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A Review of Studies in India

Methodological Challenges in Estimating the Impact of Improved Sanitation on Child Health Outcomes

In this article, the findings of selected observational studies are contrasted with that of randomised experiments conducted to estimate the impact of improved sanitation on child health in India. The estimation bias exists and could be due to the measurement error in sanitation indicators, which remained unaddressed by most observational studies. The sanitation indicators used and the inadequate questions asked to measure it, result in a measurement error.

This article aims to highlight the problems encountered in measuring the causal effect of sanitation on child health in observational studies. The focus is on the methodological issues related to sanitation indicators, health indicators, confounding bias, and selection bias, along with the controls arising in the process of assessing the impact of development efforts to imp­rove sanitation practices on health outcomes.

The impact of improved sanitation on child health has been assessed by various studies (Hammer and Spears 2016). Based on the approaches used in the estimation, the literature on the impact of imp­roved sanitation on health outcomes can be
divided into two strands: first, those studies that use randomised controlled experiments, and second, those that are based on observational data. The rando­mised experiments provide better estimates of causality by removing the biases originating from confounding. In this article, we summarise the findings of five important randomised controlled experiments; two conducted in Kenya and Bangladesh (published in 2018); two conducted in India (one each in Madhya Pradesh and Odisha) in 2014; and one in Mali in 2015. There is a consensus in the findings of these randomised experiments that des­pite a significant increase in toilet coverage and reduction in open defecation, the prevalence of diarrhoea (repor­ted for a period of one week) among children (less than five years) did not reduce, except in Bangladesh where diarrhoea reduction was modest (5.7% in controlled group and 3.5% in treated group) (Clasen et al 2014; Patil et al 2014; Pickering et al 2015; Null et al 2018; Luby et al 2018).

Contrary to the findings of randomised experiments, the observational studies show an abnormally high impact of sanitation on the reduction of diarrhoea. For example, Esrey et al (1991) reviewed, inter alia, two studies which concluded that, on an average, 30% reduction in diarrhoea was associated with improved water and sanitation conditions. Black and Walker (2019), in a review of studies, highlighted that water and sanitation ­intervention, as estimated in the observational studies, can reduce diarrhoea by 15%–40%.

Kumar and Vollmer (2013) using data from the District Level Household Survey (DLHS) and propensity score matching method found that sanitation reduces the risk of contracting diarrhoea by 2.2 percentage points in children ­under the age of five years. Geruso and Spears (2014), using the National Family Health Survey (NFHS) data, found that reducing the mean open defecation by 10 percentage points would reduce ­infant mortality by three deaths per thousand, or about 4% of the mean mortality rate. Andres et al (2017), in a study using DLHS-31 data, used propensity score matching that found 47% reduction in diarrhoea prevalence (two-week reference period) between the children (less than 48 months of age) living in a household without access to improved sanitation in a village but without coverage of improved sanitation compared to children living in a household with acc­ess to improved sanitation in a village with complete sanitation coverage. Conversely, Khanna (2008) found an incr­ease in diarrhoeal incidence in households with piped water and sanitation using the propensity score method and NFHS, 1998–99. These stark differences in findings between randomised controlled experiments and observational data studies require careful methodological examination.

Therefore, this apparent divide in the documented findings between randomised experiments and observational studies motivates to review the methodological issues that the observational studies encounter. In this context, this article highlights the methodological issues in the studies using observational data related to measurement error insa­nitation indicators, health indicators, bias arising due to confounding variables, sample selection bias, and inadequate controls. Some of the observational studies related to India have been reviewed, not with the intention of fault-finding, but for a better understanding of the methodological issues. These studies are only a few but they are prominent and, therefore, selected to highlight the effects of improved sanitation on different health outcomes—for example, child mortality, cognitive skills, stu­nting—while the focus is on the impact of sanitation on diarrhoea.

