Water, Sanitation, and Hygiene (WASH) Indicators in North African Countries

Author

Farah Arbi

Published

December 11, 2025

Abstract

This report analyzes Water, Sanitation, and Hygiene (WASH) outcomes across six North African countries using World Health Organisation (WHO) indicators retrieved from the Humanitarian Data Exchange platform. The analysis evaluates progress toward SDG 6 by assessing coverage of basic and safely managed drinking-water and sanitation services, alongside trends in water- and sanitation-related official development assistance (ODA). Temporal trends and rural–urban disparities are quantified to identify persistent service gaps, emerging stagnation, and areas of declining access, while ODA data are used to assess the extent and volatility of external financing for WASH systems.

Introduction

Access to safe drinking water, sanitation, and hygiene is a fundamental human need and a key determinant of public health outcomes. Adequate WASH services prevent infectious diseases, reduce undernutrition, and lower the risk of premature mortality. Sustainable Development Goal (SDG) 6.1 commits all countries to “achieve universal and equitable access to safe and affordable drinking water for all” by 2030, underscoring the essential role of water and sanitation systems in human development and societal well‑being United Nations, Department of Economic and Social Affairs (2025) .

This report assesses Water, Sanitation, and Hygiene (WASH) outcomes across six North African countries: Algeria, Egypt, Libya, Morocco, Sudan, and Tunisia, using World Health Organisation (WHO) health indicator datasets hosted on the Humanitarian Data Exchange (HDX) platform.

The objective is to assess regional progress toward SDG 6: Clean Water and Sanitation by examining a set of indicators that reflect access disparities, service quality, and financial investment in WASH systems. The analysis focuses on the following metrics:

Official development assistance (ODA) for water and sanitation projects that are aligned with government-coordinated spending plans (2023 constant US$ millions)
Proportion of safely treated domestic wastewater flows, capturing national wastewater management capacity
Population using at least basic sanitation services (%), representing foundational access to hygienic sanitation facilities
Population using safely managed sanitation services (%), reflecting higher service standards that ensure safe disposal and treatment
Population using safely managed drinking-water services (%), measuring access to water sources that are safe, available, and accessible on premises

By analyzing temporal trends and cross-country differences, this report quantifies progress, identifies periods of stagnation, and emerging challenges, providing an evidence-based foundation for policy evaluation and regional comparison.

Methods

library(tidyverse)
library(dplyr)
library(ggthemes)
library(gt)

Data Import and Initial Inspection

After loading the dataset, an initial exploratory step was performed using glimpse(), which allowed assessment of column formats, missing values, and metadata fields. This step guided subsequent preprocessing decisions. The initial dataset includes 939 observations and 17 variables covering indicator codes, geographic identifiers, residence types, and numeric measurements.

raw_data <- read.csv2(here::here("data/raw/water_sanitation_and_hygiene_wash_indicators.csv"))
glimpse(raw_data)

Rows: 939
Columns: 17
$ GHO..CODE.        <chr> "WSH_SANITATION_BASIC", "WSH_SANITATION_SAFELY_MANAG…
$ GHO..DISPLAY.     <chr> "Population using at least basic sanitation services…
$ GHO..URL.         <chr> "https://www.who.int/data/gho/data/indicators/indica…
$ YEAR..DISPLAY.    <int> 2002, 2003, 2014, 2011, 2010, 2004, 2015, 2013, 2023…
$ STARTYEAR         <int> 2002, 2003, 2014, 2011, 2010, 2004, 2015, 2013, 2023…
$ ENDYEAR           <int> 2002, 2003, 2014, 2011, 2010, 2004, 2015, 2013, 2023…
$ REGION..CODE.     <chr> "EMR", "EMR", "EMR", "EMR", "EMR", "EMR", "EMR", "EM…
$ REGION..DISPLAY.  <chr> "Eastern Mediterranean", "Eastern Mediterranean", "E…
$ COUNTRY..CODE.    <chr> "TUN", "TUN", "TUN", "TUN", "TUN", "TUN", "TUN", "TU…
$ COUNTRY..DISPLAY. <chr> "Tunisia", "Tunisia", "Tunisia", "Tunisia", "Tunisia…
$ DIMENSION..TYPE.  <chr> "RESIDENCEAREATYPE", "RESIDENCEAREATYPE", "RESIDENCE…
$ DIMENSION..CODE.  <chr> "RESIDENCEAREATYPE_RUR", "RESIDENCEAREATYPE_URB", "R…
$ DIMENSION..NAME.  <chr> "Rural", "Urban", "Urban", "", "", "Total", "Urban",…
$ Numeric           <chr> "51.1154", "72.61696", "83.57548", "153.1567", "129.…
$ Value             <chr> "51", "73", "84", "153.2", "129.8", "73", "96", "82"…
$ Low               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ High              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

