Influence of sanitation facilities on diarrhea prevalence among children aged below 5 years in flood-prone areas of Bangladesh: A multilevel analysis

doi:10.21203/rs.3.rs-2611812/v1

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Influence of sanitation facilities on diarrhea prevalence among children aged below 5 years in flood-prone areas of Bangladesh: A multilevel analysis

https://doi.org/10.21203/rs.3.rs-2611812/v1

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published 21 Aug, 2023

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Although the improvement of sanitation facilities has been a major contributor to improving public health, it is not guaranteed to prevent negative health outcomes. This is especially true in areas affected by severe natural disasters, such as flooding or extreme rainfall. Previous studies have focused on the association between catastrophic natural disasters and negative health outcomes. However, studies focusing on disaster-prone areas are limited. This study focused on the impact of flood risks and examined whether the improvement of sanitation facilities would be sufficient to suppress the prevalence of diarrhea in flood-prone areas. To this end, we used data from the Bangladesh Demographic and Health Survey and geodata of flood-prone areas. Results showed that the severely and low flood-prone areas had significant positive and negative associations with diarrhea prevalence, respectively. However, interactions between unimproved sanitation and moderately flood-prone areas as well as between diffused sanitation facilities and severely flood-prone areas had significant negative associations with diarrhea prevalence. These findings indicate that improved sanitation may not positively contribute to the prevention of diarrhea in these areas. Instead of installing sanitation facilities that concentrate feces in one area in flood-prone regions, the urgent need for alternative sanitation technologies should be addressed.

diarrhea

child morbidity

sanitation

flood risk

demographic and health survey

multilevel linear probability model

Although improving sanitation facilities has been a major contributor to the improvement of public health in resource-limited regions, such as low- and middle-income countries (Cha et al. 2017; Chowdhury et al. 2015; Contreras et al. 2022; Dagnew et al. 2019; Local Burden of Disease WaSH Collaborators, 2020; Luby et al. 2018; Null et al. 2018; Omotayo et al. 2021; UNICEF and WHO 2019), there has been some debate surrounding the negative effects of improved sanitation facilities, such as pit latrines, on the environment. Dzwairo et al. (2006) investigated the lateral distance between the groundwater table and tube wells that might cause groundwater contamination in rural areas of Zimbabwe. Dey et al. (2017), focusing on different hydrological regions of Bangladesh, showed that the risk of groundwater contamination was high during the wet season. Islam et al. (2016) investigated groundwater contamination, with a focus on hydrogeological conditions of floodplains, in Bangladesh. Thus, although pit latrines are a safe option, geological, seasonal, environmental, and climatological factors may influence the effect of sanitation facilities on health and are therefore important factors to consider.

Floods are a commonly occurring natural disaster that affect human health. Previous studies have argued that flood events (Chowdhury et al. 2018; Hashimoto et al. 2014; Hashizume et al. 2008a, b; Zhang et al. 2019) and recurrent disasters (Joshi et al. 2011) affect the prevalence of diarrhea. The risk of water- and vector-borne diseases is high in flood-affected areas (Abbas and Routray 2014; Levy et al. 2016), and diarrhea prevalence increases during periods of flooding (Schwartz et al. 2006).

Floods also affect sanitation facilities, causing the spread of waterborne infectious diseases. A study conducted in the flood-prone regions of Nicaragua revealed that overflowing sanitation facilities increased the likelihood of diarrhea prevalence (Denslow et al. 2010). Studies focusing on the geological features of flood-prone areas have shown that these areas generally occupy large alluvial plains in lowlands (Wesselink et al. 2015), which implies that the infrastructure in these areas is more susceptible to disruption or deterioration when compared with the infrastructure in non-flood-prone areas (Mukherjee et al. 2009). Considering the state of flood-prone environments, appropriate sanitation facilities should be developed, and existing sanitation technology should be updated and implemented. A review by Borges Pedro et al. (2020) discussed the appropriate sanitation technologies in flood-prone areas worldwide; however, the health impacts in those areas have not been fully explored.

