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    Distribution of counties in a nine-state region of the southcentral United States (Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Oklahoma, Nebraska, and Tennessee) reporting tularemia cases or with tularemia incidences exceeding 1 case per 100,000 person-years from 1990 to 2003. This figure appears in color at www.ajtmh.org.

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    Comparison of spatial patterns of county tularemia incidence in Arkansas during 1978–198221 and 1990–2003.

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    Distribution of land cover types with different perceived suitability for tick vectors of F. tularensis in the southcentral United States (Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Oklahoma, Nebraska, and Tennessee). Grass/shrub includes prairie, pasture, and shrubland. Other habitats include water, barren, agricultural crops, seasonally flooded habitat, and wetland. This figure appears in color at www.ajtmh.org.

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    Counties with predicted risk of human exposure to tularemia in Arkansas-Missouri (gray). Counties reporting cases during 1990–2003 are displayed with hatched lines.

  • View in gallery

    Areas with predicted risk of human exposure to the tularemia agent in Arkansas-Missouri (rose-colored). Counties reporting cases during 1990–2003 are shaded light blue. This figure appears in color at www.ajtmh.org.

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Ecoepidemiology of Tularemia in the Southcentral United States

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  • 1 Division of Vector-Borne Infectious Diseases, National Center for Zoonotic, Vector-Borne, and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado; Office of the State Epidemiologist, Oklahoma State Department of Health, Oklahoma City, Oklahoma

We combined county-based data for tularemia incidence from 1990 to 2003 for a nine-state region (Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Nebraska, Oklahoma, and Tennessee) in the southcentral United States with Geographic Information System (GIS)-based environmental data to determine associations between coverage by different habitats (especially dry forest representing suitable tick habitat) and tularemia incidence. High-risk counties (> 1 case per 100,000 person-years) clustered in Arkansas-Missouri and far eastern Oklahoma and Kansas. County tularemia incidence was positively associated with coverage by dry forested habitat suitable for vector ticks for Oklahoma-Kansas-Nebraska and Arkansas-Missouri but not for Illinois-Indiana-Kentucky-Tennessee. A multivariate logistic regression model predicting presence of areas with risk of tularemia based on GIS-derived environmental data was developed for the Arkansas-Missouri tularemia focus. The study shows the potential for research on tularemia ecoepidemiology and highlights the need for further modeling efforts based on acarologic data and more fine-scale point or zip code/census tract epidemiologic data.

INTRODUCTION

Tularemia is caused by the bacterium Francisella tularensis, which can be transmitted to humans through a variety of routes including tick or insect bite, handling of infected animals, contact with or ingestion of infected water, food, or soil, and inhalation of infectious aerosol.14 Approximately 90% of North American tularemia cases are considered to be caused by F. tularensis tularensis (Type A),5 but tularemia caused by F. tularensis holarctica (Type B) may be underdiagnosed or underreported. Several aspects of the epidemiology of tularemia in the United States have changed dramatically since the early 1900s. First, the average of 123 cases reported annually during 1990–2004 is an order of magnitude lower than the average of 1,181 cases reported annually during 1940–1948.2,68 This tremendous decline in national tularemia cases, which occurred primarily during the 1950s and 1960s,9 most likely was related to a general decrease in human exposure to infected animals, particularly rabbits. Second, the spatial distribution of tularemia cases changed dramatically over the 20th century. The current national focus of tularemia in Arkansas-Missouri7 is distinctly smaller than the historical one, with large numbers of cases occurring also in other eastern states (e.g., Georgia, Illinois, Indiana, Kentucky, Louisiana, Mississippi, Ohio, Tennessee, and Virginia).2 It was recently argued that ticks historically and currently play a more prominent role in the transmission of F. tularensis to humans in Arkansas-Missouri than in other parts of the eastern United States.10 This may be the primary reason for a ~2-fold reduction in tularemia in this two-state area, whereas incidence decreased ~8-fold from other eastern states after the decline in human exposure to infected lagomorphs.10 This notion is supported by the exceptional numbers of vector ticks (Dermacentor variabilis and, especially, Amblyomma americanum) that can infest dry forested habitats in Arkansas-Missouri.4,11,12

The epidemiology of tularemia in the southcentral and southeastern United States was studied intensively from the late 1940s to the late 1980s1325 but has received virtually no attention over the last two decades. Recent advances in Geographic Information System (GIS) technology and increasing availability of GIS-based environmental data have opened new avenues for ecoepidemiologic studies of associations between environmental factors and tularemia incidence and the intriguing ecology of the tularemia agent. This study, which focused on a nine-state region in the southcentral United States (Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Oklahoma, Nebraska, and Tennessee), represents an initial effort to use GIS technology to explore the linkages between the environment and risk of tularemia in the United States. Primary aims were to 1) determine associations between coverage by different habitat types (especially dry forest representing suitable tick habitat) and tularemia incidence in the nine-state region and 2) develop a multivariate logistic regression model predicting presence of areas with risk of tularemia in the Arkansas-Missouri disease focus based on associations between GIS-derived environmental data and tularemia incidence.