Data Preprocessing

Data preprocessing was conducted in R, using the tidyverse and dplyr packages.

# Select relevant columns
data <- raw_data |> 
  select(GHO..CODE., GHO..DISPLAY., YEAR..DISPLAY., REGION..CODE., REGION..DISPLAY., 
         COUNTRY..CODE., COUNTRY..DISPLAY., DIMENSION..NAME., Numeric) |>
  
  # Rename columns consistently
  rename(
    indicator_code = GHO..CODE., 
    indicator_name = GHO..DISPLAY.,
    year = YEAR..DISPLAY., 
    region_code = REGION..CODE.,
    region_name = REGION..DISPLAY., 
    country_code = COUNTRY..CODE., 
    country_name = COUNTRY..DISPLAY., 
    location_category = DIMENSION..NAME.,
    indicator_value = Numeric
  ) |>
  
  # Convert to numeric and round
  mutate(indicator_value = as.numeric(indicator_value)) |> 
  mutate(indicator_value = round(indicator_value, 2)) |>
  
  # Replace empty strings with NA
  mutate(across(where(is.character), ~ na_if(., "")))

glimpse(data)

Rows: 939
Columns: 9
$ indicator_code    <chr> "WSH_SANITATION_BASIC", "WSH_SANITATION_SAFELY_MANAG…
$ indicator_name    <chr> "Population using at least basic sanitation services…
$ year              <int> 2002, 2003, 2014, 2011, 2010, 2004, 2015, 2013, 2023…
$ region_code       <chr> "EMR", "EMR", "EMR", "EMR", "EMR", "EMR", "EMR", "EM…
$ region_name       <chr> "Eastern Mediterranean", "Eastern Mediterranean", "E…
$ country_code      <chr> "TUN", "TUN", "TUN", "TUN", "TUN", "TUN", "TUN", "TU…
$ country_name      <chr> "Tunisia", "Tunisia", "Tunisia", "Tunisia", "Tunisia…
$ location_category <chr> "Rural", "Urban", "Urban", NA, NA, "Total", "Urban",…
$ indicator_value   <dbl> 51.12, 72.62, 83.58, 153.16, 129.85, 72.98, 96.44, 8…

Filtering of Incomplete Indicators: The indicator representing the proportion of safely treated domestic wastewater (WSH_DOMESTIC_WASTE_SAFELY_TREATED) was excluded from the main analysis due to substantial data gaps, with values reported in only three years.

Creation of Subsets: A separate dataset was created for the WASH official development assistance (ODA) indicator (SDGODAWS) by selecting relevant columns and removing the residence-type dimension, which is not applicable to this metric.

location_category_levels <- c('Rural', 'Urban', 'Total')

proceeded_data <- data |>
  filter(indicator_code != "WSH_DOMESTIC_WASTE_SAFELY_TREATED") |>
  mutate(location_category = factor(location_category, levels = location_category_levels))

wash_oda_indicator <- data |>
  filter(indicator_code == "SDGODAWS") |> 
  select(-location_category)

Output

After preprocessing, the cleaned dataset was saved as:

write_csv(data, here::here("data/processed/my-processed-data.csv"))

This processed dataset constitutes the analytical base for all figures, tables, and statistical summaries presented in the report.