Bangladesh, which occupies the world’s largest river delta, is one of the most flood-prone countries globally (Mukherjee et al. 2009; Wesselink et al. 2015). It experiences various flood types annually due to the different geological and climatological features of the region (Dewan, 2015). Three main flood types, namely river floods, flash floods, and tidal surges, are observed in Bangladesh (Bangladesh Water Development Board: BWDB, Table S4). River floods are caused by the overflow of riverbanks or extreme rainfall and are mostly observed during the monsoon season (June to October). Flash floods are mainly observed in north-eastern and south-eastern hilly areas, where abnormal amounts of water surge from hillsides between early April and May. Tidal surges are observed in coastal areas and are caused by cyclones between June and September. Floods in Bangladesh are observed every year, but catastrophic severe flooding events are only observed every few years (BWDB).

The geological features of Bangladesh also affect the infrastructure in the area. Bangladesh has three major rivers that shape the Bengal Delta; these are Ganga, Brahmaputra, and Meghna, which merge into the Bay of Bengal. These rivers have deposited floodplains and shaped the Pleistocene tracts Barind and Madhupur in the central and northwestern areas, respectively (Shamsudduha and Uddin, 2007). Floodplains or alluvium sand absorb water from floods, especially in lower land basins during the monsoon season. This enriches the soil for agricultural purposes but may cause infrastructure to become unstable and deteriorate, especially in severely flood-prone areas (Brammer, 1990; Umitsu, 1993). Therefore, it is crucial to focus on the appropriateness of infrastructure construction in these areas. However, there are few studies focusing on infrastructure, especially sanitation facilities, in flood-prone areas of Bangladesh.

In 2017, Bangladesh was declared “open defecation free” (UNICEF and WHO, 2019); however, most urban and rural areas, except for some areas of mega cities such as Dhaka (Yin et al. 2021), do not yet have sufficient sewer systems or treatment plants to serve the entire population, which means that most people still rely on on-site sanitation systems such as pit latrines. Although pit latrines are an affordable option for low-income households, particularly in rural areas, the negative effects of pit latrines on floodplains have not yet been fully examined.

Bangladesh has experienced remarkable success in the health sector, albeit with high morbidity rates and low use of health services (Chowdhury et al. 2013). For example, the number of diarrhea-related deaths dropped significantly between the years 2007 and 2016 (Government of the People's Republic of Bangladesh, Ministry of Health and Family Welfare: GOB-MHFW 2019). However, the diarrhea morbidity rate remains high. Therefore, focusing on diarrhea morbidity, this study aimed to examine the effect of sanitation facilities on the incidence of diarrhea in children below 5 years of age, living in disaster-prone areas of Bangladesh with various levels of flood risk, as well as the relationship between sanitation facility types and flood risk. Furthermore, the nationwide spatial distribution of diarrhea prevalence was evaluated to identify focus areas for reducing diarrhea-related morbidity in Bangladesh. Considering the effect of environmental factors on sanitation facilities can help to assess the suitability of these facilities for disaster-prone areas of Bangladesh.

Data

Two secondary datasets were aggregated in this study. The first dataset was the Bangladesh Demographic and Health Survey (BDHS) conducted in the years 2011 (BDHS 2011), 2014 (BDHS 2014), and 2017–2018 (BDHS 2017–2018) (The DHS Program). This survey was implemented by the National Institute of Population Research and Training and Mitra and Associates of Dhaka, to obtain information, including details on caregivers, geodata, and diarrhea incidence in children < 5 years old, regarding the target population. The survey data were based on a stratified two-stage cluster design, wherein each sample was represented at both the national and semi-national levels. Each respondent represented a data point within a cluster, which in turn represents the buffer in the dataset. Each cluster consisted of data from respondents who live within its buffer zone. Cluster data also included average population data calculated within the 2 km (urban area) or 10 km (rural area) buffer surrounding the cluster location (Burgert et al. 2013). These data were combined using geodata.

In this study, the dependent variable was diarrhea incidence in children < 5 years of age. Diarrhea is defined as “the passing of a loose stool three or more times within 24 h.” When questioned if the child experienced diarrhea in the 2 weeks prior to the survey, the parent or caregiver’s response would be either “Yes,” “No,” or “I don’t know.” This study only analyzed the “Yes” and “No” responses, as “I don’t know” responses may invite uncertainties due to the inaccurate recollections of respondents, who may be unaware of the child having diarrhea in the previous 2 weeks.

