Systematic Review of Spatial Analysis of Diarrhoea

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26th Sep 2017 Health Reference this

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DR. SYED SHARIZMAN BIN SYED ABDUL RAHIM (P67288)

ABSTRACT

Spatial analysis may offer timely information on the course of a disease and other health related events making it possible to customise intervention control methods. Spatial analysis also has a huge rule in the event of epidemics especially from food and water borne diseases. This review summarises the current literatures on spatial analysis and patterns associated with diarrhoea. Search of databases related to GIS or spatial analysis and diarrhoea which included from PubMED, Medline, ScienceDirect, EBSCOhost, and Oxford Journals which consist of articles from 1997 to 2012. Articles were first screened by titles and abstracts and then full manuscripts, where afterwards the final articles were extracted. 50 articles were screened to identify the final 17 articles based upon adherence to the inclusion criteria involving english language based literatures from multiple regions. Analyses consist from descriptive analysis, pattern analysis, cluster analysis and modelling in descriptive ecological studies. Spatial patterns found were unsafe water supply, poor sanitation, unhygienic waste disposal, unhygienic practices by personal or food handlers, low socioeconomic status, poor health care access, low education, high population density, rural areas, squatters, and malnutrition. Temporal factors such as climate factors, seasonality, temperature and rainy seasons also play a role. Diarrhoeal disease may be avoided, outbreaks predicted and managed effectively if timely use of spatial analysis was practiced.

INTRODUCTION

Throughout the years in almost every field, Geographical information system (GIS) has open new doors in research and knowledge. Especially in the field of public health, it has help researchers investigate and plan intervention effectively. Spatial analysis is not actually a new field when it comes to health, however currently it is still not widely being practised. This is true with the evidence of limited studies being done using spatial analysis. In the past, John Snow had showed the value of descriptive spatial epidemiology in action on the field, stressing on importance of ‘place’ in an epidemic as a guide for control and prevention 1.

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Spatial analysis may offer timely information on the course of a disease and other health related events thus making it possible to tailor specific intervention methods. It also plays a major role in the event of epidemics as did John Snow demonstrated. Sadly this technology has been disregarded most of the time 2. By looking at the chain between place, temporality and human health, we can make deductions regarding the illness and exposures. This would largely be beneficial in developing a better healthcare system which would include more effective disease interventions and community level programmes 3.

GIS has been used in various methods when it comes to waterborne disease outbreaks and research studies. Its usage has proved hypothesis of risk factors in epidemics, microbial risks in drinking water reservoirs and contaminated daily water supply sources and structures 4. In some water borne studies, this technology has been used to study the relationship between socio-economic and demographic attributes to the disease, environmental exposure risks, spatial epidemiology and health risk prediction.

The purpose of this review was to summarise the current literatures regarding spatial analysis, its patterns with diarrhoea. Even though spatial analysis techniques were introduced a long time ago in the field of epidemiology, its current practice remains lacking.

METHODOLOGY

Articles for possible inclusion were determined through a search of databases related to GIS or spatial analysis and diarrhoea which included from PubMED, Medline, ScienceDirect, EBSCOhost, and Oxford Journals. These consist of articles from 1997 to 2012.

Only English language articles were included in the review. Articles must have both spatial and diarrhoea variables in their studies for inclusion. Search for articles started since October 2012 until December 2012. Articles were first screened by titles and abstracts and then full manuscripts (Figure 1). Final articles were extracted and organised upon a spreadsheet.

RESULTS

50 articles were screened to identify the final 17 articles based upon adherence to the inclusion criteria (Figure 1). The studies involved multiple regions, all with spatial analysis and diarrhoea included. Diarrhoea may be caused by multiple infective agents and any of these causes are included as well. All of the reports are journals, from 1997 to 2012. These articles involved various age groups and this is important so the spatial patterns relating to diarrhoea across different age groups can be observed.

Based on the articles compiled, findings are summarised in Table 1 stating the author, year, country of study, spatial analysis and association of pattern. There are 14 articles in the last 5 years. Analyses involved vary from descriptive analysis, pattern analysis, cluster analysis and modelling. All of the studies were descriptive ecological studies involving large and adequate sample size based on the population at risk.