Methodological Issues

 

Sanitation indicators: (i) Use of open defecation proxies and measurement error: There are many indicators used in the studies to represent unsafe disposal of human faeces while studying the negative health exter­nalities, including positive indicators such as the “presence of sanitation facility,” “access to toilet,” “latrine per capita,” and negative indicators such as “fraction of households who defecate in open.” All of these indicators, although, may be chosen as per the availability of data, yet they have considerable measurement ­error. The access to latrine as such does not have a causal effect on negative or positive health outcomes. While the asso­ciation, if any, has to flow through the use of toilet, which remain unmeasured. Therefore, the difference between latrine coverage and its use is accounted as a measurement error. Having a toilet but partial or no use of it is another source of measurement error. This error may not be random for several reasons. It varies as per the age group and sex composition of a household as younger members and males are relatively less likely to use the toilet. Since the use of toilet is a behavioural variable, it changes slowly over time, while the access to toilet, since promoted through policy, increases relatively faster. Thus, the measurement error is likely to increase. On the one hand, any indicator on toilet coverage suffers from inclusion error; on the other, the indicator related to open defecation measured, based on households without toilet, suffers from exclusion error. It excludes those members who have access to toilet but defecate in the open, most of the time or sometimes. The measurement error in the independent variable is a source of bias in the estimates of the observational studies.

Whilst the use of a sanitation facility (for example, a toilet) is equally important to its access, the coverage of a given population by sanitation facilities is necessary but not a sufficient condition for ensuring health benefits. Therefore, the emphasis should be on the universal ­usage of safe sanitation infrastructure (Carr 2001). However, the measurement of universal usage of toilet facilities through various surveys has been focused either on sanitation coverage or “households practising open defecation” (HOD) rather than “population practising open defecation” (POD) or “incidences of open defecation” (IOD). In India, there are many surveys undertaken inter alia to collect information about the various aspects of sanitation, including the decennial demographic survey by the Census of India, and other periodic surveys like the National Sample Survey Office (NSSO), NFHS, etc. Nevertheless, the measurement of toilet usage and open defecation in such surveys has been far from satisfactory in capturing the reality of toilet usage by all the members of the household, and regularly. The consequence of the measurement error, which finds an alternate way to impact dependent variable, other than through the independent variable, creates a bias in the estimates. The method used such as propensity score matching, instrumental variable may not be able to overcome this problem.

 

(ii) Overview of questions asked in the census and surveys: The Census of India, 2011, for example, asked the following questions to a household: “Latrine within premises,” “yes” or “no,” and if “yes” then specify “type of latrine”; if no latrine within premises,” specify “public latrine” or “open.” The question records a household’s access to a latrine and whether a household practices “open” defecation in the absence of latrine access. However, it does not put any direct question on the usage of latrine at the household or individual level to those who do have access to a latrine. It also does not confirm whether the latrine a household has is functional or non-functional. Errors in data are likely to arise because it estimates the practice of “open defecation” by those who neither have a latrine on-premises nor have access to a public latrine; this heavily underestimates even the HOD by those households that do have access either to a household latrine or a public latrine but do not use either of them. In a way, the estimates are limited only to the first expression of the swachhta (cleanliness) equation.

Another important source of information on open defecation is NSSO’s occasional surveys. One such survey was the NSSO (2012) 69th round survey on “Drinking Water, Sanitation, Hygiene and Housing Condition in India,” a household-level survey. In the NSSO (2012) survey, it was asked to a respondent, who answered the survey on behalf of a household, what was the “access to lat­rine (exclusive use of household; common use of households in the building; public/community latrine without payment; public/community latrine with payment; others; no latrine).” The res­ponse of “no latrine” to this question clearly segregates the households, which do not have access to any latrine. Therefore, it can measure the first expression of the swachhta equation.

Further, the NSSO (2012) survey asked, from those households that have access to any type of latrine, “type of latrine (used: flush/pour-flush to: piped sewer system; septic tank; pit latrine; not used.” The response “not used” can also segregate the households who have access to a latrine but did not use it. This qualifies the second expression of the swachhta equation. Further, the survey asked, from those having access to latrine, “whether all household members of categories (male of age below 15 years; male of age 15 years and above; female of age below 15 years; female of age 15 years and above) are using latrine (yes: 1, no: 2, not applicable: 3) (Viswanathan 2017). The res­ponse “no” segregates only a category of members who were not using the latrine. It, however, does not ask any further questions to those who were not using a latrine about how many times during the reference period they actually used it. The grouping also brings in a judgment bias of a respondent to confirm as “yes” when most of the household members were using a toilet but not really all of them. This shows that the survey underestimates the third expression of the swachhta equation.

