Spatial Analysis of Risk Factor of Cholera Outbreak for in a Peri-urban Area of Lusaka, Zambia

Transcription

1 Am. J. Trop. Med. Hyg., 79(3), 2008, pp Copyright 2008 by The American Society of Tropical Medicine and Hygiene Spatial Analysis of Risk Factor of Cholera Outbreak for in a Peri-urban Area of Lusaka, Zambia Satoshi Sasaki,* Hiroshi Suzuki, Kumiko Igarashi, Bushimbwa Tambatamba, and Philip Mulenga Department of Infectious Disease Control and International Medicine, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan; Lusaka District Health Management Team, Ministry of Health, Lusaka, Zambia Abstract. A cholera outbreak occurred in Lusaka city between November 28, 2003 and June 8, 2004, and 6,542 cases with 187 deaths (case fatality rata: 2.86) were reported. We analyzed 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. Chloropleth mapping of the incidences of cholera showed variation of the incidences in the study area. Our analysis indicated a significant association between the lack of latrine and drainage systems surrounding houses and high incidence of cholera. The matched case-control study showed the protective role of chlorination of drinking water and of hand washing with soap for cholera prevention. We concluded that cholera occurred because of personal behavior and the environment conditions of daily life. INTRODUCTION * Address correspondence to Satoshi Sasaki, Asahimachi-dori Chuo Ward, Niigata , Japan. ssasaki@med.niigatau.ac.jp 414 Cholera is a disease that continues to ravage developing countries and re-emerges sporadically elsewhere throughout the world. 1 According to the World Health Organization (WHO), 45 countries officially reported cholera in 2003, with a total of 111,575 cases and 1,894 deaths (case fatality rate 1.7%), and 97% of the reported cases occurred in sub- Saharan Africa. 2 The annual figures of the WHO are actually just the tip of the iceberg, because the morbidity and mortality caused by Vibrio cholerae is grossly under-reported because of surveillance difficulties and the fear of economic and social consequences. 3 Cholera is categorized as a water- and food-borne disease through fecal-oral transmission. Contaminated water is more common as the usual vehicle for transmission in lessdeveloped countries than in more-developed ones. 4 The lack of infrastructure to provide clean and safe water has made many parts of sub-saharan Africa susceptible to cholera. 5 Many studies have indicated that cholera outbreaks were associated with inadequate sanitation, poor hygiene, and limited access to safe water supplies. 6 However, cholera outbreaks have not been effectively controlled because interventions for the prevention of outbreaks were not appropriately and sufficiently undertaken. Prevention and control activities need to be carefully defined with a view to bringing intervention to a targeted population to eliminate the risk factors of cholera. In addition, identifying appropriate methods of intervention at various levels is of great importance. Recently, there has been a growing sense that purely individual-based analyses of the causes of disease are insufficient and fail to capture important disease determinants. 7,8 Therefore, in analyzing risk factors and developing preventive approaches, it has been recognized as crucial to consider not only individual-level exposure but also characteristics of the households, groups and population or contexts to which an individual belongs. Zambia, located in the middle of southern Africa and is surrounded by neighboring countries including Tanzania, Angola, Zimbabwe, Congo DRC, Namibia, Malawi, and Mozambique, experienced widespread cholera epidemics in 1991, 1992, and More than 10,000 cases were reported during the outbreaks of 1992 and In response to the large outbreak in 1999, the Government of Zambia urged the use of in-home chlorination, point-of-use water disinfection, and safe water storage. 10 The Japanese Government therefore supported the construction of deep-well facilities for a safe water supply in an unplanned settlement in Lusaka, the capital city of Zambia. However, another cholera epidemic emerged in Zambia in November A Geographical Information System (GIS) can offer an efficient and practical way to not only directly visualize the dynamics of the transmission of infectious diseases but also examine the disease s distribution and risk factors in the setting of the outbreak John Snow s classic study of epidemic cholera in 1854 in London showed an association of the disease with contaminated drinking water even before bacteria was known to exist. 