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Policy Research Working Paper 8924
An Assessment of Water, Sanitation and Hygiene Access in Bangladesh’s
Community Health ClinicsGeorge Joseph
Bushra Binte AlamAnne ShresthaKhairul Islam
Santanu LahiriSophie Ayling
Water Global PracticeJune 2019
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Produced by the Research Support Team
Abstract
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Policy Research Working Paper 8924
Adequate water, sanitation and hygiene (WASH) in health care facilities plays a critical role in ensuring improved health care utilization and reducing disease burden due to reinfection. WASH in health facilities is now gaining momentum with the new SDG targets that governments have vowed to meet. This goal calls for a baseline examina-tion of existing WASH conditions in health facilities. Using data collected through a census of all community health
clinics in Bangladesh, this paper presents an analysis of the state of WASH in Bangladesh’s rural, public health facilities highlighting that the lack of functionality of WASH facili-ties is a widespread problem across the country. The paper also identifies priority areas for action when considering the prevalence of poverty and chronic undernutrition at the upazilla level.
This paper is a product of the Water Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at [email protected].
An Assessment of Water, Sanitation & Hygiene Access in Bangladesh’s Community Health Clinics
George Joseph, Bushra Binte Alam, Anne Shrestha , Khairul Islam, Santanu Lahiri, Sophie Ayling
JEL: I15; I18, Q01 Key Words: SDG, Health Clinics, Health Centers, WASH access, Water Supply, Sanitation, Bangladesh
Corresponding author: George Joseph (Water Global Practice, The World Bank, Washington DC, USA 20433, [email protected]); Anne Shrestha (Water Global Practice, The World Bank, Washington DC, USA 20433,[email protected]); Bushra Binto Alam( Health Nutrition and Population Global Practice, The World Bank, Dhaka, Bangladesh; [email protected]; Khairul Islam, Water Aid Bangladesh, Dhaka , Bangladesh,[email protected]; Santanu Lahiri, Water Global Practice, The World Bank, Dhaka, Bangladesh, [email protected]); Sophie Ayling ([email protected], InterAmerican Development Bank, Washington DC, USA 20577
This paper is a product of an assignment conducted for the FAO and World Bank using data collected by Community Based Health Care (CBHC) with support from World Bank and WaterAid Bangladesh. This was made possible by the SwedishInternational Development Cooperation Agency and benefited from funding from the Government of Japan through theJapan Trust Fund for Scaling Up Nutrition. We would like to thank the valuable contributions of Prof. Dr. Md. Abul HashemKhan, Dr. Ashish Kumar Saha, Er. Md. Ziaul Haque, Dr. Barendra Nath Mandal, Dr. Farzana Munmun, Ms. Soma Ghosh Moulik, Mr. Arif Ahamed, Ms. Rokeya Ahmed, Mr. Abu Ahmed Mansoor Kabir, Mr. Mohammed Abu Hamid, Mr. SM Rezaul Islam, Mr.Mohammed Abu Hamid, Mr. Bakhtiar Sohag, Mr. Sahil Deo, Ms. Sanjana Krishnan, Mr. Akramul Haque and Mr. Tareq Mahamud.
The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of FAO or The World Bank, its Board of Executive Directors, or the governments they represent. FAO or The World Bank does not guarantee theaccuracy of the data included in this paper.
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Water, Sanitation & Hygiene (WASH) in Bangladesh’s Community Clinics
Introduction
Adequate WASH in health care facilities (HCFs) is critical to uphold their very purpose: to provide effective medical services. The lack of adequate WASH in HCFs not only compromises the effectiveness of service delivery but also has the potential do more harm than good as transmission of infectious diseases in public settings holds higher risks of causing large epidemics in comparison to household settings (Caincross et al.,1996). Moreover, the burden of infections is higher among patients and newborns whose immune systems are already compromised. Endemic health‐care‐associated infections are a major problem in the developing world, where HCFs are likely to have lower hygiene standards (Allegranzi et al.,2011). HCF‐acquired neonatal infection rates are three to twenty times higher in resource‐limited countries than in industrialized ones (Zaidi et al., 2005). Poor WASH in HCFs may discourage women from giving birth in health facilities or cause delay in care‐seeking (Velleman et al.,2014). This is important to note in the context of Bangladesh where the percentage of births accompanied by a skilled birth attendant is already low (32%) (Tatem et al., 2014). The WHO (2014) reports that in low income settings, an estimated 10‐15% of maternal deaths are due to infections that can be linked to unhygienic conditions. On the other hand, improving WASH conditions could help establish trust in HCFs and encourage mothers to seek prenatal care and delivery services at facilities (Russo et al. 2012).