The NSSO has also come up with a Swachhta Status Report for June 2015 (NSSO 2016) and July–December 2017 (NSSO 2017). At the household level, in both these surveys, it was asked: “Does the household have a sanitary toilet?” The fixed structure response is either “yes” or “no.” Further, about the usage of toilets, it was asked, “the number of household members (separately for “old,” “adult males,” “adult females,” and “children”) using toilet/community toilet” (old persons are defined as those with age more than 60 years, adult males and females bet­ween the age of 15 to 60 years, and children with age less than 15 years). This question although does not give the household-wise frequency of toilet use—who partially use it—yet it can give the number of households where no member is using and also the households where all members are using the toilet, and the number of members who partially uses the toilet.

The NFHS is another survey that rec­ords a household’s responses on sanitation aspects. The first question it puts to a household’s respondent is: “What kind of toilet facility do members of your household usually use?” Two responses to this question—first “dry toilet,” and second, “no facility/uses open space or field”—are together able to segregate the no access to any latrine by a household; thus, they qualify the first expression of the swachhta equation. The survey does not put any questions on latrine use, and thus underestimates the POD and IOD. It may be mentioned that the World Health Organization and United Nations Children’s Fund’s Joint Monitoring Programme (JMP) estimates of POD, for 2019, also uses NFHS’s data.

Another household small sample survey is called the National Annual Rural Sanitation Survey (NARSS), which is carried out by independent agencies for the Department of Drinking Water and Sanitation, Government of India. Its question about access to latrine is: “Whether you and your family members have ­access to a toilet; if yes, what kind of toilet facility?” One of its responses is: “no, our family does not have access to any toilet (family members usually defecate in the bush, fields, or other locations).” The response to this question segregates the households without having access to any latrine and qualifies the first expre­ssion of the swachhta equation. It is also accompanied by the observation of the interviewer to reconfirm this response, and thus, it reduces the chances of error.

Further, for those households that have a ­latrine, the surveyor needs to observe the functionality of the toilet, and record the observations as “(i) pan is completely broken; and (ii) pan is choked”; the observations may be taken as non-functionality of a toilet or “toilet not in use.” On the usage of toilet, following questions on defecation practices are posed before a respondent about each member, aged three years and above, of the household: “(i) Does the member use ­latrine always?” Responses are: “yes” or “no”; if “no” then; “(ii) Did he/she use latrine often, rarely and never in last 15 days?” Structured responses are: “often”; “rarely”; and “never.” The responses such as “always” and “never” clearly indicate the frequency of use as zero and regular. However, the subjective terms “rarely” and “often,” though convey some sense, yet are vague in capturing the ­actual frequency of usage. The questionnaire comes close to capturing the first and third expression of the swachhta equation (given below) to estimate IOD.

 

(iii) Other issues: Over a period of time, access to toilet may not necessarily be associated with reduction in the incidence of open defecation. It may be that with the increase in latrine coverage or access, the practice of open defecation may also expand. A case in point for this paradox is reflected in the Census of ­India (2011) figures: out of 16.78 crore (or 167.8 million) rural households in the Census of India, 2011, 30.7% had latrine compared to 22% (13.82 crore, or 138.2 million) in 2001, which paints a picture of an improvement. Contrary to this, there is an increase in the total number of rural households without a latrine by 1.38 crore (13.8 million) between 2001 and 2011, which means an additional 6.89 crore (68.9 million) people defecating in the open (assuming an average household size of five persons, and those who did not have a latrine, defecate in the open) (Hueso and Bell 2013).

