Estimating Typhoid Fever Risk Associated with Lack of Access to...
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Research ArticleEstimating Typhoid Fever Risk Associated with Lack ofAccess to Safe Water: A Systematic Literature Review
Vijayalaxmi V. Mogasale,1 Enusa Ramani,2 Vittal Mogasale ,2
Ju Yeon Park,3 and Thomas F. Wierzba4,5
1Epidemiology Unit, International Vaccine Institute, Seoul, Republic of Korea2Policy and Economic Research Department, International Vaccine Institute, Seoul, Republic of Korea3Biostatistics and Data Management Department, International Vaccine Institute, Seoul, Republic of Korea4Development and Delivery Unit, International Vaccine Institute, Seoul, Republic of Korea5PATH, 455 Massachusetts Avenue NW, Suite 1000, Washington, DC, USA
Correspondence should be addressed to Vittal Mogasale; [email protected]
Received 28 December 2017; Accepted 28 May 2018; Published 4 July 2018
Academic Editor: Evelyn O. Talbott
Copyright © 2018 Vijayalaxmi V.Mogasale et al.This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in anymedium, provided the originalwork is properly cited.
Background. Unsafe water is a well-known risk for typhoid fever, but a pooled estimate of the population-level risk of typhoidfever resulting from exposure to unsafe water has not been quantified. An accurate estimation of the risk from unsafe water willbe useful in demarcating high-risk populations, modeling typhoid disease burden, and targeting prevention and control activities.Methods. We conducted a systematic literature review and meta-analysis of observational studies that measured the risk of typhoidfever associated with drinking unimproved water as per WHO-UNICEF’s definition or drinking microbiologically unsafe water.The mean value for the pooled odds ratio from case-control studies was calculated using a random effects model. In addition tounimproved water and unsafe water, we also listed categories of other risk factors from the selected studies. Results. The searchof published studies from January 1, 1990, to December 31, 2013 in PubMed, Embase, and World Health Organization databasesprovided 779 publications, of which 12 case-control studies presented the odds of having typhoid fever for those exposed tounimproved or unsafe versus improved drinking water sources. The odds of typhoid fever among those exposed to unimproved orunsafe water ranged from 1.06 to 9.26 with case weighted mean of 2.44 (95% CI: 1.65–3.59). Besides water-related risk, the studiesalso identified other risk factors related to socioeconomic aspects, type of food consumption, knowledge and awareness abouttyphoid fever, and hygiene practices. Conclusions. In this meta-analysis, we have quantified the pooled risk of typhoid fever amongpeople exposed to unimproved or unsafe water which is almost two and a half times more than people who were not exposed tounimproved or unsafe water. However, caution should be exercised in applying the findings from this study in modeling typhoidfever disease burden at country, regional, and global levels as improved water does not always equate to safe water.
1. Introduction
Typhoid fever is a systemic bacterial illness of public healthimportance. The disease is transmitted person to persondue to fecal contamination of food and water [1]. Thecausative agent, Salmonella enterica serovar Typhi (S. Typhi),is exclusive to humanswho are the natural host and reservoirs[2]. Humans can become chronic carriers and food handlingpractices among carriers can result in food contaminationand S. Typhi transmission [2]. However, use of sewage con-taminated water for irrigation and domestic use is considered
critical in maintaining typhoid endemicity in developingcountries as demonstrated in Santiago, Chile [2]. Since themajor routes of transmission of typhoid fever are throughdrinking water or eating food contaminated with Salmonellatyphi, the World Health Organization (WHO) recommendsprovision of safe water as one of the preventive measures fortyphoid fever [2].
Defining and monitoring quality and ensuring watersafety in low- and middle-income countries (LMICs) arechallenging. The WHO defines microbiologically safe waterbased on the amount of Escherichia coli which should be
HindawiJournal of Environmental and Public HealthVolume 2018, Article ID 9589208, 14 pageshttps://doi.org/10.1155/2018/9589208
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Table 1: Improved and unimproved drinking water sources based on WHO/UNICEF Joint Monitoring Programme for water supply andsanitation [4].
Improved drinking water source Unimproved drinking water sourcePiped water into dwelling, yard or plot Unprotected springPublic tap or standpipe Unprotected dug wellTubewell or borehole Cart with small tank/drumProtected dug well Tanker-truckProtected spring Surface waterRainwater collection Bottled water from unimproved water source∗∗Please refer to WHO/UNICEF Joint Monitoring Programme for water supply and sanitation [4] for details. Note that any microbiologically contaminatedwater source was considered unsafe water in the analysis.
Table 2: Selection criteria for systematic literature review.
Inclusion criteria(i) Publications listed from January 1, 1990 to December 31, 2013(ii) Studies in English language(iii) Research conducted in human subjects(iv) Studies listed in PubMed database or Embase database or WHO website or PAHO website(v) Study designs: case-control, cohort, randomized control trials(vi) At least one water related exposure variable that could be categorized either as improved or unimproved drinking water source [4](vii) Water is consumed by drinkingExclusion criteria(i) Descriptive cross sectional studies that did not present odds ratio, case reports and case series(ii) Studies that did not present water related risk-factors(iii) Studies conducted in typhoid non-endemic area are excluded in the estimation of pooled odds ratio
0 CFU/100 ml [3] suggesting there should not be any fecalcontamination. Continuous monitoring of the microbio-logically safe water requires periodic laboratory testing ofwater sources which is difficult in resource poor settingsof LMICs. To simplify the process WHO-UNICEF JointMonitoring Programme (JMP) has defined alternative indi-cators, “improved water” and “unimproved water” sources[4], which deemed to represent safe water and unsafe water,respectively (Table 1).
