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Cholera risk in Lusaka: A geospatial analysis to inform improved water and sanitation provision [1]

['Peter W. Gething', 'John Curtin Distinguished Professor', 'Curtin School Of Population Health', 'Faculty Of Health Sciences', 'Curtin University', 'Bentley', 'Western Australia', 'Telethon Kids Institute', 'Perth Children S Hospital', 'Nedlands']

Date: 2023-08

Abstract Urbanization combined with climate change are exacerbating water scarcity for an increasing number of the world’s emerging cities. Water and sanitation infrastructure (WSS), which in the first place was largely built to cater only to a small subsector of developing city populations, is increasingly coming under excessive strain. In the rapidly growing cities of the developing world, infrastructure expansion does not always keep pace with population demand, leading to waterborne diseases such as cholera (Vibrio cholerae) and typhoid (Salmonella serotype Typhi). Funding gaps make targeting efficient spending on infrastructure essential for reducing the burden of disease. This paper applies geospatial analysis in Lusaka, Zambia for the cholera outbreak of October 2017—May 2018, to identify different WSS investment scenarios and their relative impact on reducing the risk of cholera in the city. The analysis uses cholera case location data and geospatial covariates, including the location of networked and non-networked WSS infrastructure, groundwater vulnerability, and drainage, to generate a high-resolution map of cholera risk across the city. The analysis presents scenarios of standalone or combined investments across sewerage expansion and maintenance; on-site sanitation improvements; piped water network expansion and quality; and ensuring the safety of point-source water. It identifies the investment most strongly correlated with the largest reduction in cholera risk as the provision of flush-to-sewer infrastructure citywide. However, it also considers the trade-offs in terms of financial cost vs. health benefits and takes note of where the next highest health benefits could be achieved for a much lower cost. Finally, the analysis was conducted during the restructuring of an existing World Bank investment, the Lusaka Sanitation Program (LSP), and identifies the most efficient investment at the time as partial sanitation scale-up and investment in piped water in wards where cholera risk was the highest.

Citation: Gething PW, Ayling S, Mugabi J, Muximpua OD, Kagulura SS, Joseph G (2023) Cholera risk in Lusaka: A geospatial analysis to inform improved water and sanitation provision. PLOS Water 2(8): e0000163. https://doi.org/10.1371/journal.pwat.0000163 Editor: Marc Jeuland, Duke University, UNITED STATES Received: November 30, 2022; Accepted: July 20, 2023; Published: August 22, 2023 Copyright: © 2023 Gething et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Publicly available data is accessible on the GitHub link https://github.com/worldbank/zambia-cholera-geospatial-modelling. Cholera case data can be requested from the Zambia National Public Health Institute (ZNPHI) or the National Health Research Authority (NHRA). Email addresses for contacting officials at either institution are Mazyanga Mazaba [email protected] and Sandra Sakala [email protected] respectively. Copy authors Solomon Sitinadziwe Kagulura and Sophie Ayling in any requests made. Funding: This work was financed by the World Bank Global Water Security and Sanitation Partnership (GWSSP). https://www.worldbank.org/en/programs/global-water-security-sanitation-partnership, received by G.J. There is no specific grant number. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript”. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests:One of the authors, Dr George Joseph is currently serving on the editorial board of Plos Water. There are no other competing interests to declare.

