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Similar a Mapping Waterborne Disease Data
Similar a Mapping Waterborne Disease Data (20)
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Mapping Waterborne Disease Data
- 1. Mapping Waterborne Disease Data Paul Gager Technical Director
- 2. Background • Mapping work conducted for Cambodia Climate Change, Water and Health Project • DRIP-SWICCH: Developing Research and Innovative Policies Specific to the Water- related Impacts of Climate Change on Health
- 3. Pathways by which climate change affects health (adapted from Patz et al, 2008)
- 4. Context
- 5. Data Inputs <- Census Data 1998 & 2008 Monthly diarrhea inpatients, by province 1998 to 2012 ->
- 7. Climatic Variables • Water level • Min Temperature • Max Temperature • Temp Difference
- 9. Correlation between climate and disease cases
- 10. Time Series Regression Analysis • The direction and magnitude of the effect of temperature on diarrhoeal disease in provinces of Cambodia (time-series analysis: red=positive association; blue=negative association) • X scale: % change for 1 unit of temp
- 13. Correlation between disease rates and other factors, by province • Flooding • Census 1998 & 2008 – Water Supply – Sanitation – Poverty – Education • Disease rates were averaged for years 97-03 and 2012
- 15. Summary – Human Factors • Main risk factors – unimproved water sources (e.g. tube wells and surface water) – poor sanitation facilities (i.e. lack of latrines and sewerage infrastructure), • Strong evidence of a protective effect of education and literacy, particularly for women and girls, against diarrhoeal disease.
- 16. Summary – Human Factors • Population size and density and factors related to employment were also significantly correlated with diarrhoeal disease incidence.
- 17. Summary - Climate • The relationship between monthly temperature, rainfall and river height and diarrhoeal disease incidence across Cambodia’s provinces proved heterogeneous • Provinces differing with respect to the direction and magnitude of these associations.
Notas del editor
- However, we look at disease rates, in this case, cases per 1,000 people, the picture is a little different. As you can see, Mondulkiri and Prey Vihear actually have very high disease rates, though the populations overall are small to other provinces. Prey Veng also has quite a high disease rate and also a high population, so eveidently there is quite a problem there. Data for some provinces is not shown here as it has not yet been compiled.
- The next type of mapping that we examined it to take the time series analysis prepared by Mr Masahiro and map this to we can better understand the spatial patterns. This map shows cases of diarreah compared to temperature difference, which is the climatic variable that generally had the highest correlations with disease cases. Unfortunately, temperature data is not available for many provinces so the picture is not that complete. Also, some of the correlations coefficients are not that significant.
- Time series analysis looks at month-by-month data at various lag e.g. Lag of 1 month, Lag of 2 months
- The final mapping analysis that we are looking at is to see if there is any correlation between disease rates and other factors, such as flooding, water supply and sanitation. Some of the information has come from the 2008 census and other data from the Mekong River Commission.
- Of all the variables analysed, a couple, such as sanitation and water supply did have moderately significant coefficients. For interest, I have also included a couple of factors that we not very significant; poverty rate and % of province that is flooded annually.