MATERIALS AND METHODS

Epidemiologic data.

In the United States, tularemia is a nationally notifiable disease. Health care providers are required by law to report cases to state or local health officials, who in turn report cases to the Centers for Disease Control and Prevention (CDC) through the National Notifiable Diseases Surveillance System (NNDSS). Both confirmed and probable cases are tallied at the national level. A confirmed case is defined as clinically compatible illness with isolation of F. tularensis from a clinical specimen or a 4-fold or greater change in antibody titer to F. tularensis antigen.26 A probable case is defined as clinically compatible illness with elevated serum antibody titer(s) to F. tularensis antigen (without a documented 4-fold change in titer) or detection of F. tularensis in a clinical specimen by fluorescent assay.26 More than 80% of reported cases meet the confirmed definition. Although reporting of tularemia cases was officially discontinued from 1995 through 1999,27 cases continued to be reported to the CDC at a steady rate during this period (CDC, unpublished data). Underreporting of tularemia cases has not been systematically studied but is assumed to occur. However, consistent underreporting should not interfere with comparisons over time or across jurisdictions.

Our study included data for county-based tularemia incidence from 1990 to 2003 for a nine-state region of the south-central United States: Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Oklahoma, Nebraska, and Tennessee. These states accounted for 838 (72%) of 1,169 confirmed and probable cases reported to CDC over this 14-year period (CDC, unpublished data). County incidences were calculated based on human population census data from 2000 (ESRI, Redlands, CA). The study region was subdivided into three state groupings based on similarities in general climate and land cover types: Oklahoma-Kansas-Nebraska to the west (with large tracts of prairie), Arkansas-Missouri in the center (with large tracts of coniferous and mixed deciduous/coniferous forest in northern Arkansas and southern Missouri), and Illinois-Indiana-Kentucky-Tennessee to the east (with agricultural landscapes interspersed with predominantly deciduous forest) (Table 1). Because of vast ecological differences within the targeted nine-state area, analyses were run separately for the aforementioned ecologically similar state groupings. Within each of these state groupings, county-based incidence of tularemia ranged from 0 to > 1 case per 100,000 person-years, with the majority of counties reporting no cases from all states expect Arkansas and Missouri (Figure 1). We also evaluated the possibility of incorporating other adjoining southcentral, southeastern, or northcentral states into the study (Texas, Mississippi, Louisiana, Alabama, Georgia, Ohio), but these were ultimately excluded because tularemia incidences were very low (mean county tularemia incidence by state of < 0.01 case per 100,000 person-years), and variation in incidence among counties was insufficient for meaningful analysis at this spatial scale.

Environmental data.

GIS-based data used in the study included 1) administrative boundaries (state, county; ESRI); 2) elevation (30 × 30-m spatial resolution; US Geological Survey national digital elevation data set); 3) long-term (1961–1990) average climate data (mean annual, January, and July minimum, mean, and maximum temperature; mean annual cooling and heating degree days and growing degree-days; mean annual, January, and July precipitation and relative humidity; mean annual snowfall; median annual length of freeze-free period; median Julian date of first and last snowfall; 2 × 2-km spatial resolution; Climate Source, Corvallis, OR); 4) annual average normalized difference vegetation index (NDVI) data from 2005 (1 × 1-km spatial resolution; derived from NOAA Advanced Very High Resolution Radiometer images); and 5) land cover classifications based on US Geological Survey state Gap analysis projects. Land cover was reclassified from state-specific classification schemes into a uniform classification scheme including the following habitat types: water, barren, urban, agricultural crops, seasonally flooded habitat, wetland, grassland (including pasture), shrubland, dry deciduous forest, dry coniferous forest, and dry mixed forest. These habitat types fall into three broad categories with regards to suitability for tick vectors: unsuitable (water, barren, agricultural crops, seasonally flooded habitat, wetland), partially suitable (grassland, shrubland, urban), and suitable (dry deciduous forest, dry coniferous forest, dry mixed forest). For the purpose of this study, dry forest refers to forested habitats not prone to seasonal flooding. The rationale for including grassland in the partially suitable, rather than suitable, category was that the human-biting life stages of both the American dog tick (D. variabilis) and the lone star tick (A. americanum) are abundant in dry forested habitats, but that only D. variabilis can be considered potentially abundant in grasslands.12,28,29 Furthermore, grasslands likely are only partially suitable even for D. variabilis in the dry environments characteristic of the western part of the targeted nine-state area.30 It is important to note that the broad habitat classifications of suitable, partially suitable, and unsuitable for vector ticks were developed to elucidate associations with tularemia incidence at the crude county scale; they do not account for more fine-scale landscape patterns potentially related to tick abundance such as presence of habitat ecotones (e.g., forest-grassland edges or forested borders of the peridomestic environment) or presence of small patches of a given habitat within another one (e.g., very small patches of dry forest within an area characterized by seasonal flooding). Furthermore, old field habitats, which are known to present risk for exposure to D. variabilis,29,30 likely were separated in the Gap land cover classification into grassland, shrubland, or forest categories depending on the degree of reforestation of an individual field at the time the classification was created.

