To meet the Sustainable Development Goal (SDG) 6.1.1. indicator of the ‘proportion of the population using safely managed drinking water services (SMDWS)’, data on the state of drinking water quality in Indonesia is needed. Therefore, a cluster survey was conducted in 2019 to investigate the access to, availability of, and quality of drinking water from 870 improved drinking water sources (IDWS) in four regions. The methods involved sanitary inspection, assessment of household water management and water quality examination. Based on four levels of sanitary risks determined by World Health Organization (WHO), the IDWS in Sumatra had the highest percentage of sources categorised as `low-risk' among the four regions. The percentage of samples in compliance with the national standards for total coliforms and Escherichia coli was 21.49 and 42.64%, respectively. Moreover, the percentage of SMDWS was 35.9%, which was higher than the previous Indonesian surveys in 2015 and 2020. These findings indicate that bacteriological contamination was present in the IDWS and sanitary risk factors were a good predictor of drinking water quality. The study suggests that proper implementation of drinking water surveillance through sanitary inspection, water quality testing and remedial actions of non-piped water sources is imperative towards achieving SDG 6.1.1.

  • This study focuses on the quality of improved drinking water sources at the point of access in four regions.

  • Sanitary inspection remains a useful tool to identify risks of drinking water supplies.

  • The proportion of bacteriological parameters that complied with the national standards was lower as opposed to physicochemical parameters.

  • The proportion of the population using safely managed drinking water services was 35.9%.

AIC

Akaike information criterion

GBD

Global burden of disease

HMD

Health Ministerial Decree

IDWS

Improved drinking water services

LMICs

Low- and middle-income countries

MAC

Maximum allowable concentration

MDGs

Millennium development goals

MICS

Multiple indicator cluster survey

MOH

Ministry of Health

MOPWH

Ministry of Public Works and Housing

PoA

Point of access

SDGs

Sustainable Development Goals

SMDWS

Safely managed drinking water services

UDWS

Unimproved drinking water sources

UNICEF

United Nations Children's Fund

WHO

World Health Organization

An indicator of 6.1.1 for Sustainable Development Goal (SDG) 6.1 is ‘the proportion of the population using safely managed drinking water services’ (SMDWS) (United Nations 2015). The criteria used to evaluate the indicator are that water should be from improved water sources, be located on-premises and available when needed, and be free of bacteriological and selected chemical contamination.

Indonesia has not yet reported its progress on SDG 6 as there has been no representative data on the proportion of population using SMDWS until the beginning of 2023. SMDWS has now been displayed in the JMP database. A survey of drinking water quality was once conducted in one province only (Special Region of Yogyakarta) in 2015 to set a national target of SMDWS in 2019 towards the end line of the SDGs in 2030. The results of that study were also used by Cronin et al. (2017) to calculate SMDWS and found it at 8.5%. Of course, it was not representative of the national situation. A second survey was conducted in four regions in 2019, adopting the methodology of the World Health Organization & United Nations Children's Fund (2012), and a third survey concerning household drinking water quality was conducted in 2020. Unfortunately, the latter has not been published yet.

There were several studies related to drinking water quality at the point of access (PoA), for instance, in Ethiopia (Tadesse et al. 2010), Jordan (Properzi 2010), Nicaragua (Aldana 2010), Nigeria (Ince et al. 2010) and Tajikistan (Aliev et al. 2010). Furthermore, 27 low- and middle-income countries (LMICs) have conducted multiple indicator cluster surveys (MICSs) containing a drinking water quality module that was used to monitor the achievement of SMDWS between 2014 and 2020 (Bain et al. 2021). However, until 2019, Indonesia had yet to conduct drinking water quality surveys applying rapid tests for drinking water quality examination. As such, it differed from many other LMICs that had already implemented the rapid test (Dorea et al. 2020). In 2020, Indonesia finally conducted a national survey on household drinking water, applying a field survey using similar methods (rapid tests) to the MICS in examining physicochemical and bacteriological parameters. The national survey targeted households using both improved drinking water services (IDWS) and unimproved drinking water sources (UDWS) as units of analysis.