1 Introduction Urban populations in developing countries continue to expand at a rapid rate. This is due to a myriad of push and pull factors, such as promising economic opportunities in urban centres, as well as climate or declining wages in rural areas [1, 2], In Africa and India, such growth is due to account for almost two-thirds of the projected increase in the world´s urban population by 2050 [3]. Furthermore, the number of large cities exposed to water scarcity is projected to increase from 193 to 284 by 2050 [4]. Alongside such rapid growth come public health challenges stemming from the inability of older infrastructure to keep pace. This is particularly the case for Water and Sanitation Service (WSS) infrastructure, as is commonly seen in informal settlements in the peri-urban areas of developing cities [5–7]. The acute need for handwashing facilities as one of the first preventative measures against the spread of COVID-19 brought WSS infrastructure deficits into focus globally once again. However, WSS infrastructure has been a long-standing challenge for much older waterborne diseases such as cholera and typhoid (Salmonella serotype Typhi) but sufficient funding has been lacking to make the needed improvements a reality. The water and sanitation utility in Lusaka has taken 131 million USD in loans [8, 9] to pay for upgrades and extensions to the sanitation systems available there, across three international funders–the African Development Bank (ADB), the World Bank, the European Investment Bank (EIB). There are also other initiatives from the Millennium Challenge Corporation (MCZ) and the Gates Foundation. The first three investments are intended to reach 1.7 million beneficiaries. Despite the seemingly large investment across several funders, the budget needs to be stretched to meet the required investment, especially for an ageing sewer network. This makes targeting essential for reducing the burden of disease. Geospatial approaches can enable spatial targeting of infrastructure investments to take place. This paper applied such methods to Lusaka, Zambia in the aftermath of the cholera outbreak of 2017–18, to identify different long-term investment scenarios and their relative impact on reducing the risk of cholera in the city. In Lusaka, the capital city of Zambia, recurrent cholera outbreaks have caused significant morbidity and mortality in recent decades [10, 11]. The cholera outbreak in Zambia in 2017–18 was declared on 6th October 2017 in Lusaka. The number of cases increased from several hundred in early Dec 2017 to peak at approximately 2,000 by early January 2018 and cumulatively resulted in at least 98 deaths (CFR = 1.8%) by May 2018 [12]. 91.7% of the suspected cases occurred among Lusaka Residents. In the short term, emergency response measures [11] such as the provision of tanked or bottled water, hyper chlorination of drinking water or oral vaccine campaigns [13–15], can reduce disease burden and shorten the duration of the outbreak. In Lusaka, multiple local and international partners engaged in the humanitarian response including the Ministry of Health, the Ministry of Water, the water utility Lusaka Water and Sewerage Company (LWSC), Lusaka City Council (LCC), the Centre for Disease Control in Zambia (CDC) and the World Health Organisation (WHO). It included the provision of safe water supplies to compounds through bowsers, emergency works to water networks in affected areas as well as provision of water through additional boreholes, kiosks, and elevated tanks. CDC conducted extensive water quality monitoring, LCC enforced pit latrine emptying and the burying of shallow wells and there was an intensified effort by LWSC to attend to sewer blockage complaints. Approximately 2 million doses of a cholera vaccine were also administered to residents over 1 year of age from Jan 10th to Feb 14th, 2018. However, these vaccines only provide short-term immunity and are not intended as a substitute for addressing underlying poor water and sanitation conditions. In fact, due to heavy flooding in March 2018, and associated widespread water shortages, there was a subsequent resurgence despite the vaccine [12]. Longer-term prevention measures necessitate a robust underlying infrastructure so that contaminant sources including human waste are disposed of safely and local populations can access routinely safe and clean drinking water. Without such longer-term investments in infrastructural renovation and maintenance, similar outbreaks often recur during the rainy season. Indeed, Zambia has seen frequent cholera outbreaks with its first reported in 1977 and its most recent in just January 2023, with a case fatality rate has ranged between 2% and 8.3% during the past three decades. The country has registered cases every year except for the 1984–1988, 1994–1995 and 2012–2015 periods. Outbreaks usually occur between October and June during the rainy season, in rural fishing camps (particularly Lake Kariba, Lake Tanganyika, and Lukanga swamps) and peri-urban areas of Lusaka and the Copperbelt provinces. Zambia is not alone in the region, and cholera continues to be a global issue. Cholera is a diarrheal disease caused by the bacterium Vibrio Cholera when the host ingests contaminated food or water. According to the Global Task Force on Cholera Control (GTFCC), there are up to 143,000 deaths worldwide and up to 4.0 million cases each year of cholera [16] due largely to inadequate access to safe water and sanitation. Without proper treatment, death can occur within hours due to severe dehydration. However, with proper treatment, the recovery from cholera is almost as dramatic as the disease’s onset. Patients get well rapidly and recover fully. According to WHO, the cholera Case Fatality Rate should be below 1%. Nonetheless, in 2020, in sub-Saharan Africa the annual Case Fatality Rate was 1.6%—the highest in the world [17] and in 2009, for instance, 98% of the 221,226 notified cases worldwide were from Africa. Further, in recent years, remarkable epidemics struck various African regions located