine the households those who fall in the north side, we have first determined the center of the study area geographically. Then we have selected the all the households those who dump in the river if their latitude is higher than then the center point.

11 We have used the similar weight (K = 8 nearest neighborhood) for both the north and south region. We have tried to show whether dumping of waste into the Danda by the upstream households impact the health of the study area. For doing this, we have only considered the Waste Danda variable for the northern part of the study area. Thus, even though we have used the same weight in the regression, it’s still addressing the issue.

Additional information

Funding

This work was supported by Open Society Foundations.