15 In this study, we investigated one of the urban unplanned settlements in Lusaka, where deep-well facilities had been installed, to identify risk factors for cholera outbreaks with a GIS and matched case-control study. MATERIALS AND METHODS Study area. The study area is called George proper and is located in the northwestern outskirts of Lusaka city (Figure 1). The population of the study area is 40,352 in 8,256 households according to a household survey in The average number of family members per household is The study area is categorized as one of the low-income resident areas in Lusaka. The source of drinking water is mainly deep-well water that is distributed throughout the community by taps after chlorination. There is one community tap for every households. Residents in the area also use shallow wells. The common method of defecation is to use pit latrines that are shared by several neighboring households. A health facility called George Health Center is the only accessible health facility for residents in the study area and covers its entire catchment area with 145,632 inhabitants. It provides health services for outpatients, mother and child health, maternity, and laboratory services. During cholera outbreaks, it is assigned as the cholera transit center where cholera-suspected patients are provided with rehydration

2 ANALYSIS OF RISK FACTORS OF CHOLERA OUTBREAK IN ZAMBIA 415 FIGURE 1. Map of Zambia, Lusaka city, and study area A, Zambia map showing nine provinces in Zambia. B, George proper (the study area), northwestern part of Lusaka city. C, The study area divided into 12 administrative zones. treatment and screened for referral to a cholera treatment center. Case definition. The Surveillance Guidelines of the Ministry of Health, Zambia, define cholera cases as individuals who have had watery stools more than three times in the 24 hours before visiting health care services and who have developed severe dehydration. For confirmation of cholera index, rectal swabs of all the suspected patients were submitted for culture to the University Teaching Hospital (UTH). After the index cases were confirmed by a laboratory test at UTH, cholera patients were identified by clinical diagnosis. These cholera patients were referred to designated cholera treatment centers, and those who lived in the study area were registered for this study. Stool specimens were collected for every 10 patients with cholera for laboratory confirmation. Patient data. The information collected from each patient at the cholera treatment centers by public health personnel was name, age, sex, date of onset of symptoms, occupation, where household is located, house address, and landmarks. Technical personnel from the public health administration, such as Environmental Health Officers and Environmental Health Technologists, traced the houses of patients, disinfected their premises, and provided technical guidance on the causes and prevention of cholera to their family members. At the same time, locations of individual patient houses were marked using a Geographic Positioning System (GPS; Garmin G72, USA). Household data. Information on households was collected through two types of surveys of the study area from 2002 to 2003 as a basic survey for the Japan International Cooperation Agency (JICA) Primary Health Care Project. One was a household survey that collected information from all the households in the study area done by a trained survey team to obtain demographic, socioeconomic, hygiene, and sanitation data. The information used for this study was the number of family members, the education level of the head of a household, and monthly income. Monthly income was estimated by the amount of daily expenses in cases without a regular source of income. The information collected on hygiene and sanitation conditions were source of drinking water, drainage condition surrounding the house, and presence of shallow wells and latrines insides the premises. Actual conditions were physically observed by a member of the survey team. Effective drainage was evaluated on the condition of no stagnant water on the premises in the rainy season. In the survey of hygiene and sanitation behavior, 500 samples were collected by a systematic random sampling method in the study area. The study area was divided into 12 administrative zones. The number of samples collected from each zone was calculated in accordance with the population of the zone. A trained survey team started visiting houses at the south end moving west along the border of the zone. The survey team collected information from households after by-