Determination of associations between habitat type and tularemia incidence.

County unit-based associations between the above-mentioned habitat types (percentages of coverage) and 12 tularemia incidence classes (0, 0.001–0.049, 0.050–0.099, 0.100–0.199, 0.200–0.299, 0.300–0.399, 0.400–0.499, 0.500–0.749, 0.750–0.999, 1.000–1.499, 1.500–1.999, and 2.000–6.500 cases per 100,000 person-years) were tested by ordinal logistic regression. Incidence classes were chosen to have similar numbers of counties (range: 17–39) for each incidence group where cases occurred. Ordinal logistic regression based on tularemia incidence classes, rather than linear regression based on tularemia incidences, was used because tularemia incidence data were not normally distributed and transformation could not make them so. Separate tests were carried out for each of the three state groupings included in the study: Oklahoma-Kansas-Nebraska, Arkansas-Missouri, and Illinois-Indiana-Kentucky-Tennessee.

Development of predictive models for risk of exposure to tularemia in Arkansas-Missouri.

Because the vast majority of cases were reported from Missouri and Arkansas and this region differs ecologically from the other groupings (Figures 1 and 3), attempts to fit a model to the entire nine state region obscured small-scale (i.e., county and sub-county) differences within the Missouri-Arkansas tularemia focus. Similarly, Yabsley and others31 showed that modeling another tick-borne disease, human mononcytic ehrlichiosis, in a similar geographic region based on subregions yielded a better overall fit than the region-wide model. Within Arkansas-Missouri, continuous environmental data (mean elevation, annual NDVI, and climate variables as listed above) were used in a multivariate logistic regression modeling approach aimed at predicting the spatial pattern of presence of counties with tularemia cases. Similar models were not developed for Oklahoma-Kansas-Nebraska and Illinois-Indiana-Kentucky-Tennessee because the numbers of tularemia cases for these state groupings were judged too low to show associations at the relatively crude county-based spatial scale.

Annual average NDVI, which is a measure of vegetation based on visible and near-infrared light that is reflected by vegetation, was found to serve as a proxy for dry forested habitat suitable for ticks in the Arkansas-Missouri area; there was a significant correlation between mean NDVI for a county and the percentage of the county covered by dry forested habitat (Spearman coefficient of rank correlation; ρs = 0.856, N = 190, P < 0.001). Exclusive use of continuous covariates with a maximum spatial resolution of 2 × 2 km allowed us to generate predictions for tularemia risk at a finer spatial scale than the original county unit scale. The validity of these fine-scale predictions will be examined in future studies.

Model development was based on a random selection (within the 12 tularemia incidence classes mentioned previously) of 75% of the total counties in Arkansas-Missouri, with the remaining 25% of counties set aside for model validation. Models were developed for the probability that an area (i.e., a county polygon or a 2 × 2-km cell within a particular county) would be classified as suitable for a tularemia case to occur. Covariates included in the forward stepwise regression model (probability to enter of 0.25) were restricted to variables yielding significant associations with presence of tularemia in univariate tests (Wilcoxon rank sum test with normal approximation; P < 0.05) and not strongly correlated with each other (Spearman rank correlation; ρs < 0.8). The output model was highly significant (whole model test; P < 0.005), and the lack of fit test indicated that the model included sufficient numbers of covariates (P > 0.05). Covariates included in the model are shown in Table 3.

The model is described by the following equation:

Logit(P)=β0+β1x1+β2x2++βkxk

where P is the probability of a cell or a county polygon being classified as a risk area and β0 is the intercept. The values β1, . , βk represent the coefficients assigned to each independent variable included in the regression, and x1, . , xk symbolize the independent variables. The probability that a particular cell or county polygon in the GIS is classified as risk or high risk, depending on the model, of exposure to the tularemia agent can be derived from equation 1 using the following expression:

P=exp(β0+β1x1++βkxk)/[1+exp(β0+β1x1++βkxk)]

At the county scale, each county was assigned a single probability value. The optimal probability cut-off value was chosen by maximizing sensitivity and specificity simultaneously using receiver operating characteristic (ROC) curves. Counties with values above the probability threshold value were classified as risk, whereas all others were considered no risk.