People who drink safe water are likely to have lowerrisk of typhoid fever compared to people drink unsafewater which is one of the several risk factors for typhoidfever. However, typhoid fever global disease burden estimatesoften extrapolate the incidence rates obtained from high-riskpopulations to rest of the populations [5, 6] which is likelyto be an overestimation. Hence, it is necessary to correct theincidence rates while extrapolating the data collected frompopulations drinking unsafe water to population drinkingsafe water. But, there is no database that provides informationon drinking safe water or unsafe water that can be used inglobal disease burden estimation. Alternately, there is globaldatabase available on access to improvedwater to populations[7] which can be used as a proxy for safe water consump-tion. Therefore, it is necessary to link the risk of unsafewater to unimproved water. Although a systematic reviewpresented earlier has showed that the microbiological safetyof improved water is inconsistent [8] but provides a measureof sanitary protection and it is the only dataset that can beapplied at the global level for water-related risk correction.
While many studies have explored the risk of typhoidfever from unsafe water, there has not been a systematic
review that presented pooled estimate of the quantitative risk.We conducted a systematic literature review to quantify theprobability of symptomatic S. typhi infection among residentswho consumed unimproved or unsafe water compared to res-idents who did not consume unimproved or unsafewater.Theprimary purpose of this review was to derive a quantitativevalue on excess risk of typhoid fever due to the consummationof unimproved or unsafe water which can be used as acorrection factor in global disease burden estimates [6].
2. Materials and Methods
A systematic literature review was conducted independentlyby each of two researchers. Each researcher first identifiedstudies on risk factors for typhoid fever and then selectedfrom those publications, papers presenting water-relatedrisks.The search results from two researchers were comparedand any differences between them were resolved based ondiscussion and agreement. If unresolved, a third independentresearcher made the final decision. All selected papers werereviewed by a third researcher before data extraction toconfirm its adherence to inclusion and exclusion criteria.
To identify studies, in addition to searching primarydatabases, PubMed and Embase, searches were also madein WHO and Pan American Health Organization (PAHO)databases. The search was limited to studies published inEnglish language, from January 1, 1990, to December 31, 2013.The detailed inclusion and exclusion criteria are provided inTable 2. The search terms used were (“typhoid” OR “typhoidfever” OR “Salmonella Typhi” OR “S. Typhi” OR “Salmonellainfection” OR “enteric fever”) AND (“risk factors” OR
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“predictive factors” OR “associated factors” OR “attributedfactors” OR “exposure factors” OR “related factors” OR“predisposing factors”). Search results are documented in aPRISMA diagram [31].
To quantify the risk, we selected one risk factor fromeach case-control study that best represented either improvedor unimproved water based on the definition providedby WHO/UNICEF-JMP (Table 1). If a water source in astudy was identified as “improved,” but was reported to be“microbiologically contaminated,” we considered the watersource as “unsafe.” We extracted the odds ratio of typhoidfever among those who got exposed to unimproved water orunsafe water compared to those who did not get exposed.A meta-analysis was conducted to pool the odds ratio ofmethodologically similar studies using a Metafor StatisticalPackages for R, version 1.9-8 [32, 33]. According to theheterogeneity test such as Q statistics and I2 [33], the meanvalue for pooled odds ratio from case-control studies werecalculated from a meta-analysis using random effects modelwith restricted maximum-likelihood estimator [33]. Cohortstudy findings were descriptively presented as they couldnot be combined with case-control study meta-analysis.We also descriptively summarized other risk factors thatshowed a statistically significant probability of symptomaticS. typhi infection from the selected studies for the betterunderstanding of overall risk factors.
3. Results and Discussion
Our review yielded a total of 779 publications from the searchdatabases (Figure 1). A total of 87 duplicates were removed,and 612 were excluded on title and abstract search becausethey lacked data on typhoid fever related risk factors. Fulltexts were accessed for remaining 80 papers. Of them, 58were excluded as they either (a) did not contain water-relatedrisk factors or (b) could not be classified into improved orunimproved water categories based on WHO-UNICEF-JMPdefinition or (c) descriptive cross- sectional studies that didnot present odd ratio. There were no randomized controltrials. Four cohort studies were presented descriptively [27–30] as the risk could not be merged and summarizedwith majority case-control studies. Finally, we could includeonly case-control studies in the estimation of pooled oddsratio. Sensitivity analysis was conducted for the decisionof study exclusion. At the beginning of the analysis, onestudy was automatically excluded due to zero-count cell[34] and another one was omitted by sensitivity analysisas an outlier [9]. Four studies had presented odds ratiofor improved water which could not be combined withodds ratio for unimproved water because inverse odds ofimproved water are technically not the same as unimprovedwater [23–26]. We cannot assume that people unexposed toimprovedwater are exposed to unimprovedwater.Thepooledodds ratio presented below include 12 case-control studiesfrom reported typhoid endemic regions and presented water-related risk factors.
3.1. Case-Control Studies. Of the 12 selected studies, five werefrom SouthAsia [12, 13, 19–21], four were from Southeast Asia
[15–17, 22], two were from Central Asia [11, 18], and one wasfrom South-Central Europe [14]. The studies included weremostly conducted in urban settings (75.00%) and only threewere outbreak investigations (25.00%, Table 3). There were915 typhoid fever cases and 1,609 nontyphoid fever controls.The exposure to unimproved water was higher among cases(62.95%; n = 576/915) compared to controls (46.30%; n =745/1609) (Figure 2). Half of the cases-controls studies havingimproved water source were microbiologically contaminatedand were considered unsafe water (Table 3). The odds oftyphoid fever among those who were exposed to unimprovedwater or unsafe water were ranged from 1.06 to 9.26 with caseweighted mean of 2.44 (95% CI: 1.65 – 3.59) (Figure 3).
Besideswater-related risk, the studies also listed other riskfactors related to socioeconomic aspects, living condition,food consumption, knowledge and awareness about typhoidfever, and hygiene practices (Table 4).