3 416 SASAKI AND OTHERS FIGURE 2. Epidemic curve of the outbreak of cholera in Lusaka city and study area that occurred from November 1, 2003 to April 18, passing the specific number of households that were calculated by dividing the total population of the zones by the number of samples to be collected. Information was collected on the method of hand washing, the type of container for storing drinking water, and chlorination of drinking water. The hand washing methods that were applied at each household were categorized into four types: washing hands in a water basin without soap, in a basin with soap, using pouring water without soap, and pouring water with soap. Geographic analysis. We used Lusaka s digital base map, which was developed by the JICA Primary Health Care project. The map was digitized based on satellite imagery (SPOT 5 with 2-m resolution) with ArcView software, and it included streets, railroads, major official buildings, and public health facilities (Figure 1). Two geographic boundaries were defined. One was based on the zonal boundaries of the Residential Development Committee of Lusaka City Council, which divide the study area into 12 zones. The other demarcated the study area with a Voronoi diagram of 125 communal water points. Zonal boundaries were categorized as administrative aggregative groups, and water point areas were represented as neighborhood groups. The distribution pattern was analyzed by two spatial statistical methods: average nearest neighbor and Moran s I analysis. The average nearest neighbor analysis was used for point data to detect clustering cholera patient locations in the study area. Furthermore, we examined distribution patterns of incident rates in the Voronoi diagram with Moran s I analysis. Arcview 9.2 (ESRI, USA) was used for developing the Voronoi diagram and spatial statistical analysis. For the analysis of risk factors, cholera incident rates and independent variables derived from the surveys were projected as layers of attributable factors both on the zonal and community water point maps. Case-control study. A case control study was applied to identify individual risk factors for cholera. All cases were residents in the study area and were admitted to George Health Center from February 9 to March 2, Two controls per case that were matched by sex and age within 2 years above and below the age of the case were systematically selected. Patients younger than 5 years of age were excluded from cases because of difficulties in interviewing. Trained surveyors visited residents of the case areas and collected information from them. Controls were identified by starting from a case house and moving in both right and left directions after bypassing the adjacent households. The surveyors walked in the same direction and questioned households until matched controls were found. If appropriate controls were not found TABLE 1 Demographic information of cases residing in the study area and Lusaka during the outbreak from November 2003 to April, 2004 Characteristic Study area Lusaka Age (years) < (27.3) 1,317 (20.1) (14.0) 771 (11.8) (6.7) 372 (5.7) (4.6) 371 (5.7) (9.4) 720 (11.0) (10.9) 818 (12.5) (9.4) 650 (9.9) (4.8) 370 (5.7) (2.5) 256 (3.9) (2.5) 151 (2.3) (1.0) 84 (1.3) (0.4) 68 (1.0) > (3.8) 165 (2.5) Unknown 13 (2.7) 429 (6.6) Total 479 (100.0) 6,542 (100.0) Sex Male 228 (47.7) 3,321 (50.8) Female 251 (52.3) 3,163 (48.3) Unknown 0 (0) 58 (0.9) Total 479 (100) 6542 (100.0) Data are no. (%) of population and cases, unless otherwise indicated.

4 ANALYSIS OF RISK FACTORS OF CHOLERA OUTBREAK IN ZAMBIA 417 FIGURE 3. Dot density maps of cholera cases distribution at 2-week intervals. Circles and squares indicate locations of patients older than 5 and younger than 5 years old, respectively. within 10 households, the process was repeated, walking in the other direction. 9 The information on sources of drinking water, chlorination of drinking water, hand washing methods, and toilet use was collected for analysis of personal risk factors. The numbers of cases and controls collected for the analysis were 40 and 80, respectively. Statistical analysis. Bivariable correlation and multivariable analyses were applied to identify attributable risk factors for