This brief report seeks to understand the status of WASH in rural, lowest tier, public HCFs in Bangladesh known as ‘community clinics’ (CCs). CCs form the first line of care for most rural Bangladeshis. In other words, they serve over 60 percent of Bangladesh’s 163 million population.
So far, the focus had been skewed towards household WASH improvements as the Millennium Development Goals (MDGs) did not include targets for WASH in HCFs and public spaces in general. However, the Sustainable Development Goals (SDGs) have now specified targets for WASH in public spaces, including HCFs, using the guidelines set forth by the Joint Monitoring Program (JMP) of the World Health Organization, as shown in Figure 1 (WHO, UNICEF, 2018). The guidelines also include indicators for health care waste management and environmental cleaning at HCFs (not shown in figure). Governments and donors are now encouraged to prioritize the goals and ensure an adequate system to enforce and monitor the guidelines in HCFS.
Adequate WASH at HCFs is, however, also crucial for other development goals (SDGS) such as poverty reduction and human development. Poor WASH has implications on long‐term poverty reduction and overall human development. The poor, usually the bottom 40 (B40) of the income distribution, are the ones who use CCs as their first line of health care. They also carry the added burden of disease and
Figure 1: JMP service ladder for WASH in HCFs
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inadequate nutrition. Together, in the short term, this puts pressure on resources of the poor and in the long term, has the potential to compromise capabilities that push them back into to the cycle of poverty. Most often, this is manifested in chronic undernutrition. Chronic undernutrition is where a child’s ability to grow to their full physical and cognitive potential is stunted, due to chronic deprivation of essential nutrients through inadequate dietary intake and/or disease (UNICEF, 1990). There is growing evidence that inadequate WASH adds to the disease causal pathway of stunting through increased burden of diarrheal diseases and environmental enteric dysfunction (EED) (Humphrey, 2009; Ngure et al., 2014; Prüss–Ustün et al. 2014). Since about 35 percent of all children under five in Bangladesh are stunted, it is important to prioritize WASH in community clinics when we consider the far‐reaching impacts it can have on human development.
Another factor that invites a closer look at community clinics in Bangladesh is its decentralized institutional framework for primary health care provision. Theoretically, a decentralized system can be more efficient and have greater accountability. However, in countries with high regional disparities in income, decentralization often reduces the redistributive powers of the central government and limits the level of transfers from richer to poorer jurisdictions, worsening equity (Akin et al.,2005). In Bangladesh, this means that the Union Parishads (UPs) are responsible for allocating resources towards CCs. The poorer the UPs, the less likely they are to have sufficient resources to allocate towards WASH in CCs in the face of competing budget demands. Therefore, without an intervention that enables such resource allocation towards WASH in HCFs, CCs in poorer UPs may not have the means to improve their conditions.
Thus, while this analysis presents a snapshot of the status of WASH in CCs at the upazila (sub‐district) level in Bangladesh, it also seeks to understand the extent of the problem in relation to the incidence of poverty and stunting. Specifically, it identifies regions with elevated levels of poverty, high stunting and low WASH in CCs, which are those where the UPs may not be able to improve outcomes on their own and prioritized interventions are called for.
The remaining sections of this brief describe the data used for this analysis and method used to create a WASH index, the status of WASH in upazilas and concludes with a discussion on the way forward.
Data Sources
This analysis uses three main sources of data to combine upazila level HCF WASH data, poverty data and stunting data for children under five.