Results

Access to drinking water and sanitation services

1. Sanitation Services in North Africa

Access to adequate sanitation is a core component of SDG 6.2, which calls for universal access to “adequate and equitable sanitation and hygiene” by 2030 United Nations, Department of Economic and Social Affairs (2025) . Sanitation outcomes in North Africa vary considerably across countries and between rural and urban populations. This section evaluates coverage of (i) at least basic sanitation services and (ii) safely managed sanitation services. Together, these measures provide insight into both service availability and quality, highlighting disparities between population groups and across countries.

Figure 1 illustrates substantial cross-country variability in sanitation outcomes in North Africa in 2022. Coverage of at least basic sanitation is generally high, with Tunisia (97.4%) and Egypt (97.5%) achieving near-universal access, followed by Libya (92.1%), Morocco (87.5%), and Algeria (85.8%). These values indicate that most countries have succeeded in extending basic sanitation infrastructure to the majority of their populations. Sudan’s coverage of 36.9% is substantially lower than that observed in the other North African countries, indicating substantial remaining gaps in access to sanitation services.

Coverage of safely managed sanitation is lower than basic sanitation across all countries. Tunisia remains highest at 81.1%, yet the gap between its near‑universal basic access and safely managed coverage points to incomplete wastewater treatment and disposal infrastructure. Egypt (67.2%), Algeria (62.4%), and Morocco (61.0%) form an intermediate cluster, while Libya’s drop from over 90% basic coverage to 23.8% safely managed, and Sudan reports no data for this indicator, limiting regional comparison.

The contrast between the two indicators shows that North African countries have expanded basic facilities but still face significant system‑level challenges in achieving safely managed sanitation.

proceeded_data |>
  filter(location_category == "Total",
         indicator_code %in% c("WSH_SANITATION_BASIC","WSH_SANITATION_SAFELY_MANAGED")) |>
  group_by(country_name, indicator_name) |>
  filter(year == max(year, na.rm = TRUE)) |>
  ungroup() |>
  ggplot(aes(x = reorder(country_name, indicator_value),
             y = indicator_value,
             fill = country_name)) +
  geom_col() +
  geom_text(aes(label = paste0(round(indicator_value, 1), "%")),
            hjust = 1.05,           
            color = "black",
            size = 3) +
  coord_flip() +
  facet_wrap(~ indicator_name, ncol = 1, scales = "free_y") +
  labs(
    x = "Country",
    y = "Population Coverage (%)",
    fill = "Country", 
    caption = "Indicator: SDG 6.2 – WASH basic and safely managed sanitation services, population using (%)\nSource: WHO Global Health Observatory" ) +
  scale_fill_brewer(type = "qual", palette = 3) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

Figure 1: Latest Sanitation Coverage in North African Countries

Table 1 further highlights that access to sanitation services is geographically uneven, with persistent and often pronounced rural–urban disparities.

Rural–urban gaps persist despite high national coverage of basic sanitation services as shown in Table 1 (a). In Egypt and Tunisia, rural coverage (95.8% and 97.1%) is close to urban levels, yielding small gaps of 3.9 and 0.5 percentage points (p.p) and indicating broadly equitable basic access. Whereas, Algeria shows a moderate gap of 8.2 p.p, while Morocco (25.0 p.p.) and Sudan (35.9 p.p.) exhibit large rural shortfalls, pointing to significant infrastructure deficits outside urban areas.