This paper conducted a study of drinking water quality in four regions in 2019, mainly covering the IDWS quality at the PoA. The main goals of the study were to determine the sanitary risk levels and the conditions of drinking water services at selected households using IDWS. In addition, the study aimed to determine whether regional data on drinking water quality in four regions complied with the Health Ministerial Decree (HMD) No. 492/2010 on drinking water quality standards for improving current drinking water quality surveillance (Ministry of Health 2010a). The standard is used to determine one of three SMDWS criteria, which is ‘free of faecal contamination and priority chemicals’. Therefore, we hypothesised that the percentage of bacteriological parameters complying with the national standards would be higher than those of the other studies covering IDWS and UDWS since the 2019 study only covered the IDWS. This is the case, as technically, the sanitary risks of the IDWS are lower than those of the UDWS, implying that the bacteriological quality of the IDWS tends to be better (Kelly et al. 2021). Furthermore, this study also differed from the other study on drinking water quality in Yogyakarta province (Cronin et al. 2017) and the household survey in 2020 in terms of its design and inclusion criteria, as explained in detail in the Methods section. As such, the findings can also be utilised to assess the capacity of such public health laboratories in four regions to improve current drinking water surveillance and develop water safety plans towards SDG 6.1.1.

Design and locations of the study

The study was a cluster survey to assess drinking water safety at the PoA (World Health Organization & United Nations Children's Fund 2012) conducted from July to October 2019. The sample selection process included defining the population using IDWS, dividing the study sample into clusters of study locations and randomly selecting clusters in which households using IDWS were available through multi-stage sampling. The selection of four regions was purposive based on geographical units of Indonesia (ISO 3166-2: ID) and the availability of accredited laboratories. The geographical units of Indonesia consist of seven large islands, namely Sumatra (ID-SM), Java (ID-JW), Kalimantan (ID-KA), Sulawesi (ID-SL), Nusa Tenggara Islands (ID-NU), Maluku Islands (ID-ML) and Papua (ID-PP) (World Atlas 2023). Moreover, each large island comprises several provinces, and we chose one province from each large island where an accredited laboratory is located. Therefore, one selected province represented one geographical unit or region, consisting of one city and one regency each. As such, the study locations consisted of four cities and four regencies in Sumatra, Java, Kalimantan and Sulawesi (Figure 1). The other three regions in the year 2019 did not have accredited laboratories for the examination of environmental health parameters, including drinking water parameters.

Based on the characteristics of the study locations, the inclusion criteria of the study were (a) IDWS used by at least 5% of people living in each cluster area, (b) drinking water quality examinations could be conducted in accredited laboratories, (c) study locations are relatively near to accredited laboratories allowing for easy access considering the maximum allowable time for water samples to be tested (less than 24 h) and (d) the laboratories were able to analyse the 10 parameters to be tested, selected from the HMD No. 492/2010 consisting of two bacteriological and eight physicochemical parameters. The type of IDWS used by most people in the study locations comprised piped water supplies, boreholes with hand pumps, protected dug wells and refilled water (packaged water). Ethical approval was obtained from The Ethics Committee of the National Institute of Health Research and Development, Indonesian MOH Number LB.02.01/2/KE.073/2019, on 13 March 2019, and informed consent was obtained from all respondents (the owners/caretakers) of the IDWS at all clusters.

Population and sample

The sample size of a cluster survey targeting the IDWS was adopted from the WHO/UNICEF (2012). The selected IDWS in four regions in this study was part of the total sample size for all regions (seven regions) in Indonesia calculated using Equation (1). Since this study only covered four regions, its sample size of IDWS was proportionally calculated based on the number of all IDWS in Indonesia, which were 870 out of 1,600:
formula
(1)
where
  • n is the required sample size;

  • P is the assumed proportion of drinking water sources with water quality exceeding the water quality target established (HMD No. 492 of 2010): 0.5;

  • D is the design effect: 4 (the ratio of the standard error using the actual sample design compared to the standard error of a simple random sample); and

  • e is the acceptable precision expressed as a proportion (0.05) as the level of accuracy required considering the variance of the estimated proportion.

