Latent variable modeling to develop a robust proxy for sensitive behaviors: application to latrine use behavior and its association with sanitation access in a middle-income country

Diarrheal disease, which is both preventable and treatable, is a persistent leading cause of morbidity and mortality for children under 5 years of age throughout the globe. Diarrhea-related mortality accounts for nearly 500,000 child deaths per year, which are concentrated in resource-limited settings [1]. Millions of children worldwide are undernourished [2], and while malnutrition has a host of causes, enteric pathogen exposure is associated with both acute and chronic malnutrition [3, 4]. Water, sanitation, and hygiene (WASH) interventions reduce exposure to enteric pathogens that cause illness [5] and it is estimated that diarrheal-related child mortality could be reduced by more than one-third in low and middle-income countries (LMICs) if sanitation interventions were implemented at full-scale [6]. Sanitation blocks multiple transmission routes of enteric pathogens, reducing individual-level exposures as well as community-level exposures [7, 8]. Indeed, sanitation programs are viewed by governments as fundamental for achieving Sustainable Development Goal (SDG) 6.2, that is, to end open defecation and provide universal access to adequate and equitable sanitation [9]. Yet, results from two recent studies of latrine construction interventions did not find an effect on child health; one rationale for these results is limited uptake of latrine use [10, 11]. Moreover, an analysis of Demographic and Health Survey data has shown a null association between access to improved sanitation facilities and prevalence of diarrhea in children throughout much of the globe [12]. These results suggest a need to emphasize latrine use in programmatic sanitation interventions. Like others, we suggest that WASH program success can be better evaluated if behavior is incorporated as an indicator of WASH success rather than simply the presence of sanitation hardware [13].

Historically, sanitation intervention success has been measured as a the presence of a household sanitation facility (traditionally and synonymously referred to as “sanitation access” within the development field and the focus of the SDGs) rather than individual sanitation practices [14]. One reason that toilet presence has been the accepted exposure variable is that it is relatively easy to measure. Latrine use, however, is difficult to measure and no gold standard of measurement exists. A limited number of intervention studies include a metric of defecation behavior, and the methods used to capture latrine use behavior vary across studies [15]. The most commonly used measure is self-reported defecation practices; however, outside social contexts where report of open defecation is not stigmatized, such as in India [16], survey questions asking about a respondent’s sanitation behaviors present challenges, such as social desirability bias due to the sensitivity of the topic [17, 18]. More recent literature has sought to move beyond a binary self-report of defecation practices, such as Jenkins et al.’s 2014 scale of defecation [19]; or Dreibelbis et al.’s 2015 [20] and Lopez et al.’s 2019 [21] use of psychosocial factors to predict latrine use. Dreibelbis et al.’s study and Lopez et al.’s study, in particular, are promising examples of integrating social theory and quantitative assessment of WASH behavior. These studies, however, rely on self-reported defecation behaviors and/or report of other’s defecation behaviors as outcomes. Such limitations potentially add misclassification and bias into their study results.

Beyond self-report or proxies of population-level psychosocial factors, hygiene behavior is commonly captured through direct observation [22, 23], which is a time-consuming effort subject to the Hawthorne effect [24]. Other measures of behavior include use of sensors inside a latrine to quantify the number of times a latrine was entered [25] and observations of the latrine to assess whether it appears to have been used [26]. While sensors may supply a more objective estimate of overall latrine use than self-report [27], neither sensors nor observations provide information necessary for assessing individual-level latrine use, or the proportion of users on the household or community levels. Such details are integral for understanding exposure risk of negative health outcomes, such as diarrheal disease and undernutrition.