Further, the positive health benefits of safe sanitation infrastructure may take time to realise its full impact as pathogens released in the environment often remain active for a long time—as long as two years, depending on environmental conditions. Therefore, explanatory variables without the time dimension may tend to underestimate the positive health outcomes of safe sanitation infrastructure. For example, the question “how long they have been using a latrine?” captures the impact better, compared to the question “does the household have a latrine or not?”

The use of negative indicators such as “open defecation” is better than “access to toilet” on two counts. First, it has direct causal relation with the negative health impacts, whereas the access to ­toilet as such does not have any direct causal relation with health impacts, rather the association flows through the toilet use which goes unmeasured. Second, the “open defecation” indicator also represents negative health externalities that arises from the defecation by other households, whereas the access to sanitation facilities excludes the health ext­ernalities’ impact. However, if open defecation is measured from access to toilet and not from its use, as is done in various studies (Table 1), it is merely a matter of interpretation of results.

Health indicators: For the measurement of health outcomes, the short period to recall the dia­rrhoea episode is suggested as better to record reported diarrhoea prevalence (Blum 1983). The recall period varies from one week to two weeks in the studies, we reviewed. The random experiments used one week while observational studies used two weeks as a recall period. Since the probability of the incidence of diarrhoea is obviously higher with the increase in the reference period, the comparisons require careful interpretation. Similarly, diarrhoea incidences are higher among children less than four years compared to children less than five years of age. The error may increase in the health indicators with the increase in the recall period due to the limitation on the capacity to recall.

 

Confounding bias: The true impact of sanitation may not be measured in the presence of a confounding bias. The confounder creates a bias that arises if the open defecation and health outcome (diarrhoea) have a sha­red cause. For example, water supply is one of the confounders, which on the one hand, improves toilet use, and on the other, its quality directly improves the health outcomes. Education and health awareness, socio-economic status, and cultural settings are important confoun­ders that simultaneously impact both open defecation and health outcomes. Further, government policies/programmes that promote toilet usage as well as those that focus on improving child health, such as food and nutrition, also work as confounders.

Seasonality is also a confounderThe diseases and infections are associated with the seasonality of wet and dry seasons (Blum 1983). A season, as an external factor, affects the water quality, health outcomes as well as sanitation behaviour. For example, the rainy season is conducive for a high density of flies that transports pathogens, and thus increases the possibility of related diseases (Collinet-Adler et al 2015). Further, the chances of contamination of water sour­ces during the monsoon season also increase (Seifert-Dähnn et al 2017). Moreover, the toilet-use behaviour also changes during the rainy and dry seasons (Sinha et al 2017). The sanitation indicators—such as “access to toilet” and “open defecation”—measured using hou­se­holds not having access to toilets, and water indicators such as “access to ­imp­roved water source” are not sensitive to these seasonal changes; therefore, they do not capture the seasonal effects. The studies, which ignore the role of seasons while assessing the health impacts of sanitation, may under or overestimate these impacts.

The consequences of confounding are the bias in the estimates of the impact of sanitation on the incidence of diarrhoea mostly in observational studies. Many studies have not included access to ­water as an independent variable or a covariate while calculating propensity scores. The methods such as propensity score matching, inverse probability weighting, G-methods, stratification-based methods, and instrumental variables can be used to remove the confounding bias.

 

Control variables: There are independent variables, other than confounders, which influence the incidence of diarrhoea without entering into the causal association between sanitation and diarrhoea. These important variables need to be controlled while calculating the regression. Regression model specification for impact assessment, omitting the relevant variables—like access to food and hygiene—is likely to give biased results. For the health outcomes, such as diarrhoea, access to clean food and hygiene is as important as sanitation. Some of the important studies reviewed have excluded these as controlled variables. There are studies that have measured the joint effect of improved water and sanitation on health outcomes (Khanna 2008). In fact, sani­tation and water supply jointly work to impact health outcomes. The absence of one may undo the benefits of the other, whereas the presence of both may give full health benefits. The studies by Khanna (2008) and Kumar and Vollmer (2013) capture the joint effect of sanitation and water and found that these toge­ther, significantly, improve the health outcomes.