Table 1 (b) demonstrates both lower overall coverage of access to safely managed sanitation services and wider rural–urban gaps than for basic services. In Algeria and Egypt, rural coverage (56.0% and 63.0%) remains below urban coverage (64.6% and 72.6%), with gaps of 8.6–9.6 p.p, indicating that rural areas are less likely to benefit from systems that ensure safe treatment and disposal. Tunisia has the most pronounced inequality, with 62.9% in rural areas versus 88.9% in urban areas (26.0 p.p), despite near‑universal basic sanitation, indicating structural constraints in extending sewerage and treatment infrastructure beyond urban centres. Missing rural and safely managed data for Morocco and Sudan limit direct comparison, but, alongside their basic sanitation gaps, suggest that safely managed services are even more unevenly distributed than basic facilities.

# 1. Build a single summary table with latest rural/urban coverage and the gap
sanitation_summary <- proceeded_data |>
  filter(
    indicator_code %in% c("WSH_SANITATION_BASIC",
                          "WSH_SANITATION_SAFELY_MANAGED"),
    location_category %in% c("Urban", "Rural")
  ) |>
  group_by(country_name, indicator_code, location_category) |>
  # Latest year per country–indicator–area
  filter(year == max(year, na.rm = TRUE)) |>
  ungroup() |>
  select(country_name, indicator_code, year,
         location_category, indicator_value) |>
  pivot_wider(
    names_from  = location_category,      # creates Rural and Urban columns
    values_from = indicator_value ) |>
  mutate(gap_urban_minus_rural = Urban - Rural) |>
  rename(
    latest_year = year,
    rural_coverage  = Rural,
    urban_coverage  = Urban) |>
  arrange(indicator_code, country_name)

# 2. Create one table for each indicator

# 2a. Basic sanitation
tbl_basic <- sanitation_summary |>
  filter(indicator_code == "WSH_SANITATION_BASIC") |>
  select(country_name, latest_year, rural_coverage, urban_coverage, gap_urban_minus_rural) |>
  mutate(across(c(rural_coverage, urban_coverage, gap_urban_minus_rural),
                ~ round(.x, 1))) |>
  gt() |>
  cols_label(
    country_name= "Country",
    latest_year= "Year",
    rural_coverage= "Rural (%)",
    urban_coverage= "Urban (%)",
    gap_urban_minus_rural = "Gap (p.p.)")

# 2b. Safely managed sanitation

# add Sudan for safely managed sanitation with NA values
sanitation_summary <- sanitation_summary |>
  bind_rows(
    tibble(
      country_name        = "Sudan",
      indicator_code      = "WSH_SANITATION_SAFELY_MANAGED",
      latest_year         = NA_integer_,
      rural_coverage      = NA_real_,
      urban_coverage      = NA_real_,
      gap_urban_minus_rural = NA_real_
    )
  ) |>
  arrange(indicator_code, country_name)

tbl_safe <- sanitation_summary |>
  filter(indicator_code == "WSH_SANITATION_SAFELY_MANAGED") |>
  select(country_name, latest_year,
         rural_coverage, urban_coverage, gap_urban_minus_rural) |>
  mutate(across(c(rural_coverage, urban_coverage, gap_urban_minus_rural),
                ~ round(.x, 1))) |>
  gt() |>
  cols_label(
    country_name= "Country",
    latest_year= "Year",
    rural_coverage= "Rural (%)",
    urban_coverage= "Urban (%)",
    gap_urban_minus_rural = "Gap (p.p.)"
  )

tbl_basic
tbl_safe

Table 1: Rural and urban coverage of basic and safely managed sanitation services in North Africa

(a) At least basic sanitation services

Country	Year	Rural (%)	Urban (%)	Gap (p.p.)
Algeria	2022	79.7	87.9	8.2
Egypt	2022	95.8	99.8	3.9
Morocco	2022	71.4	96.4	25.0
Sudan	2020	24.2	60.1	35.9
Tunisia	2022	97.1	97.6	0.5

(b) Safely managed sanitation services

Country	Year	Rural (%)	Urban (%)	Gap (p.p.)
Algeria	2022	56.0	64.6	8.6
Egypt	2022	63.0	72.6	9.6
Morocco	2022	NA	34.4	NA
Sudan	NA	NA	NA	NA
Tunisia	2022	62.9	88.9	26.0

2. Drinking Water Services in North Africa

Access to clean water is a fundamental human need, and Sustainable Development Goal (SDG) 6.1 aims to “achieve universal and equitable access to safe and affordable drinking water for all” by 2030.

