Valuing an Index of Sanitation Related Quality of Life (SanQoL-5) in urban Mozambique – a Discrete Choice Experiment

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Katana, Neiva Banze, Cremildo Manhiça, Catildo Cubai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4790952/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives 1.5 billion people live without basic sanitation. A five-attribute index of sanitation-related quality of life (SanQoL-5) designed for economic evaluation has now been applied in six countries. After rescaling, scores range 0 (no sanitation capability) to 1 (full sanitation capability). To date, SanQoL-5 valuation has been via simple methods such as rank sum, not robust methods such as discrete choice experiment (DCE). We aimed to value the SanQoL-5 index using a DCE in urban Mozambique. Methods We enrolled 601 adults in the cities of Maputo and Dondo, sampling women and men equally alongside quotas for toilet type. The DCE task was a choice between two scenarios representing combinations of SanQoL-5 attribute levels (always, sometimes, never). Each respondent completed 10 tasks and a dominance test. We fitted a mixed logit model and rescaled coefficients to derive the index, with sub-group analysis by gender. Results The highest-valued attribute was disgust (“never feel disgusted while using the toilet”), with a SanQoL-5 index value of 0.25. The other attributes had similar values (ranging 0.18–0.19). People valued “sometimes” levels at around 60% of “never” levels. Mean SanQoL-5 by toilet type followed a gradient with Sustainable Development Goal 6 categories: “open defecation” 0.30, “unimproved” 0.45, “limited” 0.60 and “at least basic” 0.70. Conclusions This is the first DCE-based valuation of any index of sanitation-related quality of life, enabling the SanQoL-5 to be used in economic evaluation. Identifying sanitation service transitions associated with the greatest quality of life gains could inform more efficient resource allocation. Health Economics & Outcomes Research discrete choice experiment preferences sanitation quality of life Mozambique toilet Figures Figure 1 Figure 2 Figure 3 Highlights No index of sanitation-related quality of life has previously been valued using robust preference elicitation methods such as a discrete choice experiment In urban Mozambique the highest-valued attribute was disgust, with other attributes (safety, privacy, shame, disease) valued slightly less. Using our findings, better sanitation economic evaluations could support more efficient allocation of the billions of dollars invested in sanitation. Introduction 1.5 billion people live without basic sanitation, 1 with billions of dollars spent every year to address this situation. 2 However, economic evaluations typically measure and value infectious disease alone, 3 when toilet users often value other benefits such as improvements in privacy, safety and dignity. 4 – 6 Under the capability approach to welfare economics, these outcomes represent what people have “reason to value” about sanitation. 7 These are therefore attributes of good quality of life (QoL), and contribute to health in its broadest sense including mental and social wellbeing, “not just the absence of disease”. 8 It is often argued in sanitation economic evaluation studies that QoL improvements arising from sanitation improvements comprise an economic benefit, but that methods for measuring and valuing QoL benefits are lacking. 9 – 11 Excluding these important benefits may result in inefficient resource allocation. A recent study developed an index of sanitation-related quality of life (SanQoL-5) in Mozambique, 12 which has now been validated in four countries (Ethiopia, Malawi, Mozambique and Zambia). 13 SanQoL-5 has five attributes (disgust, disease, privacy, shame and safety), each measured by a question with a three-level response scale (always, sometimes, never). A limitation of applications of SanQoL-5 thus far is the use of the rank sum method or attribute scoring to derive the index. 14 These methods do not fully reflect what people value because they involve weighting attributes per se rather than trading off different levels of attributes. 15 Application of discrete choice methods, as used in valuation of the EQ-5D and other influential health-related QoL indices, would be best practice. 16 , 17 In this study, we aimed is to estimate a SanQoL-5 index using a discrete choice experiment (DCE) in two different urban settings in Mozambique. We derived a SanQoL-5 index value ranging 0–1 for each of the 243 potential sanitation states in the descriptive system, and compared valuations by gender. We hypothesised that not all attributes would be valued equally and that, for all attributes, the “middle” levels would be seen as worth more than half the value of the “high” levels. The decision-makers who would use our results are considered as the Mozambican Ministry of Health and Ministry of Public Works, but we anticipate wider relevance in other countries where decisions about investments in basic sanitation are made. Methods We followed the Bridges et al. checklist for conjoint analysis in health, 18 and report against it in Supplementary Material A. Data and replication code are available at https://osf.io/38vsh/ Study population and sampling The study took place in two cities in Mozambique: Maputo (population 1.1 million) and Dondo (population 100,000). Since this is the first time DCE-based valuation has been used for any index of sanitation-related quality of life, our sampling priority was not to achieve representativeness of the cities’ population. Rather, we aimed to achieve approximate gender balance and diversity in type of toilet used (Supplementary Material B). The study population was adults aged 18 + living in two neighbourhoods/ bairros in Maputo (Polana Caniço A and Polana Caniço B) and two in Dondo (Macharote and Nhamainga). These areas were selected because they are mixed in terms of housing quality and, in particular, have a diversity of toilet types used. The majority of healthcare DCEs interview 100–300 respondents. 19 We aimed to recruit 600 respondents to meet other study objectives, as well as to allow for dropping some of the sample on data quality grounds. 20 We aimed to interview 300 women and 300 men per site, to allow exploration of whether valuation varies by gender. For toilet type, we aimed to sample 200 people using flush toilets, 340 people using pit latrines, and 60 people practising open defecation (no toilet). We achieved this by sampling based on data from existing health surveillance surveys (details in Supplementary Material B). 21 The bairros in Dondo were selected on the basis of surveillance data indicating that prevalence of open defecation was > 10%, since open defecation was uncommon in the Maputo site (Supplementary Material B). SanQoL-5 index The SanQoL-5 is a multi-attribute measure of sanitation-related quality of life, developed from primary qualitative research and supported by the literature on what people value about sanitation. 3 , 12 Its descriptive system (Table 1 ) has five questions, each measuring a capability-based attribute: disgust, disease, privacy, shame and safety. Each is measured on a three-level frequency scale (always, sometimes, never), with questions framed such that “never” is the best outcome. There are therefore 15 attribute levels to be valued in 243 (= 3 5 ) possible combinations. Following norms in health-related quality of life (HRQoL), each combination is termed a “sanitation state”. Adopting HRQoL notation, the best state is denoted 11111 (“never” for all levels) and the worst 33333, with intermediary states such as 23132, 11213, etc. Table 1 SanQoL-5 descriptive system Attribute Question* Responses Disgust How often do you feel disgusted when using the toilet? Always Sometimes Never Disease How often do you worry that the toilet spreads diseases? Privacy How often do you worry about being seen while using the toilet? Shame How often do you feel ashamed about using the toilet? Safety How often do you feel unsafe while using the toilet? * A preamble is as follows: “The following questions are about your sanitation experiences in the past 30 days, meaning defecation, urination, and anything else you do in a toilet. Please respond with always, sometimes or never.” If less literate respondents struggle with a question, it can be reformulated as “Do you feel disgusted while using the toilet? How often?”. Before the SanQoL-5 questions, the respondent is asked about the last place they defecated. If the respondent practiced open defecation (OD), e.g. in fields or wasteland, they are directed to OD-specific questions, e.g. “How often do you worry about being seen while practising open defecation?” Data collection Our study was a face-to-face survey using Open Data Kit (ODK) Collect software on tablet computers. Though the survey was administered in Portuguese in the vast majority of cases, some participants preferred to speak in the predominant local language (Changana in Maputo, Sena in Dondo). Therefore, two teams (one per site) were recruited and underwent five-day programmes of training and piloting. Data collection was undertaken during May-July 2023. The questionnaire was translated into Portuguese by NB and the translations discussed at length with field team. No incentives for participation were provided. Discrete choice study design After questions about socio-economic status and sanitation, the DCE section started with a series of warm-up tasks, to ensure participants fully understood the choices they were being asked to make. First, participants answered the SanQoL-5 questions (Table 1 ) and completed the sanitation visual analogue scale (VAS) – a 0-100 scale on which people rate how they feel about their level of sanitation today (Supplementary Material B). Second, participants watched three video vignettes on the tablet, to provide more meaning to hypothetical states and introduce the images used to frame attributes (Supplementary Material B). In each video, a hypothetical person describes the toilet they use and how it makes them feel about each of the SanQoL-5 attributes, i.e. describes their sanitation state. After each video, the participant was asked to score that person’s level of sanitation on the VAS, to get them used to the idea of comparing states. Third, participants were asked to complete a food-based menu choice card (Supplementary Material B), to emphasise that the two columns as a whole are being compared and trading items between columns was not possible. The last warm-up task involved being shown three sanitation states, and asked to choose which was worst and which was best, as well as their reasoning, to assess whether they understood the task (Supplementary Material B). For the actual DCE choice tasks, participants were shown a card with two sanitation states as profiles of SanQoL-5 atttribute levels (Fig. 1 ) with the same emoji visualisation as the warm-up tasks. Participants were asked to select which state was “better”, with no opt-out. This follows best practice from valuation protocols for the EuroQoL 5-dimension (EQ-5D) measure of HRQoL. 17 , 22 Participants were told to not consider their present toilet or level of sanitation, but instead to imagine being in the states in the scenario. Each participant undertook 10 choice tasks. We identified a 6-block efficient design using the dcreate programme in Stata 18 with a Modified Federov Algorithm (d-efficiency 10.4). 