3.2. Cohort Studies. Four cohort studies presented relativerisk of typhoid fever attributable to exposure to unimprovedwater sources compared to improved water sources [27–30].The risk of contracting typhoid fever in groups exposed todrinking from a government water supply tank in Rajasthanwas 11.10 (95% CI: 3.70 – 33.00) times greater than those inthe nonexposed group [27].Thosewho drank from combinedsources of government tank, hand pump, and personal tubewell were 3.75 (95% CI: 1.02 – 13.80) times more likely at riskof typhoid than those not exposed to the three combinedsources indicating contamination of these sources. On afloating island restaurant in France, those who drank pipedwater onboard from untreated River Seine source had noexcess risk of typhoid fever compared to those unexposed tothose sources (RR = 1.40 95% CI: 0.60 – 3.00) [28].This studyconcluded that consumption of rice and chicken washed intap water resulted in outbreak and found fecal contaminationin the tap water which was untreated. In urban Karachi,univariate analysis showed that individuals who consumedtap or bottled water had same risk (RR = 0.70; 95% CI: 0.44– 1.11) of getting typhoid fever compared to those who didnot use tap or bottled water [30]. However, using regressionmodel, after adjusting for all covariates, the study found thatoverall risk of typhoid fever is lower among households usinga safe drinking water source (RR = 0.63; 95% CI: 0.41–0.99).In Eastern Kolkata, the study found that a significantly lowerproportion of households use tap water (RR = 0.07; p value =<0.001) in typhoid fever high-risk areas compared to typhoidfever low-risk areas [29].
3.3. Case-Control Studies Excluded from Meta-Analysis. Thefour excluded case-control studies that presented odds ofexposure to improved water among typhoid fever casescompared to controls [23–26] when combined together didnot show any significant association with water source (OR =0.70; 95% CI: 0.46 – 1.05) (Figure 4). The risk factors selectedfrom these four studies included utilization of municipaldrinking water (OR = 0.75; 95% CI: 0.31 – 1.84) in Diyarbakir,Turkey [23], utilization of piped water (OR = 1.00; 95% CI:0.37 – 2.72) inUjungPandang, Indonesia [24], drinking pipedwater (OR = 0.52; 95% CI: 0.23 – 1.16) in Jakarta, Indonesia
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Full-text articles excluded (n=10) for: a)Record used in descriptive analysis (n=4) b)Presenting outlier data for meta-analysis (n=2)c)Presenting only improved water which could not be combined with odds ratio of unimproved water (n=4)
Studies included in final meta-analysis for water related risk factors
(n = 12)
PubMed search (n=292)Embase search (n=425)
Total hits (n= 717)
Extra searchWHO/PAHO
Websites (n=62)
Records excluded (n=699) due to: Being duplicates (n = 87)Irrelevance to study (n=612)
Full-text articles assessed for eligibility
(n = 22)
Records excluded based on inclusion criteria (n = 58)1) Did not contain water related risk-factors (n=31)2) Could not be classified into improved orunimproved water categories based on WHO-UNICEF JMP definition (n=20)3) Descriptive cross-sectional studies that did not present odd ratio (n=7)
Titles and abstractsscreened(n =80)
Scre
enin
gEl
igib
ility
Iden
tifica
tion
Incl
uded
Figure 1: PRISMA diagram representing search results of typhoid fever risk factors.
[25], and utilization of tap water (OR = 0.69; 95% CI: 0.34 –1.40) in Karachi, Pakistan [26].
Two case-control studies excluded from our investiga-tions at the time of sensitivity analysis were from Thailand[34] and Malaysia [9]. In Thailand study, drinking unboiledspring water had 37.80 (95%CI: 1.93 – 739.89) odds of typhoidfever compared to those who drank from either piped water,rain water, commercially bottled water, or water from wells.However, all typhoid fever cases were exposed to unboiledspring water. In Malaysian study, the accidental ingestion ofwater during swimming or bathing in a river had 32.78 (6.16
– 174.54) odds of getting typhoid fever compared to exposurefrom food items. In Figure 5 we have presented forest plotfor odds ratio without excluding this study to show how itsinclusion would have changed the results. Table 5 presentsPRISMA checklist.
3.4. Discussion. The systematic review of literature yielded 12case-control studies from 12 sites conducted in 10 differentcountries and presenting variables for water-related risk thatcould be categorized as unimproved water or unsafe waterand associated with typhoid fever. This review demonstrates
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Table 3: Characteristics of case-control and cohort studies included in the systematic literature review.
Publication year Study year Study site Studysetting
Sitepopulation
Selectedunimproved/unsafe
water sourceReasons for typhoid fever Source
Case control studies
1999 1997 Dushanbe,Tajikistan
Outbreakin
endemicarea
Urban
Water for homeobtained from outside
tap [10](contaminated
improved source =unsafe)
Water source contaminationCessation of chlorination,intermittent water supply
creating negative pressure andcontaminating water supply with
surrounding contaminants.
[11]
2013 2011 Kathmandu,Nepal Endemic Urban Use of stone spout
water
Multiple; drinking water spoutcontamination with sewage,
contamination of stored water,general sanitation issues such aslack of toilets or lack of water to
flush toilets
[12]
2009 2007 West Bengal,India
Outbreakin
endemicarea
Urbanslum
Drinking piped water[10] (contaminatedimproved source =
unsafe)
Unchlorinated water supplythrough pipes, drinking water
pipes close to open drainage andintermittent water supply
[13]
1992 1990 NeapolitanArea, Italy
Outbreakin
endemicarea
Urban Drinking non-potablewater
Multiple; foodborne, sanitation,and drinking water source
contamination due to sewageexposure to municipal water
supply
[14]
2004 2000MadayaTownship,Myanmar
Outbreakin
endemicarea
Rural Drinking untreatedriver water
Drinking water contaminationwith unchlorinated river water
which had direct sewagedrainage.