DISCUSSION

Overall, the articles published showed relationship between spatial patterns and infective diarrhoea. Most of the articles found similar patterns, consistent with other published findings.

Among the pattern of association found (Table 1), were unsafe water supply, poor sanitation, unhygienic waste disposal and unhygienic practices by personal or food handlers 1,5,6,11,12,14,16. Other than that, socioeconomic status play a role too where areas of low socioeconomic status are more at risk, with poor health care access, low education, high population density, rural areas, squatters and malnutrition has higher risk of diarrhoea spatially 6,8,9,11. Other than exhibiting spatial relationship, there are also temporal factors such as climate factors, seasonality, temperature and rainy seasons 7,13,15,17,18.

In some studies the details of spatial analysis and how the coordinates were obtained were not mentioned thus comes the issues of precision when coordinates were analysed. Biases may arise from the study design where all of the study designs were descriptive ecological study. Since all diarrhoea were taken into account, some studies did not ascertain the actual agents or if it is really infective in origin.

With that being said, these studies describe well the functions of spatial analysis and its pattern with diarrhoea occurrences and outbreaks. Most of the studies mentioned here, involved large sample size in the population affected by diarrhoeal disease. We may however miss out some important studies since we only take account English language publications.

CONCLUSION

With the findings of these articles, we can conclude that spatial analysis is important in understanding and controlling food and water borne diseases especially that causes diarrhoea. Infective diarrhoea may be avoided if proper control measures are in place and communities at risk are provided with good and safe water supply, sanitation with access to health care services. With timely use of spatial analysis, uncontrolled outbreaks can be prevented and more effectively managed.

Figure 1. Flow chart of article selection

Table 1. Articles on spatial analysis patterns and diarrhoea.

Author

Country

Spatial Analysis done

Pattern of association / risk factors

Oyedepo et al (2011) 5

Abeokuta, Nigeria

Descriptive – distance from source

  • Municipial water supply
  • contaminated river
  • poor sanitation
  • unhygienic waste disposal

Toprak, D. and Erdogan, S. (2008) 6

Turkey

Bayes empirical smoothing, Moran I, Getis Ord

  • Low socioeconomy areas
  • Village water contaminated with sewage
  • Poor health service access
  • Food handlers

Sasaki et al (2009) 7

Zambia

Kriging spatial analysis

  • Insufficient coverage of drainage networks
  • Higher in children
  • Rainy season

Ali et al (2002) 8

Bangladesh

Spatial filtering

  • High population density
  • Poor educational level
  • Proximity to surface water

Chaikaew et al (2009) 9

Chiang Mai, Thailand

Quadrant analysis, nearest neighbour analysis, spatial autocorrelation, local indicators of spatial association and kernel density

  • Hotspots migrated from urban villages to highland villages which have had limited safer water
  • Infrastructure and health systems.
  • Spatial distribution follows sociodemographic factors, environmental, sanitation and climate factors.

Nazarudin et al (2008) 10

Kota Bharu, Kelantan

Nearest Neighbour Analysis, Ripley’s K function and nearest neighbour hierarchial spatial clustering

  • Clustered cases in the district with about 6km distances.

Kazembe et al (2009) 11

Malawi

Model fitting used Markov Chain Monte Carlo simulation

  • Chilhood malnutrition
  • Increased population density
  • Sanitation
  • Access safe water
  • Children under 5
  • Maternal socioeconomic status
  • Rural children
  • Variation in urban areas
  • Squatters,

Bessong et al (2009) 12

Venda, South Africa

Cluster analysis

  • Diarrhoea occurred from water tanks sources from a river upstream
  • Water supply was not treated and being used for daily activities

Alonso et al (2012) 13

Mexico

Cluster analysis spatial autocorrelation Moran I

  • Spatial temporal related to temperature and environmental precipitation.

Cifuentes et al (1999) 14

Mexico

Descriptive

  • Spatial temporal pattern related to socioeconomy
  • Poorer municipal have higher mortality of diarrhoea cases, especially in the extreme age groups.