23 With 60 choice tasks (6x10), there were 120 sanitation states compared in all. Each block including states across the range of severity. To avoid bias from the ordering of the tasks (e.g. less care taken over later tasks) we randomised participants into 12 groups, with half of the groups doing tasks in reverse. 24 Quality Control Each interviewer undertook 8 pilot interviews (80 DCE tasks per interviewer in total), in areas outside the study sample – the data were not included in the analysis. The pilot identified issues with time taken to complete the survey, which resulted in removing some socio-demographic questions and randomising some non-DCE questionnaire modules to sub-samples. Overall, the acceptability of the tasks was good. Preliminary analysis of the pilot data indicated that DCE data were consistent. Other aspects of quality control included timestamps throughout the survey, to allow flagging when a participant completed a section extremely rapidly relative to most others. We also included a dominance test halfway through the DCE, in which one state (12121) was objectively better than the other (23232) on all five attributes, so there is a “correct” answer. Dominance test choices were not included in the analysis. For the primary analysis, we excluded data of participants who met one or more of these conditions: (i) failed the dominance test; (ii) completed the first five tasks in less than 10 seconds per task; (iii) completed the second five tasks and dominance task in less than 5 seconds per task. We also examined the choices in respect of level sum score (LSS), which is the sum of attribute levels in state notation (e.g. the LSS of 11113 is 7). We calculated difference in LSS between the two options a respondent was shown and observed the distribution of responses. Hypothetically, a larger LSS difference should increase the likelihood that a respondent chooses the option with the lower LSS. The LSS of the best state (11111) is 5 and the worst state 15. We reconfirmed fieldworkers’ classifications of toilet types by verifying photos they took of toilets’ interiors against entered data on toilet characteristics. Data Analysis We analysed choices in Stata 18 first using a conditional logit model, which assumes that preferences are not correlated across individuals. We then using a mixed logit model with correlated parameters, which aims to account for: (i) preference heterogeneity (when differences between individuals’ preferences cannot be explained by observable characteristics); and, (ii) scale heterogeneity (when unmeasured factors affect individuals’ responses to different extents). 25 We based model selection on whether there was evidence of heterogeneity, as well as the Akaike/Bayesian information criteria (AIC/BIC). Our analytical approach was based on the EQ-5D valuation protocol. 17 , 22 The model assumes that in making their choice, people are comparing the quality of life they would have in two sanitation states (Eq. 1), namely \(\:{V}_{ijl}\) (left-hand option \(\:l\) for individual \(\:i\) within DCE pair \(\:j\) ) and \(\:{V}_{ijr}\) (right-hand option \(\:r\) ). Eq. 1 represents the choice as an inequality, with the sign () decided by the participant’s response. Since there is no opt-out, the respondent cannot give them equal value. Equation 1 $$\:{V}_{ijl}=\:\alpha\:-\:{\sum\:}_{k=1}^{10}{\beta\:}_{k}{x}_{k}^{ijl}+\:{e}_{i}^{lj}\:\:\:>\:?<\:\:\:\:\:{V}_{ijr}=\:\alpha\:-\:{\sum\:}_{k=1}^{10}{\beta\:}_{k}{x}_{k}^{ijr}+\:{e}_{i}^{rj}$$ The variable \(\:x\) represents a sanitation state using 10 dummy (binary) variables. The first two dummies refer to “sometimes” and “always” levels of the disgust dimension. If the state involves being “sometimes” disgusted (Table 1 ), the “sometimes” dummy takes the value 1. For the “always” level its respective dummy takes the value 1. If both dummies are 0, then the state include “never” being disgusted. The other 8 dummies are the equivalents for the remaining attributes. If all 10 dummies are zero then the state is 11111 (full sanitation capability), its value denoted by α, which cancels out once the participant makes their choice. The parameter β is a 10 × 1 vector aligning to the dummies. Its first two elements reflect decrements of “sometimes” or “always” being disgusted against the value of “never” being disgusted. Since estimated coefficients are decrements, they are expected to be negative. The overall decrement of moving from “never” to “always” is the sum of the coefficients for “sometimes” and “always”. Error terms are assumed to follow an extreme value distribution. We rescaled estimated coefficients to a 0–1 index, whereby 0 is the value of the worst state and 1 the value of the best. This is achieved by dividing through by the sum of the coefficients of the worst levels (i.e. always). We included a sub-group analysis by sex. First, we explored whether differences in preferences between women and men were explained solely by differences in randomness of choices by sub-groups, i.e. scale heterogeneity. 26 We assessed this using the Swait-Louviere test, 27 effectively a likelihood ratio test comparing the log likelihood statistics of the pooled model with sub-group models. Following Mott et al. 25 , our main sub-group analysis was to compare relative attribute importance (RAI) in the two sub-group regressions. To estimate RAI scores, we calculated ratios of each attribute’s “always” coefficient to that of the lowest-valued attribute. Attributes with higher RAI therefore have a higher value. We estimated RAI difference by subtracting RAI scores in the men’s sample from those in the women’s sample, and estimated confidence intervals using nlcom in Stata 18. Ethics The study received prior approval from the Comité Institucional de Ética at the Instituto Nacional de Saúde in Mozambique (ref: 028 /CIE-INS/2023), and the Research Ethics Committee at the London School of Hygiene and Tropical Medicine (Ref: 28190). Informed, written consent was obtained from all participants. Results We enrolled 601 participants between May-July 2023, after approaching 605 individuals (response rate 99%). We included 541 participants in the final analysis, dropping data for 41 (7%) who failed the dominance test and 19 (3%) who completed tasks faster than the minima set out above. Of the respondents included in the analysis, 54% were women, and 63% had access to on-plot piped water (Table 2 ). Table 2 Sample characteristics Maputo (n = 292) Dondo (n = 249) Total (n = 541) Respondent demographic characteristics Respondent is female 149 (51%) 143 (57%) 292 (54%) Age category 18–29 103 (35%) 85 (34%) 188 (35%) 30–44 91 (31%) 72 (29%) 163 (30%) 45–59 56 (19%) 44 (18%) 100 (18%) 60+ 42 (14%) 48 (19%) 90 (17%) Household size 5.2 (2.5) 5.2 (2.4) 5.2 (2.4) Completed primary school or above 208 (71%) 158 (63%) 366 (68%) Moderate problems walking about (or worse)* 10 (7%) 16 (13%) 26 (10%) Moderate pain (or worse)* 17 (12%) 28 (22%) 45 (17%) Dwelling characteristics Sealed floor 278 (95%) 164 (66%) 442 (82%) Solid exterior wall 285 (98%) 135 (54%) 420 (78%) Households with fridge 167 (57%) 47 (19%) 214 (40%) Households with television 247 (85%) 126 (51%) 373 (69%) Access to electricity connection 271 (93%) 184 (74%) 455 (84%) Access to on-plot piped water 254 (87%) 86 (35%) 340 (63%) Respondent rents dwelling 39 (13%) 7 (3%) 46 (9%) Toilet type used Cistern-flush toilet with water seal 67 (23%) 17 (7%) 84 (16%) Pour-flush toilet with water seal 77 (26%) 25 (10%) 102 (19%) Off-set pit latrine with pour-flush but no water seal 80 (27%) 139 (56%) 219 (40%) Pit latrine with concrete slab 45 (15%) 9 (4%) 54 (10%) Pit latrine with non-concrete slab (soil, tyres, wood) 23 (8%) 17 (7%) 40 (7%) Open defecation 0 (0%) 42 (17%) 42 (8%) Sanitation service characteristics Uses on-plot toilet 291 (> 99%) 190 (76%) 481 (89%) Shares toilet with other households 90 (31%) 53 (26%) 143 (29%) Number of households sharing toilet (amongst sharers) 3.0 (1.4) 3.6 (2.1) 3.2 (1.7) Number of people sharing toilet (amongst sharers) 9.2 (4.5) 12.4 (6.9) 10.4 (5.7) Note: Data are n (%) for categorical variables and mean (standard deviation) for numerical variables. * indicates questions asked of a random half of the sample The most common sanitation type (61%) was an off-set pit latrine with pour-flush but no water seal. In this design, the pit is not directly visible to the user but there is no water seal (u-bend) in the connecting pipe to stop the passage of smells or flies. It is therefore a step down in toilet quality from a pour-flush toilet with water seal, which was the second most common toilet type used (19%). Photos of common toilet types are in Supplementary Material C. Amongst participants using toilets, 29% shared the toilet with 1 or more other households. Background characteristics of participants in the two cities are summarized in Table 2 (and by gender in Supplementary Material D). Considering participants’ levels of SanQoL-5, the attribute for which people had the worst outcomes was perception of disease risk, with 41% selecting “always” (Supplementary Material D). Maximum SanQoL-5 was reported by 5% of participants and minimum by 3%. In both regression models (Table 3 ), coefficients for every attribute level were negative and significant at the 1% level. In the mixed logit model, every standard deviation (SD) was significant at the 1% level, which is evidence of preference heterogeneity and suggests that the mixed logit is more appropriate than conditional logit. Selection of the mixed logit is supported by the higher log-likelihood and lower AIC, with the higher BIC contradicting this slightly. However, BIC has a larger penalty than AIC for the number of model parameters, and 55 are added when allowing for correlated parameters (as is advisable, to account for heterogeneity). Table 3 Regression output Conditional logit Mixed logit Coeff. SE Coeff. SE Disgust (sometimes) -1.26*** 0.06 -2.22*** 0.20 Disgust (always) -2.17*** 0.10 -4.18*** 0.37 Health (sometimes) -0.93*** 0.07 -2.01*** 0.24 Health (always) -1.60*** 0.09 -3.24*** 0.36 Privacy (sometimes) -1.60*** 0.07 -1.99*** 0.21 Privacy (always) -0.96*** 0.08 -3.25*** 0.37 Shame (sometimes) -1.60*** 0.06 -1.79*** 0.23 Shame (always) -0.96*** 0.08 -3.14*** 0.32 Safety (sometimes) -1.61*** 0.06 -1.67*** 0.23 Safety (always) -0.80*** 0.08 -2.98*** 0.32 SD (Disgust, sometimes) 1.18*** 0.17 SD (Disgust, always) 2.03*** 0.27 SD (Health, sometimes) 1.5*** 0.31 SD (Health, always) 1.9*** 0.26 SD (Privacy, sometimes) 1.52*** 0.21 SD (Privacy, always) 1.75*** 0.26 SD (Shame, sometimes) 1.21*** 0.25 SD (Shame, always) 1.58*** 0.28 SD (Safety, sometimes) 1.14*** 0.27 SD (Safety, always) 1.51*** 0.22 Number of choices 10,820 10,820 Number of participants 541 541 Log likelihood -2,231 -2,072 AIC 4,482 4,274 BIC 4,555 4,748 *, **, *** indicate significance at the 10, 5 and 1 percent level. Coeff: coefficient; SE: standard error; SD: standard deviation; BIC: Bayesian information Criterion; Mixed logit models estimated using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm with 500 Halton draws and uncorrelated parameters used as starting values. After rescaling to a 0–1 index (Fig. 2 ), disgust was the highest-valued attribute, with its “never” level valued at 0.25 after rescaling. Safety was the lowest-valued (0.18 for its “never” level). The “sometimes” levels were valued at 53–62% of the “never” levels of each attribute (Fig. 2 ), indicating that respondents perceived moving from the middle level to the worst as a bigger decrement than moving from the best level to the middle. The value set is provided with standard errors in Supplementary Material D. SanQoL-5 by toilet type followed a gradient with objective toilet quality according to categories used in Sustainable Development Goal 