How Global Information Management System (GIMS) can help save lives?

Transcription

1 How Global Information Management System (GIMS) can help save lives? Rifat Hossain Lead, Global Information Management System on Environment and Health 1

2 Data to evidence to effective decision making to reduced disease burden to lives saved Background, concerns, challenges Climate affecting sustainable development What is GIMS and how can it help? Global Burden of Water-borne Disease What's next? 2

3 Background and challenges 3

4 WHO and Public Heath WHO HQ: Environment: Health Securities and Environment cluster Public Health & Env (PHE) dept of HSE Water Sanitation Hygiene & Health (WSH) Major role in climate change and public health Leading data integration, wash, climate and health Monitored WASH sector since 1950s Lead international initiatives eg. Millennium Development Goals WSH official monitor for MDG and access, also Key player in WHO Risk Assessment Working Group (RAWG), led by Health Action in Crises (HAC) Country, regional and global WASH monitoring Drinking-water guidelines WASH in emergencies Global Cholera programme 4

5 Why are we concerned? 2.2 million deaths per year (water-borne diseases) Second highest disease burden Higher than HIV, malaria and TB combined (for under 14) Close to 1 in 5 defecate in the open and 1 in 7 lack potable water Worse under climate change scenarios Limited knowledge of environmental factors' links to health. Deaths could be prevented, with evidence based policies and adaptation, but: Lack of a comprehensive information system for adequate planning and focusing of resources. Monitoring activities on health and environment are fragmented 5

6 Challenges of monitoring the WASH sector Access to water: Sources that by nature of its construction adequately protects the source from outside contamination in particular with fecal matter Access to sanitation: Facilities that by nature of its construction and regular intervention adequately separates human excreta from human contact Safe water: compliant with WHO guidelines Lack of comparable data: no global estimates 6

7 Challenges in Health Information Management Fragmentation and duplication Are climate data used by WASH specialists? Lack of use of standards (case definitions, identifiers, geocoding, data exchange) Lack of interoperability between systems Weak data quality assessment and transparency Lack of data portability 7

8 Climate change affecting Sustainable development 8

9 Health sector: Climate knowledge gap How much climate change affects MDG/access? How much climate variability affects access? How climate change/variability affect water quality/quantity? Weak monitoring of water quality and related health outcomes Can we handle/mitigate climate impacts? Can we prevent disasters related to climate? What's the public health fallout of climate change? Need to reduce fragmentation in related initiatives to close knowledge gap to make effective policy 9

10 We need To strengthen the evidence base Links between sanitation/water and health (disease) Links between different environmental issues (watsan, CC etc.) To correlate basic service levels to poverty, BoD, climate change etc. To collect appropriate environmental health information: Improve quality and types of health surveillance and the environmental data Better connect existing databases Address data gaps Build country capacity 10

11 What's GIMS and How can it help? 11

12 Global Information Management System to identify needs and target interventions Provide a platform for linking environmental determinants with socio-economic indicators (water access/quality) and health Data integration Integrate real-time data on selected environmental determinants of health to construct early warning systems (e.g. on water vulnerability to climate, cholera). Early warning system 12

13 IMS launched at OECD world forum 2009 A comprehensive, dynamic, wiki style data management system, also available in stand-alone version Data collection Data analysis Data dissemination Data linking system (data integration) Data to be shared with interested parties (interoperable and open source) Incremental build-up to develop an Early Warning System for water-borne diseases: Vulnerability and risk analyses as a learning step 13

14 Integrating remotely sensed and in-situ data into GIMS Data collection from earth observation satellites (2-35m 2 resolution) Water quality data Hydrological data Land use Risk assessment (catchment) Risk assessment (waterrelated disease) Trends Role of GEO/GEOSS? In-situ data: Validation of coverage da Validation of access data Input to models 14

15 In a nutshell, GIMS will facilitate Data integration Climate data with access and disease data Model for other data integration Map and model vulnerability and risk assessment What's the global burden of disease due to climate change? Downscale to regional and local scenarios Pilot in selected countries Buy-in by the government agencies How to overcome technological limitations of developing countries? Real-time early warning system 15

16 Climate change impacting Global burden of water borne disease 16

17 Preparing for climate change 17

18 2020 regional and global estimates 18

19 What's next? 19

20 Plethora of data Satellite data In-situ data What are the obstacles? How to combine all the relevant data? Are they interoperable? How to integrate and in what system? Integration of data for effective policy formulation Willingness to collaborate Buy-in by the governments How to overcome technological limitations of developing countries? 20

21 Collaboration with NOAA GEO? Climate change (IPCC Assessment Report models), and short term climate variability scenarios How climate change/variability will induce extreme conditions Hydrological model What will be affected (run-off water causing flooding) Resilience model What service levels are not resilient? How does that affect MDGs etc.? Global burden of disease Lower service level induces higher disease burden Repeat for regional focused studies/hotspot analysis Results will help build early warning system 21

22 Roadmap Short term (2011): post-doc for risk mapping+ data integration Run IPCC AR models (flooding) Feed results into global hydro model Overlay resilience model CC resilient data are fed into burden of disease model Longer term ( ): Run IPCC AR5 with other climate change conditions: temperature change, sea-level rise etc. Integrate water quality and quantity aspects Short-term climate variability impacts Feed results to regional and local hydro model (downscaling) Build GIMS Early Warning System Pilot in identified countries Guideline for scale-up of GIMS EWS 22

23 Opportunities for collaboration WHO attendance at GEOSS meet in Addis (February 2011): Missing link: health and water vis-à-vis climate/environment Access monitoring, how GEOSS can help How to accelerate progress towards MDG target Climactic issues in the water/health sector Possible collaboration: Water quality monitoring Water scarcity monitoring Data integration Linking health to climate and atmospheric research Anything else? 23

24 THANK YOU For further information: Rifat HOSSAIN ) 24