Data collection

Data collection included sanitary inspection and water sampling of the IDWS in four cities and four regencies. Moreover, interviews with 175 householders using a structured questionnaire were also conducted to obtain information on the availability of water when needed and whether the location of water source is on the premises and free from faecal and priority chemical contamination. A total 175 householders were selected from each cluster in four regions who used the IDWS, and they were considered as 20% of the sample size of the IDWS studied. The World Health Organization & United Nations Children's Fund (2012) suggests that selected householders using the IDWS studied (at least 10%) should also be studied to obtain additional information to determine the state of drinking water quality and water management (World Health Organization & United Nations Children's Fund 2012). Data collectors were trained by the research team, and they were divided into eight sub-teams to prepare the field organisation and supervise the data collection process to ensure compliance with the study protocol.

Sanitary inspection

Sanitary inspection is a method of determining risk factors through a set of standardised binary questions (yes/no). The answer of ‘yes’ represented the risk of the physical conditions of the water source and its surroundings. This study used the sanitary inspection forms provided by the MOH (2010a), adopting the old version of WHO's sanitary inspection forms. Sanitary inspections along with water sampling have been part of drinking water quality surveillance programmes in Indonesia since WHO (1997) released ‘The Guidelines for Drinking-Water Quality, Volume 3’, concerning the surveillance and control of community supplies in 1997. The risk for each completed sanitary checklist was calculated from the total ‘yes’ answer from each type of drinking water supply, where each question held an equal value of ‘0’ (answer ‘no’) or ‘1’ (answer ‘yes’). The outcome of the sanitary inspection was then categorised into four levels, namely, low risk (0–2), medium risk (3–5), high risk (6–8) and very high-risk (9–10).

Collection of drinking water samples

The collection of water samples was also conducted by trained enumerators. Each water sample was collected from one of 870 IDWS for bacteriological testing using a sterile bottle with a minimum volume of 250 mL. Testing each IDWS requires 200 mL water, so there will be a sufficient air gap on the top of the bottle. All bottles for collecting treated water samples were dosed with 0.2 mL of a 3% solution of sodium thiosulfate to deactivate any chlorine. The sample bottles were then labelled and immediately placed in a lightproof insulated box containing artificial ice packs with water to ensure rapid cooling and easy delivery to the designated laboratories. The box to carry the sample bottles for bacteriological testing was cleaned and disinfected after each use to avoid contamination. Moreover, a water sample of 200 mL was also collected from each IDWS and stored in a polyethylene bottle at a low temperature (e.g. 4 °C) for physicochemical testing. The sample bottles for physicochemical testing were clean but not sterile. The collected water samples were taken to the designated public health centre of each cluster for further checking by laboratory personnel on its volume, label, and packing prior to delivery to the designated laboratory of each region for examination.

Drinking water examination

pH and colour were tested on site using a pH metre and colorimeter, respectively. The other parameters were tested in the assigned laboratories, including two bacteriological parameters (total coliforms and Escherichia coli), one physical parameter (turbidity), and five chemical parameters (iron, manganese, fluoride, total hardness and nitrate) using standard methods of accredited laboratories. The bacteriological parameters were tested using the most probable number methods. In contrast, the chemical parameters were examined using induced coupled plasma–optical emission spectroscopy, aligned with the procedures of American Public Health Association (APHA) standards.

Data analysis

Sanitary inspection data were analysed to generate descriptive data in four risk levels for each type of IDWS, and the most common risk for each type of IDWS was identified. Similarly, descriptive data on drinking water quality were also analysed to generate the proportion of each parameter in complying with the standards of drinking water quality stated in HMD No. 492/2010. A set of bivariate analyses were also carried out to determine the relationship between sanitary inspection scores and bacteriological scores, as well as with a combination of physical and chemical parameters using Stata/SE 13.1 (Hosmer et al. 2013). The Akaike information criterion (AIC) was used to analyse multiple model alternatives and to determine which models will explain the greatest amount of variation with the fewest possible independent variables.