6. Specifically, people practising OD had the lowest mean SanQoL-5 (0.30) and people with “at least basic” sanitation had the highest (0.70), with “unimproved” and “limited” sanitation in-between (0.45 and 0.60 respectively). When comparing responses based on LSS differences, the pattern is as expected (Fig. 3 ), which brings confidence in data quality. When there was a LSS difference of ± 3 or greater, 91–97% of choices were for the expected option. There was also an approximately equal split in choosing A versus B when the LSS difference was 0 (Fig. 3 ). The Swait-Louviere test did not reject the null hypothesis of equal preference structure in the two sub-groups (p = 0.24), allowing us to proceed with RAI comparison. There was no significant gender difference in valuation of attributes (Supplementary Material D), with no statistically significant differences in RAI score. However, there was suggestive evidence (p = 0.08) for men valuing disgust more highly than women, with women considering disgust to be 1.29 times as important as safety (the attribute lowest valued by both sexes) compared to 1.43 times for men. Discussion In this study, an index of sanitation related quality of life was obtained from discrete choice valuation of the SanQoL-5 descriptive system. A set of index values for 243 sanitation states was derived from the stated preferences of 541 adults in urban Mozambique, after excluding data for 60 participants on quality grounds. The highest-valued attributed was disgust (“never feeling disgusted while using the toilet”), with a SanQoL-5 index value of 0.25. The other attributes were valued similarly to one another (ranging 0.18–0.19). Our study is in the first assessment of the relative value to people of SanQoL-5 attributes using robust preference elicitation methods. SanQoL-5 has now been validated in four countries. 13 The SanQoL-5 value set we report here (Fig. 2 ) can be used to evaluate sanitation investments in Mozambique, as well as in other countries when a local value set is not available. People valued the “sometimes” levels of attributes at around 60% of the “never” levels. This indicates that moving moving from the middle level to the worst level was a bigger decrement in QoL than moving from the best level to the middle. Such patterns in levels are commonly observed in value sets for EQ-5D and other HRQoL indices, including in African countries. 28 , 29 Since this is the first DCE of SanQoL-5, it is not possible to compare the relative value of attributes or levels to other studies. We are confident in the quality of the data, for several reasons. First, we dropped 10% of the original sample on the grounds of failing the dominance test or completing tasks excessively quickly. A relatively low proportion (7%) failed the dominance test, indicating that the tasks were understandable. 20 Second, the pattern of choices in relation to LSS (Fig. 3 ) align with theory and are similar patterns in to EQ-5D DCEs. 30 , 31 Third, we carefully monitored interviewer performance. After the first 15 interviews we noted that one interviewer in Maputo had consistently very low durations for the DCE section compared to other interviewers, as well as some geolocations well outside the study area. They agreed to leave the study team and their 15 observations were dropped, with 15 additional Maputo households sampled to replace them. Fourth, half of the participants were randomised into answering their block of choice tasks in reverse. It is of potential concern that the highest-weighted attribute (Fig. 2 ) was also the first listed in the choice tasks (Fig. 1 ). It is possible that this is down to chance, but it is also possible that participants paid more attention to the attributes higher up the choice card. However, we see two reasons not to be too concerned. First, several methodological studies exploring attribute ordering in DCEs found no evidence that order influences results. 32 – 34 Second, in our gender subgroup analysis, women valued privacy slightly higher than disease (which was higher up in the card) though the difference was not statistically significant (Supplementary Material D). Nonetheless, in future SanQoL-5 DCEs we recommend randomising participants into blocks with different attribute orderings, to avert the risk of bias. SanQoL-5 can be used to monitor and evaluate sanitation programmes, e.g. to measure differences over time or between groups, and subsequently in economic evaluation. These use cases of SanQoL-5 are similar to those of HRQoL indices such as the EQ-5D which, since its inception in 1987, had been used in over 17,000 studies by 2015 to inform efficient allocation of resources for health. 35 With the sustainable development goal target for sanitation off-track, 1 sanitation investments will need to be evaluated for many years to come. SanQoL-5 has already been included as an outcome in two ongoing trials of sanitation interventions. Since it is gender-sensitive (can be answered by anyone), SanQoL-5 can also be used to evaluate gender gaps. However, a key feature of its design is the weighting of attribute levels demonstrated in this study, which allows it to be used to measure and value benefits in economic evaluation such as benefit-cost and cost-effectiveness analysis. 36 Sanitation economic evaluations have previously described QoL benefits as intangible. 9 – 11 SanQoL-5 makes them tangible, with DCE valuation in this study a first step. The next step for including QoL benefits in a benefit-cost study is monetary valuation of SanQoL-5 gains based on willingness to pay, as is done with health gains through monetary valuation of quality-adjusted life years. 37 Our study has a number of limitations. First, as set out above, choice cards presented attributes in a consistent order to all participants, so we are unable to account for the risk of attribute order bias in our results. Second, attributes may be interpreted differently by different people. Through use of video vignettes we aimed to illustrate the multiple meanings of attributes (e.g. one video discussed safety in terms of pit collapse, and another video in terms of assault risk), but there was only time for 3 vignettes. Third, the exclusion of an opt-out in the DCE forces the participant to choose one option even if they consider both to be of equal value. However, this follows norms in health state valuation (e.g. EQ-5D), 17 and risk of bias is minimal. Fourth, the sample was designed to demonstrate proof of concept amongst users of diverse types of sanitation, rather than to be representative of a given population. In the future, nationally-representative samples would ideally be used, so that societal preferences are reflected where resource allocation is to be informed. Conclusion Our study presents the first discrete choice valuation of the SanQoL-5 index, or indeed any measure of sanitation-related quality of life. Our results suggest that discrete choice valuation is feasible and acceptable in resource-constrained settings, and only 7% of participants failed the dominance test. We expect that the availability of this value set will facilitate economic evaluation of sanitation programmes in Mozambique and beyond. We also hope it will support broader research into sanitation-related quality of life. Declarations Author Contributions : Concept and design: Ross, Katana, Capitine, Banze Acquisition of data: Banze, Capitine, Manhiça, Cubai, Viera, Fulai, Katana Analysis and interpretation of data: Ross, Katana Drafting of the manuscript: Ross, Katana Critical revision of the paper for important intellectual content: Banze, Capitine, Manhiça, Cubai, Viera, Fulai, Cumming, Viegas Obtaining funding: Cumming, Viegas, Ross Administrative, technical, or logistic support: Banze, Capitine, Manhiça, Cubai, Viera, Fulai, Cumming, Viegas, Katana Supervision: Ross, Cumming, Viegas Funding. This work was funded by the Bill & Melinda Gates Foundation (OPP1137224). Ian Ross acknowledges the support of a post-doctoral fellowship from the Reckitt Global Hygiene Institute in the period when the paper was drafted. Role of the Funder : The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Acknowledgements. We greatly appreciated the cooperation of survey participants in providing their time, as well as the efforts of the fieldworkers in Dondo and Maputo. Conflict of interest. None declared. References UNICEF & WHO (2023) Progress on household drinking water, sanitation and hygiene 2000–2022: special focus on gender WHO (2022) Strong systems and sound investments: evidence on and key insights into accelerating progress on sanitation, drinking-water and hygiene. UN-Water global analysis and assessment of sanitation and drinking-water (GLAAS) 2022 report Ross I, Cumming O, Dreibelbis R, Adriano Z, Nala R, Greco G (2021) How does sanitation influence people's quality of life? Qualitative research in low-income areas of Maputo, Mozambique. Soc Sci Med 272:113709–113709. 10.1016/j.socscimed.2021.113709 Elmendorf M, Buckles PK Appropriate Technology for Water Supply and Sanitation. 1980;(December) Jenkins M, Curtis V (2005) Achieving the 'good life': Why some people want latrines in rural Benin. Soc Sci Med 61(11):2446–2459. 10.1016/j.socscimed.2005.04.036 Solomons N (1978) Resume of the discussion on 'water and other environmental interventions'. Am J Clin Nutr 31(11):2124–2126 Sen A (1993) Capability and well-being73. Qual life 30:270–293 WHO. Constitution of The World Health Organization (1948) 978 92 4 165047 2. http://www.who.int/governance/eb/who_constitution_en.pdf Hutton G, Patil S, Kumar A, Osbert N, Odhiambo F (2020) Comparison of the costs and benefits of the Clean India Mission. World Dev 134(105052). 10.1016/j.worlddev.2020.105052 Hutton G, Rodriguez UP, Winara A et al (2014) Economic efficiency of sanitation interventions in Southeast Asia. J Water Sanitation Hygiene Dev 4(1):23–23. 10.2166/washdev.2013.158 Whittington D, Radin M, Jeuland M (2020) Evidence-based policy analysis? The strange case of the randomized controlled trials of community-led total sanitation. Oxf Rev Econ Policy 36(1):191–221. 10.1093/oxrep/grz029 Ross I, Greco G, Opondo C et al (2021) Measuring and valuing broader impacts in public health: Development of a sanitation-related quality of life instrument in Maputo, Mozambique. Health Econ 31(3):466–480. 10.1002/hec.4462 Akter F, Banze N, Capitine I et al (2024) The Sanitation-related Quality of Life index (SanQoL-5)–validity and reliability in rural and urban settings in Ethiopia, Malawi, Mozambique, and Zambia Stillwell WG, Seaver DA, Edwards W (1981) A comparison of weight approximation techniques in multiattribute utility decision making. Organizational Behav Hum Perform 28(1):62–77. https://doi.org/10.1016/0030-5073(81)90015-5 Brazier J, Ratcliffe J, Salomon J, Tsuchiya A (2016) Measuring and Valuing Health Benefits for Economic Evaluation. Oxford University Press Xie F, Gaebel K, Perampaladas K, Doble B, Pullenayegum E (2014) Comparing EQ-5D valuation studies: a systematic review and methodological reporting checklist. Med Decis Making 34(1):8–20 Oppe M, Devlin NJ, van Hout B, Krabbe PF, de Charro F (2014) A program of methodological research to arrive at the new international EQ-5D-5L valuation protocol. Value Health 17(4):445–453 Bridges JF, Hauber AB, Marshall D et al (2011) Conjoint analysis applications in health–a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health Jun 14(4):403–413. 10.1016/j.jval.2010.11.013 de Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA (2015) Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide. Patient Oct 8(5):373–384. 