Carrel et al (2009) 15

Matlab, Bangladesh

Pattern and cluster analysis (SaTScan)

  • Spatial and temporal patterns which include shift from dry season to rainy seasons clusters and delayed clustering in the flood protected areas.
  • Flood protection have significant impact on diarrhoeal cases mainly cholera endemic areas.

Shittu et al (2010) 16

Abeokuta, Nigeria

Point buffer zone and Cluster analysis

  • More cases clustered in areas with dense population
  • Poor sanitation
  • Sewage disposal systems and unsafe (pipeline leakage and feacal contamination) or not properly treated (low chlorine) municipal water supply.

McCormick et al (2012) 17

Thailand

Spatially weighted panel regression model

  • Strong association in seasonality occurrence with daily mean temperature and precipitation.
  • In relation to per capita GDP and population density.
  • Seasonality of diarrhoeal disease is dampened iin affluent urban populations.

Myaux et al (1997) 18

Bangladesh

Point buffer zone and Cluster analysis- Cuzick and Edwards

  • Clusters appeared consistently in determined are, however reseacher couldn’t find relations with local environment.
  • Socioeconomically, the clusters were almost similar with lower education level, dense population and lower hygiene.
  • Cases also showed seasonality trend over the years.

Luquero et al (2011) 1

Guinea-Bissau

Cluster analysis, Kernel smoothing, K functions, Kulldorff’s spatial scan stats

  • In one area, there’s a market that’s at risk because of food handlers, large inflow of people or waste in the streets or insufficient latrines.
  • It could also be due to poor general water and sanitation systems.

Giebultowicz et al (2011) 19

Matlab, Bangladesh

Moran’s I

  • Comparing spatial and social network analysis, found clusters follows relation of space and seldom with social networks.
  • For example cholera transmits mostly from environment rather than person to person.

Kim et al (2008) 20

Vietnam

Bayesian disease mapping model

  • Usage of unsafe water (untreated open well), close proximity to the hospital has higher risk.
  • While community practising religion and has better economic status has lower risk to shigellosis.

REFERENCES

1. Luquero F, Banga C, Remart’inez D, Palma P, Baron E, Grais R. Cholera epidemic in Guinea-Bissau (2008): the importance of “place”. PloS one. 2011;6(5):e19005.

2. Tanser F, Le Sueur D. International Journal of Health Geographics. Int J Health Geogr. 2002;1:4.

3. Ali M, Emch M, Ashley C, Streatfield P. Implementation of a medical geographic information system: concepts and uses. Journal of Health, Population and Nutrition. 2011;19(2):100-10.

4. Kistemann T, Dangendorf F, Schweikart J. New perspectives on the use of Geographical Information Systems (GIS) in environmental health sciences. Int J Hyg Environ Health. 2002;205(3):169-81.

5. Oyedepo J, Shittu O, Popoola T, Adeofun C, Ogunshola E. Rapid Epidemiological Mapping of Cholera in Some Parts of Abeokuta Metropolis: A GIS-Supported Post-Epidemic Assessment. COLERM Proceedings. 2012;1:167-76.

6. Toprak D, Erdougan S. Spatial analysis of the distribution of typhoid fever in Turkey. Int Arch Photogram Remote Sens Spatial Inform Sci. 2008;:1367-72.

7. Sasaki S, Suzuki H, Fujino Y, Kimura Y, Cheelo M. Impact of drainage networks on cholera outbreaks in Lusaka, Zambia. Journal Information. 2009;99(11)

8. Ali M, Emch M, Donnay J, Yunus M, Sack R. Identifying environmental risk factors for endemic cholera: a raster GIS approach. Health & place. 2002;8(3):201-10.

9. Chaikaew N, Tripathi N, Souris M. International Journal of Health Geographics. Int J Health Geogr. 2009;8:36.

10. Safian N, Shah S, Idrus S, sor Hamzah W. Cluster analysis of typhoid cases in Kota Bharu, Kelantan, Malaysia. .

11. Kazembe L, Muula A, Simoonga C. Joint spatial modelling of common morbidities of childhood fever and diarrhoea in Malawi. Health and place. 2009;15(1):165-72.