Sanitary inspection and risk level

The sanitary inspection was conducted for all the 870 IDWS studied. Figure 2 presents the breakdown of risk categories for each region and evidence about the variation in the proportion of risk levels among the four regions, depending on the IDWS types and their conditions of physical construction. Regardless of the type of IDWS, Sumatra had the highest percentage (89.9%) of IDWS with a low-risk score, followed by Sulawesi (67.5%), Java (53.2%) and Kalimantan (52.9%). As such, the physical conditions of the IDWS and the surroundings in the Sumatra region were the best among the four regions. In comparison with the risk levels of the same four locations of the survey in 2020, the percentages of IWDS with a low-risk score in Sumatra and Java were lower: 67.9 and 66.5%, respectively. In contrast, Kalimantan and Sulawesi had 72.6 and 74.1%, respectively, meeting the low-risk criteria (Ministry of Health 2021). Moreover, Figure 2 shows that Sumatra only had a small percentage of IDWS that fell into the high-risk category (0.5%), whereas Kalimantan had the highest percentage of IWDS that felt into the high-risk category (19.0%).
Figure 1

Map of study locations in four regions of Indonesia.

Figure 1

Map of study locations in four regions of Indonesia.

Close modal
Figure 2

Percentage of IDWS and its risk levels in four regions, 2019.

Figure 2

Percentage of IDWS and its risk levels in four regions, 2019.

Close modal
The prevalence of common risk factors associated with refilled water sources, piped water supply, protected dug wells and boreholes with hand pumps were 83.5, 69.4, 65.2 and 55.3%, respectively (Figure 3). The leading risk factors of boreholes with hand pumps and protected dug wells were the same, namely, the existence of a latrine within 10 m of the well. The predominant risk factor of a refilled water source was the existence of an open waste bin near water treatment plant. In contrast, the most common risk factor for piped water is present when drinking water is not directly sourced from household piped connections.
Figure 3

Percentage of IDWS and its typical risk factors in four regions, 2019.

Figure 3

Percentage of IDWS and its typical risk factors in four regions, 2019.

Close modal

Compliance of drinking water quality

The compliance of bacteriological parameters was defined as free or absent from total coliforms and E. coli for 100 mL water samples confirming <1 colony forming unit according to HMD 492/2010. From 870 water samples, the compliance of total coliforms and E. coli was met by only 187 (21.49%) and 371 samples (42.64%), respectively. The compliance rates of total coliforms were better in cities than in regencies, except for the Sulawesi region. Similarly, E. coli compliance was better in cities than in regencies, except in Sumatra.

The percentage of IDWS that comply with the limits for physical parameters (turbidity and colour) was 91.95 and 96.10%, respectively. The standards of each chemical parameter, including pH and concentration of iron, manganese, fluoride, total hardness and nitrate parameters, can be seen in Table 1. The percentage of the IDWS with a pH value meeting the HMD 492/2010 standard was 61.15% for all locations, and only Banjarmasin city was found to meet the standards. In terms of iron, two cities and one regency were totally compliant with the standards (Bandung, Bogor and Banjarmasin). The highest percentage of IDWS complying with the pH standard was found in Banjarmasin (100%) and the lowest was in Palembang (8.86%). The IDWS samples that met the HMD No. 492/2010 standard for manganese concentration were 91.95% for all locations. Banjarmasin, Ogan Ilir and Minahasa Utara were the city and regencies with 100% compliance. All samples from all locations met the water quality standard for fluoride concentration. As for the nitrate parameter, nearly all water samples met the standard with 99.89% compliance (Table 1).

Table 1

Percentage of IDWS samples complying with standards of HMD 492/2010

Region/Municipality/DistrictpH (6.5–8.5), %Iron (≤0.3 mg/L), %Manganese (≤0.4 mg/L), %Fluoride (≤1.5 mg/L), %Total hardness (≤500 mg/L), %Nitrate (≤50 mg/L), %
Sumatra
Palembang (n = 158) 

8.86 

98.1 

99.37 

100.0 

100.0 

99.37 
 Ogan Ilir (n = 50) 10.0 98.0 100.0 100.0 100.0 100.0 
Java
Bandung (n = 128) 

85.93 

100.0 

82.03 

100.0 

100.0 

100.0 
 Bogor (n = 120) 33.33 100.0 65.83 100.0 100.0 100.0 
Kalimantan
Banjarmasin (n = 114) 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 
 Banjar (n = 60) 28.34 86.67 96.67 100.0 100.0 100.0 
Sulawesi
Manado (n = 176) 