[15]
2005 2002Son La
Province, N.Vietnam
Endemic Urban Drinking untreatedwater
Drinking water contaminationand consumption of
unchlorinated water (dislike forchlorine smell)
[16]
2005 1996-1997
DongThapProvince,MekongDelta, S.Vietnam
Endemic Urban Drinking unboiledwater
Multiple; drinking river waterwhich had sewage (latrine)
drainage, drinking water sourcesfrom deep wells and ponds
contaminated with drainage fromlatrines situated in the proximity
[17]
2007 2002-2003SamarkandOblast,
UzbekistanEndemic Urban and
Rural
Consumption ofunboiled surfacewater outside the
home
Water source contamination. Thedrinking of un-boiled surfacewater outside home during thehot and dry summer months.
[18]
2009 2005-2006Darjeeling,West Bengal,
IndiaEndemic Rural Stream water
Multiple; foodborne, sanitationissues, and drinking water sourcecontamination. Untreated watersupply from unprotected springsand natural streams, untreatedwater supply from venders.
[19]
2007 2003-2004Dhaka slum,Kamalapur,Bangladesh
Endemic Urbanslum
Drinking unboiledwater at home
Multiple; sanitation issues, anddrinking water sourcecontamination. Partial
chlorination of municipal watersupply exposed to
contamination, drinking ofuntreated water
[20]
1998 1994 Karachi,Pakistan Endemic Urban
Drinking water atwork (improved or
unimprovedunknown)
Multiple; foodborne, sanitationissues, and drinking water source
contamination at workplace[21]
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Table 3: Continued.
Publication year Study year Study site Studysetting
Sitepopulation
Selectedunimproved/unsafe
water sourceReasons for typhoid fever Source
2001 1992-1994 Samarang,Indonesia Endemic Urban
Drinkingnon-municipal water
source
Multiple; foodborne, sanitationissues, and drinking of
unchlorinated water fromvenders
[22]
2005 2001-2003 Diyarbakir,Turkey Endemic Urban &
rural
Municipality drinkingwater (contaminatedimproved watersource = unsafe)
Consumption of raw vegetablesirrigated with sewage water from
the city[23]
1997 1990-1991Ujung
Pandang,Indonesia
Endemic Urban
Piped water(contaminatedimproved watersource = unsafe)
Street food consumption [24]
2004 2001-2003 Jakarta,Indonesia Endemic Urban
Piped water(contaminatedimproved watersource == unsafe)
Hygienic practices such as no useof soap for handwashing, sharing
of food, and no toilet in thehousehold and household
crowding at home
[25]
2008 1999-2001 Karachi,Pakistan Endemic Urban
Piped water(contaminatedimproved watersource = unsafe)
Hygienic practices such as lack ofsoap availability at handwashingplace, frequently eating outsidehome and crowing at home
[26]
Cohort studies
2010 2007 Rajasthan,India
Outbreakin
endemicarea
Rural
Drinking water fromgovernment tank,hand pump andpersonal tube well
Contaminated sources due to anopen well supplying water to allthe three water supply facilities
[27]
2000 1998 River Seine,Paris, France Outbreak Urban Drinking untreated
river water Fecal contamination of tap water [28]
2007 2003 – 2004EasternKolkata,India
Endemic Urban Drinking unsafedrinking water NA [29]
2012 2003 – 2006 Karachi,Pakistan Endemic Urban Drinking tap water NA [30]
NA – Not provided.
that unimproved water and unsafe water are associatedwith quantifiable odds of having typhoid fever. The resultsummary has been used in estimation of typhoid fever diseaseburden in LMICs [6] which demarcates high-risk populationwho would benefit maximum from typhoid interventionssuch as improving water and sanitation or vaccination. Othersignificant risk factors associated with the occurrence oftyphoid fever were related to food consumption, socioeco-nomic status, hygiene and sanitary practices, living condition,and water storage and handling. These factors should bequantified in future analyses and should be included in futuretyphoid disease burden estimates. We have not accounted forenvironmental factors such as rain fall and temperatures, andanthropological measures such as age in this review whichshould be the other considerations in future disease burdenstudies.
The importance contaminatedwater as amajor risk factorfor typhoid fever is undisputable. During high-endemicperiod of typhoid fever in Santiago, Chile, the sewage con-tamination of food chain was demonstrated as the most
important factor contributing to typhoid fever transmission,more than the typhoid carrier state in the family members[2]. The past epidemiological studies have demonstrated theimportance of waterborne transmission, showing that onlysmall inocula is sufficient for waterborne typhoid transmis-sion, while foodborne transmission requires large inocula[2]. The key role of water and sanitation in typhoid fevertransmission is further validated by a correlation betweeninstallment of water and sanitation system and decline intyphoid fever cases in industrialized countries. The pro-gressive introduction of water filtration system in later 19thcenturywas correlatedwith decline in typhoid fevermortalityin United States of America (USA) [35]. A more decisivecorrelation was demonstrated in Philadelphia, USA, wherewater filtration system was serially introduced in six differentdistricts between 1902 and 1909 [36]. When the conditionof water supply and cause specific death rates for variousdiseases were examined, only typhoid fever deaths werefound declining significantly following the introduction ofwater filtration system.
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rovi
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arka
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aran
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94
Cases exposedCases unexposed
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Figure 2: Exposure to unimproved water among typhoid fever cases and controls in selected studies.