Independent variables were selected based on previous studies (Kamal et al. 2015; Sinmegn Mihrete et al. 2014). For individuals, the characteristics of each child, namely age, sex, mother’s age, mother’s education, access to sanitation facilities, access to drinking water facilities, wealth index, religion, and residence, were included in this study. The effect of sanitation facility type on diarrhea incidence was also examined. Sanitation facilities were categorized into three types— “improved,” “unimproved,” and “no facilities”—based on the definitions provided in the Joint Monitoring Program (JMP) for Water Supply, Sanitation and Hygiene sanitation ladder shown in Table S1 (UNICEF and WHO 2019). Furthermore, this study added an additional category, namely the disposal route for human excreta (Table S2). “Concentrated” refers to excreta held in an underground pit or tank for a specified period, which carries a probability of exposure to pathogens. “Diffused” refers to excreta placed on the ground with a wider surface area rather than in a designated pit or tank. “Transferred” refers to excreta placed directly into piped systems and transferred to a treatment facility. This categorization was applied in this study due to the geological setting of Bangladesh, which occupies the Bengal Basin, one of the largest sedimentary basins worldwide (Shamsudduha and Uddin 2007). The local geology comprises shallow alluvial soils susceptible to natural disasters, such as extreme rains, river flooding, and tidal surges (Mukherjee et al. 2009). This study used two analytical models; Model I used the variable “sanitation facilities” following JMP ladders, whereas Model II used “sanitation facilities” based on the type of excreta (Table S3).

The second dataset included geodata of flood risk levels in Bangladesh, provided by the Bangladesh Agricultural Research Council (BARC). Depending on geological characteristics, BARC classifies floods as river floods, flash floods, and tidal surges. BARC also categorizes four levels of flood risk— “severely flood-prone (SFP),” “moderately flood-prone (MFP),” “low flood-prone (LFP),” and “not flood-prone (NFP)” (Tables S4 and S5, Figs. S1 and S2). This categorization is based on the frequency and severity of floods experienced in the last 10 years. The severity differs year on year; however, normal floods occur yearly and inundate approximately 25% of the total land area (Dewan 2015). No catastrophic floods were reported during the survey period (BWDB, 2011, 2014, 2017). This study applied the four flood-prone levels to the BDHS data using geodata.

This study excluded high population density and severe drought-prone areas, which were assumed to have different health risk factors. Densely populated areas with > 20,000 people were excluded using the geodata of the primary sampling unit, which includes groups of approximately 10–20 households. Severe drought-prone areas were excluded by applying a drought risk area dataset provided by BARC (Fig. S3). Geodata on flood and drought risk are available in the Humanitarian Data Exchange (The Humanitarian Data Exchange, 2021).

Statistical analysis

The independent variables used in the analysis are shown in Table S6. Multivariate analysis was performed to examine all covariates and include community level as a random intercept. As BDHS involves two-stage stratified cluster sampling, the collected individual data at the community level are nested as a cluster. The structure multilevel random intercept model was implemented for such nested data. A multilevel logistic regression model is usually applied for binary dependent variables; however, this study used a linear probability model to assess the probability of sanitation and flood risk impact on diarrhea incidence. Linear regression is applied for non-binary dependent variables, but it can also be appropriate when the area of interest concerns the effectiveness of an estimation (Mood 2010). The interpretation is straightforward, and interaction terms are not influenced by random intercepts (Beier 2018; Mood 2010). However, the linear probability model may estimate values > 1 or < 0, which is not a fundamental problem if most coefficients fall within the range and have residue heteroscedasticity, because the dependent variable is binary, although this can be avoided by applying robust standard errors.

Confidence intervals (CIs) and the coefficients (β) estimated in linear probability models were used for analysis in this study. As this study examined how sanitation type influences the effect of flood risk on diarrhea prevalence and vice versa, the interaction terms of sanitation type and flood risk were applied to the models.

For the spatial distribution of diarrhea prevalence, choropleth maps of each survey year were created in the aggregated BDHS. This study used districts as spatial enumeration units for diarrhea prevalence. The analyses were performed using the “lmerTest” package, and the map was created using the “sf” package for R version 4.1.2 (R core team 2021).

A total of 24,232 samples from children < 5 years of age were obtained from the BDHS for 2011, 2014, and 2017–2018. After removing all missing and excluded data, 14,404 data points were used for analysis, of which 697 were diarrhea cases. Table 1 shows the characteristics of BDHS target populations in 2011, 2014, and 2017–2018.

The spatial distribution of diarrhea prevalence during each survey year is shown in Figs. S4–S6. Figure S7 indicates the average prevalence for all years. The districts of Manikganji in Dhaka, Nawabganji in Rajshahi, coastal areas of Barisal and Habiganji in Sylhet, and the western part of Chittagong reported high diarrhea prevalence.