At the sub-county scale for each county, the maximum probability value among 2 × 2-km raster cells populating each county was extracted using zonal statistics (ArcGIS9.2 Spatial Analyst; ESRI). The optimal probability cut-off value was chosen by maximizing sensitivity and specificity simultaneously. Counties containing at least one raster cell with a probability value at least equal to the optimal value were classified as containing risk areas, whereas all other counties were considered to lack risk areas in our evaluation matrix. Statistical analyses were carried out using Version 5.1 of the JMP statistical package,32 and the results were considered significant when P < 0.05.

RESULTS

Spatial patterns of tularemia incidence.

Mean county tularemia incidence per 100,000 person-years ranged from 0.02 for Indiana to 0.98 for Arkansas, and peak county values ranged from 0.35 for Indiana to 6.21 for Missouri (Table 1). Pairwise tests revealed that the Arkansas-Missouri grouping had a higher county tularemia incidence per 100,000 person-years (median, 0.52; mean, 0.80 ± 0.97) than either the Oklahoma-Kansas-Nebraska grouping (median, 0; mean, 0.15 ± 0.44) or the Illinois-Indiana-Kentucky-Tennessee grouping (median, 0; mean, 0.05 ± 0.14; Wilcoxon rank sum test with normal approximation; z ≥ 11.20; df ≥ 1,463; P < 0.001 in both cases). Although there was a trend toward higher county tularemia incidence for the Oklahoma-Kansas-Nebraska grouping than the Illinois-Indiana-Kentucky-Tennessee grouping, the difference was not statistically significant (P = 0.06). Counties with reported tularemia cases and, especially, with tularemia incidences exceeding 1 case per 100,000 person-years from 1990 to 2003 clustered in an area encompassing Arkansas, southern Missouri, and eastern Oklahoma and Kansas (Figure 1). We also were able to compare county tularemia incidence for Arkansas during 1978–198221 and 1990–2003 (Figure 2). This comparison showed both a general decline in tularemia incidence and a shift in the primary risk area toward the north in Arkansas from 1978–1982 to 1990–2003.

Determination of associations between habitat type and tularemia incidence.

The distribution of land cover types with different perceived suitability for tick vectors of F. tularensis (suitable: dry forested habitats; partially suitable: grassland, shrubland, urban; unsuitable: water, barren, agricultural crops, seasonally flooded habitat, wetland) in the nine-state region is shown in Figure 3. County tularemia incidence was strongly positively associated with coverage by dry forested habitat suitable for vector ticks for the Arkansas-Missouri and Oklahoma-Kansas-Nebraska groupings (P < 0.001 in both cases; Table 2). A breakdown of dry forest categories (deciduous, coniferous, mixed) showed positive associations between tularemia incidence and all three forest categories for both state groupings (P < 0.05 in all cases; Table 2). In striking contrast, coverage by dry forested habitat suitable for vector ticks was negatively associated with county tularemia incidence for the Illinois-Indiana-Kentucky-Tennessee grouping. Other positive associations with county tularemia incidence (P < 0.05) included coverage by grassland for the Arkansas-Missouri and Illinois-Indiana-Kentucky-Tennessee groupings and by water for the Oklahoma-Kansas-Nebraska grouping (Table 2). Finally, negative associations with county tularemia incidence (P < 0.05) included coverage by croplands for the Arkansas-Missouri and Oklahoma-Kansas-Nebraska groupings, by seasonally flooded habitats and wetlands for the Oklahoma-Kansas-Nebraska grouping, and by shrubland or urban areas for the Illinois-Indiana-Kentucky-Tennessee grouping (Table 2).

Predictive model for areas in Arkansas-Missouri with risk of exposure to tularemia.

A multivariate logistic regression model was developed for presence of areas with risk of tularemia (Figures 4 and 5; based on annual NDVI, annual maximum temperature, annual relative humidity, and precipitation in July; whole model test: χ2 = 21.88, df = 4, P < 0.001). Lack of fit test indicated that the model included sufficient numbers of covariates (P = 0.35). Details for covariates included in the model are shown in Table 3.

Before extrapolating the model to the sub-county scale, we evaluated model performance at the county scale (Table 4; Figure 4). Producer accuracy for classifying counties that reported cases as counties at risk was 77.1% for the build set and 70.6% for the validation set. For counties predicted to pose a risk, 86.2% of the build set and 85.7% of the validation set reported cases.