5 418 SASAKI AND OTHERS point areas. In analyzing risk factor at the household level, we identified cholera patient households after matching a patient name and address, and applied logistic regression analysis to examine risk factors among the same independent variables as we had applied regression analysis to. In the multivariable analysis, multicollinearity among the independent variables was examined and a collinear variable was removed. These analyses were calculated by using SPSS 11.5 version (SPSS, USA). Matched odds ratio (mor), P value, and confidence interval (CI) were calculated to analyze the association between cholera and personal risk factors for the case-control study. The Mantel-Haenszel test was applied for matched-pair analysis with SPSS FIGURE 4. Choropleth map layers of cholera incident rates (per 1,000 population) by RDC zones (A) and water tap boundaries by using a Voronoi diagram (B). the incidence of cholera in the study area. The dependent variable for the analysis was the incidence of cholera, and the independent variables were indicators collected through household and sample surveys. Pearson correlation analysis was applied for identifying association among incident rates and independent variables of 12 zones. Regression analysis was used as a multivariable analysis to examine the association of cholera with the independent variables of 125 water RESULTS A total of 6,542 cholera cases and 187 deaths (case fatality rate: 2.86) were reported in Lusaka between November 28, 2003 and June 8, The laboratory-confirmed rate after sending sample rectal swabs of the registered patients to UTH was 68%, and all cases were characterized in the laboratory as V. cholerae (EL Tor Ogawa). The total number of patients in the study area was 479 (7.3% of the 6,542). The epidemic pattern of cholera outbreaks in Lusaka city and the study area indicated two peaks in Weeks 4 5 and (Figure 2). The second peak was lower than the first peak in both cases. The age distribution of cholera patients showed that the leading age group was younger than 5 years old, accounting for 27.3% in the study area and 22.8% in Lusaka city (Table 1). Children younger than 9 years old accounted for 41% and 34.6%, respectively. The first case in the study area was recorded on December 27, 2003, 1 month after the first case in Lusaka city. Dot density maps of cholera cases indicated that patients were distributed sparsely in the George compound, and almost all patients were adults in the first stage of the outbreak (Figure 3). As the outbreak progressed, the density of patient dots became more apparent, and the number of affected infants and children younger than 5 years old increased gradually. TABLE 2 Pearson s correlation matrix of incident rates of cholera and risk factor variables by zones Coefficient Variables N Mean SD Incident rate No of family members Education* Average monthly income Households with shallow well 6 Households drinking water from shallow well 7 Households without latrine Households without drainage 9 Washing hands with soap Keeping water in containers with lid 11 Chlorinating dirking water * Percentage of head of household who complete primary level education. US dollars. Percentages. P < P < 0.01.

6 ANALYSIS OF RISK FACTORS OF CHOLERA OUTBREAK IN ZAMBIA 419 TABLE 3 Regression analysis of incident rates of cholera and risk factors Variables N Mean SD B 95% CI P value No of family member Average monthly income* Households without latrine < 0.05 Households without drainage Drinking water from shallow well < 0.01 * US dollars. Percentages. The study area was divided into 12 zones by the Residential Development Committee of the Lusaka City Council and into 125 communal water point areas using a Voronoi diagram, as indicated on the map (Figure 4). The incidence of cholera in the study area by zone and water point area ranged from 3.95 to (mean, 11.46; standard deviation [SD], 4.29) and 0 to (mean, 12.56; SD, 8.9) per 1,000 people, respectively. The average nearest neighbor analysis of individual point data indicated that cholera patients were significantly clustered (Z score, 6.01; P < 0.01). Moran I analysis for group data also showed significant clustering of cholera incidents (Z score, 5.71; P < 0.01). Risk factors were analyzed at the administrative zone, community water point area, and household levels. Bivariable correlation analysis of cholera incident rates in relation to independent variables at a zone level, including the number of family members, education level, monthly income, households with shallow wells, households without latrines, households without drainage, washing hands with soap, type of water storage container, and chlorination of water, is shown in Table 2. Analysis of