10.1007/s40271-015-0118-z Tervonen T, Schmidt-Ott T, Marsh K, Bridges JF, Quaife M, Janssen E (2018) Assessing rationality in discrete choice experiments in health: an investigation into the use of dominance tests. Value Health 21(10):1192–1197 Rendição AJ (2022) Análise da percepção dos munícipes da cidade de Maputo em relação aos impactos socioambientais do saneamento básico: caso de gestão de esgotos domésticos no bairro Polana Caniço A Feng Y, Devlin NJ, Shah KK, Mulhern B, van Hout B (2018) New methods for modelling EQ-5D-5L value sets: An application to English data. Health Econ (United Kingdom) 27(1):23–38. 10.1002/hec.3560 Carlsson F, Martinsson P (2003) Design techniques for stated preference methods in health economics. Health Econ 12(4):281–294. 10.1002/hec.729 Nguyen TC, Le HT, Nguyen HD, Ngo MT, Nguyen HQ (2021) Examining ordering effects and strategic behaviour in a discrete choice experiment. Econ Anal Policy 70:394–413 Mott DJ, Shah KK, Ramos-Goñi JM, Devlin NJ, Rivero-Arias O (2021) Valuing EQ-5D-Y-3L health states using a discrete choice experiment: do adult and adolescent preferences differ? Med Decis Making 41(5):584–596 Vass CM, Wright S, Burton M, Payne K (2018) Scale heterogeneity in healthcare discrete choice experiments: a primer. Patient-Patient-Centered Outcomes Res 11:167–173 Swait J, Louviere J (1993) The role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res 30(3):305–314 Welie AG, Gebretekle GB, Stolk E et al (2020) Valuing health state: an EQ-5D-5L value set for Ethiopians. Value health Reg issues 22:7–14 Yang F, Katumba KR, Roudijk B et al (2022) Developing the EQ-5D-5L value set for Uganda using the ‘lite’protocol. PharmacoEconomics. :1–13 Sun S, Chuang L-H, Sahlén K-G, Lindholm L, Norström F (2022) Estimating a social value set for EQ-5D-5L in Sweden. Health Qual Life Outcomes 20(1):167 Gutierrez-Delgado C, Galindo-Suárez R-M, Cruz-Santiago C et al (2021) EQ-5D-5L health-state values for the Mexican population. Appl Health Econ Health Policy 19(6):905–914 Logar I, Brouwer R, Campbell D (2020) Does attribute order influence attribute-information processing in discrete choice experiments? Resour Energy Econ 60:101164 Norman R, Kemmler G, Viney R et al (2016) Order of presentation of dimensions does not systematically bias utility weights from a discrete choice experiment. Value Health 19(8):1033–1038 Mulhern B, Norman R, Lorgelly P et al (2017) Is dimension order important when valuing health states using discrete choice experiments including duration? PharmacoEconomics 35:439–451 Devlin NJ, Brooks R (2017) EQ-5D and the EuroQol Group: Past, Present and Future. Appl Health Econ Health Policy 15(2):127–137. 10.1007/s40258-017-0310-5 Ross I (2021) Measuring and valuing quality of life in the economic evaluation of sanitation interventions (PhD thesis) . 10.17037/PUBS.04661119 Ryen L, Svensson M (2015) The willingness to pay for a quality adjusted life year: a review of the empirical literature. Health Econ 24(10):1289–1301 Additional Declarations The authors declare no competing interests. Supplementary Files supplementary.pdf Katana_Mozambique_DCE_sanitation_supplementary Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4790952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330897219,"identity":"bb8f2a3c-9423-4992-84b2-3b23d55f9b3d","order_by":0,"name":"Patrick V. Katana","email":"","orcid":"","institution":"London School of Hygiene and Tropical Medicine, UK","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"V.","lastName":"Katana","suffix":""},{"id":330897220,"identity":"574764d5-24a4-42b6-86c4-54f3ac6e5ed3","order_by":1,"name":"Neiva Banze","email":"","orcid":"","institution":"Instituto Nacional de Saúde, Mozambique","correspondingAuthor":false,"prefix":"","firstName":"Neiva","middleName":"","lastName":"Banze","suffix":""},{"id":330897221,"identity":"350d10c8-fcbb-4943-a938-e3fb2eb9a25e","order_by":2,"name":"Cremildo Manhiça","email":"","orcid":"","institution":"Instituto Nacional de Saúde, Mozambique","correspondingAuthor":false,"prefix":"","firstName":"Cremildo","middleName":"","lastName":"Manhiça","suffix":""},{"id":330897222,"identity":"c30ab7c1-34c8-495f-818f-da709f56d73a","order_by":3,"name":"Catildo Cubai","email":"","orcid":"","institution":"Instituto Nacional de Saúde, 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Ross","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNhiDnx3BZiZOi2QzkH2AGC1wYHCYWC187GcPMFdU1NkbH2Z/9vhjDoM8fwOPsQFeh/HkJTCeOXM4cdthHnODg9sYDGcc4DFOwKuFIceAsbHtQILZYR42CaAWxg0MPMYH8GrhfwPSAnRYM/szkBZ7wlokwLYwM25gZjADaUkEacHvMIk3BgcbgH6ZcZjHTOLsNonkGYfZivF6X74/x/BhAzDE+Nvbn0lUbrOx7W9v3iyBTwsIILtcguiIHAWjYBSMglGABwAAFMk/JF05H4MAAAAASUVORK5CYII=","orcid":"","institution":"London School of Hygiene and Tropical Medicine, UK","correspondingAuthor":true,"prefix":"","firstName":"Ian","middleName":"","lastName":"Ross","suffix":""}],"badges":[],"createdAt":"2024-07-23 19:40:02","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4790952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4790952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61098731,"identity":"30de802f-deab-4a50-b012-34693b0927ba","added_by":"auto","created_at":"2024-07-25 14:40:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134090,"visible":true,"origin":"","legend":"\u003cp\u003eExample choice task\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4790952/v1/56136b59cc410030af3c83f3.jpeg"},{"id":61098734,"identity":"e49e3f74-b5a7-4adc-a6e4-6d1f8d7b6208","added_by":"auto","created_at":"2024-07-25 14:40:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128882,"visible":true,"origin":"","legend":"\u003cp\u003eFinal SanQoL-5 value set\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4790952/v1/a5b91f8a503c9a6a220e6865.jpeg"},{"id":61098732,"identity":"e73fa7e0-941f-44c9-96e8-35adcbbda067","added_by":"auto","created_at":"2024-07-25 14:40:55","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139283,"visible":true,"origin":"","legend":"\u003cp\u003eChoice (A or B) by difference in level sum score (A minus B)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4790952/v1/6c41718b906f04d32bdca9ac.jpeg"},{"id":61099281,"identity":"57ab1a84-7a74-4f5b-8538-44df92a42915","added_by":"auto","created_at":"2024-07-25 14:48:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1062111,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4790952/v1/04e5fbde-e6da-4ef5-91d2-f36694b72bb5.pdf"},{"id":61098730,"identity":"263eb2a5-c11e-4015-a8b4-4992c6236121","added_by":"auto","created_at":"2024-07-25 14:40:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":748440,"visible":true,"origin":"","legend":"\u003cp\u003eKatana_Mozambique_DCE_sanitation_supplementary\u003c/p\u003e","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4790952/v1/412772738e1b68df4f1f51c5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eValuing an Index of Sanitation Related Quality of Life (SanQoL-5) in urban Mozambique – a Discrete Choice Experiment\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eNo index of sanitation-related quality of life has previously been valued using robust preference elicitation methods such as a discrete choice experiment\u003c/li\u003e\n \u003cli\u003eIn urban Mozambique the highest-valued attribute was disgust, with other attributes (safety, privacy, shame, disease) valued slightly less.\u003c/li\u003e\n \u003cli\u003eUsing our findings, better sanitation economic evaluations could support more efficient allocation of the billions of dollars invested in sanitation.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003e1.5\u0026nbsp;billion people live without basic sanitation,\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e with billions of dollars spent every year to address this situation.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e However, economic evaluations typically measure and value infectious disease alone,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e when toilet users often value other benefits such as improvements in privacy, safety and dignity.\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Under the capability approach to welfare economics, these outcomes represent what people have \u0026ldquo;reason to value\u0026rdquo; about sanitation.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These are therefore attributes of good quality of life (QoL), and contribute to health in its broadest sense including mental and social wellbeing, \u0026ldquo;not just the absence of disease\u0026rdquo;.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIt is often argued in sanitation economic evaluation studies that QoL improvements arising from sanitation improvements comprise an economic benefit, but that methods for measuring and valuing QoL benefits are lacking.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Excluding these important benefits may result in inefficient resource allocation. A recent study developed an index of sanitation-related quality of life (SanQoL-5) in Mozambique,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e which has now been validated in four countries (Ethiopia, Malawi, Mozambique and Zambia).\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e SanQoL-5 has five attributes (disgust, disease, privacy, shame and safety), each measured by a question with a three-level response scale (always, sometimes, never).\u003c/p\u003e \u003cp\u003eA limitation of applications of SanQoL-5 thus far is the use of the rank sum method or attribute scoring to derive the index.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e These methods do not fully reflect what people value because they involve weighting attributes per se rather than trading off different \u003cem\u003elevels\u003c/em\u003e of attributes.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Application of discrete choice methods, as used in valuation of the EQ-5D and other influential health-related QoL indices, would be best practice.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this study, we aimed is to estimate a SanQoL-5 index using a discrete choice experiment (DCE) in two different urban settings in Mozambique. We derived a SanQoL-5 index value ranging 0\u0026ndash;1 for each of the 243 potential sanitation states in the descriptive system, and compared valuations by gender. We hypothesised that not all attributes would be valued equally and that, for all attributes, the \u0026ldquo;middle\u0026rdquo; levels would be seen as worth more than half the value of the \u0026ldquo;high\u0026rdquo; levels. The decision-makers who would use our results are considered as the Mozambican Ministry of Health and Ministry of Public Works, but we anticipate wider relevance in other countries where decisions about investments in basic sanitation are made.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe followed the Bridges et al. checklist for conjoint analysis in health,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and report against it in Supplementary Material A. Data and replication code are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/38vsh/\u003c/span\u003e\u003cspan address=\"https://osf.io/38vsh/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and sampling\u003c/h2\u003e \u003cp\u003eThe study took place in two cities in Mozambique: Maputo (population 1.1\u0026nbsp;million) and Dondo (population 100,000). Since this is the first time DCE-based valuation has been used for any index of sanitation-related quality of life, our sampling priority was not to achieve representativeness of the cities\u0026rsquo; population. Rather, we aimed to achieve approximate gender balance and diversity in type of toilet used (Supplementary Material B). The study population was adults aged 18\u0026thinsp;+\u0026thinsp;living in two neighbourhoods/\u003cem\u003ebairros\u003c/em\u003e in Maputo (Polana Cani\u0026ccedil;o A and Polana Cani\u0026ccedil;o B) and two in Dondo (Macharote and Nhamainga). These areas were selected because they are mixed in terms of housing quality and, in particular, have a diversity of toilet types used.