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Future Outlook

Increasing the Predictive Capability of Mapping Technologies for Healthcare in Disaster Management

Dr. Simon O. M. Adebola


Health as defined by the WHO/EUROs Health 21 is 'the reduction in mortality, morbidity and disability due to detectable disease and disorder, and an increase in the perceived level of health' (WHO, 1999). Health Impact Assessment (HIA) is a combination of procedures, methods and tools by which a policy, program or project may be judged as to its potential effects on the health of a population, and the distribution of those effects within the population (WHO, 1999).

The use of HIA in disaster management has usually been as part of the Environmental Health Impact Assessment (EHIA) which is a part of the Environmental Impact Assessment (EIA) (Rainer, 1999). However, the use of Health Impact Assessment as a tool in disaster management has been taking on an increasing role in recent years (Russell and Saunders, 2007). The role of such assessments in providing a decision support tool for decision makers responsible for health protection and relief efforts is crucial because disaster response requires well coordinated action. This involves a spectrum of decision makers, tools and equipments, on-ground relief workers and other support personnel. Therefore the role of assessments to enhance decision making and also guide a coordinated response at the disaster site, for the immediate, short term and long term phases, is very important.

Various systems have been developed for Health Impact Assessments, HIA in public health and policy settings, and some of these have been adapted successfully for disaster management efforts (Ruijten, 2007). The aim of this paper is to develop a tool that would guide decision and policy making towards a proactive response and also to ensure a well coordinated post-disaster response, addressing both individual and community needs, by using remote sensing as a tool for disaster management. It is for strengthening the utility of satellite imagery by the end users, which in this case would be the decision makers, public health authorities, public health policy makers, doctors and other relief workers. Information is crucial to the carrying out of an effective and efficient post-disaster healthcare response. The quality of information provided by satellite imagery is very useful in determining how features and changes in the environment can affect the health of individuals and the community. This implies that in conducting Health Impact Assessments, there should be an added advantage to be enjoyed by specifically integrating satellite imagery.

Using post-disaster images as a base, criteria would be defined to be used in defining the health impact of a disaster and also in augmenting the effectiveness of the healthcare response. It would involve analyses of satellite imagery to enhance prediction of likely disease occurrence using landscape and meteorological factors in disease prediction. It would also involve:

  1. Assessing the extent of destruction to healthcare infrastructure and indices,
  2. Estimating affected and at-risk populations,
  3. Determination of likely diseases and morbidities following the disaster,
  4. Recommendations on healthcare response in terms of facilities, quantity and location of staff, volunteers, mobile units, emergency response activities, and other disease prevention efforts.

This would take the form of a rapid health impact assessment to provide basic information useful in different phases of the disaster management cycle. It would also help in understanding and defining the healthcare needs of the affected population.

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Figure 1: Impact of satellite imagery in HIA for disaster management. (Modified from Earth Observation Magazine).

In modelling the likelihood of disease occurrence using environmental data, one must consider that there are uncertain variations in the relationship of these data and their influence on disease occurrence and transmission. Thus it is expected that not only should the best evidence be relied upon but attention should be paid to the differences that could exist in disease transmission trends even in areas that are close to each other. This is because most environmental models are based on indirect estimations. Thus for example, in modelling for malaria using the changes in the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), one is actually using them as parameters to measure the temporal and spatial variations in the vegetation and their effect on disease vector populations. This is in turn influenced by the biology of the vector which is based on the genetics of the vector species and their adaptation to the environment. All these are further derived from the premise that changes in the disease vector population would inadvertently affect the transmission of a vector-borne disease. Now even though that is likely, there are other factors such as host resistance, treatment and prevention efforts, and even the existence of vector predators that could make this premise invalid in predicting the transmission of disease. Thus it is better to correlate such environmental data with vector biology factors, host indicators and apportion weights based on the statistical methods that best simulate the relationship between these factors in disease transmission.

This process would require that there be access to accurate and reliable local health data and ground data. In many cases however, in the days immediately after the occurrence of a disaster event, such data may not be easy to come by. This further underscores the need to have excellent coordination of disaster mitigation and preparedness activities on both local and international levels, with an emphasis on data that would be requisite for developing high quality mapping solutions. This would ensure the availability of such data in the crises moments following a disaster. Local capacity building efforts and stronger international cooperation between development agencies, departments and organizations that are involved in the use of geo-information solutions for disaster management and the humanitarian community at large, would further enhance the output of solutions developed for such purposes. Extra attention should also be paid by disaster-prone areas to having detailed information about their territory, indicating both geographic and social factors, and also to maintaining solid links with the disaster management community. The advantage of foresight can never be overemphasised when discussing the necessity of shifting the emphasis of disaster management practice from relief and recovery to preparedness and mitigation.

Another factor is the issue of uptake of mapping services by members of the disaster management community especially those responsible for providing medical relief. The emphases on the clinical and hygienic aspects, which bear more on the acute and long term phases of providing care, make interest in the use of predictive tools, as part of preparation for relief efforts, less attractive. The doctor wants to know the risk of disease spread; how many people are dead, injured or in need of immediate attention; how many healthcare facilities are functional, their location, staff strength and extent of damage. Thus it is necessary to demonstrate that although geo-information solutions may not have the answers to all these questions, they still have a role to play by their ability to:v

  1. Provide information to aid planning and logistics in the pre-arrival stages.
  2. Model the disease risk in certain areas using environmental data and background knowledge of disease trends.
  3. Serve as a decision making tool in the post-disaster planning of strength and location of medical relief, due to their ability to make preliminary damage assessments and show areas where recovery efforts such as location of healthcare posts or location of refugee camps are likely to be sustainable.
  4. Visually display the relationship between populations (usually from ground data) and services such as water sources, food distribution points, roads, health posts etc. This would aid the monitoring of factors influencing health and disease spread, and also facilitates the planning of public health interventions such as vaccinations, enlightenment campaigns, mass distribution etc.
  5. Demonstrate the need, where necessary, to pay attention to existing risks that may necessitate a major operational change, such as an evacuation. This is through been able to detect and predict changes in geographical and meteorological factors, interpret the consequences, and visually represent these in a manner that makes it easy for 'lay' people to utilize.

When these issues are well considered, it is evident that the use of mapping solutions by the healthcare community if encouraged, can only guarantee a greater depth of preparation. This would in turn strengthen the soundness of practice, reduce the risk of untoward events and maximize the effective use of resources and relief personnel.

No. Activity Telecom Earth Observation Geo- Navigation
1 Early Warning
2 Decision Support
3 Disease Risk Assessment
4 Logistics
5 Search and Rescue
6 Telecom and Telemedicine Support
7 Assessing the Viability of Environmental Support

Table 1: Summary of Satellite Solutions used in Disaster Management

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