Although poorwater and sanitation system is not the onlyrisk for typhoid transmission, its undisputable importancemakes it a key risk factor in defining high-risk groups.Demarcating the typhoid fever risk groups is especiallyimportant in effectively targeting control measures such asvaccination programs.TheWHOhas recommended targetedvaccination of high-risk population with existing typhoidpolysaccharide vaccine [1]. The significance of defining high-risk groups has increased with impending availability oftyphoid conjugate vaccine [37], which may necessitate revis-iting of WHO policies on vaccination strategies based onwell-delineated target population. Most surveillance studieswere conducted in known typhoid high-risk populations,which cannot be simply extrapolated to general populationbecause their risk of typhoid fever is lower [6, 10]. One ofthe several risk corrections that can be made in applying thetyphoid fever incidence from high-risk population to generalpopulation is correct for water-related risk. However, thereis no data available at global level of safe water drinking,but there is a database available on improved water andunimproved water [7]. Whereas improved water is represen-tative of safe water and unimproved water is representativeof unsafe water, the only available database can be appliedat the global level for water-related correction in diseaseburden estimate. Computing the excess risk associated withthe consumption of unsafe water or unimproved waterwill help in understanding the additional typhoid risk incertain populations and helps in measuring risk-differential
typhoid fever incidence in different communities [6]. Suchcharacterization of disease burden that can be linked toaccess to improved water can help in developing risk-basedvaccination strategies and forecasting vaccine demand [38],identifying high-risk populations within countries and tar-geting vaccination to specific population, estimate its impact,calculate cost-effectiveness, and compare the efficiency oftargeted vaccination versus vaccination of whole population.
3.5. Limitations. Our study has many limitations. First, weused a basic definition of improved water to represent safewater because this variable is officially reported by WHO-UNICEF-JMP and a global data base is available that can beapplied to LMICs in computing risk-differential typhoid feverdisease burden. However, improved water does not alwaysequate to safe water in many LMICs [8, 39] and in this paperhalf of the case-control studies reported microbiologicalcontamination of improved water sources. Although micro-biologically unsafe water sources were combined with unim-proved water sources to estimate the excess risk of typhoidfever associated with unsafe water, the results may not begeneralizable to country levels as this study representedonly small number of countries. Similarly, caution shouldbe applied in generalizing the finding to unimproved wateras we included both unimproved and unsafe water in onecategory. Second, evidence from randomized control trialsis valued the highest followed by longitudinal prospectivecohort studies and case-control studies based on hierarchy
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0.69 [ 0.34 , 1.40 ]
0.52 [ 0.23 , 1.16 ]
1.00 [ 0.37 , 2.72 ]
0.75 [ 0.31 , 1.84 ]
0.70 [ 0.46 , 1.05 ]
Odds Ratio [95% CI]
RE Model
0.50 2.00 7.00Odds Ratio (log scale)
Karachi, Pakistan
Jakarta, Indonesia
Ujung Pandang, Indonesia
Diyarbakir, Turkey
72
7
11
55
16
62
39
9
143
119
9
114
22
548
32
14
Exposed Unexposed Exposed Unexposed
Typhoid Control
Place, Country
Figure 3: Forest plot showing odds ratio for typhoid fever for exposure and nonexposure to unimproved water.
of strength of evidence. We had to exclude four prospectivecohort studies from meta-analysis approach because it wasnot possible to integrate the findings from those studies intothe analysis. However, these cohort studies have suggestedunimproved water as an important risk factor for typhoidfever. Third, it is worth noting that we used only one variablefrom each case-control study that best matched “unimprovedwater” to keep the analysis simple. It was challenging tocategorize some water sources as improved or unimprovedas they did not fit into any category and we had to choose onefrom the remaining variables. Selection of any other variablesmay have presented different values or may have resultedin ambiguous findings. Fourth, some of the water sourcesmatched the definition of improved water but a statementfrom investigators revealed a case of clear contamination ofimproved water due to reasons such as proximity to sewagepipes and breakage in water supply systemsmade it necessaryto reconsider the improved water as unsafe. This actuallydeviated from the definition of unimproved water but repre-sented unsafe water whichwas criticalmeasure for risk differ-entials.Wehad club these two categories in our analysis. Fifth,the typhoid fever risk from unsafe water is represented onlyby 12 studies in our systematic review. Number of studies istoo small to generalize and mostly represent Asia. Caution isnecessary in the application of results to global disease burdenestimation. Sixth, we could have missed some vital papers on
water-related risk factors for typhoid fever published in otherlanguages besides English because of search criteria. Also, oursearch did not include unpublished literature such as confer-ence abstracts, doctoral thesis, or meeting presentations.Thismay have resulted in publication bias. Lastly, we have usedonly those papers containing water-related risk factors in ourreview and, hence, many other significant typhoid fever riskfactors outside selected papers may not have been capturedin this review. We could have missed some important otherrisk factors not presented in these studies.
3.6. Conclusions. In conclusion, based on literature reviewwe demonstrated that the exposure to unimproved water orunsafe water is significantly associated with typhoid fever.Our findings suggest that the population without access tosafe water may be considered as one indicator to delineatehigh-risk population for typhoid related interventions. Thehigh-risk population decided based on lack of access to safewater can be targeted for typhoid vaccination in additionto ongoing effort to improve water and sanitation infras-tructure. Future research should focus on demarcating andquantifying other factors associated with typhoid fever inaddition to water-related one, so that more comprehensiverisk-association mapping based on geographical informationsystem could be developed and used for targeting typhoidinterventions.
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Table4:Other
significantrisk
factorsfor
typh
oidfeveridentified
inreview
edpapers.