This measurement conundrum for latrine use behavior is not unique to the sanitation field. Rather, many sub-fields of public health that rely on self-report of sensitive behaviors face similar issues of misclassification and measurement error. For example, epidemiology studies focusing on sexually transmitted infections have long been challenged by measurement of behavior [28], with self-reported condom use, a key intervention, presenting misclassification within the study [29, 30]. Here, we take a latent variable approach to capturing latrine use in a LMIC population with high latrine access, variable latrine use, and stigmatized self-report of defecation practices. Our approach is rooted in health behavior theory that recognizes both demographic factors and social processes as determinates of individual-level behavior [31]. Using psychosocial indicators of latrine use behavior, we first apply latent variable modeling to create a proxy-indicator of latrine use at the individual level. Latent class modeling specifically refers to a group of techniques that identify one’s underlying propensities to respond to particular questions and classifies a person into a group given their responses [32]; thus, it lends itself well to measuring sensitive concepts that may not be easily captured by one direct question. We hypothesize that individuals in this population will generally belong to one of three classes: those who always use a latrine for defecation, those who sometimes use a latrine for defecation, and those who never use a latrine. With an assigned likelihood of latrine use, we next test the association between the latent variable measure of latrine use and household-level access to an improved, non-shared sanitation facility.

The study sample was young (median age = 23), predominately female (65%) and Afro-Ecuadorian (63%). Most participants did not complete high school (81%), and of those that worked in the last year, it was most common to receive cash remittance. Slightly more than half of respondents live in households with non-cement walls (51%) and in households with access to basic sanitation (53%). Sixty-one percent of participants were from asset-deprived homes, and overall access to improved sanitation is high in the population (86%), with half of the sample having access to a privately owned, improved sanitation facility (i.e., basic sanitation) (Table 1).

The responses to the 16 indicators are presented in Table 2. Survey questions asking about within-home latrine sharing and about convenience of latrine use while outside of the home had more evenly split responses relative to the rest of the survey questions. While most survey questions generated a yes or no response, indicators asking about descriptive norms of latrine use by men and neighbors tended to have more don’t know responses.

For latrines to reduce pathogen transmission they must remain clean and be used. Thus, research and programmatic evaluation need to have accurate defecation behavior measurements. To this end, our findings reveal three important insights into the complicated relationship between latrine presence and latrine use. First, our model predicts that less than a quarter of this population practices inconsistent latrine defecation (22%, Table 2), even when access to improved sanitation is high (86%, Table 1). Although we do not present self-reported latrine use data, we suspect that misclassification would have been high in this variable given that less than 10% of respondents disagreed with the statement “I use a latrine daily” (see Additional file 7 for further comparisons). Additionally, we can also find insight from prior qualitative research on this topic. From qualitative work, we know that defecation behaviors besides latrine use are not uncommon and that self-reported defecation behaviors in a quantitative survey context are likely misclassified. The triangulation of misclassification in self-report latrine use for defecation and prior research in the study site suggests that psychosocial indicator variables are a potential surrogate for latrine defecation that minimizes misclassification bias. Second, we found no evidence that the presence of basic sanitation within the home was associated with latrine use. This result suggests that private, within-home sanitation does not ensure the use of said facility for every family member each time one needs to defecate. Our model result is consistent with data from India, which present variable sanitation practices among individuals living in a household with a sanitation facility [16, 50]. Some individuals will defecate in the household’s latrine, while others practice open defecation. Our null finding also provides an explanation for the null relationship between latrine access and child health observed in a number of settings [10‐12]. And third, we found that five simple indicators could be used as reasonable indicators of the two latent classes of individuals in this population, as evidenced by a high entropy value for the final model that approached one (0.86). This high entropy value indicates limited misclassification in group assignment and is also an overall reflection of the indicators’ high conditional item probabilities in the consistent latrine use class [51, 52]. The strength of these indicators suggests that they are plausible underlying manifestations of latrine defecation. In further examining which indicators were informative for group classification, social theories of behavior provide additional rationale for these model results.

