Environmental Influences on Infection Disease Risk: Studies at Different Spatial Scales

Transcription

1 Environmental Influences on Infection Disease Risk: Studies at Different Spatial Scales Mark L. Wilson Department of Epidemiology Department of Ecology and Evolutionary Biology The University of Michigan 20 January, 2014 l Institut de Recherche en Santé Publique de l Université de Montréal

2 Perspective Behavioral Ecology Evolutionary Biology Applied Ecology, Epidemiology Complexity, systems dynamics, multilevel analysis "Social" drivers of disease risk

3 Characterizing Environment Abiotic (physical/chemical) * weather (temp., precip., RH, sunlight) * climate (same variables, but long-term trends) * electromagnetic radiation * atmospheric chemicals, particulates Biotic * vegetation, land cover, land use * animal predators or parasites * habitats, biomes Socio-economic * housing * hygiene * jobs * social stress * education Nature of environmental changes * most changes involve many factors from areas above * occur in time (seasonal, long-term), place (local, regional) * change can be greater/lesser: mean, variation, extremes

4 Classic Epidemiologic Triad Environment (biophysical, psycho-social, etc.) Agent (diverse exposures, including noncontagious ) Host (animal, plant, ultimately human)

5 Examples Involving Infectious Diseases Environment longevity & infectivity outside host host distribution, abundance, infection e.g. cholera hantaviral disease hookworm schistosomiasis nutrition hygiene treatment housing e.g. TB, HIV/AIDS, diarrheal diseases, acute respiratory infections Agent Host

6 Environment Agent Host Exposure probability, host immunity, support networks, availability of treatment

7 Some implications: Strong Interactions, and Feedback among variables Evolution Likely under time scale of changes "Environment" largely defined by People, Technologies and Economies

8 Examples of environmental data that may be important Climate patterns variability perhaps change Land Use / Land Cover patterns Housing, living quarters, work environments Vector abundance and diversity Toxin distribution / pollution prevalence Soil, topography, plants (forests, cropland, etc.) Economic development, nutrition, hygiene Human demography, migration MUCH MORE Each of these is historically changing in time and space

9 Life is complicated!!! Role of environment is changing in context of other variables "Environment" should be broadly defined to include social, economic, behavioral as well as other bio-physical exposures Many contemporary studies don't recognize or analyze these additional variables.

10 Environmental Determinants of Human Disease Social and Economic Policies Institutions (including medical care) Living Conditions Social Relationships Individual Risk Factors Genetic/Constitutional Factors Pathophysiologic pathways Individual/Population Health Modified from Kaplan, 2002

11 Research Challenge Analyze and understand interactions! Social and Economic Policies Institutions (including medical care) Living Conditions Social Relationships Individual Risk Factors Genetic/Constitutional Factors Pathophysiologic pathways Individual/Population Health

12 Each environmental exposure should be considered in context of: PERSON (age, behavior, gender, SES, etc.) TIME (year, season, adjacent periods, etc.) PLACE (geographic location, habitat, proximity, etc.) Many epidemiological studies only superficially consider this for environmental exposures: PERSON often involves standard descriptors that do not include "social" characteristics or other environmental risks. TIME is rarely dynamic, considers only recent past, and environmental pattern over long periods not always available. PLACE often ignored or not carefully evaluated (e.g. spatial autocorrelation, clustering of cases, environmental exposures, etc.).

13 Nature of Epidemiological Data Many variables, qualitatively different Complex interactions, rich interconnections Temporal variation (seasonal, inter-annual) Spatial variation (scale-dependent) So, need for spatially- and temporally-explicit tools to enhance research and direct prevention efforts

14 Recent Studies at Different Spatial Scales West Nile virus risk US Dengue Latin America Malaria Africa

15 West Nile virus transmission risk within a U.S. County

16 Old World flavivirus described 1937 (Uganda) Introduction into U.S. in 1999 (NYC) Rapid spread (few years) throughout N. Amer. Mosquito-borne (many spp) Bird reservoirs (many spp) Spillover transmission to others including humans West Nile Virus Basics