Stud
ySite
Risk
Category
Expo
sure
factor
AOR(95%
CI;p-value
ifany)
Source
Dushanb
e,Tajik
istan
Water
storage
andhand
ling
Unb
oiledwater
6.5(3.0–24.0;p
<0.05)∗
[11]
Food
hand
lingandconsum
ption
Eatin
gfood
obtained
from
streetvendo
r2.9(1.4–7.2
;p<0.05)∗
Kathmandu
,Nepal
Socio-econ
omic
Hou
seho
ldmon
thlyincome<
US$125
2.55
(1.1–6.0;p<0.05)
[12]
Hygiene
&sanitary
Hou
seho
ldlatrine
8.52
(1.8–40.1;
p<0.05)
Water
storage
&hand
ling
Water
storedaft
ercollection
3.09
(1.2–8.1;p<0.05)
Eatenstreetfood
<2weeks
2.34
(1.4–4.0;p<0.05)
WestB
engal,India
Food
hand
lingandconsum
ption
Food
from
sweetsho
p6.2(2.4–2.2)∗∗
[19]
Socio-econ
omic
Mon
thlyfamily
income<
1500
6(1.3–26.8)
INR(eqvt.US$
34)
Living
cond
ition
Hou
seho
ld>4mem
bers
4.2(1.7–11.1)
Living
cond
ition
Anyon
eillin
neighb
orho
od2.5(1.2–5.2)
Food
hand
lingandconsum
ption
Eatin
gof
Paratha(
flatbread
inlayers)
2.1(0.87–5.3)
Neapo
litan
Area,Ita
ly
Food
hand
lingandconsum
ption
Any
rawShellfish
13.3(5.5–32.8;p
<0.05)∗∗
[14]
Food
hand
lingandconsum
ption
RawOysters
9.3(1.7–67.3;p
<0.05)∗∗
Food
hand
lingandconsum
ption
RawMussels
8.9(3.9–21.1;
p<0.05)∗∗
Food
hand
lingandconsum
ption
RawHen
Clam
s8.1(2.8–23.7;p
<0.05)∗∗
Food
hand
lingandconsum
ption
RawSeaT
ruffles
6.4(1.4–32.9;p
<0.05)∗∗
MadayaT
ownship,Myanm
arLiving
cond
ition
Con
tactwith
typh
oidpatie
nt10.9(2.0-79.7
)[15]
SonLa
Province,N
orthernVietnam
Socio-econ
omic
Beingun
educated
2.0(1.0–3.7;p=0.03)
[16]
Living
cond
ition
Con
tactwith
typh
oidpatie
nt3.3(1.7–6.2;p<0.05)
Samarkand
Oblast,Uzbekistan
Socio-econ
omic
Stud
entasp
rimaryoccupatio
n4.0(1.4–11.3;p
<0.05)
[18]
Takenantim
icrobialsin2weeks
before
illnesson
set
12.2(4.0–37.0;p
<0.05)
Darjeeling,WestB
engal,India
Food
hand
lingandconsum
ption
Eatin
grawcabb
age
2.8(1.7-4.8)∗
[19]
Water
storage
&hand
ling
Scoo
ping
water
from
acon
tainer
with
acup
2.5(1.3-4.7)∗
Food
hand
lingandconsum
ption
Con
sumptionof
butte
r2.3(1.3-4.1)∗
Food
hand
lingandconsum
ption
Con
sumptionof
Yoghurt
2.3(1.4-3.7)∗
Food
hand
lingandconsum
ption
Eatin
gun
washedgrapes
2.2(1.3-4.0)∗
Food
hand
lingandconsum
ption
Eatin
grawon
ion
2.1(1.2
-3.9)∗
Food
hand
lingandconsum
ption
Eatin
grawcarrot
2.1(1.2
-3.9)∗
Dhaka
slum,K
amalapur,B
angladesh
Water
storage
&hand
ling
Con
sumptionof
foul-smellin
gwater
7.4(2.1–25.4;p
=0.002)
[20]
Food
hand
lingandconsum
ption
Con
sumptionof
unwashedPapaya
5.2(1.2–22.2;p
=0.03)
Karachi,Pakista
n
Takenantim
icrobialsin2weeks
priortoillness
3.0(1.4–6.5)
[21]
Food
hand
lingandconsum
ption
Eatin
gatrestaurant
betweenJulyandAu
gust
2.7(1.1-6.6;p=0.01)∗
Food
hand
lingandconsum
ption
Eatin
gatroad
sidec
abin
betweenJulyandAu
gust
2.4(1.0-5.6;p
=0.03)∗
Food
hand
lingandconsum
ption
Eatin
gou
t>1p
erweekbetweenJulyandAu
gust
2.3(1.0-5.2;p
=0.02)∗
Food
hand
lingandconsum
ption
Eatin
gicec
ream
1.7(1.0-3.1;
p=0.03)∗
Food
hand
lingandconsum
ption
Eatin
gac
ommercialbrandof
icec
ream
1.6(1.0-2.9;p
=0.04)∗
Semarang,Indo
nesia
Hygiene
&sanitatio
nNever
orsometim
eswashing
hand
sbeforee
ating
3.97
1.22-12.93;p=0.022)
[22]
Hygiene
&sanitatio
nOpensewages
ystem
orno
drainage
syste
min
house
7.19(1.33
-38.82;p
=0.022)
Socio-econ
omic
Beingun
employed
orpart-timejob
ber
31.3(3.08-317.4
;p=0.036)
Mekon
gDelta,Vietnam
Living
cond
ition
Recent
contactw
ithtyph
oidfeverc
ase
4.3(1.4-13.4;p=0.04
)[17]
Socio-econ
omic
Lowecon
omiclevel
2.5(1.3-5.1;
p=0.01)
RiverS
eine,Paris,
France
Eatin
gchickenon
boat
2.0(0.3–13.7)#
[28]
Eatin
gric
eonbo
at2.9(0.4–19.8)#
Easte
rnKo
lkata,India
Usin
glatrinein-ho
uselatrin
efor
defecatio
n5.32
(p=0.9)
[29]
Karachi,Pakistan
Living
indensely
popu
lated
area
2.43
(1.27–4.64
;p=0.01)
[30]
∧Frozen
mam
eypu
lpim
ported
from
Guatemalau
sedin
fruitshake
∧∧CigKo
fteisatraditio
nalraw
food
madefrom
rawmeatrolledin
aballform
∗MOR–Matched
odds
ratio
∗∗OR–Odd
sratio
# RR–Re
lativ
erisk
ratio
.