far from the coast. For instance, in 2008–2009, Zimbabwe experienced the largest cholera outbreak ever recorded in Africa, with more than 100 000 cases and more than 4,300 deaths [18]. These examples stress the need to better characterize cholera outbreaks in non-coastal regions of Africa and how to use various measures to reduce the cholera risk in susceptible environments. The analysis presented in this paper uses cholera case location data and geospatial covariates to include the location of networked and non-networked WSS infrastructure as well as groundwater data to generate a high-resolution map of cholera risk across the city. It has sought to explain patterns of risk with known risk factors for the disease from the literature, identify priority areas and compare alternative strategies for improved infrastructure. The analysis presents scenarios of standalone or combined investments across sewerage coverage and maintenance; on-site sanitation improvements; piped water network coverage and quality. It also looks at ensuring the safety of point source water and in each case, the estimated cost and number of people it would impact (i.e. cost efficiency). It identifies the investment most strongly correlated with the largest reduction in cholera risk would be the provision of flush-to-sewer infrastructure citywide. However, such an investment would also be disproportionately the most expensive. It identifies the next most impactful stand-alone investment would be the expansion of piped water city-wide, followed by addressing water quality in the existing network. Finally, the analysis was carried out in the context of a considered restructuring of an existing World Bank investment called the Lusaka Sanitation Program (LSP) and identified the most efficient combined initiative was partial sanitation investment scale-up and investment in piped water in 10 priority wards, costing 134 million USD, and reducing the risk of cholera city-wide by some 48%. In the following sections, this paper outlines the relevant data for Lusaka that was assembled to complete the analysis, such as case location data on cholera cases, water supply and sanitation infrastructure and other potential predictors of cholera to conduct a geospatial analysis for addressing three important objectives: i) use cholera case location data and geospatial covariates to generate a high-resolution map of cholera risk across the city; ii) seek to explain patterns of risk with putative causal factors; iii) compare current water and sanitation infrastructure and access to the pattern of cholera risk, identify priority areas and compare alternative strategies for improved infrastructure. In the results section, the paper presents a high-resolution map of cholera risk followed by the results of scenario analysis exploring the difference in the level of association between the covariates and risk reduction. It also presents the results of combined scenarios which mirror a series of alternatives proposed under a World Bank investment which was being restructured at the time of the analysis. It ends with a discussion and limitations section. This paper contributed to informing the targeting of existing investments, and the methods employed here can continue to be used to inform future investments by the Government of the Republic of Zambia once these projects are closed. The granularity of the analysis which has brought together multiple data sources enhances our understanding of the pattern of underlying risk across the city, the role of different putative risk factors, and the potential impact of different infrastructure provisions or other mitigation strategies. High-quality, spatially referenced, data have been generated on the location of the cholera cases themselves, the configuration and quality of existing infrastructure, population access to safe water and sanitation, and other relevant environmental variables. The modelling techniques used here contribute to a growing field of geospatial modelling work in disease modelling that aims to identify and estimate relative contributions of factors that contribute to outbreaks [19, 20], including cholera. For example in Bangladesh, Ali et al defined areas of high cholera risk based on environmental risk factors such as proximity to surface water, high population density and low educational status [21, 22]; in Ghana, Anamzui-Ya measured proximity to refuse dumps and water reservoirs within communities of Kumasi and used a spatial conditional autoregressive (CAR) model to determine the spatial dependency of cholera prevalence on both digitized imagery and RapidEye image. They found an inverse spatial relationship between cholera prevalence and proximity to both refuse dumps and classified reservoirs [23]. In Zimbabwe [24] identified a spatial pattern in the distribution of cholera cases in an epidemic with Harare, characterised by a lower cholera risk in suburbs with the highest elevation. A parallel study to this one was also conducted in Harare, Zimbabwe taking into account a similar suite of geospatial covariates [25]. There have also been other efforts to map cholera risk in Zambia using spatial methods. For example, Tambatamba et al. analysed the distribution of cholera cases, the mode of cholera transmission, and the risk factors affecting cholera infection in a peri-urban area of Lusaka by using a Geographic Information System (GIS) and a matched case-control method [26] while other authors looked explicitly at precipitation patterns and the association between drainage network availability and cholera cases with a regression analysis [27]. Mwamba et al took a broader look using a cholera risk in Zambia between 2008 and 2017, identifying 16 districts at higher risk using a discrete Poisson-based space-time scan statistic to allow for variation over the ten-year study period [28]. However, this study provides a unique contribution in terms of the combination of data sources that it brings together, considering multiple known environmental risk factors, and its granular focus on Lusaka.