For the WASH indicators, data from a 2017 rapid assessment survey of WASH by the Community Based Health Care (CBHC), Directorate General of Health Services (DGHS) and the Ministry of Health and Family Welfare (MoH&FW) was used. The survey covers 63 zilas (districts) and 469 upazilas out of 492 upazilas. As of June 2016, DGHS registry1 showed that 13, 394 CCs were in operation. The rapid assessment was designed for scale rather than depth. It only contains six questions on the type of water, sanitation and hand‐washing facility and the state of their functionality that the CCs self‐reported online.2 The questions on functionality highlights the value‐added of this survey as this information is not usually captured in surveys. To verify the reliability of this data, CBHC used a validation survey (WaterAid Bangladesh, 2018)3 , the results of which we compare herein.
1 http://facilityregistry.dghs.gov.bd/index.php 2 For details, see Annex‐Table 1 3 Two CCs from each upazila were randomly selected
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The poverty figures at the upazila level are derived from the predicted poverty estimates from Steele et al. (2017).4 It uses overlapping data from i) traditional household surveys (DHS, HIES, 2010 and Census 2011); ii) remote sensing data such as night‐time lights, distance to roads, distance to closest urban settlements, climate variables; and iii) call detail records at varying spatial resolutions to estimate poverty rates using Bayesian geostatistical models (BGMs) (See Annex‐Figure 1 for a upazila level map with poverty estimates). The only other poverty estimate available to us at the time of analysis were the national estimates from Census 2011, which we deemed outdated to use alongside 2017 WASH data.
The upazilas level stunting estimates for children under five were taken from the Small‐Area Estimation of Child Undernutrition in Bangladesh report by Haslett et al. (2014), Bangladesh Bureau of Statistics (BBS) and the World Food Programme (WFP). The estimates combine survey data from the Child and Mother Nutrition Survey of Bangladesh 2012 (CMNS) and the Health and Morbidity Status Survey 2011 (HMSS) which some additional data from the BBS Census 2011.
Method
In addition to descriptive statistics, the primary objective of this analysis is to have a upazila level spatial snapshot of the state of WASH in CCs. To this end, we first create tiers for each category of WASH using the available survey data. The questions in the survey are not comprehensive enough to create the complete JMP tiers. However, we follow the JMP guidelines to the extent possible with the available data. Specifically, each of the three category tiers are coded as shown in Table 1.5 Table 2 shows the number and proportion of CCs that fall under each tier.
Each of the three tiers are then scaled into an index between 0 and 1. The WASH score for each HCF is the aggregate of the three indices combined. And the WASH index is the mean of the HCF WASH scores at the upazila level, scaled between 0 and 1. The upazilas with WASH index below 0.5 are categorized as ranking “low” in WASH, whereas those with a score above 0.5
4 Mean probability of households being below $2.50/day using the Progress Out of Poverty Index (PPI) computed using survey data. 5 The survey includes whether there was arsenic testing done at the HCF, however, no further information on water treatment or quality was solicited. The survey also includes a question on when the pit latrine was last emptied. However, this information is not complete enough to make a proper assessment of the state of sanitary facilities beyond functionality. Therefore, we leave those out in constructing our index. See Annex‐Table 1 for questionnaire.
Table 2: HCFs at each WASH Tier, by category
Water Freq. %
0 7,690 64.67 1 3,692 31.05 2 509 4.28
Total 11,891 100
Sanitation Freq. Percent
0 3,539 30.03 1 6,402 54.33 2 1,586 13.46 3 183 1.55 4 74 0.63
Total 11,891 100
Handwashing Freq. Percent
0 1,697 14.27 1 4 0.03 2 10,190 85.7
Total 11,891 100
Table 1: WASH Tiers
Tier Water Sanitation Handwashing
0 No supply/ Not
functional No latrine/ Not
functional No facility
1 Functional hand‐pump
One functional latrine
Facility with water
2 Functional piped water
Two functional latrines
Facility1 with water and soap
3 ‐ Three functional
latrines ‐
4 ‐ Four functional
latrines ‐
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are categorized as ranking “high” in WASH. However, it is important to note that a perfect score of 1 indicates relatively better WASH but not necessarily adequate WASH based on JMP standards. Therefore, it is most useful to read these results to compare across upazilas keeping in mind that bad scores indicate very poor WASH in CCs but good scores do not necessarily reflect adequate WASH.