12. Bessong P, Odiyo J, Musekene J, Tessema A. Spatial distribution of diarrhoea and microbial quality of domestic water during an outbreak of diarrhoea in the Tshikuwi community in Venda, South Africa. J Health Popul Nutr. 2009;27(5):652.

13. Alonso W, Acu~na-Soto R, Giglio R, Nuckols J, Leyk S, Schuck-Paim C et al. Spatio-temporal patterns of diarrhoeal mortality in Mexico. Epidemiol Infect. 2012;140(01):91-9.

14. Cifuentes E, Hern’andez J, Venczel L, Hurtado M. Panorama of acute diarrhoeal diseases in Mexico. Health & place. 1999;5(3):247-55.

15. Carrel M, Emch M, Streatfield P, Yunus M. Spatio-temporal clustering of cholera: The impact of flood control in Matlab, Bangladesh, 1983–2003. Health & place. 2009;15(3):771-82.

16. Shittu O, Akpan I, Popoola T, Oyedepo J, Ogunshola E. Epidemiologicai features of a GIS-supported investigation of cholera outbreak in Abeoukta, Nigeria. J. Pub. Health Epidem. 2010;2(7):152-62.

17. McCORMICK B, Alonso W, Miller M. An exploration of spatial patterns of seasonal diarrhoeal morbidity in Thailand. Epidemiol Infect. 2011;1(1):1-8.

18. Myaux J, Ali M, Felsenstein A, Chakraborty J, De Francisco A. Spatial distribution of watery diarrhoea in children: identification of “risk areas” in a rural community in Bangladesh. Health & place. 1997;3(3):181-6.

19. Giebultowicz S, Ali M, Yunus M, Emch M. A comparison of spatial and social clustering of cholera in Matlab, Bangladesh. Health & place. 2011;17(2):490-7.

20. Kim DR, Ali M, Thiem VD, Park JK, von Seidlein L, Clemens J. Geographic analysis of shigellosis in Vietnam. Health Place. 2008;14(4):755-67.

DR. SYED SHARIZMAN BIN SYED ABDUL RAHIM (P67288)

ABSTRACT

Spatial analysis may offer timely information on the course of a disease and other health related events making it possible to customise intervention control methods. Spatial analysis also has a huge rule in the event of epidemics especially from food and water borne diseases. This review summarises the current literatures on spatial analysis and patterns associated with diarrhoea. Search of databases related to GIS or spatial analysis and diarrhoea which included from PubMED, Medline, ScienceDirect, EBSCOhost, and Oxford Journals which consist of articles from 1997 to 2012. Articles were first screened by titles and abstracts and then full manuscripts, where afterwards the final articles were extracted. 50 articles were screened to identify the final 17 articles based upon adherence to the inclusion criteria involving english language based literatures from multiple regions. Analyses consist from descriptive analysis, pattern analysis, cluster analysis and modelling in descriptive ecological studies. Spatial patterns found were unsafe water supply, poor sanitation, unhygienic waste disposal, unhygienic practices by personal or food handlers, low socioeconomic status, poor health care access, low education, high population density, rural areas, squatters, and malnutrition. Temporal factors such as climate factors, seasonality, temperature and rainy seasons also play a role. Diarrhoeal disease may be avoided, outbreaks predicted and managed effectively if timely use of spatial analysis was practiced.

INTRODUCTION

Throughout the years in almost every field, Geographical information system (GIS) has open new doors in research and knowledge. Especially in the field of public health, it has help researchers investigate and plan intervention effectively. Spatial analysis is not actually a new field when it comes to health, however currently it is still not widely being practised. This is true with the evidence of limited studies being done using spatial analysis. In the past, John Snow had showed the value of descriptive spatial epidemiology in action on the field, stressing on importance of ‘place’ in an epidemic as a guide for control and prevention 1.