98.29 

100.0 

98.3 

100.0 

100.0 

100.0 
 Minahasa Utara (n = 64) 92.18 98.44 100.0 100.0 100.0 100.0 
Total (n = 870) 61.15 98.5 91.95 100.0 100.0 99.89 
Region/Municipality/DistrictpH (6.5–8.5), %Iron (≤0.3 mg/L), %Manganese (≤0.4 mg/L), %Fluoride (≤1.5 mg/L), %Total hardness (≤500 mg/L), %Nitrate (≤50 mg/L), %
Sumatra
Palembang (n = 158) 

8.86 

98.1 

99.37 

100.0 

100.0 

99.37 
 Ogan Ilir (n = 50) 10.0 98.0 100.0 100.0 100.0 100.0 
Java
Bandung (n = 128) 

85.93 

100.0 

82.03 

100.0 

100.0 

100.0 
 Bogor (n = 120) 33.33 100.0 65.83 100.0 100.0 100.0 
Kalimantan
Banjarmasin (n = 114) 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 
 Banjar (n = 60) 28.34 86.67 96.67 100.0 100.0 100.0 
Sulawesi
Manado (n = 176) 

98.29 

100.0 

98.3 

100.0 

100.0 

100.0 
 Minahasa Utara (n = 64) 92.18 98.44 100.0 100.0 100.0 100.0 
Total (n = 870) 61.15 98.5 91.95 100.0 100.0 99.89 
Table 2

Bivariate regression analyses between sanitary risks with drinking water quality

ModelIndependent variableDependent variablep-valueR2AIC
Logistic regression Sanitary risk level Physical–chemical scores 0.010* 0.006 1,179.5 
Logistic regression Sanitary risk level Bacteriological scores <0.001* 0.099 777.3 
Logistic regression Sanitary risk level Physical–bacteriological–chemical scores < 0.001* 0.094 589.0a 
Ordinal log regression Sanitary risk level Chemical–physical category 0.711 0.000 1,186.5 
Ordinal log regression Sanitary risk level Bacteriological category 0.082 0.003 859.4 
Ordinal log regression Sanitary risk level Physical–bacteriological–chemical category <0.001* 0.020 637.0 
Linear regression Sanitary risk level Physical–chemical category 0.032** 0.004 1,549.9 
Linear regression Sanitary risk level Bacteriological category 0.072 0.002 2,043.5 
Linear regression Sanitary risk level Physical–bacteriological–chemical scores 0.437 0.000 1,727.2 
Pearson's chi-square Sanitary risk level Chemical–physical category 0.737  1,727.2 
Pearson's chi-square Sanitary risk level Bacteriological category 0.050**  1,727.2 
Pearson's chi-square Sanitary risk level Physical–bacteriological–chemical category 0.001*  1,727.2 
ModelIndependent variableDependent variablep-valueR2AIC
Logistic regression Sanitary risk level Physical–chemical scores 0.010* 0.006 1,179.5 
Logistic regression Sanitary risk level Bacteriological scores <0.001* 0.099 777.3 
Logistic regression Sanitary risk level Physical–bacteriological–chemical scores < 0.001* 0.094 589.0a 
Ordinal log regression Sanitary risk level Chemical–physical category 0.711 0.000 1,186.5 
Ordinal log regression Sanitary risk level Bacteriological category 0.082 0.003 859.4 
Ordinal log regression Sanitary risk level Physical–bacteriological–chemical category <0.001* 0.020 637.0 
Linear regression Sanitary risk level Physical–chemical category 0.032** 0.004 1,549.9 
Linear regression Sanitary risk level Bacteriological category 0.072 0.002 2,043.5 
Linear regression Sanitary risk level Physical–bacteriological–chemical scores 0.437 0.000 1,727.2 
Pearson's chi-square Sanitary risk level Chemical–physical category 0.737  1,727.2 
Pearson's chi-square Sanitary risk level Bacteriological category 0.050**  1,727.2 
Pearson's chi-square Sanitary risk level Physical–bacteriological–chemical category 0.001*  1,727.2 

*p < 0.01; **p < 0.05.

aThe lowest value of AIC indicates that this logistic regression is the best model for predicting the relationship between sanitary risk levels and drinking water quality parameters.