Figure 2 presents trends in safely managed drinking-water services for North African countries; however, due to data limitations, only Algeria, Morocco, and Tunisia have consistent data available.

All three countries have made measurable progress since 2000; however, improvements have slowed considerably in recent years, with both Tunisia and Algeria experiencing a decline in access, indicating emerging challenges in maintaining water service quality and coverage, while Morocco has reached 75% of the population with safely managed drinking-water services. In all three cases, a persistent rural–urban gap remains, with rural coverage systematically below urban coverage despite overall national progress.

proceeded_data |>
  filter(indicator_code == "WSH_WATER_SAFELY_MANAGED") |>
   ggplot(aes(x = year,
              y = indicator_value,
              color = location_category)) +
    geom_point() +
    geom_line(size = 1) +
    labs(
       x = "Year",
       y = "Population Coverage (%)",
       color = "Area",
       caption = "Indicator 6.1 - WASH safely managed drinking-water services, population using (%) \nSource: WHO Health Indicators (HDX platform)") +
  facet_wrap(~ country_name, scales = "free_y", ncol = 1) + 
  scale_fill_brewer(type = "qual", palette = 3)  +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

Figure 2: Evolution of Safely Managed Drinking Water Across Geographic Areas

Water- and sanitation-related Official development assistance (ODA)

The indicator “amount of water- and sanitation-related official development assistance that is part of a government‑coordinated spending plan” measures external financial resources disbursed to the WACH sector that are aligned with the recipient government’s own budgetary and planning frameworks. According to the World Health Organisation (WHO) definition, it captures ODA commitments and disbursements for WASH that are recorded in national budgets or medium-term expenditure plans, signaling not only the volume of aid but also the extent to which it is coordinated, predictable, and integrated into country-led strategies for achieving SDG 6.a World Health Organization (2020) .

In this analysis, coordinated ODA flows were examined for six North African countries between 2010 and 2023. Summary statistics for each country are presented in Table 2.

Algeria, Morocco, and Sudan experienced large declines in coordinated ODA, with reductions ranging from −75% to −97%, indicating a significant withdrawal of external financing over the period. Egypt also shows a moderate decline of −17%, although it remains the region’s largest and most consistently funded recipient, as reflected by its high mean ODA and variability. In contrast, Tunisia is the only country with a strong positive trajectory, recording a +95% increase in coordinated ODA and a relatively high long-term average. Libya shows a sharp percentage increase driven by very low initial values, but continues to receive only minimal absolute.

oda_summary <- wash_oda_indicator |>
  group_by(country_name) |>
  rename(country = country_name) |> 
  summarise(
    n_years    = n(),
    first_year = min(year, na.rm = TRUE),
    last_year  = max(year, na.rm = TRUE),
    oda_first  = indicator_value[year == first_year][1],
    oda_last   = indicator_value[year == last_year][1],
    abs_change = oda_last - oda_first,
    pct_change = (oda_last - oda_first) / oda_first * 100,
    mean_oda   = mean(indicator_value, na.rm = TRUE),
    sd_oda     = sd(indicator_value, na.rm = TRUE),
    .groups    = "drop") 

# create gt table and format
oda_summary |>
  gt() |>
  fmt_number(columns = c(oda_first, oda_last, abs_change, pct_change, mean_oda, sd_oda),
    decimals = 2) |>
  tab_style(style = cell_text(color = "red"),
    locations = cells_body(columns = abs_change, rows = abs_change < 0)) |>
  tab_style(style = cell_text(color = "red"),
    locations = cells_body(columns = pct_change, rows = pct_change < 0))