Similarly, the upazila level poverty probabilities and stunting estimates are also rescaled to range
between 0 and 1, and then categorized into “low” or “high” poverty/stunting based on the 0.5 score cut‐off. Again, “low” and “high” should be read in relative terms only.
Using the WASH, poverty and stunting indices, we identify and map upazilas in Bangladesh that rank high in poverty and in stunting in relation to their WASH status. The base maps for the upazilas are from the Global Administrative Areas database.
The Status of WASH in Community Clinics: A Snapshot
The community clinics in Bangladesh still have a long way to go to achieve adequate WASH facilities that are critical to providing safe, hygienic environments that help promote proper treatment and well‐being of patients. Overall, the rapid assessment data shows that adequate handwashing facilities at CCs are becoming near universal, whereas adequate water and sanitation are far from adequate.
A large majority of the community clinics had limited WASH facilities only. Table 3 shows the number of CCs that met individual WASH category criteria. Less than 36 percent of the community clinics had basic water supply, i.e. a functional improved water source. Around 30 percent did not have any functional sanitation facility. Only 16 percent had two functional latrines on premises. According to the 2008 WHO guidelines, HCFs must have at least 4 functional, improved sanitation facilities on premises. Less than 2 percent of the community clinics met this WHO criterion. Eighty‐five percent of the community clinics have adequate handwashing facility with soap and water. However, it is unclear to what extent the handwashing facilities were affected by the lack of functional water supply.
The CBHC survey revealed that a major challenge is that many community clinics did not have any type of WASH facility within premises; 2,280 community clinics (19 percent) did not have any functional water, sanitation or hand‐washing facility.6
Of the 11,891 CCs surveyed, 3,660 community clinics reached the ‘limited’ JMP standard detailed in Figure 1. In other words, only about 31 percent of the community clinics had at least a functional improved water source (handpump or piped), at least one functional latrine (type unknown), and a handwashing station with water and soap. If we set a stricter standard of at least two functioning latrines (one for each gender) as suggested as one of the criterion in the JMP ‘basic’ sanitation tier, the number of community clinics with adequate overall WASH falls to 1,003. These preliminary numbers show that most rural and peri‐urban residents in Bangladesh have less than reliable primary health services in CCs, which are often the first line of care for patients.
6 For further details, please refer to Annex‐Table 3 and Annex‐Table 4.
Table 3:HCFs with limited/ basic WASH, by category
WASH Category Freq. %
Basic Water 4,201 35.33
Limited Sanitation ≥ 1 latrines 8,352 70.24
Limited Sanitation ≥ latrines 1,950 16.4
Limited Sanitation ≥ 4 latrines 181 1.52
Limited Handwashing 10,190 85.7
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Most community clinics (87 percent) relied on hand‐pumps as their water source. However, only 14.5 percent of the them had conducted an arsenic test7. Due to limited data, the result of the test or treatment measures taken to improve water quality before use is unknown.
While all the community clinics had technologically improved sources of water supply such as hand‐pump, rain water jar or piped water, 64.8 percent of them were not functional. This statistic adds a challenging dimension to the ICDDR,B figure reported in WHO (2015)8, where 97% of HCFs are said to have water coverage without considering the functionality of the water source. It is unclear how the community clinics meet their water needs when their primary source is non‐functional and whether the secondary sources were improved sources. The validation survey showed that only 49.1 percent of the water facilities were non‐functional. However, this could be attributed to the difference in time periods9 of assessment and/or sampling.
Reliability of water on premises is key for HCFs. Time spent collecting water can take time away from treating patients. Due to the high frequency of non‐functional water sources, the water index in Figure 2 shows a need for progress at scale in terms of water coverage in CCs in Bangladesh. Mymensingh and Barisal divisions have the highest concentration of upazilas with the lowest water index scores, although this is widespread in other divisions as well.
Non‐functionality was a significant challenge for sanitation facilities as well. While 99 percent of the CCs had at least one latrine, over 28 percent of them did not have any functional latrines. Around 13 percent had two functional latrines and about 2 percent had three or more functional latrines. Figure 3 show the map of the sanitation index by upazila. Again, Mymensingh and the south of Dhaka appear to have the highest number of upazilas with poor sanitation at community clinics. It is important to note that the validation survey showed a lower prevalence of non‐functional sanitation facilities at the CCs by about 10 percentage points. The validation survey also collected data on whether the status of latrines was affected due to flooding. The results showed no significant difference.