10 Journal of Environmental and Public HealthTa
ble5:PR
ISMA2009
checklist.
Section/topic
#Ch
ecklist
item
Repo
rted
onpage
#TI
TLE
Title
1Identifyther
eportasa
syste
maticreview
,meta-analysis,
orbo
th.
Cover
page
Abstr
act
2,3
ABS
TRAC
T
Structured
summary
2
Providea
structuredsummaryinclu
ding
,asa
pplicable:backgroun
d;ob
jectives;
datasources;stu
dyeligibilitycriteria
,partic
ipants,
andinterventio
ns;study
appraisaland
synthesis
metho
ds;results;
limitatio
ns;con
clusio
nsandim
plications
ofkeyfin
ding
s;syste
maticreview
registratio
nnu
mber.
Abstr
act
INTR
OD
UCT
ION
Ratio
nale
3Describethe
ratio
naleforthe
review
inthec
ontext
ofwhatisa
lreadykn
own.
1
Objectiv
es4
Providea
nexplicitstatem
ento
fquestions
beingaddressedwith
referenceto
participants,
interventio
ns,com
paris
ons,ou
tcom
es,and
study
desig
n(PIC
OS).
1
MET
HODS
Protocoland
registr
ation
5Indicateifar
eviewprotocolexists,ifandwhere
itcanbe
accessed
(e.g.,Web
address),and
,ifavailable,provider
egistratio
ninform
ationinclu
ding
registratio
nnu
mber.
NA
Eligibilitycriteria
6Specify
study
characteris
tics(e.g
.,PICO
S,leng
thof
follow-up)
andrepo
rtcharacteris
tics(e.g
.,yearsc
onsid
ered,langu
age,pu
blicationsta
tus)used
ascriteria
fore
ligibility,givingratio
nale.
2,16
Inform
ationsources
7Describea
llinform
ationsources(e.g
.,databaseswith
dateso
fcoverage,contactw
ithstu
dyauthorstoidentifyadditio
nalstudies)inthes
earchanddatelastsearched.
2
Search
8Presentfullelectronics
earchstrategy
fora
tleaston
edatabase,inclu
ding
anylim
itsused,suchthatitcouldbe
repeated.
2,Table2
Stud
yselection
9Statethe
processfor
selectingstu
dies
(i.e.,
screening,eligibility,includedin
syste
maticreview
,and
,ifapp
licable,
inclu
dedin
them
eta-analysis).
2,Table2
Datac
ollection
process
10Describem
etho
dof
dataextractio
nfro
mrepo
rts(e.g
.,pilotedform
s,independ
ently,indu
plicate)andanyprocessesfor
obtainingandconfi
rmingdata
from
investigators.
2,Figure
1
Dataitems
11Listanddefin
eallvaria
bles
forw
hich
dataweres
ought(e.g
.,PICO
S,fund
ing
sources)andanyassumptions
andsim
plificatio
nsmade.
9,Table3
Risk
ofbias
inindividu
alstu
dies
12Describem
etho
dsused
fora
ssessin
gris
kof
bias
ofindividu
alstu
dies
(inclu
ding
specificatio
nof
whether
thiswas
done
atthes
tudy
orou
tcom
elevel)
,and
howthis
inform
ationisto
beused
inanydatasynthesis.
NA
Summarymeasures
13Statethe
principalsum
marymeasures(e.g
.,ris
kratio
,difference
inmeans).
3,16
Synthesis
ofresults
14Describethe
metho
dsof
hand
lingdataandcombining
results
ofstu
dies,ifd
one,
inclu
ding
measureso
fcon
sistency(e.g.,I2)for
each
meta-analysis.
2,3
Figure
3Risk
ofbias
across
studies
15Specify
anyassessmento
frisk
ofbias
thatmay
affectthe
cumulativee
vidence(e.g
.,pu
blicationbias,sele
ctiver
eportin
gwith
instu
dies).
NA
Additio
nalanalyses
16Describem
etho
dsof
additio
nalanalyses(e.g
.,sensitivityor
subgroup
analyses,
meta-regressio
n),ifd
one,indicatin
gwhich
werep
re-specified.
NA
Journal of Environmental and Public Health 11
Table5:Con
tinued.
Section/topic
#Ch
ecklist
item
Repo
rted
onpage
#RE
SULT
S
Stud
yselection
17Given
umbersof
studies
screened,assessedfore
ligibility,and
inclu
dedin
the
review
,with
reason
sfor
exclu
sions
ateach
stage,ideallywith
aflow
diagram.
Figure
1
Stud
ycharacteris
tics
18Fo
reachstu
dy,present
characteris
ticsfor
which
dataweree
xtracted
(e.g.,stu
dysiz
e,PICO
S,follo
w-upperio
d)andprovidethe
citatio
ns.
Table3
Risk
ofbias
with
instu
dies
19Presentd
atao
nris
kof
bias
ofeach
study
and,ifavailable,anyou
tcom
elevel
assessment(seeitem
12).
NA
Results
ofindividu
alstu
dies
20Fo
rallou
tcom
esconsidered
(benefitsor
harm
s),present,for
each
study
:(a)
simple
summarydatafore
achinterventio
ngrou
p(b)effectestim
ates
andconfi
dence
intervals,ideally
with
aforestp
lot.
Figures3
and4
Synthesis
ofresults
21Presentresultsof
each
meta-analysisdo
ne,including
confi
denceintervalsand
measureso
fcon
sistency.
Figures3
and4
Risk
ofbias
across
studies
22Presentresultsof
anyassessmento
frisk
ofbias
acrossstu
dies
(see
Item
15).