Relationship between sanitary risk levels and drinking water quality

Twelve bivariate analyses were conducted to determine the impact of environmental conditions (sanitary risk levels) on the results of the IDWS test as dependent variables (drinking water quality scores/categories) (Table 2). As a result, seven models showed statistical significance between the sanitary inspection results and the drinking water quality results. However, among seven significant models, the best-fit model was a logistic regression, as it had the lowest AIC value (p < 0.001, R2 = 0.0948 and AIC value of 589).

Safely managed drinking water services

This study found that the percentage of Indonesian people using SMDWS at the household level was 35.9%, based on the sample size of 175 households and three parameters (E. coli, fluoride and nitrate). This percentage of Indonesian people using SMDWS in this study was much higher than that of SMDWS from the national survey in 2020, which produced an estimate of 11.9% (Ministry of Health 2021). The 2020 survey used five parameters: E. coli, nitrate, nitrite, total dissolved solids and pH. The first similar survey in Yogyakarta province in 2015 (Cronin et al. 2017) found that the population using SMDWS was much lower (8.5%) based on the E. coli parameter alone.

The low risk levels were dominated by piped water supplies and refilled water supplies. Conversely, the high and very high risk levels were dominated by non-piped water supplies, particularly dug wells. Figure 3 also demonstrates that the typical potential sources of contamination of non-piped water (shallow groundwater sources) are related to the contamination from any improper on-site disposal of faeces and household wastes within the risk distance. Of course, the standardised sanitary inspection forms could not capture all potential drinking water contamination, as they only captured visually horizontal sources of contamination and construction of drinking water sources. Therefore, remedial actions should be focused on the improvement of non-piped water sources, particularly the sources with high and very high risks.

Concerning the predominant risk factor by the type of IDWS in Figure 3, Indonesia can be compared to five other countries, namely, Nigeria, Tajikistan, Jordan, Nicaragua and Ethiopia. The most common risk factor of piped water in Nigeria was taps located outside the premises (81.0%) (Ince et al. 2010); in Tajikistan, water obtained from more than one source (24.4%) (Aliev et al. 2010); in Jordan, water stored in a container (51.3%) (Properzi 2010); in Nicaragua, the unsanitary area around the tap (34.9%) (Aldana 2010); and in Ethiopia the most common risk factor was the presence of animals that have access to the area around the pipe (69.8%) (Tadesse et al. 2010). The most common risk to piped water in Indonesia was the same with Nigeria, although the percentage of piped water in Nigeria (81.0%) was higher than Indonesia (69.4%).

The study also revealed issues with bacteriological contamination as only 42.64% water samples were free from E. coli contamination. The percentage of IDWS that achieved total coliform compliance was much lower than that of E. coli as total coliforms indicated not only the occurrence of water contamination caused by human faeces but also animal waste. This finding was similar to the majority of LMICs studied (Bain et al. 2021). The percentages of faecal contamination of 27 countries were from 16 to 90% at PoA, with the average SMDWS being 31%.

The chemical parameters from overseas studies that can be compared to Indonesia's were only iron, fluoride and nitrate. Interestingly, the compliance percentages of the IDWS having the three parameters were as good as Jordan's (Properzi 2010). Among the six parameters, only fluoride and nitrate have been proven to pose significant health effects. Nevertheless, the other four chemical parameters (iron, manganese, pH and total hardness) can also influence the acceptance of consumers as well as the function of drinking water treatment and distribution systems.

Statistical analyses using 12 models suggest that logistic regression was the best model to predict the dependent variables consisting of two bacteriological and eight physicochemical parameters. It can be interpreted that there was approximately 9% influenced by the environment around the drinking water sources studied, and other factors influenced the remaining proportion. Several studies concerning the relationship between sanitary risks and drinking water quality use statistical analysis techniques (Ercumen et al. 2017; Snoad et al. 2017). Kelly et al. (2020) reviewed 21 studies and found that 57% of sanitary inspection results were significantly associated with drinking water quality. A year later, Kelly et al. (2021) also conducted a study on the same topic in 12 countries in sub-Saharan Africa. They revealed that a different statistical model using a different framework of determining variables could reflect a causal pathway of contamination of boreholes with hand pumps, indicating that its model was more advanced than commonly established methods.