Spatial analysis may offer timely information on the course of a disease and other health related events thus making it possible to tailor specific intervention methods. It also plays a major role in the event of epidemics as did John Snow demonstrated. Sadly this technology has been disregarded most of the time 2. By looking at the chain between place, temporality and human health, we can make deductions regarding the illness and exposures. This would largely be beneficial in developing a better healthcare system which would include more effective disease interventions and community level programmes 3.

GIS has been used in various methods when it comes to waterborne disease outbreaks and research studies. Its usage has proved hypothesis of risk factors in epidemics, microbial risks in drinking water reservoirs and contaminated daily water supply sources and structures 4. In some water borne studies, this technology has been used to study the relationship between socio-economic and demographic attributes to the disease, environmental exposure risks, spatial epidemiology and health risk prediction.

The purpose of this review was to summarise the current literatures regarding spatial analysis, its patterns with diarrhoea. Even though spatial analysis techniques were introduced a long time ago in the field of epidemiology, its current practice remains lacking.

METHODOLOGY

Articles for possible inclusion were determined through a search of databases related to GIS or spatial analysis and diarrhoea which included from PubMED, Medline, ScienceDirect, EBSCOhost, and Oxford Journals. These consist of articles from 1997 to 2012.

Only English language articles were included in the review. Articles must have both spatial and diarrhoea variables in their studies for inclusion. Search for articles started since October 2012 until December 2012. Articles were first screened by titles and abstracts and then full manuscripts (Figure 1). Final articles were extracted and organised upon a spreadsheet.

RESULTS

50 articles were screened to identify the final 17 articles based upon adherence to the inclusion criteria (Figure 1). The studies involved multiple regions, all with spatial analysis and diarrhoea included. Diarrhoea may be caused by multiple infective agents and any of these causes are included as well. All of the reports are journals, from 1997 to 2012. These articles involved various age groups and this is important so the spatial patterns relating to diarrhoea across different age groups can be observed.

Based on the articles compiled, findings are summarised in Table 1 stating the author, year, country of study, spatial analysis and association of pattern. There are 14 articles in the last 5 years. Analyses involved vary from descriptive analysis, pattern analysis, cluster analysis and modelling. All of the studies were descriptive ecological studies involving large and adequate sample size based on the population at risk.

DISCUSSION

Overall, the articles published showed relationship between spatial patterns and infective diarrhoea. Most of the articles found similar patterns, consistent with other published findings.

Among the pattern of association found (Table 1), were unsafe water supply, poor sanitation, unhygienic waste disposal and unhygienic practices by personal or food handlers 1,5,6,11,12,14,16. Other than that, socioeconomic status play a role too where areas of low socioeconomic status are more at risk, with poor health care access, low education, high population density, rural areas, squatters and malnutrition has higher risk of diarrhoea spatially 6,8,9,11. Other than exhibiting spatial relationship, there are also temporal factors such as climate factors, seasonality, temperature and rainy seasons 7,13,15,17,18.

In some studies the details of spatial analysis and how the coordinates were obtained were not mentioned thus comes the issues of precision when coordinates were analysed. Biases may arise from the study design where all of the study designs were descriptive ecological study. Since all diarrhoea were taken into account, some studies did not ascertain the actual agents or if it is really infective in origin.

With that being said, these studies describe well the functions of spatial analysis and its pattern with diarrhoea occurrences and outbreaks. Most of the studies mentioned here, involved large sample size in the population affected by diarrhoeal disease. We may however miss out some important studies since we only take account English language publications.

CONCLUSION

With the findings of these articles, we can conclude that spatial analysis is important in understanding and controlling food and water borne diseases especially that causes diarrhoea. Infective diarrhoea may be avoided if proper control measures are in place and communities at risk are provided with good and safe water supply, sanitation with access to health care services. With timely use of spatial analysis, uncontrolled outbreaks can be prevented and more effectively managed.

Figure 1. Flow chart of article selection

Table 1. Articles on spatial analysis patterns and diarrhoea.