 

Health externalities: The finding that open defecation of one’s neighbours, rather than the household’s own practice, matters most for child survival (Geruso and Spears 2014; Vyas et al 2016) underlines the strong effect of externalities arising from open defecation by others; therefore, it requires considerable methodological attention while estimating the impact of safe sanitation on health outcomes. Generally, the regression analysis conducted to measure the health impacts of safe sanitation uses cross-sectional data at the household and/or district level, which does not account for the health externalities. The negative health impacts of unsafe disposal of excreta are more of a “local phenomenon.” For example, the practice of open defecation in one village is more likely to affect the residents of that village rather than those of a village located far away. In view of this, measuring the negative health impacts of open defecation using district-level data may not necessarily take into account the distribution of open defecation within that particular district.

Most of the studies, including the randomised experiments, are limited to the measurement of direct impacts while ign­oring the external effects of sanitation. The study by Andres et al (2017), which estimates the direct and external effect of safe sanitation on the incidence of diarrhoea, found that most of the gains from safe sanitation are indirect.

 

Selection bias: The observational studies, which assesses the impact of safe/improved sanitation on child health, select the households with children within the range of a particular age, and drop the other obser­vations. This selection process tends to create a “selection bias” for several reasons. For example, the poor households may have more probability of selection as they have higher probability of having children under the specified age (due to higher birth rate in poor households) and they are also likely to have lower acc­ess to latrines. In this sample, irrespective of the causal relation between toilet access and child health, there would be an association due to their economic status. The access to sanitation is not random or, in other words, depend on many factors; therefore, the unobserved factors, which enhance or dec­rease the probability of adoption of toilet by a household, create a selection bias. The studies (for example, Kumar and Vollmer 2013) claim to overcome this selection bias using propensity score mat­ching; however, they did not discuss in detail the important factors influencing the probability of toilet adoption. Further, purposive sampling of two groups of households—one having access to sanitation and the other without access to sanitation—may also create a sampling bias. The presence of a sampling bias in the data, and the process of assessment without using appropriate sampling correction methods, may tend to yield biased results.

Suggestions

The most important remedy for the rem­oval of this measurement error bias in estimates is measuring the variable more precisely. Using “incidence of open defecation” is one of the important indicators to improve the estimates. In the absence of the incidence of open defecation, the studies have relied on other proxies of sanitation that may or may not yield the true results. These proxies do not capture an individual member’s use habits and frequency in a household. More­over, for children’s health impact studies, the information on toilet use is very crucial compared to only “access to ­toilet” as the child faeces may not be ­disposed off safely (WSP and UNICEF 2015).

In view of the above, one of the objectives of this article is to focus on the broad framework of the estimation of IOD, which is more comprehensive compared to that of estimating POD. The IOD not only counts the persons engaged in open defecation but also the frequency of open defecation, which is important in the Indian context, as not all members of a family always use a latrine despite having access to one. In many households, some members never use the toilet, whilst some others use it only irregularly; such behaviour is not fully captured by the existing surveys mentioned. The incidence of open defecation is important to capture the change in ­the behaviour towards the practice of safe sanitation. It is more important in the present context where coverage or availability of latrines for households has inc­reased to a great extent, and the focus of policy attention is growing increasingly towards bringing a behavioural change towards the regular use of latrines. Moreover, the positive and negative health outcome externalities of open defecation are more precisely linked to IOD rather than to POD and HOD.

The proposed “framework of estimation” discusses the nature of questions to be put to potential respondents about households and their individual members in order to capture the finer nuances of open defecation behaviour. Figure 1 shows the flow of questions to be asked to a household under the proposed framework of estimation. The first question is about access to a latrine and to be asked to all the households; the second question is about its use to be asked to those who have access to a latrine. The third question is only referred to individual members of those households that have, and are also using, a ­latrine; it is about the toilet usage frequency of each household member.

The aggregation process of incidence of open defecation is shown in the swachhta equation. The first expression of the equation counts the number of households (shown as j) without a latrine multiplied by the number of members (shown as i) in each household and multiplied by the frequency (shown as π) of open defecation during the reference period. This expression is also determined by the household coverage of latrines. Out of those households who have a latrine, the second expression counts IOD among these for a segment who has access to toilet but never ever used one during the reference period. The third expression counts individual member’s frequency of latrine use during the reference period. The incidence(s) of open defecation is/are counted by subtracting frequency of use (shown as π) from maximum number of times expected to use (shown as r) capped to the reference period.

It may be mentioned that the first expression of the swachhta equation accounts for the behaviour of a household to adopt a latrine, while the second and third together account for the behavioural change of the household/individuals towards lat­rine use. Three indicators can be used to measure open defecation: (i) HOD, that is, households engaged in open defecation; (ii) POD, that is, population engaged in open defecation; and (iii) IOD, that is, incidence of open defecation.

The swachhta equation thus goes:

X = Number of households

N = Number of members in a household

r = Reference period

π = Frequency of latrine use during the reference period (π ≤ r).

Discussion and Conclusions

This article highlights the various methodological issues related to sanitation indicators, health indicators, confounding bias, and selection bias in the process of estimating the causal effect of improved sanitation on child health in the observational studies. The divide ­between the findings of observational studies and the randomised experiments conducted to estimate the impact of improved sanitation on child health, raises doubts about their methodological robustness. On the one hand, the randomised experiments find almost no causal effect of sanitation on diarrhoea in India; on the other, the observational studies found a significantly higher association. The case of India is of utmost importance as out of a total of 4,77,000 global diarrhoeal deaths of children under five years of age, 1,02,000 (or 21%) occurred in India in 2016. In 2015, 40% of the total population of India defecated in the open. A large number of child deaths were attributed to open defecation and/or unsafe disposal of human excreta.

The United Nation’s Sustainable Development Goal 6.2 aims, inter alia, to end open defecation by 2030. Efforts have been made in mission mode across Africa and Asia to implement Community Led Total Sanitation (CLTS) programme. In India, a remarkable progress has been made to increase the coverage of sanitation facilities since the launch of the Swachh Bharat Mission (Clean India Mission) in 2014 and before that through the Total Sanitation Programme (TSP).

We reviewed a total of six observational studies that were mostly based on the DLHS data and conducted bet­ween 2008 and 2014. One of the major limitation of all these studies is the presence of bias linked to measurement ­error. The sanitation indicator used in these studies is based on “access to sanitation,” which excludes the information on the actual use of latrine facilities. The other indicator “proportion of population that practice open defecation” suffers from the “exclusion error” amounting to those who,
despite having access to latrine, practice open defecation. The measurement error-induced bias was hardly recognised by the studies, and adequate remedial measures were not taken. The method used, such as propensity score matching, may not be able to give unbiased results in the presence of a measurement errors. The best way to counter the limitation is to precisely measure open defecation. Therefore, we suggested the “incidence of open defecation” indicator, which is a more precise measure of open defecation, and should be able to facilitate the estimates of sanitation impact on child health.

All the reviewed studies, including the randomised experiments, except for Andres et al (2017), which measures the impact of sanitation on diarrhoea, focused on the direct impact of sanitation whilst the indirect impacts were largely ignored. As per the findings of Andres et al (2017), the indirect impacts of sanitation are even more important.

The definition of health indicator used in various studies also varies, in terms of child age and reference period, to record the reported incidence of diarrhoea. Since the probability of the incidence of diarrhoea is sensitive to age and the recall period, comparing the quantitative results may not be possible.

The important confounders include education and health awareness, socio-economic status, cultural settings, govern­ment policy, seasonality, and access to water because the impacts on both open defecation and health outcomes simultaneously generate confounding bias. Tho­ugh some studies used propensity score matching to neutralise the confounding bias, yet many confounders were excluded while generating the propensity score. Moreover, the selection bias arising during the selection of children less than a particular age and self-selection of households in the adoption of toilet facilities needs more attention while estimating the impact of sanitation on child health outcomes.

Note

1 First District-Level Household and Facility Survey was published for 1998–99, second for 2002–04, and DLHS-3 for 2007–08.

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Updated On : 5th Jun, 2022
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