The results of multilevel linear probability models I and II are shown in Table S7; Table 2 only shows the results which this study considered. In Model I, the JMP ladder had no significant association with diarrhea incidence, with or without interactions between sanitation and flood risk categories, whereas the diffused sanitation type in Model II showed positive associations with diarrhea prevalence with interactions (β = 0.027, CI: 0.004, 0.05). Some flood-prone areas were associated with diarrhea incidence. In Model I without interactions, LFP areas showed significant negative associations with diarrhea incidence (β = − 0.013, CI: − 0.024, − 0.003), and SFP areas exhibited significant positive associations with diarrhea incidence (β = 0.015, CI: 0.001, 0.027). Furthermore, in Model I with interactions, MFP areas showed significant positive associations with diarrhea incidence (β = 0.012, CI: 0.001, 0.024). In Model II, LFP areas showed significant negative associations with diarrhea incidence, both with (β = − 0.113, CI: − 0.02, − 0.001) and without (β = − 0.013, CI: − 0.023, − 0.003) interactions. SFP areas reported significant positive associations with the incidence of diarrhea both with (β = 0.022, CI: 0.008, 0.035) and without (β = 0.015, CI: 0.003, 0.027) interactions. Interaction effects were observed in both models. In Model I, unimproved sanitation and MFP interactions showed significant negative associations with diarrhea prevalence (β = − 0.022, CI: − 0.040, − 0.005). In Model II, diffused sanitation type and SFP interactions showed significant negative associations with diarrhea prevalence (β = − 0.060, CI: − 0.095, − 0.023). As this study excluded high population density areas before analysis, transferred sanitation prevalence decreased. A sensitivity analysis was conducted by including all cases except severe drought areas, and it showed comparable results.

Table 1

Target population demographic data based on BDHS from 2011, 2014, and 2017–18.
	Diarrhea = No	Diarrhea = Yes
Cases	13,707	697
BDHS survey year (%)
2011	4829 (35.2)	245 (35.2)
2014	4274 (31.2)	208 (29.8)
2017–2018	4604 (33.6)	244 (35.0)
Child age (%)
0–11 months	2348 (17.1)	148 (21.2)
12–23 months	2507 (18.3)	232 (33.3)
24–35 months	2690 (19.6)	128 (18.4)
36–47 months	2996 (21.9)	101 (14.5)
48–60 months	3166 (23.1)	88 (12.6)
Sex of child
Male (%)	7070 (51.6)	392 (56.2)
Female (%)	6637 (48.4)	305 (43.8)
JMP ladder^a
Improved (%)	7785 (56.8)	372 (53.4)
Unimproved (%)	5922 (43.2)	325 (46.6)
Sanitation type^b(%)
Concentrated	12,347 (90.1)	616 (88.4)
Diffused	1253 (9.1)	73 (10.5)
Transferred	107 (0.8)	8 (1.1)
Toilet shared/individual (%)
Individual	7892 (57.6)	362 (51.9)
Shared	5212 (38.0)	300 (43.0)
No facilities	603 (4.4)	35 (5.0)
Water facilities
Unimproved (%)	370 (2.7)	14 (2.0)
Households with electricity
Yes (%)	8577 (62.6)	434 (62.3)
Households with a refrigerator
Yes (%)	2350 (17.1)	108 (15.5)
Literacy (%)
Yes	8435 (61.5)	413 (59.3)
Some	1618 (11.8)	90 (12.9)
3654 (26.7)	194 (27.8)
Religion (%)
Islam	12,482 (91.1)	651 (93.4)
Hindu	1144 (8.3)	44 (6.3)
Other religion	81 (0.6)	2 (0.3)
Mother’s education (%)
High school	1198 (8.7)	55 (7.9)
Secondary school	5821 (42.5)	264 (37.9)
Primary school	4320 (31.5)	261 (37.4)
No education	2368 (17.3)	117 (16.8)
Mother’s age [mean (SD^*)]	26.67 (5.88)	25.76 (5.54)
Residential area
Urban (%)	25.76 (5.54)	199 (28.6)
Wealth index (%)
Poorest	3804 (27.8)	200 (28.7)
Poorer	2909 (21.2)	163 (23.4)
Middle	2529 (18.5)	141 (20.2)
Richer	2465 (18.0)	94 (13.5)
Richest	2000 (14.6)	99 (14.2)
Division (%)
Dhaka	2657 (19.4)	154 (22.1)
Chittagong	2433 (17.8)	121 (17.4)
Sylhet	2335 (17.0)	135 (19.4)
Khulna	1689 (12.3)	62 (8.9)
Barisal	1668 (12.2)	95 (13.6)
Rajshahi	1415 (10.3)	67 (9.6)
Rangpur	1510 (11.0)	63 (9.0)
Population of children < 5 years of age [mean (SD)]	1503.27 (3796.48)	1720.75 (4328.00)
Population density [mean (SD)]	1803.81 (2426.04)	1843.10 (2529.63)
Flood-prone area (%)
NFP	4756 (34.7)	246 (35.3)
LFP	3140 (22.9)	116 (16.6)
MFP	4151 (30.3)	223 (32.0)
SFP	1660 (12.1)	112 (16.1)
^a WHO/UNICEF Joint Monitoring Program sanitation ladder
^b Sanitation type, categorized by storage or transfer of excreta
^* Standard deviation (SD)

Table 2

Multilevel linear probability models (Model I and Model II).
	Model I: JMP ladder without interactions		Model I: JMP ladder with interactions		Model II: Sanitation type without interactions		Model II: Sanitation type with interactions
	β	95% CI	β	95% CI	β	95% CI	β	95% CI
JMP ladder
Improved	(reference)		(reference)
Unimproved	0.003	(–0.004, 0.018)	–0.015	(–0.0004, 0.026)
Sanitation type
Concentrated					(reference)		(reference)
Transferred					0.023	(–0.019, 0.068)	0.030	(–0.02, 0.07)
Diffused					0.004	(–0.013, 0.019)	0.027	(0.004, 0.05)
Toilet shared / private
Private	(reference)		(reference)		(reference)		(reference)
Shared	0.009	(0.0005, 0.016)	0.009	(0.002, 0.017)	0.009	(0.001, 0.016)	0.009	(0.0009, 0.016)
No facilities/bush	0.008	(–0.009, 0.025)	0.007	(–0.011, 0.028)	0.005	(–0.017, 0.027)	0.0001	(–0.02, 0.02)
Flood-prone level
NFP	(reference)		(reference)		(reference)		(reference)
LFP	–0.013	(–0.024, − 0.003)	–0.009	(–0.023, 0.005)	–0.013	(–0.023, − 0.003)	–0.113	(–0.02, − 0.001)
MFP	0.003	(–0.005, 0.012)	0.012	(0.001, 0.024)	0.003	(–0.005, 0.012)	0.005	(–0.004, 0.014)
SFP	0.015	(0.001, 0.027)	0.024	(–0.003, 0.030)	0.015	(0.003, 0.027)	0.022	(0.008, 0.035)
Interaction (Model I)
JMP Unimproved * LFP			–0.009	(–0.029, 0.007)
JMP Unimproved * MFP			–0.022	(–0.040, − 0.005)
JMP Unimproved * SFP			0.003	(–0.019, 0.026)
Interaction (Model II)
Sanitation transferred * LFP							–0.025	(–0.121, 0.083)
Sanitation diffused * LFP							–0.021	(–0.057, 0.012)
Sanitation transferred * MFP							–0.066	(–0.224, 0.099)
Sanitation diffused * MFP							–0.021	(–0.055, 0.012)
Sanitation transferred * SFP							0.037	(–0.145, 0.210)
Sanitation diffused * SFP							–0.060	(–0.095, − 0.023)
Dependent variable: diarrhea = yes, Model I: multilevel linear probability model of JMP ladder, Model II: multilevel linear probability model of sanitation type

This study examined the influence of various sanitation facility types and flood risk levels and their effect on diarrhea prevalence among children < 5 years of age in Bangladesh. Although previous studies have explored the effects of improved sanitation on health (Clasen et al. 2014; Knee et al. 2018; Patil et al. 2014; Sinha et al. 2017), this study added another categorization to sanitation facilities, focusing on transfer or storage of excreta in the environment. The study also focused on how the interactions between flood risk and sanitation facilities affect diarrhea prevalence. Previous research has focused on the influence of flood events or extreme weather on diarrhea prevalence in low- and middle-income countries (Carlton et al. 2014; Chowdhury et al. 2018; Dimitrova and Muttarak 2020; Hashimoto et al. 2014; Hashizume et al. 2008a, b; Milojevic et al. 2012). While these studies focused on specific events and regions, the current study focused on assessing flood frequency and severity using a national-level survey. This study showed how varying flood-prone levels influence diarrhea prevalence, depending on the type of sanitation facility available.

The spatial distribution of diarrhea prevalence highlighted regional inequalities. Prevalence was high in the southern coastal area and parts of the eastern area of Bangladesh. These results aligned with those of previous studies showing high malnutrition rates in southeastern regions, including Barisal, Chittagong, and Sylhet (Akram et al. 2018; Saha et al. 2019). As diarrhea prevalence is associated with growth stunting (Guerrant et al. 2013), this study also indicates that the southeastern and western regions may require additional measures to address child malnutrition and diarrhea. The northwestern regions exhibited a relatively low diarrhea prevalence, with the exception of the western part of the Rajshahi division; however, this area is prone to a severe drought risk and was thus excluded from the study (Figs. S1, S3, and S7).

The multilevel linear probability model showed no association between the JMP ladder in Model I and diarrhea prevalence with or without interactions; however, the interaction between unimproved facilities and MFP exhibited significant positive associations with diarrhea prevalence. In Model II, diffused sanitation type showed significant positive associations with diarrhea prevalence with interactions, whereas the interaction between diffused sanitation type and SFP showed significant negative associations with diarrhea prevalence. This result may indicate that diffused sanitation type reduces the possibility of contact with pathogens in SFP areas, as excreta are more likely to be flushed away by floodwaters. This result is consistent with the control of a case study conducted in Ecuador during the wet season (Bhavnani et al. 2014). These findings imply that while environmental factors in SFP areas may affect health, pathogen concentration in such areas is low, thus reducing the risk of possible pathogen contact.

Although flood-prone areas or floodplains provide suitable soils for agriculture, they are unstable and insecure environments for people who are forced to evacuate when water inundates the region (Brammer 2016). Presumably, the risk is reduced if households do not have facilities or hanging latrines, because feces are not concentrated in their living environment. Model II showed that LFP was negatively associated with diarrhea prevalence compared with NFP. This could be explained by climatological factors and the characteristics of pathogens that cause enteric infections. For example, cholera is highly prevalent during the wet season, especially during and after flood events; however, diarrhea caused by rotavirus is more prevalent than cholera when humidity and temperature are low (Bray et al. 2019; Jagai et al. 2012; Schwartz et al. 2006). NFP areas have a higher risk of diarrhea, especially in the dry season, and exhibit a higher prevalence than that in LFP areas.

This study showed that shared sanitation facilities were positively associated with diarrhea prevalence in both Models I and II, supporting the JMP ladder, which stated that shared sanitation facilities considered limited sanitation services rather than improved services (UNICEF and WHO 2019). Studies examining the Mapsan trial have shown that improving shared sanitation facilities prevents human fecal contamination in target communities (Holcomb et al. 2020, 2021). The diarrhea prevalence rate among households with children < 5 years of age and shared sanitation facilities was significantly higher than that in households with non-shared facilities (Fuller et al. 2014). Furthermore, a previous study applied a mathematical model with fictitious pathogens and communities, demonstrating that high coverage of shared sanitation facilities increases the likelihood of contact with infected people and the risk of enteric infections (Just et al. 2018). Although numerous factors, such as socioeconomic status and caretaker education level, should also be considered (Heijnen et al. 2014), the result emphasizes that shared sanitation itself is a key risk factor resulting in adverse health outcomes.

Although the result of this study contradicted the JMP ladder criteria, some studies also showed mixed results regarding improved sanitation. For example, the Total Sanitation Campaign, one of the largest intervention projects conducted in India that aimed to end the practice of open defecation, revealed that installing sanitation facilities had no significant effect on reducing diarrhea prevalence despite decreasing the frequency of open defecation (Patil et al. 2014). Another intervention conducted in rural areas of Kenya and Bangladesh, also known as WaSH Benefits, showed different results. Contreras et al. (2022) showed that individual sanitation interventions (such as pit latrine installation, providing tools to clean child feces, and promoting behavioral change) reduced diarrhea prevalence, whereas Pickering et al. (2019) found that only integrated interventions involving drinking water, sanitation facilities, and nutrition effectively reduced the risk of helminth infection. Another community-level study, focusing on hotspot districts of diarrhea prevalence in children, reported increased rates in communities with low ratios of improved sanitation facilities (Azage et al. 2016). To summarize, community environmental and meteorological factors should also be considered.

Geological factors may be another key element. In this study, the interactions between flood risk level and sanitation type were significantly associated with diarrhea prevalence. In Model II, SFP areas had a significant positive association with diarrhea prevalence, but the interaction between SFP and diffused sanitation type had a significant negative association. This result may be explained by geological features in Bangladesh, which is located in the Bengal Delta, comprising the Ganges and Brahmaputra-Jamuna Deltas and the Sylhet Basin (Mukherjee et al. 2009; Umitsu 1987). As it is located in a coastal region and an alluvial lowland, the Bengal Delta experiences river flooding as well as flooding due to tidal surges (Umitsu 1987; Wesselink et al. 2015). As this alluvial floodplain contains sandy silt or sand, any infrastructure installed in this environment carries a high risk of inundation, destruction, or deterioration due to unstable sediments. In this environment, sanitation systems such as pit latrines, which collect and store fecal sludge in vaults for years, may not be safe, as fecal sludge can contaminate the surrounding aquifers in rural areas (Islam et al. 2016; Ravenscroft et al. 2017) or surface floods in urban areas (Jenkins et al. 2015). Unlike improved sanitation facilities, open defecation, or bucket latrines, the diffused sanitation type defined in this study may not contaminate the surrounding aquifer, because it does not collect sludge at a specific place, which in turn may avoid pathogen concentration. Similar results were observed for Model I—MFP had significant positive associations with diarrhea prevalence but a negative association when interacting with unimproved sanitation types. Further studies are needed; however, improved sanitation facilities did not exhibit a clear association with the reduction in diarrhea prevalence in flood-prone areas.

This study showed an increased risk of diarrhea in MFP and SFP areas, where prevalence was not reduced by improved or concentrated sanitation systems. However, this does not mean that unimproved sanitation, such as pit latrines without slabs or open defecation areas, presents a better option. A potential future system may involve fecal sludge emptying from storage vaults at specific intervals, followed by transfer and appropriate treatment. The effectiveness of implementing alternative sanitation systems, such as “constructed wetland system (CWS)” or “ecological sanitation toilets,” has been discussed, especially in low- and middle-income countries (Bydałek and Myszograj 2019; Jehawi et al. 2020; Langergraber and Muellegger 2005). In Bangladesh, the Reed Bed System, a CWS, has been proposed (Biswas 2014) and has recently been introduced to Rohingya camps (Kabir et al. 2020; Saeed et al. 2022). In this system, fecal sludge can be emptied, transferred, and treated appropriately and safely. Other advantages of these systems include their low cost and potential as a fertilizer source for agricultural use (Kabir et al. 2020). Examining and expanding alternative systems, including those mentioned previously, in flood-prone areas will contribute to the development of appropriate sanitation systems in Bangladesh.

This study has several limitations. Firstly, the geodata for each cluster in all BDHS results were randomly displaced from their original location to maintain respondent confidentiality. Geodata could be displaced outside the district but could not cross divisional boundaries. The choropleth map was created at the district level; therefore, some clusters might have been displaced outside their original district. Secondly, because diarrhea episodes were based on caregivers’ self-reports, they were subject to recall bias.

Using aggregated BDHS data, this study showed the influence of flood risk and sanitation facilities on diarrhea prevalence among children < 5 years of age in Bangladesh. The MFP and SFP areas affected by unimproved sanitation showed negative associations with diarrhea prevalence. The findings indicated an increased risk of diarrhea in MFP and SFP areas. In addition, installing improved sanitation facilities may not directly contribute to diarrhea prevention. Geological and climatological features should be considered together to develop more appropriate sanitation technologies in regions with large floodplains, such as Bangladesh. In addition, future studies should focus on specific regions, such as SFP or NFP areas, to investigate latent factors that may affect diarrhea prevalence among children < 5 years of age.

Funding

No funds, grants, or other support were received during the preparation of this manuscript.

Data availability

All data and materials associated with this study are presented within the manuscript.

Author contributions

MK: conceptualization, methodology, data curation, formal analysis, writing (original draft preparation), validation, formal analysis, investigation, and review and editing.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author has no relevant financial or non-financial interests to disclose.

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Influence of sanitation facilities on diarrhea prevalence among children aged below 5 years in flood-prone areas of Bangladesh: A multilevel analysis

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