17 Mosquitoes collected (N.J. light traps) at 22 sites every 3 nights, May-September ID to species and counted, pooled by species & sample location Mosquito pools tested for WNV RNA (RT-RTPCR assay) Wild dead birds oral swab tested for WNV (Vec-Test) Sentinel pheasants tested weekly for WNV antibodies (IEA assay)

18 Culex pipiens 37,225 mosquitoes sampled Three species dominated: Culex pipiens, Culex restuans, Aedes vexans Mosquito abundance (dotted) varied seasonally and annually Temporal association with 2-wk avg. weather variables Culex restuans

19 Temporal pattern of Corvid bird infection with WNV associated with Culex spp. infection, suggesting role in enzootic transmission

20 Space-time clusters found each year, but in different locations Summary: Analysis of surveillance system data demonstrated spatial and temporal patterns of WNV transmission risk, and role of specific vectors and reservoirs

21 A different problem and much finer spatial scale: Dengue virus transmission household-level risk in Ecuador

22 Dengue Basics Mosquito-transmitted flavivirus Human-mosquito-human cycle Four major serotypes (den-1 den-4) 2.5 Billion people live in dengueendemic areas Million cases dengue per year Increasing geographic area, cases, severity

23 Dengue Basics Aedes aegypti mosquitoes primary vector Globally distributed in tropics and sub-tropics Eggs laid in any containers holding water Rapid development in containers Adult mosquitoes: peri-domestic & anthropophagic

24 Households (N=199) randomly selected across geographic extent of Borbon in May 2010 (after rainy season) All water containers in/around each house counted and classified Aedes aegypti mosquito larvae, pupae removed for later identification, counting

25 1,185 containers of various types 58 (~5%) w/ immature mosquitoes 80% uncovered; 61% outdoors; 77% no organic material Larger, uncovered, chlorinated containers more productive

26 Mosquito stage present Larvae, Pupae Adults Larvae, Pupae, Adults None present

27 Target households <10m from immature-positive households had 11 times odds of also being positive Spatial Analysis Proximity to other Positive and Negative households Negative neighbor Positive neighbor 40m 30m Target household Positive neighbor Negative neighbor

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29 Summary Strong evidence for household-level clustering of Aedes aegypti at <20 m Clustering stronger for large containers (>40 L) Relationships hold after adjusting for container characteristics Patterns likely attributable to short dispersal distances rather than shared characteristics Important implications for human disease risk and mosquito control efforts.

30 Now something different using various metrics and diverse spatial scales: Malaria in Sub-Saharan Africa

31 Characteristics of Malaria Affecting Control and Prevention Parasites Plasmodium protozoa Multiple parasite species, changing antigenicity, Resistance to treatment, no vaccine Vectors Anopheles mosquitoes Multiple species, different ecologies Insecticide resistance increasing Humans Transmission, Infection, Disease Personal protection not simple, SES and KAP factors Asymptomatic infection complicates transmission Changing Geographical Distribution Movement/transport constant problem Rapid long-distance travel, tourism

32 Drivers of Malaria Dynamics Deforestation Environment Civil Strife / War Agriculture/Water Vector Agent Host Rapid Urbanization Drug Resistance Plasmodium spp. Rapid Global Travel Insecticide Resistance Rural Poverty

33 Study area: highlands NE of Lake Victoria Near Nandi Forest; elev. 1,829 m - 2,132 m Patches of land deforested and planted Large swamp sections drained for agriculture Complete census (pop. ~3,700); all houses geo-located; satellite image of LU/LC Passive surveillance for malaria at local clinic,

34 Houses widely distributed through various habitats and elevations House designated as positive if one case of malaria during year Household-level and proximal environmental variables analyzed using multivariable models Tea and swamp Forest edge

35 Geographic clustering of "hotspots" ( times risk) Clusters consistent across years regardless of incidence Lower altitude, and greater distance to forest and swamp independently increased risk Suggests interventions focused on specific ecological areas are more cost-effective Unknown whether findings are widely generalizable