attributable risk factors indicated that the incidence of cholera was only significantly associated with lack of a latrine (coefficient 0.70, P < 0.05) and/or proper drainage (coefficient 0.71, P < 0.01) on the house premises. However, other independent variables such as the number of family members, education level, monthly income, presence of shallow well, hand washing method, water container, and the chlorination of drinking water showed no significant association with the disease. Multivariable analysis was used to examine the association of cholera incident with the number of family members, monthly average income, households without latrines, households without drainage, and households drinking water from shallow well after verifying multicollinearity of the variables. Regression analysis at a communal water point level indicated that households without latrines (B 27.84, 95% CI , P < 0.05) and drinking water from shallow wells (B 24.71, 95% CI , P < 0.01) were associated with cholera incidents (Table 3). Other variables did not show any significant association with incidents of cholera. Regarding the investigation risk factors at the household level, results of logistic regression analysis indicated that households that do not have toilets on their premises (OR 2.33, 95% CI , P < 0.01) and drink water taken from shallow wells (OR 2.27, 95% CI , P < 0.01) were statistically associated with incidents of cholera (Table 4). On the personal risk factor analysis, 40 cases and 80 controls were analyzed with Mantel-Haenszel analysis. Regarding age distribution, the 25- to 34-year-old age group was the leading group among cases and controls and accounted for 32.5% (Table 5). The analysis indicated that source of drinking water, method of hand washing, and sharing a latrine was associated with cholera incidence (Table 6). Drinking water collected from shallow wells was statistically associated with cholera (mor 7.25, 95% CI , P < 0.05). Drinking water without chlorination also showed significant association with incidence of cholera (mor 2.23, 95% CI , P < 0.05). Washing hands with pouring water and soap significantly reduced the risk of cholera (mor 0.22, 95% CI , P < 0.05). However, the timing of washing hands both before eating and after defecating did not indicate any significant difference. Although lack of a latrine within a house s premises did not have any significant association with cholera (mor 2.00, 95% CI , P 0.19), sharing a latrine with neighboring households was statistically associated with cholera incidence (mor 4.25, 95% CI , P < 0.05). DISCUSSION This analysis, through the plotting of the cholera patients in the study area, indicated almost all patients to be adults in the first stage of the outbreak, whereas the numbers of affected infants and children younger than 5 years old increased gradually as the epidemic continued. V. cholerae has not been documented to spread directly from one person to another TABLE 4 Logistic regression analysis of incident rates of cholera and risk factors Household characteristics (%) Logistic regression analysis Variables Patient (N 267) Non-patient (N 7,989) Odds ratio 95% CI P value No. of family members Mean 4.89 Mean SD 2.43 SD 2.65 Average monthly income (US$) Mean Mean SD SD Households without latrine 74 (27.7) 1,091 (13.7) < 0.01 Households without drainage 80 (30.0) 2,117 (26.9) Drinking water from shallow well 56 (21.0) 747 (9.4) < 0.01

7 420 SASAKI AND OTHERS TABLE 5 Demographic information of a matched cases-control study residing in the study area and Lusaka Characteristic Case Control Age (years) (27.5) 21 (26.25) (17.5) 16 (20.0) (32.5) 25 (31.25) (15.0) 12 (15.0) (2.5) 3 (3.75) > 55 2 (5.0) 3 (3.75) Total 40 (100.0) 80 (100.0) Sex Male 23 (57.5) 46 (57.5) Female 17 (42.5) 34 (42.5) Total 40 (100.0) 80 (100.0) Data are no. (%) of population and cases, unless otherwise indicated. without the intermediary of food or contamination water. 