Next, we evaluated model performance at the sub-county scale. Among the 105 counties in the build set that reported tularemia cases, 89 (85%) were classified by our model as containing at least one 2 × 2-km risk area. Similarly, 82% of counties classified as containing risk areas reported tularemia cases (Table 5; Figure 5). Among counties from which tularemia cases were not reported, 47% were classified as not containing risk areas. For counties classified as not containing risk areas, 53% did not report cases. Evaluation of the same model based on counties that were not included in model construction revealed similar accuracy (Table 5). For counties reporting tularemia cases, 88% were classified by the model as containing risk areas. When a county was classified as containing risk areas, 88% of counties reported cases. Furthermore, 69% of counties not reporting cases were classified as lacking risk areas and 69% of counties classified as lacking risk areas did not report cases.

DISCUSSION

We present here the first GIS-based predictive spatial model for tularemia risk in the United States. The study included the national Arkansas-Missouri tularemia focus and surrounding areas to the west (Oklahoma-Kansas-Nebraska) and east (Illinois-Indiana-Kentucky-Tennessee). GIS-based environmental data were found to be highly useful for studies of the ecoepidemiology of tularemia, including the development of a predictive spatial risk model for the Arkansas-Missouri tularemia focus. Our modeling efforts both generated testable hypotheses for fine-scale patterns of tularemia risk within Arkansas-Missouri and yielded new knowledge regarding habitat associations of tularemia in a nine-state region that will help unravel the natural maintenance cycles of the tularemia agent F. tularensis. This initial study also highlighted the need for further modeling efforts including a combination of field-based data for acarologic risk of vector exposure and more fine-scale point, zip code, or census tract–based epidemiologic data.

As expected from previous studies including the southcentral United States,7,23 we found that counties with elevated incidence of tularemia clustered in an area encompassing western and northern Arkansas, southern Missouri, and far eastern Oklahoma and Kansas (Figure 1). The distinct spatial pattern of tularemia risk in Oklahoma and Kansas, with increasing likelihood of counties with tularemia cases in the far eastern parts of these states, is readily explained by land cover type. Tularemia risk is low in the prairie landscapes to the west but increases toward the Arkansas and Missouri borders as the prairie is replaced by dry forested habitats suitable for tick vectors (D. variabilis adults and A. americanum nymphs and adults) of F. tularensis2,4,10,12 (Figure 3). Although the black-legged tick (Ixodes scapularis) has been implicated as a vector of F. tularensis33 and found to be naturally infected with the tularemia agent in Oklahoma,33 this tick can be considered to play a minor role in the transmission of F. tularensis to humans in the southcentral United States because only the adult stage commonly infests humans in this part of the country, and the adults are not active during the summer months when most tularemia cases occur.10 The positive association between county tularemia incidence and coverage by water in Oklahoma-Kansas-Nebraska is not surprising because ticks in the dry prairie landscapes characteristic of these states undoubtedly cluster along water sources such as rivers and streams.

The spatial patterns of counties with elevated incidence of tularemia in Arkansas and Missouri (Figure 1) also can be explained by the general distribution of dry forested habitat suitable for vector ticks (Figure 3), which occurs widely in western and northern Arkansas and southern Missouri but is sparse in the Mississippi River valley of southeastern Arkansas and the grasslands of northern Missouri where tularemia in humans occurs only infrequently. Indeed, our predictive spatial model for risk of tularemia in Arkansas-Missouri was based in part on a variable indicative of dry forests (NDVI). The association between habitat suitable for vector ticks and tularemia incidence in Arkansas-Missouri is in accordance with the most recent epidemiologic study from Arkansas,21 which indicates that tick bite accounts for the majority of human exposures to F. tularensis in the state. The discrepant result from the Illinois-Indiana-Kentucky-Tennessee area, where county tularemia incidence was found to be negatively associated with coverage by dry forest habitat suitable for ticks, is intriguing. This may have resulted from a variety of reasons including differences in species composition or abundance of vector ticks or vertebrate reservoirs or from transmission to humans occurring more frequently through routes other than tick bite (e.g., handling of lagomorphs). Alternatively, forested habitats may be more important for tick survival, relative to grasslands, in the drier environments to the west (Oklahoma-Kansas-Nebraska and the eastern part of Arkansas-Missouri) than in moister areas to the east (Illinois-Indiana-Kentucky-Tennessee). On the other hand, one can speculate that intense enzootic transmission of F. tularensis and elevated risk of human pathogen exposure is related to presence of dry mixed deciduous/coniferous forest, which occurs commonly in the Arkansas-Missouri tularemia focus but not in the Illinois-Indiana-Kentucky-Tennessee area. Finally, these differences could be related to the habitat associations of F. tularensis Types A and B, such that Type A is more likely to occur in dry forested habitat than Type B. Type A is considered to be more strongly associated with ticks relative to Type B,2,4 thus supporting this hypothesis. Intriguingly, 78% of samples from tularemia patients originating from Arkansas-Missouri and 94% of samples from Oklahoma-Kansas-Nebraska were classified as Type A, whereas 76% of samples from the Illinois-Indiana-Kentucky-Tennessee area were classified as Type B.34 These considerations highlight the need for renewed studies comparing both the ecology and epidemiology of tularemia across a habitat gradient ranging from Oklahoma-Kansas to Arkansas-Missouri and Kentucky-Tennessee.