36 What environmental characteristics might help explain the observed spatial pattern of elevated risk?

37 Malaria data from used to explore microhabitats linked with transmission risk Topographic wetness index generated from digital terrain model of landscape Land-cover/land-use from highresolution satellite image (objectbased classification) Topographic and land-cover variables used to predict household-level malaria (iterative split-sample model fitting)

38 Topographic wetness variables most useful in predicting malaria-positive households Human modified environmental variables (LU/LC) also associated, but did not improve statistical models Topographic wetness values in this highly varied terrain most accurate predictor of risk Control strategies in similar settings may want to use topographic and geographic characteristics to identify high-risk regions for surveillance

39 Drivers of Malaria Dynamics Deforestation Environment Civil Strife / War Agriculture/Water Vector Agent Host Rapid Urbanization Drug Resistance Plasmodium spp. Rapid Global Travel Insecticide Resistance Rural Poverty

40 Environmental conditions in urban settings may not be conducive to transmission there Undertook unmatched casecontrol study of risk factors for malarial anemia in urban Kisumu, Kenya Cases (n = 80) hospitalized patients with a severe anemia, Plasmodium infection; Controls (n = 826) healthy respondents to a concurrent survey

41 Multiple environmental and behavioral risk factors identified Use of mosquito coils and bed net, characteristics of housing were NOT significant Children spending >1 night per month in rural area at 9-fold higher risk Implication: exposure during rural travel is important risk of malaria Future studies of urban malaria should consider travel history

42 How might access to prevention help explain the observed spatial patterns of elevated risk?

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47 What explains observed spatial patterns of elevated risk when mosquito vectors are virtually absent?

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49 Ten villages randomly selected from TA Sitola (N=6) and TA Nsamala (N=4) Census (dry season 2011) of all households with <5 yo children Administered malaria rapid diagnostic test (RDT) to children and questionnaire to parents Households with RDT-positive child designated as "cases"; RDT-negative were "controls" Environmental data: peri-domestic land cover <50 m radius Multivariate logistic regression (MLR) and multi-level multivariate logistic regression (MLLR) Spatial clustering of RDT status and environmental factors analyzed using Getis- Ord Gi*

50 Results 390 children enrolled; 45.6% positive for Plasmodium (RDT) Household proximity to agriculture (<25 m radius) highly predictive of positivity (controlled for sex age, bed net ownership, and elevation) Spatial clustering (Gi* z>2.58, p<0.01) of Pearson residuals (MLR), no clustering using MLLR

51 Univariate Gi* Hot Spots Malaria clusters at different scales Malaria cases Village-Level <25m from agriculture TA-Level Bednet ownership Study Area-Level

52 Summary Evidence of significant, dry-season spatial heterogeneity of malaria prevalence Pattern strongly linked to peri-domestic land use, particularly nearby crop production Elevated risk seen at very fine geographic scales (tens of meters), and during dry season Important implications for implementing effective surveillance and control

53 Malaria Transmission Cycle - Intervention Points Personal Protection Treatment Longevity Contact Frequency Human Vector Alternative Hosts Biting Rate Vector Human Competence Abundance Proximity to Breeding Sites Parasitemia Prevalence Personal Protection: repellants, bednets, coils, etc. decrease biting success Treatment: reduced duration of parasitemia diminishes acquisition by mosquito Contact Frequency: human abundance, house construction, movement, etc. Alternative Hosts: certain animals reduce number of bites on humans (zooprophylaxis) Competence: certain vector species better able to acquire, develop, transmit Abundance: number of vectors influences average number of bites Proximity to Breeding: closer sites reduce distance needed to find human host Parasitemia Prevalence: more infective people increase population risk Biting Rate: number of partial bloodmeals taken per egg-laying cycle Longevity: infected mosquito must survive parasite development and next bloodmeals

54 Summary Environmental drivers of disease risk involve more than just the biophysical Environmental impacts are spatially-dependent, operating at different spatial scales Spatial analyses of infection transmission provides insights into exposure risks (reservoirs, habitats, treatment and prevention, etc.) Questions being asked should determine the data needed, spatial scale, and analytic methods used Ignoring geographical patterns and spatial dependence is likely to lead to misleading results

55 Knowing is not enough; we must apply. Willing is not enough; we must do. (Goethe)