NA
Additio
nalanalysis
23Giver
esultsof
additio
nalanalyses,ifdo
ne(e.g.,sensitivityor
subgroup
analyses,
meta-regressio
n[see
Item
16]).
Figure
4
DIS
CUSS
ION
Summaryof
evidence
24Summarizethe
mainfin
ding
sincluding
thes
treng
thof
evidence
fore
achmain
outcom
e;consider
theirrele
vancetokeygrou
ps(e.g.,healthcare
providers,users,
andpo
licymakers).
8
Limitatio
ns25
Disc
usslim
itatio
nsatstu
dyandou
tcom
elevel(e.g.,ris
kof
bias),andatreview
-level
(e.g.,incompleter
etrie
valofidentified
research,reportin
gbias).
7,8
Con
clusio
ns26
Providea
generalinterpretationof
ther
esultsin
thec
ontext
ofothere
vidence,and
implications
forfuturer
esearch.
8,Figure
2
FUND
ING
Fund
ing
27Describes
ources
offund
ingforthe
syste
maticreview
andothersup
port(e.g.,
supp
lyof
data);roleof
fund
ersfor
thes
ystematicreview
.9
From
[31].
12 Journal of Environmental and Public Health
RE Model
0.50 2.00 7.00
Odds Ratio (log scale)
Karachi, Pakistan
Jakarta, Indonesia
Ujung Pandang, Indonesia
Diyarbakir, Turkey
72
7
11
55
16
62
39
9
143
119
9
114
22
548
32
14
0.69 [ 0.34 , 1.40 ]
0.52 [ 0.23 , 1.16 ]
1.00 [ 0.37 , 2.72 ]
0.75 [ 0.31 , 1.84 ]
0.70 [ 0.46 , 1.05 ]
Exposed Unexposed Exposed Unexposed
Typhoid Control
Place, Country Odds Ratio [95% CI]
Figure 4: Forest plot showing odds ratio for typhoid fever for exposure and nonexposure to improved water.
RE Model
0.50 2.00 7.00
Odds Ratio (log scale)
Anita S, MJM, 2012
Semarang, Indonesia; 1992−94
Karachi, Pakistan; 1994
Dhaka slum, Bangladesh; 2003−04
Darjeeling, West Bengal, India;2005−06
Samarkand Oblast, Uzbekistan; 2002−03
Dong Thap Province; Vietnam; 1996−97
Son La Province, Vietnam; 2002
Madaya Township, Myanmar; 2000
Neapolitan area, Italy; 1990
West Bengal, India; 2007
Kathmandu, Nepal; 2011
Dushanbe, Tajikistan; 1997
10
67
50
37
81
35
124
70
30
6
39
27
10
2
8
50
4
42
62
25
20
3
45
26
22
32
9
57
97
48
70
51
231
79
27
5
14
56
10
59
18
103
34
53
139
57
101
25
97
51
80
106
Exposed Unexposed Exposed Unexposed
Typhoid Control
Place, Country, Data collection year
32.78 [ 6.16 , 174.53 ]
2.64 [ 1.07 , 6.54 ]
1.06 [ 0.66 , 1.72 ]
6.55 [ 2.14 , 20.11 ]
1.46 [ 0.87 , 2.45 ]
1.54 [ 0.91 , 2.60 ]
1.22 [ 0.73 , 2.06 ]
4.47 [ 2.51 , 7.97 ]
9.26 [ 2.51 , 34.16 ]
2.59 [ 0.75 , 8.92 ]
5.46 [ 2.53 , 11.83 ]
1.75 [ 0.91 , 3.39 ]
3.31 [ 1.27 , 8.66 ]
2.78 [ 1.80 , 4.32 ]
Odds Ratio [95% CI]
Figure 5: Forest plot showing odds ratio for typhoid fever for exposure and nonexposure to unimproved water after including one outlierstudy [9].
Journal of Environmental and Public Health 13
Abbreviations
S. Typhi: Salmonella enterica serovar TyphiWHO: World Health OrganizationLMICs: Low- and middle-income countriesCFU: Colony forming unitsUNICEF: United Nations International Children
Emergency Relief FundJMP: Joint Monitoring ProgrammePAHO: Pan American Health OrganizationRR: Relative riskOR: Odds ratioUSA: United States of America.
Data Availability
All data related to the research is available in the manuscript.
Ethical Approval
Research involved analysis of secondary data available inpublic domain. No human subjects are involved in this study.No ethical approval was sought.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Authors’ Contributions
Vittal Mogasale has conceptualized the study, advised ondeveloping search terms, served as the third reviewer inconducting the search, guided the analysis, interpreted theresults, and rewrote the final manuscript. Vijayalaxmi V.Mogasale has served as first reviewer in conducting thesearch, developed search terms, extracted data, conductedthe analysis, and wrote the first draft of manuscript. EnusaRamani has served as second reviewer in conducting thesearch, assisted in data extraction and analysis, and reviewedand put together the final manuscript. Ju Yeon Park esti-mated weightedmean sensitivity using random effects modeland drew forest plots. Thomas F. Wierzba provided overalltechnical advice for conceptualization and data analysisand reviewed and edited the manuscript. All authors haveapproved the final manuscript.
Funding
This work was supported the Vi-based Vaccines for Asia(VIVA) Initiative, which is funded by the Bill and MelindaGates Foundation (Grant no. 417.01). International VaccineInstitute receives core funding by the Governments of Koreaand Sweden.
Acknowledgments
The authors thank Dr. Jin Kyung Park for statistical support.This work was supported by the Vi-based Vaccines for Asia(VIVA) Initiative, which is funded by the Bill and Melinda
Gates Foundation (Grant no. 417.01). International VaccineInstitute received core funding from the Governments ofKorea and Sweden.
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