The extent of unimproved facilities in community clinics is unknown, as there is no information about the type of latrine provided in the survey. Moreover, it also does not contain any information on whether the facilities are separated for staff and patients, segregated by gender, or if they are accessible to people with limited mobility. This additional data will be crucial for
7 In Bangladesh, naturally occurring Arsenic in ground water is a major problem in many regions of the country. 8In addition, the report puts sanitation coverage at 0.53 and hygiene coverage 0.79. Again, these figures do no take functionality into account. 9 The validation survey was conducted 1‐2 months after the CBHC rapid assessment.
Figure 2: Community Clinics with Basic Water Access, by Upazila
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Bangladesh to establish a baseline status of WASH in community clinics in order to adequately measure their progress towards the SDGs.
The survey showed that over 85 percent of the CCs have some type of handwashing facility. Fourteen percent of the CCs did not have any facility for handwashing, which is critical to prevent the spread of diseases. 74 percent of the community clinics had bucket and soap available for handwashing. Six percent of community clinics used basin with soap, and another six used tippy taps with soap. It is likely that handwashing facilities lack water in the community clinics where the water sources were not functional. However, assuming that the facilities did have both water (from secondary sources) and soap, handwashing was adequate in most of the upazila community clinics by JMP’s ‘limited’ standard. Figure 4 shows that Mymensingh and south of Dhaka, again, have the most upazilas that rank the lowest.
The validation survey showed a significant difference
in the prevalence of handwashing arrangement available in CCs. It reports that less that 66 percent had any type of handwashing arrangement. This survey collected this data by observation, and therefore is likely to be more valid. Thus, Figure 4 might be showing a more optimistic picture than reality. However, in order to establish the adequacy by JMP standards, more data on the type, number and location of handwashing facilities in necessary.
Figure 5 shows an aggregated picture of the status of WASH in community clinics at the upazila level. The upazilas that had the worst WASH conditions ‐‐with an index score below 0.4‐‐ appear in orange or red and are labeled in the map (a list of these upazilas can also be found in Annex‐Table 5). Mymensingh, Dhaka and Rajshahi divisions have the most number of upazilas with community clinics with low WASH. Rangpur, on the other hand, ranks higher in terms of WASH score despite ranking high in poverty rate. Nonetheless, keeping in mind that higher scores do not necessarily mean adequacy in absolute terms, more information is needed to ensure that CCs in high poverty areas have adequate WASH for proper service delivery.
.
Figure 4: Community Clinics with Handwashing Coverage, by Upazila
Figure 3: Community Clinics with Limited Sanitation Access, by Upazila
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Figure 5: WASH Index by Upazila
Overlaying areas of high/low WASH in the CCs over areas of high/low poverty Figure 6a) and areas of high/low stunting Figure 6b), we find that there isn’t much of a correlation between poverty and status of WASH in community clinics. Whereas, we find some correlation between the areas with relatively higher WASH and areas with low stunting. Notably, the western half appears to have higher levels of WASH in community clinics, lower levels of stunting despite higher levels of poverty. This reveals that stunting is an issue that transcends class lines in Bangladesh. It is an interesting area for further research to understand the underlying causal relationships and to draw lessons for the eastern part which fares
worse in terms of these outcomes.
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Figure 6a and 6.b show that Mymensingh appear in both maps as a region with high poverty and high stunting with low levels of WASH in community clinics. However, the problem is more widespread; there are upazilas scattered throughout Bangladesh with high poverty, high stunting and low WASH in community clinics. Annex‐Figure 2 shows the upazilas with high stunting and high poverty whereas, Annex‐Figure 3 shows its subset where health center WASH also ranks low. The list of upazilas in the latter figure is also available in Annex‐Table 6. These are the upazilas with the highest need but are most likely faced with the highest budget constraints to improving WASH facilities in health care and public domains in general
Figure 6.a & 6.b: State of WASH in Community clinics and Poverty, by Upazila (Source: DGHS, WB, 2017; Steel et al. 2017)
Discussion
Preliminary results from the rapid assessment data indicate that the state of WASH in community clinics in Bangladesh is a matter of high concern. While there is a long road ahead in achieving adequate levels of WASH in community clinics, the recent WASH guidelines in the works for WASH in community clinics by led by MoH&FW and WHO –and the community clinic WASH improvement programs being led by non‐profits such as WaterAid‐‐ show that Bangladesh is already on the path to addressing the challenges highlighted by this CBHC survey.
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One of the main findings is that the prevalence of non‐functional water and sanitation facilities is a significant issue. More information is necessary to establish what the secondary sources are, and if they are adequate for effective health service delivery. There is also a need for more comprehensive data to be collected not just on where arsenic testing has been carried out, but what the results were and whether proper treatment was administered to mitigate the issue before usage.
In relation to our health indicator for human development, the degree of overlap between upazilas with high levels of stunting and low levels of WASH is remarkably high and calls for a closer look at the determinants of stunting in those regions.
Despite low income levels, the west of Bangladesh had relatively higher levels of WASH in its community clinics. As noted in the above sections, there are two main caveats to keep in mind. One, that the index is a relative measure and a perfect score of 1 is by no means indicative of adequate WASH. Two, it is worth entertaining the possibility that there could have been measurement errors in the collection of data for the rapid assessment. If that is the case, we can expect to see over‐ or under‐estimated rates of WASH at community clinics.
While the lowest income areas are not necessarily the ones where WASH is the poorest, it is worth noting that the Union Parishad’s limited revenues, particularly own resource mobilization, will most probably be a constraint to achieving adequate level of WASH by the SDG standards. Therefore, the local governments who are accountable for the improvement of community clinics need support at the national level through dedicated budget accounts. This pattern where WASH levels are higher in poorer regions could also reflect that more Bangladeshis in poorer regions use community clinics whereas those who live in richer regions bypass community clinics and may prefer to use private health services or hospitals even for the first line of care, causing greater neglect of community clinics in richer regions. If this happens to be the case, then the poorest of the poor in relatively higher income regions are likely to be left out to a greater degree than the poorest of the poor in relatively low‐income regions.
However, this assessment only gives us a partial picture. To understand the depth of the problem, a more extensive analysis would be necessary. The simplicity of the rapid assessment survey affords representativeness at the cost of depth of information. Therefore, while this analysis provides a preliminary account of the state of WASH in community clinics, it does not sufficiently establish a baseline against which progress towards SDGs can be measured. The DGHS an MoHFW will benefit from adopting the core survey components designed to assess SDG indicators detailed in the WHO/UNICEF (2018) guidelines, which can then be adjusted to fit the Bangladeshi context.
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ANNEX
Annex‐Table 1: Rapid Assessment Questionnaire
Water Sanitation Hygiene What type of drinking water supply do you have in your community clinic?
How many latrines do you have within community clinic building and how many latrines outside building, but within community clinic premises?
Is there any hand washing arrangement in your community clinic?
▪Type of supply (eg. Handpump, piped)
▪Number of toilets within and outside building ▪Hand washing facility- yes/no
▪Functional or notfunctional ▪Number of functional and non-functional toilets within and outside building
▪Type of hand washing arrangement (bucket, tap etc.)