Recently, Indonesia has reported the proportion of Indonesian population using SMDWS based on the criteria of World Health Organization & United Nations Children's Fund (2017), and it was displayed along with many countries. However, there have been differences in terms of types of IDWS and priority chemical contamination. Indonesia has not included packaged water as IDWS. Moreover, there are 19 obligatory parameters stated in the new HMD No. 2/2023 (Ministry of Health 2023) as a replacement of HMD 492/2010 that had more obligatory parameters (Ministry of Health 2010b). The different proportions of SMDWS mainly showed that Indonesia has calculated the progress of its SMDWS, started from the pilot study in Yogyakarta province in 2015 (Cronin et al. 2017), followed by a study in four regions and national survey in 2020 (Ministry of Health 2021). Moreover, based on the data from 2020, JMP was calculated using global criteria so that it can be compared with other countries. The achievement of SMDWS of more than 35% in 2022 was much higher than the national target (15%) in 2024. However, harmonisation of SMDWS criteria should be conducted by key stakeholders concerning water, sanitation and hygiene programmes. This is important not only for comparing to global achievement but also to improve policy to strengthen the implementation of drinking water quality surveillance covering all drinking water supplies towards SDG 6.1.

Indonesia has already implemented drinking water quality surveillance programmes since the beginning of the millennium development goal era, but the progress has not been satisfactory. A qualitative inquiry was also conducted embedded with this study. Several problems were encountered, including the need for more capacity of the majority of personnel, the limited availability of appropriate infrastructure and limited operational funds managed by local governments due to the decentralisation system policy (Ministry of Health 2020). However, the role of the central government as a key stakeholder in water and sanitation programmes, such as the Ministry of Public Works and Housing (MOPWH), only focuses on providing and managing piped water and non-piped water supplies managed by drinking water companies or community organisations (Ministry of Public Works & Housing 2023). The MOPWH has introduced the implementation of water safety plans by preparing guidelines and training materials aligned with the WHO Water Safety Plan Manual (2005) and other guidelines. Similarly, the MOH is also preparing several modules for capacity building and recruiting water safety plan auditors as stated in the new HMD No. 2/2023 (Ministry of Health 2023).

Some limitations of this study involved the design, sample size and coverage. A cluster survey design was chosen and carried out cross-sectionally without considering the influence of seasonal factors concerning drinking water quality, such as Indonesia's rainy and dry seasons.

This article focused on IDWS quality in four regions of Indonesia, indicating the state of progress and conditions of drinking water services in the first five years of the implementation of SDGs. The sanitary inspections and drinking water quality examination indicated gross microbial contamination; however, chemical parameters were compliant with the HMD 492/2010. The use of sanitary inspection forms to assess risks to drinking water sources is still useful as part of important tools for risk management. Particular attention should be given to regions with many risk factors (Java and Kalimantan regions). Improving the physical construction of IDWS, which are dominated by non-piped supplies, by reducing the risk factors will be urgent.

The existence of E. coli as an indicator of faecal contamination of drinking water was quite useful as one of the parameters to inform the proportion of the population using SMDWS in Indonesia and other countries. Although the proportions of people using SMDWS varied and were relatively low compared to the results reported from other upper middle-income countries, such as Malaysia and Jordan, these can be used to advocate to policymakers. These results will be useful to inform relevant policymakers that the conditions of drinking water quality surveillance programmes should be enhanced, including implementing water safety plans in the framework of health-based targets that has been accommodated in the new HMD No. 2/2023. Enormous challenges remain for the key stakeholders and all Indonesian people who need safe drinking water, regardless of their socio-economic status, to maintain their health and protect their dignity as part of their human rights through the achievement of SDG 6.1.1.

The authors acknowledge the Health Development Policy Agency (formerly the National Institute of Health Research and Development) of the Ministry of Health, The Republic of Indonesia, which provided a research grant (No. HK.02.04/I/507/2019) for conducting this study. Finally, the authors also acknowledge the enumerators who collected data in eight municipalities/districts of four regions.

SI and ID were the primary contributors in designing, statistical analysing and writing the manuscript, whereas other co-authors were minor contributors to the finalisation of the manuscript. All authors read and agreed with the final version of the manuscript.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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