4 Therefore, we assume that adults transmit V. cholerae to young children by the interfamilial spread of infection through family contacts, and this is accelerated under the conditions of non-hygienic circumstances. From a public health perspective and the prevention and control of cholera point of view, not only early detection of epidemics but also education to promote good personal hygiene among adults should be emphasized. The geographic clustering of cholera patients indicated that there were indigenous factors that provide favorable conditions for the transmission of V. cholerae. In this study, spatial statistical analysis with average nearest neighbor and Moran I analysis showed that the distribution of cholera patients was statistically clustered within the study area. A chloropleth map of cholera cases showed that a higher incident rate of cholera within an administrative boundary was statistically associated with lower coverage of latrines and effective drainage systems. The regression analysis in a neighborhood group indicated an association of cholera incident only with an insufficient number of toilets. The insufficient facilities for excreta disposal and drainage increased the risk of transmission of V. cholerae through the defecation of patients in areas surrounding their houses and its spread because of rain water floods. Thus, the improvement of hygiene conditions including the construction and maintenance of sewage disposal facilities must be a high priority for the long-term prevention of cholera in collaboration with international and nongovernmental organizations. However, these improvements are not happening rapidly in most regions where cholera is prevalent. In addition to the lack of infrastructure, the spread of the disease is because of the fact that cholera is known to primarily affect individuals with insufficient knowledge and inappropriate attitudes toward hygiene and sanitation practices. In our study, drinking water collected from shallow wells was identified as an important risk factor for cholera. The result supports the assumption that transmission of V. cholerae happens when rain flood water causes feces from defecation around cholera patients houses to contaminate shallow wells. Chlorination of drinking water is widely recognized as an important factor to prevent cholera. Even if the water is clean when it is first drawn from a deep well, it can be heavily contaminated by the time it reaches the unsuspecting recipient. Our study also indicated that using water without home chlorination was statistically associated with spreading the disease. Several studies have emphasized the importance of the microbial safety of water immediately before consumption for reducing diarrheal rates Because most of households in less-developed countries do not have individual water connections, the water is vulnerable to contamination during transport and storage. Thus, our study strongly supports the association between the incidence of cholera and the nature of water and water treatment. Simple and inexpensive methods of domestic water disinfection and storage reduce the risk of cholera and diarrhea. Therefore, we would like to emphasize again use of in-home chlorination, point-of-use water disinfection and safe water storage and support the construction of deep-well facilities for safe water supplies to unplanned settlements in Lusaka. This study also showed the protective role of hand washing with soap for cholera prevention. This finding is consistent with other studies that suggest hand washing reduces the risk of diarrhea by > 40%. 20 The Centers for Disease Control and Prevention were invited to Zambia to assist in a study of the present outbreak and implicated that the presence of soap in the home, which served as proxy for improved hygiene, was protective against cholera during this study. 9,10 From a public health view point, health education to promote good personal hygiene with emphasis of a proper hand washing method with soap is strongly required for prevention and control of cholera. This study had certain limitations. The methodology of selection of controls could have controlled out local-level variables. The controls were selected after bypassing the adjacent neighboring to avoid the same exposure of the cases. We assume residents in the study area share their latrines and hygiene facilities with neighboring households. However, because controls were selected within 10 households from the case, there were still possibilities to control out local-level variables. The risk factor analysis by the case-control study and the TABLE 6 Mantel-Haenszel analysis to identify risk factor for cholera outbreak Independent variables Case (%) Control (%) Odds ratio 95% CI P value Drink water from a shallow well 9 (22.5) 5 (6.3) < 0.05 Not chlorinate drinking water 28 (70.0) 37 (49.3) < 0.05 Wash hands before eating 32 (80.0) 67 (83.8) Wash hands after defecating 30 (75.0) 67 (83.8) Wash hands with pouring water and soap 4 (10.0) 25 (33.3) < 0.05 Not have a latrine inside premises 7 (17.5) 7 (8.8) Share a latrine with more than two households 38 (95.0) 63 (78.8) < 0.05