Table 2: Summary statistics of water- and sanitation-related ODA allocated through government-coordinated spending plans (2010–2023)

country	n_years	first_year	last_year	oda_first	oda_last	abs_change	pct_change	mean_oda	sd_oda
Algeria	14	2010	2023	12.21	0.35	−11.86	−97.13	6.67	5.14
Egypt	14	2010	2023	146.27	121.56	−24.71	−16.89	253.42	126.89
Libya	10	2011	2023	0.21	5.59	5.38	2,561.90	1.33	1.73
Morocco	14	2010	2023	170.93	141.11	−29.82	−17.45	235.72	73.26
Sudan	14	2010	2023	111.32	27.79	−83.53	−75.04	83.98	59.44
Tunisia	14	2010	2023	129.85	253.08	123.23	94.90	169.19	73.61

Figure 3 illustrates substantial year-to-year volatility in WASH-related ODA across the region, rather than steady or predictable investment patterns. In both Egypt and Morocco, WASH‑related ODA recorded in government‑coordinated spending plans rises in the early to mid‑2010s before declining in subsequent years, indicating a concentration of external financing in a limited number of high‑disbursement years followed by a period of reduced inflows.

Sudan receives substantial but irregular inflows, with sharp declines in the most recent years that are consistent with aid disruptions during periods of political and macroeconomic instability. Tunisia, by contrast, exhibits a steadier upward trajectory, characterized by fewer abrupt fluctuations and an increase in coordinated ODA during the late 2010s and early 2020s, in line with broader efforts to strengthen and reform the water and sanitation sector World Bank (2009) .

Algeria and Libya remain at consistently low ODA levels, indicating a relatively limited and often project‑based donor engagement in the water and sanitation sector rather than sustained programmatic investment.

ggplot(wash_oda_indicator,
       aes(x = year, y = indicator_value, group = country_name)) +
  geom_col(aes(fill = country_name), alpha = 0.6) +
  geom_line(aes(color = country_name), size = 0.7) +
  geom_point(aes(color = country_name), size = 1.5) +
  facet_wrap(~ country_name, ncol = 2, scales = "free_y") +
  labs(
    x = "Year",
    y = "ODA (constant US$ millions)",
    caption = "Indicator: SDG 6.a – Water and sanitation related official development assistance\nSource: WHO Global Health Observatory"
  ) +
  scale_fill_brewer(type = "qual", palette = 3) +
  scale_color_brewer(type = "qual", palette = 3) +
  theme_minimal() +
  theme(plot.title= element_text(hjust = 0.5))

Figure 3: Water- and Sanitation-related ODA in North African Countries

Conclusions

Basic sanitation coverage is high in most North African countries, but safely managed sanitation remains substantially lower, revealing major gaps in wastewater treatment and safe disposal.
Safely managed drinking-water services have improved since 2000 in Algeria, Morocco, and Tunisia, but recent stagnation and reversals indicate growing challenges in sustaining service quality and reliability.
Rural–urban inequalities are a persistent feature of both sanitation and drinking-water services, with rural areas consistently lagging behind urban populations despite national-level progress.
External financing for WASH, measured as water- and sanitation-related ODA in government-coordinated plans, shows declining or volatile trends in several countries, raising concerns about the predictability of resources needed to close remaining gaps.

References

United Nations, Department of Economic and Social Affairs. 2025. “Sustainable Development Goal 6: Clean Water and Sanitation.” https://sdgs.un.org/goals/goal6.

World Bank. 2009. “Republic of Tunisia: Water and Sanitation Strategy.” World Bank; https://openknowledge.worldbank.org/entities/publication/0cf53864-dfb7-51b1-9e3a-1fd941b2b3ef.

World Health Organization. 2020. “Water- and Sanitation-Related ODA: Official Development Assistance That Is Part of a Government-Coordinated Spending Plan.” https://www.who.int/data/gho/data/indicators/indicator-details/GHO/amount-of-water--and-sanitation-related-official-development-assistance-that-is-part-of-a-government-coordinated-spending-plan.