\u003c/p\u003e \u003cp\u003eThe majority of healthcare DCEs interview 100\u0026ndash;300 respondents.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e We aimed to recruit 600 respondents to meet other study objectives, as well as to allow for dropping some of the sample on data quality grounds.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e We aimed to interview 300 women and 300 men per site, to allow exploration of whether valuation varies by gender. For toilet type, we aimed to sample 200 people using flush toilets, 340 people using pit latrines, and 60 people practising open defecation (no toilet). We achieved this by sampling based on data from existing health surveillance surveys (details in Supplementary Material B).\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e The \u003cem\u003ebairros\u003c/em\u003e in Dondo were selected on the basis of surveillance data indicating that prevalence of open defecation was \u0026gt;\u0026thinsp;10%, since open defecation was uncommon in the Maputo site (Supplementary Material B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSanQoL-5 index\u003c/h2\u003e \u003cp\u003eThe SanQoL-5 is a multi-attribute measure of sanitation-related quality of life, developed from primary qualitative research and supported by the literature on what people value about sanitation.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Its descriptive system (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) has five questions, each measuring a capability-based attribute: disgust, disease, privacy, shame and safety. Each is measured on a three-level frequency scale (always, sometimes, never), with questions framed such that \u0026ldquo;never\u0026rdquo; is the best outcome. There are therefore 15 attribute levels to be valued in 243 (=\u0026thinsp;3\u003csup\u003e5\u003c/sup\u003e) possible combinations. Following norms in health-related quality of life (HRQoL), each combination is termed a \u0026ldquo;sanitation state\u0026rdquo;. Adopting HRQoL notation, the best state is denoted 11111 (\u0026ldquo;never\u0026rdquo; for all levels) and the worst 33333, with intermediary states such as 23132, 11213, etc.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSanQoL-5 descriptive system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttribute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestion*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisgust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow often do you feel disgusted when using the toilet?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAlways \u003c/p\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow often do you worry that the toilet spreads diseases?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow often do you worry about being seen while using the toilet?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShame\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow often do you feel ashamed about using the toilet?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow often do you feel unsafe while using the toilet?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* A preamble is as follows: \u0026ldquo;The following questions are about your sanitation experiences in the past 30 days, meaning defecation, urination, and anything else you do in a toilet. Please respond with always, sometimes or never.\u0026rdquo; If less literate respondents struggle with a question, it can be reformulated as \u0026ldquo;Do you feel disgusted while using the toilet? How often?\u0026rdquo;. Before the SanQoL-5 questions, the respondent is asked about the last place they defecated. If the respondent practiced open defecation (OD), e.g. in fields or wasteland, they are directed to OD-specific questions, e.g. \u0026ldquo;How often do you worry about being seen while practising open defecation?\u0026rdquo;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eOur study was a face-to-face survey using Open Data Kit (ODK) Collect software on tablet computers. Though the survey was administered in Portuguese in the vast majority of cases, some participants preferred to speak in the predominant local language (Changana in Maputo, Sena in Dondo). Therefore, two teams (one per site) were recruited and underwent five-day programmes of training and piloting. Data collection was undertaken during May-July 2023. The questionnaire was translated into Portuguese by NB and the translations discussed at length with field team. No incentives for participation were provided.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiscrete choice study design\u003c/h2\u003e \u003cp\u003eAfter questions about socio-economic status and sanitation, the DCE section started with a series of warm-up tasks, to ensure participants fully understood the choices they were being asked to make. First, participants answered the SanQoL-5 questions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and completed the sanitation visual analogue scale (VAS) \u0026ndash; a 0-100 scale on which people rate how they feel about their level of sanitation today (Supplementary Material B). Second, participants watched three video vignettes on the tablet, to provide more meaning to hypothetical states and introduce the images used to frame attributes (Supplementary Material B). In each video, a hypothetical person describes the toilet they use and how it makes them feel about each of the SanQoL-5 attributes, i.e. describes their sanitation state. After each video, the participant was asked to score that person\u0026rsquo;s level of sanitation on the VAS, to get them used to the idea of comparing states. Third, participants were asked to complete a food-based menu choice card (Supplementary Material B), to emphasise that the two columns as a whole are being compared and trading items between columns was not possible. The last warm-up task involved being shown three sanitation states, and asked to choose which was worst and which was best, as well as their reasoning, to assess whether they understood the task (Supplementary Material B).\u003c/p\u003e \u003cp\u003eFor the actual DCE choice tasks, participants were shown a card with two sanitation states as profiles of SanQoL-5 atttribute levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with the same emoji visualisation as the warm-up tasks. Participants were asked to select which state was \u0026ldquo;better\u0026rdquo;, with no opt-out. This follows best practice from valuation protocols for the EuroQoL 5-dimension (EQ-5D) measure of HRQoL.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Participants were told to not consider their present toilet or level of sanitation, but instead to imagine being in the states in the scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach participant undertook 10 choice tasks. We identified a 6-block efficient design using the \u003cem\u003edcreate\u003c/em\u003e programme in Stata 18 with a Modified Federov Algorithm (d-efficiency 10.4).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e With 60 choice tasks (6x10), there were 120 sanitation states compared in all. Each block including states across the range of severity. To avoid bias from the ordering of the tasks (e.g. less care taken over later tasks) we randomised participants into 12 groups, with half of the groups doing tasks in reverse.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQuality Control\u003c/h2\u003e \u003cp\u003eEach interviewer undertook 8 pilot interviews (80 DCE tasks per interviewer in total), in areas outside the study sample \u0026ndash; the data were not included in the analysis. The pilot identified issues with time taken to complete the survey, which resulted in removing some socio-demographic questions and randomising some non-DCE questionnaire modules to sub-samples. Overall, the acceptability of the tasks was good. Preliminary analysis of the pilot data indicated that DCE data were consistent. Other aspects of quality control included timestamps throughout the survey, to allow flagging when a participant completed a section extremely rapidly relative to most others. We also included a dominance test halfway through the DCE, in which one state (12121) was objectively better than the other (23232) on all five attributes, so there is a \u0026ldquo;correct\u0026rdquo; answer. Dominance test choices were not included in the analysis.\u003c/p\u003e \u003cp\u003e For the primary analysis, we excluded data of participants who met one or more of these conditions: (i) failed the dominance test; (ii) completed the first five tasks in less than 10 seconds per task; (iii) completed the second five tasks and dominance task in less than 5 seconds per task. We also examined the choices in respect of level sum score (LSS), which is the sum of attribute levels in state notation (e.g. the LSS of 11113 is 7). We calculated difference in LSS between the two options a respondent was shown and observed the distribution of responses. Hypothetically, a larger LSS difference should increase the likelihood that a respondent chooses the option with the lower LSS. The LSS of the best state (11111) is 5 and the worst state 15. We reconfirmed fieldworkers\u0026rsquo; classifications of toilet types by verifying photos they took of toilets\u0026rsquo; interiors against entered data on toilet characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eWe analysed choices in Stata 18 first using a conditional logit model, which assumes that preferences are not correlated across individuals. We then using a mixed logit model with correlated parameters, which aims to account for: (i) preference heterogeneity (when differences between individuals\u0026rsquo; preferences cannot be explained by observable characteristics); and, (ii) scale heterogeneity (when unmeasured factors affect individuals\u0026rsquo; responses to different extents).\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e We based model selection on whether there was evidence of heterogeneity, as well as the Akaike/Bayesian information criteria (AIC/BIC).\u003c/p\u003e \u003cp\u003eOur analytical approach was based on the EQ-5D valuation protocol.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The model assumes that in making their choice, people are comparing the quality of life they would have in two sanitation states (Eq.\u0026nbsp;1), namely \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{ijl}\\)\u003c/span\u003e\u003c/span\u003e (left-hand option \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:l\\)\u003c/span\u003e\u003c/span\u003e for individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e within DCE pair \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{ijr}\\)\u003c/span\u003e\u003c/span\u003e (right-hand option \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e). Eq.\u0026nbsp;1 represents the choice as an inequality, with the sign (\u0026lt;\u0026thinsp;or \u0026gt;) decided by the participant\u0026rsquo;s response. Since there is no opt-out, the respondent cannot give them equal value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEquation 1\u003c/h2\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{V}_{ijl}=\\:\\alpha\\:-\\:{\\sum\\:}_{k=1}^{10}{\\beta\\:}_{k}{x}_{k}^{ijl}+\\:{e}_{i}^{lj}\\:\\:\\:\u0026gt;\\:?\u0026lt;\\:\\:\\:\\:\\:{V}_{ijr}=\\:\\alpha\\:-\\:{\\sum\\:}_{k=1}^{10}{\\beta\\:}_{k}{x}_{k}^{ijr}+\\:{e}_{i}^{rj}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents a sanitation state using 10 dummy (binary) variables. The first two dummies refer to \u0026ldquo;sometimes\u0026rdquo; and \u0026ldquo;always\u0026rdquo; levels of the disgust dimension. If the state involves being \u0026ldquo;sometimes\u0026rdquo; disgusted (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the \u0026ldquo;sometimes\u0026rdquo; dummy takes the value 1. For the \u0026ldquo;always\u0026rdquo; level its respective dummy takes the value 1. If both dummies are 0, then the state include \u0026ldquo;never\u0026rdquo; being disgusted. The other 8 dummies are the equivalents for the remaining attributes.\u003c/p\u003e \u003cp\u003eIf all 10 dummies are zero then the state is 11111 (full sanitation capability), its value denoted by α, which cancels out once the participant makes their choice. The parameter β is a 10 \u0026times; 1 vector aligning to the dummies. Its first two elements reflect decrements of \u0026ldquo;sometimes\u0026rdquo; or \u0026ldquo;always\u0026rdquo; being disgusted against the value of \u0026ldquo;never\u0026rdquo; being disgusted. Since estimated coefficients are decrements, they are expected to be negative. The overall decrement of moving from \u0026ldquo;never\u0026rdquo; to \u0026ldquo;always\u0026rdquo; is the sum of the coefficients for \u0026ldquo;sometimes\u0026rdquo; and \u0026ldquo;always\u0026rdquo;. Error terms are assumed to follow an extreme value distribution. We rescaled estimated coefficients to a 0\u0026ndash;1 index, whereby 0 is the value of the worst state and 1 the value of the best. This is achieved by dividing through by the sum of the coefficients of the worst levels (i.e. always).\u003c/p\u003e \u003cp\u003eWe included a sub-group analysis by sex. First, we explored whether differences in preferences between women and men were explained solely by differences in randomness of choices by sub-groups, i.e. scale heterogeneity.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e We assessed this using the Swait-Louviere test,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e effectively a likelihood ratio test comparing the log likelihood statistics of the pooled model with sub-group models. Following Mott et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, our main sub-group analysis was to compare relative attribute importance (RAI) in the two sub-group regressions. To estimate RAI scores, we calculated ratios of each attribute\u0026rsquo;s \u0026ldquo;always\u0026rdquo; coefficient to that of the lowest-valued attribute. Attributes with higher RAI therefore have a higher value. We estimated RAI difference by subtracting RAI scores in the men\u0026rsquo;s sample from those in the women\u0026rsquo;s sample, and estimated confidence intervals using \u003cem\u003enlcom\u003c/em\u003e in Stata 18.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eThe study received prior approval from the \u003cem\u003eComit\u0026eacute; Institucional de \u0026Eacute;tica\u003c/em\u003e at the \u003cem\u003eInstituto Nacional de Sa\u0026uacute;de\u003c/em\u003e in Mozambique (ref: 028 /CIE-INS/2023), and the Research Ethics Committee at the London School of Hygiene and Tropical Medicine (Ref: 28190). Informed, written consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe enrolled 601 participants between May-July 2023, after approaching 605 individuals (response rate 99%). We included 541 participants in the final analysis, dropping data for 41 (7%) who failed the dominance test and 19 (3%) who completed tasks faster than the minima set out above. Of the respondents included in the analysis, 54% were women, and 63% had access to on-plot piped water (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaputo (n\u0026thinsp;=\u0026thinsp;292)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDondo (n\u0026thinsp;=\u0026thinsp;249)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;541)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent is female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 (35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted primary school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate problems walking about (or worse)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate pain (or worse)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDwelling characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSealed floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e442 (82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid exterior wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285 (98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420 (78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds with fridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds with television\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e373 (69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to electricity connection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to on-plot piped water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e340 (63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent rents dwelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eToilet type used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCistern-flush toilet with water seal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePour-flush toilet with water seal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff-set pit latrine with pour-flush but no water seal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePit latrine with concrete slab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePit latrine with non-concrete slab (soil, tyres, wood)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen defecation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSanitation service characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUses on-plot toilet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291 (\u0026gt;\u0026thinsp;99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e481 (89%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShares toilet with other households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of households sharing toilet (amongst sharers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of people sharing toilet (amongst sharers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Data are n (%) for categorical variables and mean (standard deviation) for numerical variables. * indicates questions asked of a random half of the sample\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe most common sanitation type (61%) was an off-set pit latrine with pour-flush but no water seal. In this design, the pit is not directly visible to the user but there is no water seal (u-bend) in the connecting pipe to stop the passage of smells or flies. It is therefore a step down in toilet quality from a pour-flush toilet with water seal, which was the second most common toilet type used (19%). Photos of common toilet types are in Supplementary Material C. Amongst participants using toilets, 29% shared the toilet with 1 or more other households. Background characteristics of participants in the two cities are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (and by gender in Supplementary Material D). Considering participants\u0026rsquo; levels of SanQoL-5, the attribute for which people had the worst outcomes was perception of disease risk, with 41% selecting \u0026ldquo;always\u0026rdquo; (Supplementary Material D). Maximum SanQoL-5 was reported by 5% of participants and minimum by 3%.\u003c/p\u003e \u003cp\u003eIn both regression models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), coefficients for every attribute level were negative and significant at the 1% level. In the mixed logit model, every standard deviation (SD) was significant at the 1% level, which is evidence of preference heterogeneity and suggests that the mixed logit is more appropriate than conditional logit. Selection of the mixed logit is supported by the higher log-likelihood and lower AIC, with the higher BIC contradicting this slightly. However, BIC has a larger penalty than AIC for the number of model parameters, and 55 are added when allowing for correlated parameters (as is advisable, to account for heterogeneity).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression output\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eConditional logit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMixed logit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoeff.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoeff.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisgust (sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisgust (always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth (sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.93***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth (always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy (sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.99***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy (always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.96***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShame (sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShame (always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.96***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety (sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.67***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety (always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.98***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Disgust, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Disgust, always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Health, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Health, always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Privacy, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Privacy, always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Shame, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Shame, always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Safety, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (Safety, always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of choices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10,820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-2,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-2,072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4,482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4,274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4,748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*, **, *** indicate significance at the 10, 5 and 1 percent level. Coeff: coefficient; SE: standard error; SD: standard deviation; BIC: Bayesian information Criterion; Mixed logit models estimated using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm with 500 Halton draws and uncorrelated parameters used as starting values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter rescaling to a 0\u0026ndash;1 index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), disgust was the highest-valued attribute, with its \u0026ldquo;never\u0026rdquo; level valued at 0.25 after rescaling. Safety was the lowest-valued (0.18 for its \u0026ldquo;never\u0026rdquo; level). The \u0026ldquo;sometimes\u0026rdquo; levels were valued at 53\u0026ndash;62% of the \u0026ldquo;never\u0026rdquo; levels of each attribute (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that respondents perceived moving from the middle level to the worst as a bigger decrement than moving from the best level to the middle. The value set is provided with standard errors in Supplementary Material D. SanQoL-5 by toilet type followed a gradient with objective toilet quality according to categories used in Sustainable Development Goal 6. Specifically, people practising OD had the lowest mean SanQoL-5 (0.30) and people with \u0026ldquo;at least basic\u0026rdquo; sanitation had the highest (0.70), with \u0026ldquo;unimproved\u0026rdquo; and \u0026ldquo;limited\u0026rdquo; sanitation in-between (0.45 and 0.60 respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen comparing responses based on LSS differences, the pattern is as expected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which brings confidence in data quality. When there was a LSS difference of \u0026plusmn;\u0026thinsp;3 or greater, 91\u0026ndash;97% of choices were for the expected option. There was also an approximately equal split in choosing A versus B when the LSS difference was 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Swait-Louviere test did not reject the null hypothesis of equal preference structure in the two sub-groups (p\u0026thinsp;=\u0026thinsp;0.24), allowing us to proceed with RAI comparison. There was no significant gender difference in valuation of attributes (Supplementary Material D), with no statistically significant differences in RAI score. However, there was suggestive evidence (p\u0026thinsp;=\u0026thinsp;0.08) for men valuing disgust more highly than women, with women considering disgust to be 1.29 times as important as safety (the attribute lowest valued by both sexes) compared to 1.43 times for men.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, an index of sanitation related quality of life was obtained from discrete choice valuation of the SanQoL-5 descriptive system. A set of index values for 243 sanitation states was derived from the stated preferences of 541 adults in urban Mozambique, after excluding data for 60 participants on quality grounds. The highest-valued attributed was disgust (\u0026ldquo;never feeling disgusted while using the toilet\u0026rdquo;), with a SanQoL-5 index value of 0.25. The other attributes were valued similarly to one another (ranging 0.18\u0026ndash;0.19). Our study is in the first assessment of the relative value to people of SanQoL-5 attributes using robust preference elicitation methods. SanQoL-5 has now been validated in four countries.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The SanQoL-5 value set we report here (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) can be used to evaluate sanitation investments in Mozambique, as well as in other countries when a local value set is not available.\u003c/p\u003e \u003cp\u003ePeople valued the \u0026ldquo;sometimes\u0026rdquo; levels of attributes at around 60% of the \u0026ldquo;never\u0026rdquo; levels. This indicates that moving moving from the middle level to the worst level was a bigger decrement in QoL than moving from the best level to the middle. Such patterns in levels are commonly observed in value sets for EQ-5D and other HRQoL indices, including in African countries.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Since this is the first DCE of SanQoL-5, it is not possible to compare the relative value of attributes or levels to other studies.\u003c/p\u003e \u003cp\u003eWe are confident in the quality of the data, for several reasons. First, we dropped 10% of the original sample on the grounds of failing the dominance test or completing tasks excessively quickly. A relatively low proportion (7%) failed the dominance test, indicating that the tasks were understandable.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Second, the pattern of choices in relation to LSS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) align with theory and are similar patterns in to EQ-5D DCEs.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Third, we carefully monitored interviewer performance. After the first 15 interviews we noted that one interviewer in Maputo had consistently very low durations for the DCE section compared to other interviewers, as well as some geolocations well outside the study area. They agreed to leave the study team and their 15 observations were dropped, with 15 additional Maputo households sampled to replace them. Fourth, half of the participants were randomised into answering their block of choice tasks in reverse.\u003c/p\u003e \u003cp\u003eIt is of potential concern that the highest-weighted attribute (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was also the first listed in the choice tasks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is possible that this is down to chance, but it is also possible that participants paid more attention to the attributes higher up the choice card. However, we see two reasons not to be too concerned. First, several methodological studies exploring attribute ordering in DCEs found no evidence that order influences results.\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Second, in our gender subgroup analysis, women valued privacy slightly higher than disease (which was higher up in the card) though the difference was not statistically significant (Supplementary Material D). Nonetheless, in future SanQoL-5 DCEs we recommend randomising participants into blocks with different attribute orderings, to avert the risk of bias.\u003c/p\u003e \u003cp\u003eSanQoL-5 can be used to monitor and evaluate sanitation programmes, e.g. to measure differences over time or between groups, and subsequently in economic evaluation. These use cases of SanQoL-5 are similar to those of HRQoL indices such as the EQ-5D which, since its inception in 1987, had been used in over 17,000 studies by 2015 to inform efficient allocation of resources for health.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e With the sustainable development goal target for sanitation off-track,\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e sanitation investments will need to be evaluated for many years to come. SanQoL-5 has already been included as an outcome in two ongoing trials of sanitation interventions. Since it is gender-sensitive (can be answered by anyone), SanQoL-5 can also be used to evaluate gender gaps. However, a key feature of its design is the weighting of attribute levels demonstrated in this study, which allows it to be used to measure and value benefits in economic evaluation such as benefit-cost and cost-effectiveness analysis.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Sanitation economic evaluations have previously described QoL benefits as intangible.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e SanQoL-5 makes them tangible, with DCE valuation in this study a first step. The next step for including QoL benefits in a benefit-cost study is monetary valuation of SanQoL-5 gains based on willingness to pay, as is done with health gains through monetary valuation of quality-adjusted life years.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study has a number of limitations. First, as set out above, choice cards presented attributes in a consistent order to all participants, so we are unable to account for the risk of attribute order bias in our results. Second, attributes may be interpreted differently by different people. Through use of video vignettes we aimed to illustrate the multiple meanings of attributes (e.g. one video discussed safety in terms of pit collapse, and another video in terms of assault risk), but there was only time for 3 vignettes. Third, the exclusion of an opt-out in the DCE forces the participant to choose one option even if they consider both to be of equal value. However, this follows norms in health state valuation (e.g. EQ-5D),\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and risk of bias is minimal. Fourth, the sample was designed to demonstrate proof of concept amongst users of diverse types of sanitation, rather than to be representative of a given population. In the future, nationally-representative samples would ideally be used, so that societal preferences are reflected where resource allocation is to be informed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study presents the first discrete choice valuation of the SanQoL-5 index, or indeed any measure of sanitation-related quality of life. Our results suggest that discrete choice valuation is feasible and acceptable in resource-constrained settings, and only 7% of participants failed the dominance test. We expect that the availability of this value set will facilitate economic evaluation of sanitation programmes in Mozambique and beyond. We also hope it will support broader research into sanitation-related quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eConcept and design: Ross, Katana, Capitine, Banze\u003c/p\u003e\n\u003cp\u003eAcquisition of data: Banze, Capitine, Manhi\u0026ccedil;a, Cubai, Viera, Fulai, Katana\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: Ross, Katana\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Ross, Katana\u003c/p\u003e\n\u003cp\u003eCritical revision of the paper for important intellectual content: Banze, Capitine, Manhi\u0026ccedil;a, Cubai, Viera, Fulai, Cumming, Viegas\u003c/p\u003e\n\u003cp\u003eObtaining funding: Cumming, Viegas, Ross\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or logistic support: Banze, Capitine, Manhi\u0026ccedil;a, Cubai, Viera, Fulai, Cumming, Viegas, Katana\u003c/p\u003e\n\u003cp\u003eSupervision: Ross, Cumming, Viegas\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding. \u003c/strong\u003eThis work was funded by the Bill \u0026amp; Melinda Gates Foundation (OPP1137224). Ian Ross acknowledges the support of a post-doctoral fellowship from the Reckitt Global Hygiene Institute in the period when the paper was drafted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the Funder\u003c/strong\u003e: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements. \u003c/strong\u003eWe greatly appreciated the cooperation of survey participants in providing their time, as well as the efforts of the fieldworkers in Dondo and Maputo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest.\u003c/strong\u003e None declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUNICEF \u0026amp; WHO (2023) \u003cem\u003eProgress on household drinking water, sanitation and hygiene 2000\u0026ndash;2022: special focus on gender\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO (2022) Strong systems and sound investments: evidence on and key insights into accelerating progress on sanitation, drinking-water and hygiene. UN-Water global analysis and assessment of sanitation and drinking-water (GLAAS) 2022 report\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss I, Cumming O, Dreibelbis R, Adriano Z, Nala R, Greco G (2021) How does sanitation influence people's quality of life? Qualitative research in low-income areas of Maputo, Mozambique. Soc Sci Med 272:113709\u0026ndash;113709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2021.113709\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2021.113709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElmendorf M, Buckles PK Appropriate Technology for Water Supply and Sanitation. 1980;(December)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenkins M, Curtis V (2005) Achieving the 'good life': Why some people want latrines in rural Benin. Soc Sci Med 61(11):2446\u0026ndash;2459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2005.04.036\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2005.04.