2 Ethics statement This study was approved by the Zambian Ministry of Health with approval number MH53/2/43.

5 Discussion The main contribution of this paper is the demonstration of how geospatial methods can be employed to target water supply and sanitation investments to attain desired outcomes- reduction of cholera risk in the city of Lusaka in Zambia. This study has shown how a wide variety of very detailed geospatial data can be brought together in a formal spatial statistical analysis to explore the geographic patterns and potential drivers of cholera risk during an urban epidemic. The high-resolution risk map provides a highly granular information source for decision-makers considering how to reduce the risk of future outbreaks, clearly delineating neighbourhoods with elevated underlying risk from those where, even amid the epidemic, the risk was low. The analysis has also demonstrated how cholera risk arises from a complex set of interacting factors. Unsurprisingly, water and sanitation access play a key role, but this is mediated by environmental factors such as poor drainage and variations in the vulnerability to contamination of the underlying groundwater. The approach presented here has also enabled estimation of the possible impact of different cholera risk mitigation strategies to improve water and sanitation infrastructure, quality, and access, including their relative efficiency and potential to target the highest impact parts of the city. The fact that two Wards (Kanyama and Harry Nkumbula) were ranked as first and second highest impact for nearly all mitigation scenarios reflects the clear need to prioritise this region of the city. The potential for the greatest impact here likely stems from the confluence of several factors: the very high concentration of cholera risk (meaning predicted incidence rates are high), the dense population (meaning larger numbers can potentially be averted), and the poor levels of access to water and sanitation at the time (meaning there was large scope to improve on current provision). All three factors, in turn, were driven by the nature of these neighbourhoods which were characterised by high rates of poverty and informal settlements with inadequate infrastructure. The approach developed here has some limitations. All analyses stemmed from a model that sought to explain the spatial variation in reported cases across the city. These case data were geolocated according to the home residence of the patient, but of more interest is the location at which the infection was acquired. In many cases, this may be in or close to the home, but in others, the exposure may have occurred somewhere else entirely–for example at a workplace, when visiting another household in a different part of the city, or in a public venue. In future analyses, it may be possible to obtain data representing patterns of human movement (for example as collected via mobile phone records) and include these in the analysis of spatial risk patterns [44]. A second limitation concerns the counterfactual analysis. This relied on the necessary assumption that the empirical relationships captured in the modelling between cholera risk and each putative risk factor were causal rather than mere associative. For example, it was assumed that the observed relationship between poor sanitation and cholera meant that improving sanitation would reduce cholera risk. This seems largely defensible since there are established causal pathways linking the two, and since other factors were controlled for in the analysis but results nevertheless remain contingent on the assumption of causation. For some risk factors, the causal pathway may be more indirect. The elimination of E. coli contamination in piped water, for example, was estimated to yield reductions in cholera risk. While E. coli is, of course, not the pathogen responsible for cholera, it is plausible that actions taken to eliminate it by improving water infrastructure and treatment would also be effective against Vibrio cholerae itself. A third limitation pertains to the distinction between access and use of water and sanitation infrastructure. This study deals solely with the question of access and does not claim to know the extent to which city residents are using the available WSS infrastructure available. There is a lack of available data on the extent of household use of WSS infrastructure, but if it were later to become available, it could be incorporated into the model. To compare the possible effort required to implement different scenarios we used the beneficiary population in each geographic area that would have to receive the intervention. This is an inevitably crude proxy that does not account for the different unit or per-capita costs of, for example, providing a household with flush to sewer versus providing piped water. Further analyses could attempt to draw up actual costings to enable more refined comparisons.

6 Conclusion Ongoing epidemics in cities across the developing world demonstrate the persistent threat of Cholera and its potentially devastating impact. International efforts led by the Global Task Force on Cholera Control (GTFCC) have been focused on rapid response through stepping up vaccine availability, while long-term solution rests in the provision of clean water and safe sanitation infrastructure facilities. Basic improvements in infrastructure, alongside more specific time-limited interventions, can dramatically reduce risk but, in heavily resource-constrained settings, must be effectively chosen and targeted to maximise their impact. This study has showcased how the growing availability of geospatial data on water and sanitation infrastructure, environmental characteristics and other risk factors can be coupled with spatial statistical analysis to provide granular and robust information to support more precise and impactful infrastructure interventions to improve public health.

Acknowledgments The authors gratefully acknowledge Lusaka Water and Sanitation Company for their collaboration namely Jonathan Kampata (Managing Director, LWSC), Kennedy Mayumbelo (Project Manager–LSP), Mwansa Nachula Mukuka (Sanitation Specialist, LSP) Kalikeka Malate (Senior Engineer, Project Planning, LSP), Lusungu Nyirenda (Wastewater Specialist, LSP). The cholera case data was made available with thanks to the Zambian Ministry of Health and Mazyanga Mazaba Liwewe, Prof Victor Mukonka, Prof Nathan Kapata, and Dr. Muzala Kapina from the Zambian National Institute of Public Health (ZNPHI). The water and sanitation household survey data collection was made possible with Field Coordinators Sensio Banda and Gertrude Namitala, and survey firms Fibonacci Engineering and Palm Associates. From inside the World Bank, the work was made possible with the support of operational Task Team Leaders (TTLs) Josses Mugabi, Odete Muximpua, Ruth Kennedy Walker, and Ai-ju Huang from the Water Global Practice of the World Bank as well as Collins Chansa, Senior Economist, Health Global Practice. Support was gratefully received from the Zambia Country Management Unit (CMU) of the World Bank. This work was financed by the World Bank Global Water Security and Sanitation Partnership (GWSSP). The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments whom they represent. The World Bank does not guarantee the accuracy of the data included in this work. Official delimitation of areas and borders might not reflect the official position of the World Bank Group. Country borders or names do not necessarily reflect the World Bank Group’s official position. These maps are for illustrative purposes and do not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.

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