Of the 16 indicators examined, those that distinguished between class memberships asked individuals about community defecation norms (i.e., other’s latrine use for defecation) and latrine sharing between households. We also conducted a sensitivity analysis with these five societal-level indicators to determine whether they were useful in distinguishing between three classes of latrine users; the three-class model results were reasonable, however, the sample size of the groups was too small to permit well-powered comparisons. The strength of community level norm indicators to classify latent class membership, coupled with social theories of behavior, suggests potential of these indicators to serve as a proxy measure for individual behavior. Social theories of behavior view individual factors alone as limited metrics of behavior [53]. Sociocultural factors shaping daily habits in invisible ways [54]; humans often exhibit the behavior of those around them, where normative expectations playing a key role in one’s own actions [55]. For example, the LCA suggests that agreement with indicators asking whether others use latrines presents a very high probability that one is a consistent latrine user. Moreover, agreement with statements regarding latrine sharing between households may reflect a strong commitment to latrine use; that is, an individual implies that they would willingly face a variety of barriers to use someone else’s latrine (inconvenience, distance, dirty environment, etc.) if within-household access to a latrine did not exist. The psychological literature supports commitment as one important driver to behavior [56], again providing evidence of a plausible mechanism driving latrine defecation. If we apply traditional metrics of Hill’s exposure-outcome causality criteria to the study findings, our results exhibit strength, consistency (with the body of research), plausibility, and coherence [57]. Use of the Hill criteria provides evidence that these indicators are valid proxies of latrine defecation behavior.

Although the indicators used in the model were plausible, consistent, and coherent, our approach does not validate that the selected psychosocial variables are robust indicators of latrine use. Given the challenges of collecting unbiased measurements of latrine use, validating the predictive power of these indicators is difficult. Nevertheless, to address this limitation, we conducted extensive fieldwork to assess drivers of latrine defecation [21]. Beyond this initial work to define the 16 indicator variables that have potential to be surrogate indicators of latrine use, our LCA analysis suggests that community-level norms of latrine defecation are the strongest indicators. Thus, there is good potential for these indicators to generalize to other populations. Indeed the social environment tends to influence health behaviors [58]. Related to defecation, social norms and the behaviors of others in our social networks have been identified as determinants of latrine use behavior in India and Ethiopia [59‐61]. The Community-Led Total Sanitation Campaign, a sanitation program targeting LMICs communities that seeks to end open defecation and has received lots of attention throughout the globe, operates through levels of behavior change that include community-pressure to promote latrine use [62, 63]. Our research was conducted in a very specific population; and it plausible that the latrine use and its drivers differ across the 20 communities included in our study as well as from drivers in other cultural contexts. Prior comparisons of the determinants of latrine defecation across cultures and geographical location highlight the role of shared community values, as well as perceptions of latrine cleanliness, on influencing behavior [21]. However, because prior studies are not void of self-report defecation behavior, it is plausible that identified determinants may be biased. Thus, determining which social norms of latrine defecation are applicable indicators across cultures requires further research. Furthermore, because social norms surrounding defecation behavior may also be influenced by the maintenance or cleanliness of a latrine, incorporating additional information on the latrine would add to this exploration and further the generalizability of this research.

As an often neglected side of the epidemiologic triad, we focus on human behavior at the interface of the environment and biological agent of disease. Our approach, illustrated here for latrine defecation, follows methods commonly used to measure socially stigmatized actions, such as intimate partner violence [64], aggression and bullying [65], or substance abuse [66]. Our work within the WASH sector, which allows us to test common assumptions of how people defecate, a critical component to disease transmission. More research is needed, however, to refine the selection of latrine use indicators. First and foremost, gender specific indicators, which may be different by life course stage [39], will likely provide better insight into population-level drivers of behavior and more accurate classification of latrine users (see our sensitivity analysis in Additional file 4). Second, inconsistent latrine use may have a different set of determinants than consistent latrine use, as these behaviors are not strictly opposites; thus, additional work is required to determine indicators of inconsistent latrine use and whether said indicators can distinguish groups of people. Third, because psychosocial norms, attitudes, and beliefs may change over time, longitudinal analysis are required to determine if these indicators are temporally consistent. Finally, future research is needed to test hypotheses assessing whether latrine use measures are adequate indicators of reduced exposure to enteric pathogens.

Regardless of the disease system of study, epidemiologists interested in population dynamics that lead to health and disease should include metrics of behavior and social process into their research. Development of psychosocial indicators of sensitive behaviors and application of LCA is a useful tool for measurement of sensitive behavioral risk factors, such as hygiene or sexual activity, where self-report data suffer from misclassification and no gold standard measurement exists.