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomons N (1978) Resume of the discussion on 'water and other environmental interventions'. Am J Clin Nutr 31(11):2124\u0026ndash;2126\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSen A (1993) Capability and well-being73. Qual life 30:270\u0026ndash;293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Constitution of The World Health Organization (1948) 978 92 4 165047 2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.who.int/governance/eb/who_constitution_en.pdf\u003c/span\u003e\u003cspan address=\"http://www.who.int/governance/eb/who_constitution_en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutton G, Patil S, Kumar A, Osbert N, Odhiambo F (2020) Comparison of the costs and benefits of the Clean India Mission. World Dev 134(105052). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.worlddev.2020.105052\u003c/span\u003e\u003cspan address=\"10.1016/j.worlddev.2020.105052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutton G, Rodriguez UP, Winara A et al (2014) Economic efficiency of sanitation interventions in Southeast Asia. J Water Sanitation Hygiene Dev 4(1):23\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2166/washdev.2013.158\u003c/span\u003e\u003cspan address=\"10.2166/washdev.2013.158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhittington D, Radin M, Jeuland M (2020) Evidence-based policy analysis? The strange case of the randomized controlled trials of community-led total sanitation. Oxf Rev Econ Policy 36(1):191\u0026ndash;221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/oxrep/grz029\u003c/span\u003e\u003cspan address=\"10.1093/oxrep/grz029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss I, Greco G, Opondo C et al (2021) Measuring and valuing broader impacts in public health: Development of a sanitation-related quality of life instrument in Maputo, Mozambique. Health Econ 31(3):466\u0026ndash;480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hec.4462\u003c/span\u003e\u003cspan address=\"10.1002/hec.4462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkter F, Banze N, Capitine I et al (2024) The Sanitation-related Quality of Life index (SanQoL-5)\u0026ndash;validity and reliability in rural and urban settings in Ethiopia, Malawi, Mozambique, and Zambia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStillwell WG, Seaver DA, Edwards W (1981) A comparison of weight approximation techniques in multiattribute utility decision making. Organizational Behav Hum Perform 28(1):62\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0030-5073(81)90015-5\u003c/span\u003e\u003cspan address=\"10.1016/0030-5073(81)90015-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrazier J, Ratcliffe J, Salomon J, Tsuchiya A (2016) Measuring and Valuing Health Benefits for Economic Evaluation. Oxford University Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie F, Gaebel K, Perampaladas K, Doble B, Pullenayegum E (2014) Comparing EQ-5D valuation studies: a systematic review and methodological reporting checklist. Med Decis Making 34(1):8\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOppe M, Devlin NJ, van Hout B, Krabbe PF, de Charro F (2014) A program of methodological research to arrive at the new international EQ-5D-5L valuation protocol. Value Health 17(4):445\u0026ndash;453\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBridges JF, Hauber AB, Marshall D et al (2011) Conjoint analysis applications in health\u0026ndash;a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health Jun 14(4):403\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jval.2010.11.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jval.2010.11.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA (2015) Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide. Patient Oct 8(5):373\u0026ndash;384. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40271-015-0118-z\u003c/span\u003e\u003cspan address=\"10.1007/s40271-015-0118-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTervonen T, Schmidt-Ott T, Marsh K, Bridges JF, Quaife M, Janssen E (2018) Assessing rationality in discrete choice experiments in health: an investigation into the use of dominance tests. Value Health 21(10):1192\u0026ndash;1197\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRendi\u0026ccedil;\u0026atilde;o AJ (2022) An\u0026aacute;lise da percep\u0026ccedil;\u0026atilde;o dos mun\u0026iacute;cipes da cidade de Maputo em rela\u0026ccedil;\u0026atilde;o aos impactos socioambientais do saneamento b\u0026aacute;sico: caso de gest\u0026atilde;o de esgotos dom\u0026eacute;sticos no bairro Polana Cani\u0026ccedil;o A\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng Y, Devlin NJ, Shah KK, Mulhern B, van Hout B (2018) New methods for modelling EQ-5D-5L value sets: An application to English data. Health Econ (United Kingdom) 27(1):23\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hec.3560\u003c/span\u003e\u003cspan address=\"10.1002/hec.3560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlsson F, Martinsson P (2003) Design techniques for stated preference methods in health economics. Health Econ 12(4):281\u0026ndash;294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hec.729\u003c/span\u003e\u003cspan address=\"10.1002/hec.729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen TC, Le HT, Nguyen HD, Ngo MT, Nguyen HQ (2021) Examining ordering effects and strategic behaviour in a discrete choice experiment. Econ Anal Policy 70:394\u0026ndash;413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMott DJ, Shah KK, Ramos-Go\u0026ntilde;i JM, Devlin NJ, Rivero-Arias O (2021) Valuing EQ-5D-Y-3L health states using a discrete choice experiment: do adult and adolescent preferences differ? Med Decis Making 41(5):584\u0026ndash;596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVass CM, Wright S, Burton M, Payne K (2018) Scale heterogeneity in healthcare discrete choice experiments: a primer. Patient-Patient-Centered Outcomes Res 11:167\u0026ndash;173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwait J, Louviere J (1993) The role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res 30(3):305\u0026ndash;314\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelie AG, Gebretekle GB, Stolk E et al (2020) Valuing health state: an EQ-5D-5L value set for Ethiopians. Value health Reg issues 22:7\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang F, Katumba KR, Roudijk B et al (2022) Developing the EQ-5D-5L value set for Uganda using the \u0026lsquo;lite\u0026rsquo;protocol. PharmacoEconomics. :1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun S, Chuang L-H, Sahl\u0026eacute;n K-G, Lindholm L, Norstr\u0026ouml;m F (2022) Estimating a social value set for EQ-5D-5L in Sweden. Health Qual Life Outcomes 20(1):167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutierrez-Delgado C, Galindo-Su\u0026aacute;rez R-M, Cruz-Santiago C et al (2021) EQ-5D-5L health-state values for the Mexican population. Appl Health Econ Health Policy 19(6):905\u0026ndash;914\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogar I, Brouwer R, Campbell D (2020) Does attribute order influence attribute-information processing in discrete choice experiments? Resour Energy Econ 60:101164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman R, Kemmler G, Viney R et al (2016) Order of presentation of dimensions does not systematically bias utility weights from a discrete choice experiment. Value Health 19(8):1033\u0026ndash;1038\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulhern B, Norman R, Lorgelly P et al (2017) Is dimension order important when valuing health states using discrete choice experiments including duration? PharmacoEconomics 35:439\u0026ndash;451\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevlin NJ, Brooks R (2017) EQ-5D and the EuroQol Group: Past, Present and Future. Appl Health Econ Health Policy 15(2):127\u0026ndash;137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40258-017-0310-5\u003c/span\u003e\u003cspan address=\"10.1007/s40258-017-0310-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss I (2021) \u003cem\u003eMeasuring and valuing quality of life in the economic evaluation of sanitation interventions (PhD thesis)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17037/PUBS.04661119\u003c/span\u003e\u003cspan address=\"10.17037/PUBS.04661119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyen L, Svensson M (2015) The willingness to pay for a quality adjusted life year: a review of the empirical literature. Health Econ 24(10):1289\u0026ndash;1301\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"discrete choice experiment, preferences, sanitation, quality of life, Mozambique, toilet","lastPublishedDoi":"10.21203/rs.3.rs-4790952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4790952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003e1.5\u0026nbsp;billion people live without basic sanitation. A five-attribute index of sanitation-related quality of life (SanQoL-5) designed for economic evaluation has now been applied in six countries. After rescaling, scores range 0 (no sanitation capability) to 1 (full sanitation capability). To date, SanQoL-5 valuation has been via simple methods such as rank sum, not robust methods such as discrete choice experiment (DCE). We aimed to value the SanQoL-5 index using a DCE in urban Mozambique.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe enrolled 601 adults in the cities of Maputo and Dondo, sampling women and men equally alongside quotas for toilet type. The DCE task was a choice between two scenarios representing combinations of SanQoL-5 attribute levels (always, sometimes, never). Each respondent completed 10 tasks and a dominance test. We fitted a mixed logit model and rescaled coefficients to derive the index, with sub-group analysis by gender.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe highest-valued attribute was disgust (\u0026ldquo;never feel disgusted while using the toilet\u0026rdquo;), with a SanQoL-5 index value of 0.25. The other attributes had similar values (ranging 0.18\u0026ndash;0.19). People valued \u0026ldquo;sometimes\u0026rdquo; levels at around 60% of \u0026ldquo;never\u0026rdquo; levels. Mean SanQoL-5 by toilet type followed a gradient with Sustainable Development Goal 6 categories: \u0026ldquo;open defecation\u0026rdquo; 0.30, \u0026ldquo;unimproved\u0026rdquo; 0.45, \u0026ldquo;limited\u0026rdquo; 0.60 and \u0026ldquo;at least basic\u0026rdquo; 0.70.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis is the first DCE-based valuation of any index of sanitation-related quality of life, enabling the SanQoL-5 to be used in economic evaluation. Identifying sanitation service transitions associated with the greatest quality of life gains could inform more efficient resource allocation.\u003c/p\u003e","manuscriptTitle":"Valuing an Index of Sanitation Related Quality of Life (SanQoL-5) in urban Mozambique – a Discrete Choice Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 14:40:51","doi":"10.21203/rs.3.rs-4790952/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9201b358-5797-4ed8-bc4b-d0c94902d102","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35032602,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2024-07-25T14:40:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-25 14:40:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4790952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4790952","identity":"rs-4790952","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}