Has anyone conducted arsenic test for hand pump? ▪Yes/no
When was the pit emptied last? ▪Emptied/Not emptied ▪ If emptied, year
▪Use of soap (yes/no)
Annex‐Table 2: WHO standards on WASH in Community clinics (Source: WHO, 2008)
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Annex‐Figure 1: Predicted Poverty Prevalence
Annex‐Table 3: Crosstabulation of Community clinics with limited water, sanitation (at least one latrine) and hand‐washing facilities
Handwashing
Limited None
Sanitation (<=1) None Limited None Limited
Water None 897 581 2,280 3,932 Limited 44 179 318 3,660
Annex‐Table 4: Crosstabulation of CCs with limited water, sanitation (at least two latrines) and hand‐washing facilities
Handwashing
Limited None
Sanitation (<=2) None Limited None Limited
Water None 1,361 117 5,420 792 Limited 185 38 2,975 1,003
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Annex‐Table 5: Upazilas with WASH Index Less than 0.4
Division District Upazila Wash Index Poverty Rate Stunting Rate
Barisal Barisal Mehendiganj 0.34 73.81 0.40
Barisal Patuakhali Dashmina 0.37 75.70 0.42
Barisal Patuakhali Dumki 0.38 70.11 0.38
Barisal Pirojpur Nazirpur 0.15 77.28 0.37
Barisal Pirojpur Pirojpur Sadar 0.26 75.11 0.37
Chittagong Bandarban Rowangchhari 0.38 79.65 0.46
Chittagong Brahmanbaria Nasirnagar 0.36 80.74 0.45
Chittagong Brahmanbaria Bijoynagar 0.40 80.06 0.43
Chittagong Comilla Homna 0.06 74.77 0.44
Chittagong Comilla Titas 0.23 72.74 0.41
Chittagong Comilla Comilla Adarsha Sadar 0.26 70.96 0.39
Chittagong Comilla Nangalkot 0.29 71.60 0.41
Chittagong Cox's Bazar Kutubdia 0.33 76.05 0.46
Chittagong Cox's Bazar Chakoria 0.37 73.40 0.43
Chittagong Khagrachhari Panchhari 0.30 78.08 0.46
Chittagong Noakhali Subarnachar 0.10 72.97 0.47
Chittagong Noakhali Senbagh 0.15 70.44 0.42
Dhaka Faridpur Alfadanga 0.21 77.07 0.38
Dhaka Faridpur Bhanga 0.28 75.78 0.40
Dhaka Faridpur Madhukhali 0.35 77.31 0.39
Dhaka Faridpur Nagarkanda 0.39 76.71 0.39
Dhaka Gopalganj Tungipara 0.18 77.21 0.36
Dhaka Gopalganj Muksudpur 0.20 76.70 0.38
Dhaka Kishoregonj Karimganj 0.12 77.81 0.43
Dhaka Madaripur Kalkini 0.02 74.33 0.39
Dhaka Madaripur Madaripur Sadar 0.34 73.99 0.42
Dhaka Manikganj Ghior 0.06 77.57 0.40
Dhaka Mymensingh Ishwarganj 0.00 77.22 0.44
Dhaka Mymensingh Gauripur 0.07 76.53 0.43
Dhaka Mymensingh Phulpur 0.33 77.11 0.45
Dhaka Mymensingh Nandail 0.39 77.05 0.45
Dhaka Narayanganj Sonargaon 0.26 72.38 0.43
Dhaka Netrakona Atpara 0.04 77.04 0.44
Dhaka Netrakona Kendua 0.29 78.87 0.43
Dhaka Netrakona Kalmakanda 0.31 78.49 0.45
Dhaka Netrakona Madan 0.38 78.13 0.45
Dhaka Shariatpur Shariatpur Sadar 0.13 74.82 0.40
Dhaka Sherpur Nalitabari 0.32 78.26 0.41
Dhaka Sherpur Nakla 0.36 78.55 0.41
Dhaka Sherpur Jhenaigati 0.40 78.35 0.42
Dhaka Tangail Ghatail 0.23 76.73 0.40
Dhaka Tangail Gopalpur 0.37 78.26 0.41
Khulna Jessore Bagherpara 0.36 77.88 0.40
Khulna Khulna Dighalia 0.27 76.44 0.42
Khulna Magura Shalikha 0.21 78.82 0.37