Although our predictive model for risk of exposure to the tularemia agent in Arkansas-Missouri was based on relatively coarse county-based incidence data, it clearly showed the potential for GIS-based modeling of tularemia ecoepidemiology and highlighted the need for further efforts based on more fine-scale point, zip code, or census tract–based epidemiologic data. Modern GIS techniques and an explosion in GIS-based environmental data have opened new avenues for epidemiologic research. We now, however, are faced with the challenge of improving the quality of the epidemiologic data available for use in GIS-based modeling approaches.35 Recent GIS-based models for plague ecoepidemiology in the southwestern United States3638 provide powerful examples of what can be accomplished in terms of modeling of spatial risk of vector-borne diseases when reliable data on pathogen exposure sites are available. Furthermore, a recent study on Lyme disease in California39 showed the value of assessing risk based on combined acarologic vector data and epidemiologic data and of calculating and displaying disease incidence at zip code rather than county scales. We expect similar future approaches for tularemia to generate improved spatial risk models for this disease and hope that our initial effort has adequately underscored the critical need for reliable fine-scale tularemia case information including not only the address of residence of afflicted persons but also evaluations of likely pathogen exposure site.

In the absence of exhaustive case studies, the address of residence will likely remain the best estimate for a probable point of pathogen exposure for tularemia cases in the United States. Such data are, however, restricted to areas where people reside. In contrast, risk models based on vector abundance can be extrapolated to publicly owned lands where people do not reside but where exposure could occur during recreational activities.3941 Therefore, field-based studies on vector ticks are also needed to improve the accuracy of spatial risk assessments for exposure to F. tularensis. Vector abundance data collected using systematic sampling methods could be useful for generating acarologic risk models for key vector species (A. americanum, D. variabilis) and elucidating environmental correlates of tick abundance.40 These models could also be useful for assessing risk of other tick-borne diseases occurring in the southcentral and southeastern United States (e.g., human monocytic ehrlichiosis, Rocky Mountain spotted fever, southern tick-associated rash illness).31,4249 Fine-scale models for spatial risk patterns will help to target the use of limited tick control resources to areas with especially high risk and inform the local medical community and public in such areas of the elevated risk of exposure to F. tularensis.

Our predictive model of tularemia risk in Arkansas-Missouri accurately classified counties reporting cases as containing risk areas. User and producer accuracy for predicting case occurrences were notably higher for the sub-county scale model evaluation than for the county scale one. This suggests that the model, which was developed based on associations between 2 × 2-km scale GIS-based environmental data and county scale tularemia incidence data, is informative at sub-county scales. The accuracy of the sub-county scale predictions will be examined in future studies by comparisons of coverage of predicted risk areas and observed tularemia incidence by census tract or zip code for Arkansas-Missouri. Overall accuracy was reduced because a high proportion of counties that did not report any tularemia cases during 1990–2003 were identified by the model as containing risk habitat. This type of error is consistent with infrequently occurring diseases (Arkansas and Missouri combined for an average of ≈ 50 cases per year during the study period) and also was observed in the above-mentioned plague risk model for the southwestern United States.3638 Anomalies in tularemia occurrence with cases lacking from counties classified by the model as solid risk and bordered on all sides by counties reporting cases (Figure 5) could be related to either incomplete case reporting or persons with mild cases of tularemia not seeking medical attention. Alternatively, the error could result from behavioral factors associated with county-specific risk of pathogen exposure (e.g., variation in the amount of time spent outdoors in tick habitat or rabbit hunting) and not considered in this model. Overall model accuracy was also reduced when the model did not identify risk areas from a county but cases were reported. This type of error may result from travel-related exposure and underscores the importance of the medical community routinely determining whether a tularemia case likely was acquired in the peridomestic environment, outside the peridomestic environment but within the county of residence, or outside of the county of residence. Misclassification of counties in northern Missouri could be related to the distribution of F. tularensis Types A and B. Recent molecular analysis of F. tularensis isolates from humans showed that Type A is common in southern Missouri and Arkansas, whereas Type B is most common in northern Missouri.34 It is possible that our model is predictive primarily of the risk of exposure to tick-borne Type A and that cases from northern Missouri were exposed to Type B.