8 ANALYSIS OF RISK FACTORS OF CHOLERA OUTBREAK IN ZAMBIA 421 Pearson correlation analysis of groups showed different results. The case-control study statistically proved that chlorination of drinking water and washing hands with running water and soap play preventative roles against cholera transmission. However, according to the correlation analysis, statistical association between these two variables and incidence of cholera was not identified. We assume that the case-control study is suitable to investigate risk factors in relation to personal behavior, whereas the group analysis shows advantages in analyzing the potential risk factors in the lack of infrastructure and the conditions of the environment for daily life. This means that risk factors could be identified differently depending on the unit of population for the study. Therefore, a combination of the analysis of behavioral risk factors from the case-control study and the analysis from a geographical perspective enabled us to investigate the cholera outbreak from multiple dimensions. Some literature has proven that a combination of one or more interventions in improving water supply, sanitation, and hygiene can substantially reduce the rate of morbidity of cholera. 19,21,22 Our study suggests that appropriate interventions need to be taken at a targeted risk level. Received August 8, Accepted for publication May 20, Authors addresses: Satoshi Sasaki, Hiroshi Suzuki, and Kumiko Igarashi, Asahimachi-dori Chuo Ward, Niigata , Japan, Tel: , Fax: Bushimbwa Tambatamba and Philip Mulenga, 5231 Makishi Road, Lusaka, Zambia, Tel: , Fax: REFERENCES 1. Naidoo A, Patric K, Cholera: a continuous epidemic in Africa. J R Soc Health 122: WHO, Cholera Wkly Epidemiol Rec 79: Shears P, Emerging and reemerging infections in Africa: the need for improved laboratory services and disease surveillance. Microbes Infect 2: Sack DA, Sack RB, Nair GB, Siddique AK, Cholera. Lancet 363: Griffith DC, Kelly-Hope LA, Miller MA, Review of reported cholera outbreaks worldwide, Am J Trop Med Hyg 75: Zuckerman JN, Rombo L, Fisch A, The true burden and risk of cholera: implications for prevention and control. Lancet Infect Dis 7: Diez Roux AV, The study of group-level factors in epidemiology: rethinking variables, study designs, and analytical approaches. Epidemiol Rev 26: Diez Roux AV, Neighborhoods and health: where are we and were do we go from here? Rev Epidemiol Sante Publique 55: Dubois AE, Sinkala M, Kalluri P, Makasa-Chikoya M, Quick RE, Epidemic cholera in urban Zambia: hand soap and dried fish as protective factors. Epidemiol Infect 134: Centers for Disease Control and Prevention, Cholera epidemic associated with raw vegetables Lusaka, Zambia, MMWR Morb Mortal Wkly Rep 53: Becker KM, Glass GE, Brathwaite W, Zenilman JM, Geographic epidemiology of gonorrhea in Baltimore, Maryland, using a geographic information system. Am J Epidemiol 147: Moore DA, Carpenter TE, Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev 21: Ali M, Emch M, Donnay JP, Yunus M, Sack RB, Identifying environmental risk factors for endemic cholera: a raster GIS approach. Health Place 8: Whitman S, Silva A, Shah A, Ansell D, Diversity and disparity: GIS and small-area analysis in six Chicago neighborhoods. J Med Syst 28: Newsom SW, Pioneers in infection control: John Snow, Henry Whitehead, the Broad Street pump, and the beginnings of geographical epidemiology. J Hosp Infect 64: Quick RE, Kimura A, Thevos A, Tembo M, Shamputa I, Hutwagner L, Mintz E, Diarrhea prevention through household-level water disinfection and safe storage in Zambia. Am J Trop Med Hyg 66: Durrheim D, Prevention of cholera. Lancet 363: Thevos AK, Kaona FA, Siajunza MT, Quick RE, Adoption of safe water behaviors in Zambia: comparing educational and motivational approaches. Educ Health (Abingdon) 13: Fewtrell L, Kaufmann RB, Kay D, Enanoria W, Haller L, Colford JM Jr, Water, sanitation, and hygiene interventions to reduce diarrhoea in less developed countries: a systematic review and meta-analysis. Lancet Infect Dis 5: Curtis V, Cairncross S, Effect of washing hands with soap on diarrhoea risk in the community: a systematic review. Lancet Infect Dis 3: Esrey SA, Potash JB, Roberts L, Shiff C, Effects of improved water supply and sanitation on ascariasis, diarrhoea, dracunculiasis, hookworm infection, schistosomiasis, and trachoma. Bull World Health Organ 69: Eisenberg JN, Scott JC, Porco T, Integrating disease control strategies: balancing water sanitation and hygiene interventions to reduce diarrheal disease burden. Am J Public Health 97: