Environmental Epidemiology in Small Areas By Dr Nik Nor Ronaidi bin Nik Mahdi
IntroductionEnvironmental epidemiology studiesenvironmental risk factors and their impact onthe health of exposed people;These factors may be natural or anthropogenicThe risk factors derive from the people’sexposure to chemical, physical or biologicalstressors.The stressors come from point, line or areasources and reach the population by way ofmatrices ( air, water, soil, foods and space forelectomagnetic waves ).
Introduction (cont.)The environmental risk adds or synergicallyinteracts with the basic risk of contracting anillness.The environmental risk is as great as is exposureand individual physiological and anamnesticsusceptibility.The exposure to environmental risk factors mayoccur in an external environment (outdoor air )or an internal environment (indoor air ).
Introduction (cont.)• Spatial epidemiology is concerned with describing and understanding spatial variation in disease risk.• Small areas definition: – no hard-and-fast rules – Any region containing fewer than 20 cases of disease – refer to counties and subcounty areas like cities, census tracts, ZIP code areas, and even individual blocks – They range from less than an acre to thousands of square miles, and from no inhabitants to many millions
Environmental Epidemiology Objectives• Environmental Epidemiology assesses the added risk ( real or potential ) to the population exposed to environmental pollutants with the purpose of identifying the sources responsible for the pollution.
Risk factors interactionThe added risk from environmental factors interacts with non environmental risk factors:• Behavioural (smoking, drugs, alcohol abuse)• Socio-health (hygiene, nutrition, stress)• Genetic (hereditary susceptibility)• Anamnestic (previous diseases and medication)• Physiological ( age, sex, pregnancy, weight, height and respiration)• Professional exposure
Why environmental epidemiology on small areas?• The complexity of interaction among risk factors hinders the risk assessement with conventional statistic tools used for large populations.• We have to study the disaggregate non sampled and territory related data to indentify a clusters of increased incidence of disease and then filter from them cases with non environmental risk factors.• This is only possible for small populations living on small areas concerned with a small number of risk factors.• Provides a qualitative answer about the existence of an association (e.g. between environmental variable and health outcome)
Commonly used data sources• Censuses: – Most industrialized countries conduct reliable censuses of the entire population at regular intervals (e.g., every five or 10 years).• Administrative Records: – records kept by federal, state, and local governments provide small-area data for years after or between censuses.• Sample Surveys: – The limitation is that sample sizes are generally too small to provide reliable estimates for small areas.
ProblemsThe small areas considered must be sufficientlypopulated for the clusters significance, especiallyfor stochastic damages.We have to make use of all computeriseddatabases : territorial, private, health, andenvironmental.During data transfer and assessement, privacymust be guaranteedThe health data needs to include family,physiological, pathological, behaviouraland occupational exposure and mobility data .
Problems (cont)• Latency problems: – The neoplastic, reproductive and development diseases begin a long time from exposure. – therefore the emission sources have to be considered taking latency time into account. – The affected subjects have verified for different exposure for home changes. – In the course of latency time, the health risks cannnot be prevented, therefore a risk estimation of possible exposure and effects is better than the epidemiological survey of disease cases.
Solutions• In low population density areas, the health stochastic environmental damages is very little.• All the institutions have adequate computerized database systems.• It is possible to use the private data without access to subjects names on screen.• We may obtain the informations on the environmental risk factors from questionnaires administered by the family doctor.
Necessary resources and collaborationsTerritorial, health and environmental institutionshave to form a coordinated operative team.The databases have to be to coordinated onwork station capable of building, to managingand to querying the geodatabases.The clusters filtering process requires theelaboration and administration of questionnairesthrough family doctors.
Operative processA)Identify the suspicious sources and risk areas from emissions register, environmental data and modelingB) Choose a study area, including risk areas, with a population of suitable dimensionsC) Build the thematic map of the study areaD) Acquiring and georeference the road, socio- health and personal databases
Operative process ( cont )E) Identify possible health damage and environmental diseasesF) Show evidence of the environmental disease clusters associated with selected factorsG) Filter the clusters from non environmental risk factor casesH) Verify the filtered clusters by biochemical methods on tissue
A+B ) Study area identificationExamine the emissions registers andenvironmental data in air, soil, foods, water andspace.Identify the hazardous substances and stressorscarried by matrices.Fate and diffusion modeling of hazardoussubstances and stressors.Risk areas identification.Link the risk areas with synergic stressors.Choose a study area including risk and stressor freeareas.
C+D) Geodatabase buildingAcquire raster map of study areaMap vectorialization for residential, productionand service structure and sensitive sitesAcquire personal and health databases on themap layers for geodatabase building
E) Possible environmental diseasesReduced fertility, spontaneous abortionLower birth weight, malformationsRespiratory, gastroenteric and kidney diseasesImmune, endocrine and neoplastic diseasesNervous and mental diseasesDermatological and sense organ diseasesInfectious and parasitic diseasesCardiocirculatory and muscle-skeleton diseases
F) clusters identification• Health data layer may show clusters with a greater incidence of disease caused by the environment causes G) clusters purification•Patients ( or at relatives in case of death ) ofthese clusters have to be given a questionnaireto identify and exclude non prevalentenvironmental cases
G) Anamnestic questionnaire for cluster filteringFamily anamnesis ( disease cases in relatives notliving in the cluster )Work and behavioural anamnesis ( exposure toprofessional and behavioural risk factors )socioeconomic, pathologic and pharmacologicalanamnesis ( factors modifying exposure,susceptibility or prognosis )
H) Clusters biochemical checkEven the most careful cluster purification notconfirm the relationship between environmentalfactors and diseasesTherefore we must research metabolic markers,i.e.matabolites of pollutants, in tissues ( hairs ornails ) or biological fluids ( blood, urine, salivaand mother’s milk ) in affected people or inrandom sample for comparison with subjectsoutside the cluster.
Environmental risk communication• The communication should be able to disseminate risk information in a timely, reliable and targeted manner• Communication should include: method description, uncertainty factors and scientific bibliography.• The assessement receivers who manage the environmental risk take responsability for using the assessement in environmental protection and health prevention decisions.
• Objective: – To assess environmental causes of outdoor falls using a small urban community in Hong Kong as an example.• Data collection by collaboration with A&E Department of the Kwong Wah Hospital (94% of HK population seek medical care from public hospitals)
• ‘geocoding’ or ‘address matching’ is a process that involves assigning a geographic coordinate to position a fall location and linking its descriptive attributes• Using Centamap—a free web map service in Hong Kong
• Data analysis:2.Incident mapping – uses points as the smallest representation of a fall incident – Each point location is associated with a number of attributes about the faller – enables a better understanding of incidental factors and their spatial patterns3.Cluster analysis – involves the detection of hot spots – These hot spots are speculated as the correct targets for implementing improvement or preventive measures.
1. Associative study – to explain relationships between geographical phenomena – enable the identification of potential hot spots of falls and their likely causes• On-site inspection at target locations to identify specific circumstances surrounding the falls.
Problems• Confounding factors: – demographic characteristics, personal traits (including gait and balance, visual condition), past medical history and long term use of medication, as well as activities engaged at the time can increase or decrease the risk of falls.• No official data about the location of falls available→ collaboration with the A&E Department of the Kwong Wah Hospital
Problems (cont)• fall injuries either treated in other hospitals or by other means (e.g. traditional therapy) or not treated will not be included.• Research conducted with consent from the patients and on a voluntary basis – it would be wise for the government, to integrate data on fall injuries into the medical records of all hospitals under the mandate of the Hospital Authority
REFERENCES• Alessandro Menegozzo (2010), slide presentation: Environmental Epidemiology on small areas. Agenzia Regionale Prevenzione Protezione Ambientale Veneto ( Italy ).• P. Elliott, J. Cuzick, D. English, R.Stern (1992). Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Oxford University Press Inc., New York.• Paul Elliott and David A. Savitz (2008). Design Issues in Small-Area Studies of Environment and Health. Environmental Health Perspectives, 116, 1098-1104.• Poh-Chin Lai, Wing-Cheung Wong, Chien-Tat Low, Martin Wong, Ming-Houng Chan (2010). A Small-Area Study of Environmental Risk Assessment of Outdoor Falls. J Med Syst• Stanley K. Smith (2003). Small-area Analysis. Encyclopedia of Population. Farmington Hills, MI: Macmillan Reference, 898-901.