Recent advances in genetic characterization of F. tularensis have allowed for determination of not only Type A versus Type B but also different subtypes of Type A that may differ in their pathogenicity to humans.34,5054 This provides intriguing possibilities for expanding our initial studies on the ecoepidemiology of tularemia to also include F. tularensis types and subtypes with differing perceived transmission routes to or pathogenicity in humans. For example, Type A is considered to be associated with ticks and lagomorphs, whereas Type B in addition to being detected from ticks also is thought to be associated with water and rodents such as beavers and voles.1,2,4,55,56 Future studies are needed to determine environmental correlates of Type A versus Type B and, especially, subtypes of Type A with high pathogenicity in humans.

Table 1

County-based tularemia incidence and environmental characteristics for states included in the study

Mean ± SD county climate data*
StateNumber of countiesTotal tularemia cases, 1990–2003Mean ± SD (peak) county incidence of tularemia per 100,000 person-years, 1990–2003Mean ± SD county percentage of dry forest areasMean July temperature (°C)Annual rainfall (mm)Mean July relative humidity (%)Annual snowfall (mm)
* Mean values for 1961–1990 based on GIS-derived data.
Oklahoma-Kansas-Nebraska grouping
    Oklahoma77950.25 ± 0.59 (4.07)20 ± 2027.6 ± 0.6949 ± 20459.4 ± 3.3172 ± 84
    Kansas105490.15 ± 0.42 (2.99)5 ± 826.0 ± 0.7754 ± 20059.8 ± 3.8426 ± 106
    Nebraska93280.09 ± 0.28 (1.52)3 ± 323.8 ± 0.9633 ± 11861.8 ± 3.4747 ± 144
Arkansas-Missouri grouping
    Arkansas752720.98 ± 0.95 (4.31)41 ± 2626.9 ± 0.81,293 ± 8068.7 ± 2.2110 ± 86
    Missouri1152700.68 ± 0.96 (6.21)32 ± 2425.3 ± 0.61,056 ± 9067.5 ± 1.7419 ± 107
Illinois-Indiana-Kentucky-Tennessee grouping
    Illinois102450.09 ± 0.19 (1.03)13 ± 1024.3 ± 1.01,009 ± 8869.8 ± 1.4547 ± 171
    Indiana92130.02 ± 0.06 (0.35)21 ± 2023.6 ± 0.81,053 ± 8469.4 ± 1.1583 ± 236
    Kentucky120260.05 ± 0.15 (0.92)42 ± 2424.5 ± 0.91,222 ± 7471.2 ± 1.4354 ± 64
    Tennessee95400.04 ± 0.10 (0.41)47 ± 2224.9 ± 1.21,380 ± 9472.5 ± 1.7228 ± 132
Table 2

County unit-based associations between percentage coverage by different habitat types and tularemia incidence class, 1990–2003, in the south-central United States

Arkansas-Missouri (N = 190 counties)Oklahoma-Kansas-Nebraska N (= 275 counties)Illinois-Indiana-Kentucky-Tennessee (N = 409 counties)
Habitat type*Association with tularemia incidence class †‡PAssociation with tularemia incidence class†‡PAssociation with tularemia incidence class†‡P
* Standardized classifications based on U.S. Geological Survey state Gap analysis projects.
† Association, based on ordinal logistic regression, classified as positive (P < 0.05), negative (P < 0.05), or none (P > 0.05).
‡ Based on 12 tularemia incidence classes: 0, 0.001–0.049, 0.050–0.099, 0.100–0.199, 0.200–0.299, 0.300–0.399, 0.400–0.499, 0.500–0.749, 0.750–0.999, 1.000–1.499, 1.500–1.999, and 2.000–6.500 cases per 100,000 person-years.
Suitable habitat for vector ticks
    Dry forest—all types combinedPositive< 0.001Positive< 0.001Negative< 0.001
    Dry deciduous forestPositive0.02Positive0.01Negative< 0.001
    Dry coniferous forestPositive0.02Positive0.01None0.31
    Dry mixed forestPositive< 0.001Positive< 0.001None0.16
Partially suitable habitat for vector ticks
    ShrublandNone0.10None0.28Negative0.03
    Grassland (including pasture)Positive0.03None0.70Positive0.004
    UrbanNone0.24None0.13Negative0.01
Unsuitable habitat for vector ticks
    WaterNone0.75Positive0.02None0.44
    BarrenNone0.14None0.11None0.08
    Agriculture—cropsNegative< 0.001Negative0.01None0.90
    Seasonally flooded habitats and wetlandsNone0.22Negative0.02None0.16
Table 3