Author

Country

Spatial Analysis done

Pattern of association / risk factors

Oyedepo et al (2011) 5

Abeokuta, Nigeria

Descriptive – distance from source

  • Municipial water supply
  • contaminated river
  • poor sanitation
  • unhygienic waste disposal

Toprak, D. and Erdogan, S. (2008) 6

Turkey

Bayes empirical smoothing, Moran I, Getis Ord

  • Low socioeconomy areas
  • Village water contaminated with sewage
  • Poor health service access
  • Food handlers

Sasaki et al (2009) 7

Zambia

Kriging spatial analysis

  • Insufficient coverage of drainage networks
  • Higher in children
  • Rainy season

Ali et al (2002) 8

Bangladesh

Spatial filtering

  • High population density
  • Poor educational level
  • Proximity to surface water

Chaikaew et al (2009) 9

Chiang Mai, Thailand

Quadrant analysis, nearest neighbour analysis, spatial autocorrelation, local indicators of spatial association and kernel density

  • Hotspots migrated from urban villages to highland villages which have had limited safer water
  • Infrastructure and health systems.
  • Spatial distribution follows sociodemographic factors, environmental, sanitation and climate factors.

Nazarudin et al (2008) 10

Kota Bharu, Kelantan

Nearest Neighbour Analysis, Ripley’s K function and nearest neighbour hierarchial spatial clustering

  • Clustered cases in the district with about 6km distances.

Kazembe et al (2009) 11

Malawi

Model fitting used Markov Chain Monte Carlo simulation

  • Chilhood malnutrition
  • Increased population density
  • Sanitation
  • Access safe water
  • Children under 5
  • Maternal socioeconomic status
  • Rural children
  • Variation in urban areas
  • Squatters,

Bessong et al (2009) 12

Venda, South Africa

Cluster analysis

  • Diarrhoea occurred from water tanks sources from a river upstream
  • Water supply was not treated and being used for daily activities

Alonso et al (2012) 13

Mexico

Cluster analysis spatial autocorrelation Moran I

  • Spatial temporal related to temperature and environmental precipitation.

Cifuentes et al (1999) 14

Mexico

Descriptive

  • Spatial temporal pattern related to socioeconomy
  • Poorer municipal have higher mortality of diarrhoea cases, especially in the extreme age groups.

Carrel et al (2009) 15

Matlab, Bangladesh

Pattern and cluster analysis (SaTScan)

  • Spatial and temporal patterns which include shift from dry season to rainy seasons clusters and delayed clustering in the flood protected areas.
  • Flood protection have significant impact on diarrhoeal cases mainly cholera endemic areas.

Shittu et al (2010) 16

Abeokuta, Nigeria

Point buffer zone and Cluster analysis

  • More cases clustered in areas with dense population
  • Poor sanitation
  • Sewage disposal systems and unsafe (pipeline leakage and feacal contamination) or not properly treated (low chlorine) municipal water supply.

McCormick et al (2012) 17

Thailand

Spatially weighted panel regression model

  • Strong association in seasonality occurrence with daily mean temperature and precipitation.
  • In relation to per capita GDP and population density.
  • Seasonality of diarrhoeal disease is dampened iin affluent urban populations.

Myaux et al (1997) 18

Bangladesh

Point buffer zone and Cluster analysis- Cuzick and Edwards

  • Clusters appeared consistently in determined are, however reseacher couldn’t find relations with local environment.
  • Socioeconomically, the clusters were almost similar with lower education level, dense population and lower hygiene.
  • Cases also showed seasonality trend over the years.

Luquero et al (2011) 1

Guinea-Bissau

Cluster analysis, Kernel smoothing, K functions, Kulldorff’s spatial scan stats

  • In one area, there’s a market that’s at risk because of food handlers, large inflow of people or waste in the streets or insufficient latrines.
  • It could also be due to poor general water and sanitation systems.

Giebultowicz et al (2011) 19

Matlab, Bangladesh

Moran’s I

  • Comparing spatial and social network analysis, found clusters follows relation of space and seldom with social networks.
  • For example cholera transmits mostly from environment rather than person to person.

Kim et al (2008) 20

Vietnam

Bayesian disease mapping model

  • Usage of unsafe water (untreated open well), close proximity to the hospital has higher risk.
  • While community practising religion and has better economic status has lower risk to shigellosis.