Khulna Magura Mohammadpur 0.38 76.37 0.39
Khulna Meherpur Gangni 0.26 83.06 0.41
Rajshahi Bogra Dhupchanchia 0.29 81.83 0.37
Rajshahi Bogra Dhunat 0.31 80.38 0.42
Rajshahi Naogaon Raninagar 0.19 84.06 0.39
Rajshahi Naogaon Porsha 0.32 87.43 0.44
Rajshahi Natore Baraigram 0.32 80.78 0.38
Rajshahi Natore Bagatipara 0.38 81.07 0.37
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Rajshahi Pabna Sujanagar 0.32 80.89 0.42
Rajshahi Pabna Atgharia 0.38 79.45 0.40
Rajshahi Sirajganj Belkuchi 0.33 79.22 0.50
Rajshahi Sirajganj Ullah Para 0.36 80.24 0.43
Rajshahi Sirajganj Kamarkhanda 0.37 78.11 0.44
Rajshahi Sirajganj Chauhali 0.40 80.31 0.46
Rangpur Kurigram Chilmari 0.07 80.23 0.44
Sylhet Sunamganj Dharampasha 0.27 76.63 0.47
Sylhet Sylhet Bishwanath 0.40 72.01 0.43
Annex‐Figure 2: Levels of Stunting and Poverty
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Annex‐Figure 3: Upazilas with Low WASH in CCs, High Poverty and High Stunting
18
Annex‐Table 6: Hotspots
Division District Upazila Wash Index Poverty Rate Stunting Rate
Chittagong Bandarban Rowangchhari 0.38 79.65 0.46
Chittagong Brahmanbaria Nasirnagar 0.36 80.74 0.45
Chittagong Brahmanbaria Bijoynagar 0.40 80.06 0.43
Chittagong Brahmanbaria Ashuganj 0.41 77.68 0.43
Chittagong Brahmanbaria Sarail 0.50 80.72 0.43
Chittagong Khagrachhari Panchhari 0.30 78.08 0.46
Chittagong Noakhali Hatiya 0.41 89.54 0.47
Chittagong Rangamati Rajasthali 0.46 77.45 0.44
Chittagong Rangamati Baghaichhari 0.50 80.02 0.44
Dhaka Jamalpur Dewanganj 0.47 80.86 0.47
Dhaka Kishoregonj Karimganj 0.12 77.81 0.43
Dhaka Kishoregonj Itna 0.41 77.46 0.45
Dhaka Kishoregonj Bajitpur 0.45 77.95 0.45
Dhaka Kishoregonj Austagram 0.46 78.93 0.45
Dhaka Mymensingh Ishwarganj 0.00 77.22 0.44
Dhaka Mymensingh Phulpur 0.33 77.11 0.45
Dhaka Mymensingh Nandail 0.39 77.05 0.45
Dhaka Mymensingh Trishal 0.41 77.76 0.44
Dhaka Mymensingh Dhobaura 0.45 78.23 0.46
Dhaka Mymensingh Haluaghat 0.49 78.40 0.44
Dhaka Mymensingh Muktagachha 0.50 78.31 0.45
Dhaka Narsingdi Narsingdi Sadar 0.49 78.01 0.44
Dhaka Netrakona Atpara 0.04 77.04 0.44
Dhaka Netrakona Kendua 0.29 78.87 0.43
Dhaka Netrakona Kalmakanda 0.31 78.49 0.45
Dhaka Netrakona Madan 0.38 78.13 0.45
Dhaka Tangail Dhanbari 0.40 79.18 0.44
Rajshahi Naogaon Porsha 0.32 87.43 0.44
Rajshahi Naogaon Sapahar 0.47 86.71 0.43
Rajshahi Sirajganj Belkuchi 0.33 79.22 0.50
Rajshahi Sirajganj Ullah Para 0.36 80.24 0.43
Rajshahi Sirajganj Kamarkhanda 0.37 78.11 0.44
Rajshahi Sirajganj Chauhali 0.40 80.31 0.46
Rajshahi Sirajganj Royganj 0.47 80.54 0.44
Rangpur Kurigram Chilmari 0.07 80.23 0.44
Rangpur Kurigram Nageshwari 0.47 79.08 0.43
Rangpur Lalmonirhat Lalmonirhat Sadar 0.42 79.63 0.43
Rangpur Nilphamari Jaldhaka 0.49 84.32 0.44
Rangpur Rangpur Gangachara 0.44 82.48 0.43
Rangpur Rangpur Mithapukur 0.49 82.19 0.43
Sylhet Habiganj Baniachong 0.44 77.88 0.44
Sylhet Habiganj Chunarughat 0.46 77.60 0.43
Sylhet Habiganj Lakhai 0.50 79.49 0.47