Parameter estimates and likelihood ratio tests for variables included in a multivariate logistic regression model for presence of areas with tularemia risk in Arkansas-Missouri

Parameter estimatesLikelihood ratio test
Model variableEstimateSEχ2dfP
Intercept−19.370215.6479
Precipitation in July0.04920.02364.3410.037
Annual NDVI−0.06540.03713.1010.078
Annual relative humidity0.34730.20182.9610.085
Annual maximum temperature0.01240.14160.0110.930
Table 4

County-scale validation results for multivariate logistic regression model for presence of counties with tularemia risk based on build set or validation set for Arkansas-Missouri

Model classification*Actual classification Tularemia reportedNot reportedPercent correct†
* Probability cut-off value of P ≥ 0.7141 was used to classify risk.
† User accuracy (commission error).
‡ Producer accuracy (omission error).
Build set
    Predicted risk811386.17
    No risk predicted242551.02
    Percent correct‡77.1465.79
Evaluation set
    Predicted risk24485.71
    No risk predicted10947.37
    Percent correct‡70.5969.23
Table 5

Sub-county scale validation results for multivariate logistic regression model for presence of areas with tularemia risk based on build set or validation set for Arkansas-Missouri

Actual classification
Model classification*Tularemia reportedNot reportedPercent correct †
* Probability cut-off value of P ≥ 0.30 was used to classify risk.
† User accuracy (commission error).
‡ Producer accuracy (omission error).
Build set
    Predicted risk892081.65
    No risk predicted161852.96
    Percent correct‡84.7647.37
Evaluation set
    Predicted risk30488.24
    No risk predicted4954
    Percent correct‡88.2469.23
Figure 1.
Figure 1.

Distribution of counties in a nine-state region of the southcentral United States (Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Oklahoma, Nebraska, and Tennessee) reporting tularemia cases or with tularemia incidences exceeding 1 case per 100,000 person-years from 1990 to 2003. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 78, 4; 10.4269/ajtmh.2008.78.586

Figure 2.
Figure 2.

Comparison of spatial patterns of county tularemia incidence in Arkansas during 1978–198221 and 1990–2003.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 78, 4; 10.4269/ajtmh.2008.78.586

Figure 3.
Figure 3.

Distribution of land cover types with different perceived suitability for tick vectors of F. tularensis in the southcentral United States (Arkansas, Illinois, Indiana, Kansas, Kentucky, Missouri, Oklahoma, Nebraska, and Tennessee). Grass/shrub includes prairie, pasture, and shrubland. Other habitats include water, barren, agricultural crops, seasonally flooded habitat, and wetland. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 78, 4; 10.4269/ajtmh.2008.78.586

Figure 4.
Figure 4.

Counties with predicted risk of human exposure to tularemia in Arkansas-Missouri (gray). Counties reporting cases during 1990–2003 are displayed with hatched lines.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 78, 4; 10.4269/ajtmh.2008.78.586

Figure 5.
Figure 5.

Areas with predicted risk of human exposure to the tularemia agent in Arkansas-Missouri (rose-colored). Counties reporting cases during 1990–2003 are shaded light blue. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 78, 4; 10.4269/ajtmh.2008.78.586

*

Address correspondence to Rebecca J. Eisen, PO Box 2087, Fort Collins, CO 80522. E-mail: dyn2@cdc.gov

Authors’ addresses: Rebecca J. Eisen, Paul S. Mead, Andrew M. Meyer, Liza E. Pfaff, Kristy K. Bradley, and Lars Eisen, Division of Vector-Borne Infectious Diseases, National Center for Zoonotic, Vector-Borne and Enteric Diseases, Centers for Disease Control and Prevention, 3150 Rampart Road, Fort Collins, CO 80522, Tel: 970-221-6408, Fax: 970-221-6476, E-mail: dyn2@cdc.gov.

Acknowledgments: The authors thank Karen Yates and Bao-Ping Zhu of the Missouri Department of Health and Senior Services for helpful comments on the manuscript.

Financial support: This study was funded by a grant from the Colorado State University College of Veterinary Medicine and Biomedical Sciences to L. Eisen.

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Author Notes

Reprint requests: Rebecca J. Eisen, Division of Vector-Borne Infectious Diseases, National Center for Zoonotic, Vector-Borne and Enteric Diseases, Centers for Disease Control and Prevention, 3150 Rampart Road, Fort Collins, CO 80521, E-mail: dyn2@cdc.gov.
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