REFERENCES

1. Luquero F, Banga C, Remart’inez D, Palma P, Baron E, Grais R. Cholera epidemic in Guinea-Bissau (2008): the importance of “place”. PloS one. 2011;6(5):e19005.

2. Tanser F, Le Sueur D. International Journal of Health Geographics. Int J Health Geogr. 2002;1:4.

3. Ali M, Emch M, Ashley C, Streatfield P. Implementation of a medical geographic information system: concepts and uses. Journal of Health, Population and Nutrition. 2011;19(2):100-10.

4. Kistemann T, Dangendorf F, Schweikart J. New perspectives on the use of Geographical Information Systems (GIS) in environmental health sciences. Int J Hyg Environ Health. 2002;205(3):169-81.

5. Oyedepo J, Shittu O, Popoola T, Adeofun C, Ogunshola E. Rapid Epidemiological Mapping of Cholera in Some Parts of Abeokuta Metropolis: A GIS-Supported Post-Epidemic Assessment. COLERM Proceedings. 2012;1:167-76.

6. Toprak D, Erdougan S. Spatial analysis of the distribution of typhoid fever in Turkey. Int Arch Photogram Remote Sens Spatial Inform Sci. 2008;:1367-72.

7. Sasaki S, Suzuki H, Fujino Y, Kimura Y, Cheelo M. Impact of drainage networks on cholera outbreaks in Lusaka, Zambia. Journal Information. 2009;99(11)

8. Ali M, Emch M, Donnay J, Yunus M, Sack R. Identifying environmental risk factors for endemic cholera: a raster GIS approach. Health & place. 2002;8(3):201-10.

9. Chaikaew N, Tripathi N, Souris M. International Journal of Health Geographics. Int J Health Geogr. 2009;8:36.

10. Safian N, Shah S, Idrus S, sor Hamzah W. Cluster analysis of typhoid cases in Kota Bharu, Kelantan, Malaysia. .

11. Kazembe L, Muula A, Simoonga C. Joint spatial modelling of common morbidities of childhood fever and diarrhoea in Malawi. Health and place. 2009;15(1):165-72.

12. Bessong P, Odiyo J, Musekene J, Tessema A. Spatial distribution of diarrhoea and microbial quality of domestic water during an outbreak of diarrhoea in the Tshikuwi community in Venda, South Africa. J Health Popul Nutr. 2009;27(5):652.

13. Alonso W, Acu~na-Soto R, Giglio R, Nuckols J, Leyk S, Schuck-Paim C et al. Spatio-temporal patterns of diarrhoeal mortality in Mexico. Epidemiol Infect. 2012;140(01):91-9.

14. Cifuentes E, Hern’andez J, Venczel L, Hurtado M. Panorama of acute diarrhoeal diseases in Mexico. Health & place. 1999;5(3):247-55.

15. Carrel M, Emch M, Streatfield P, Yunus M. Spatio-temporal clustering of cholera: The impact of flood control in Matlab, Bangladesh, 1983–2003. Health & place. 2009;15(3):771-82.

16. Shittu O, Akpan I, Popoola T, Oyedepo J, Ogunshola E. Epidemiologicai features of a GIS-supported investigation of cholera outbreak in Abeoukta, Nigeria. J. Pub. Health Epidem. 2010;2(7):152-62.

17. McCORMICK B, Alonso W, Miller M. An exploration of spatial patterns of seasonal diarrhoeal morbidity in Thailand. Epidemiol Infect. 2011;1(1):1-8.

18. Myaux J, Ali M, Felsenstein A, Chakraborty J, De Francisco A. Spatial distribution of watery diarrhoea in children: identification of “risk areas” in a rural community in Bangladesh. Health & place. 1997;3(3):181-6.

19. Giebultowicz S, Ali M, Yunus M, Emch M. A comparison of spatial and social clustering of cholera in Matlab, Bangladesh. Health & place. 2011;17(2):490-7.

20. Kim DR, Ali M, Thiem VD, Park JK, von Seidlein L, Clemens J. Geographic analysis of shigellosis in Vietnam. Health Place. 2008;14(4):755-67.

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