Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.

The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined.

Taiwan is geographically located in a region that spans both tropical and subtropical climates (22–25°N and 120–122°E). The Taiwan Centers for Disease Control have found that the ecological habitat of Aedes aegypti appears only south of 23.5°N. Low temperatures may contribute to this particular habitat distribution of Ae. aegypti under the influence of the East Asian winter monsoon. However, the threshold condition related to critically low temperatures remains unclear because of the lack of large-scale spatial studies. This topic warrants further study, particularly through national entomological surveillance and satellite-derived land surface temperature (LST) data. We hypothesized that the distribution of Ae. aegypti is highly correlated with the threshold nighttime LST and that a critical low LST limits the survival of Ae. aegypti. A mosquito dataset collected from the Taiwan Centers for Disease Control was utilized in conjunction with image data obtained from the moderate resolution imaging spectroradiometer (MODIS) during 2009–2011. Spatial interpolation and phi coefficient methods were used to analyze the correlation between the distributions of immature forms of Ae. aegypti and threshold LST, which was predicted from MODIS calculations for 348 townships in Taiwan. According to the evaluation of the correlation between estimated nighttime temperatures and the occurrence of Ae. aegypti, winter had the highest peak phi coefficient, and the corresponding estimated threshold temperatures ranged from 13.7 to 14 °C in the ordinary kriging model, which was the optimal interpolation model in terms of the root mean square error. The mean threshold temperature was determined to be 13.8 °C, which is a critical temperature to limit the occurrence of Ae. aegypti. An LST of 13.8 °C was found to be the critical temperature for Ae. aegypti larvae, which results in the near disappearance of Ae. aegypti during winter in the subtropical regions of Taiwan under the influence of the prevailing East Asian winter monsoon.

Aedes mosquitoes in Taiwan mainly comprise Aedes albopictus and Ae. aegypti. However, the species contributing to autochthonous dengue spread and the extent at which it occurs remain unclear. Thus, in this study, we spatially analyzed real data to determine spatial features related to local dengue incidence and mosquito density, particularly that of Ae. albopictus and Ae. aegypti. We used bivariate Moran’s I statistic and geographically weighted regression (GWR) spatial methods to analyze the globally spatial dependence and locally regressed relationship between (1) imported dengue incidences and Breteau indices (BIs) of Ae. albopictus, (2) imported dengue incidences and BI of Ae. aegypti, (3) autochthonous dengue incidences and BI of Ae. albopictus, (4) autochthonous dengue incidences and BI of Ae. aegypti, (5) all dengue incidences and BI of Ae. albopictus, (6) all dengue incidences and BI of Ae. aegypti, (7) BI of Ae. albopictus and human population density, and (8) BI of Ae. aegypti and human population density in 348 townships in Taiwan. In the GWR models, regression coefficients of spatially regressed relationships between the incidence of autochthonous dengue and vector density of Ae. aegypti were significant and positive in most townships in Taiwan. However, Ae. albopictus had significant but negative regression coefficients in clusters of dengue epidemics. In the global bivariate Moran’s index, spatial dependence between the incidence of autochthonous dengue and vector density of Ae. aegypti was significant and exhibited positive correlation in Taiwan (bivariate Moran’s index = 0.51). However, Ae. albopictus exhibited positively significant but low correlation (bivariate Moran’s index = 0.06). Similar results were observed in the two spatial methods between all dengue incidences and Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The regression coefficients of spatially regressed relationships between imported dengue cases and Aedes mosquitoes (Ae. aegypti and Ae. albopictus) were significant in 348 townships in Taiwan. The results indicated that local Aedes mosquitoes do not contribute to the dengue incidence of imported cases. The density of Ae. aegypti positively correlated with the density of human population. By contrast, the density of Ae. albopictus negatively correlated with the density of human population in the areas of southern Taiwan. The results indicated that Ae. aegypti has more opportunities for human–mosquito contact in dengue endemic areas in southern Taiwan. Ae. aegypti, but not Ae. albopictus, and human population density in southern Taiwan are closely associated with an increased risk of autochthonous dengue incidence.

Purpose: This study focused on using Moran’s tests and logistic regression to detect changes in spatial clustering for females and males. Methods: For spatial distribution analysis, an average morbidity rate for a 7-year period was calculated. Medical cases from Taiwan National Health Insurance (NHI) were used as the numerator, and the denominator was the average mid-year population. Spatial analysis techniques, with a morbidity-smoothing coefficient estimate based on the empirical Bayesian method, were incorporated and applied to global and local Moran tests. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. Results: The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. There is great interest in discovering the relationship between leading health care problems and spatial risk factors. For example, in Taiwan, the geographic distribution of clusters where neoplasms were prevalent was found to closely correspond to the locations in the arseniasis-endemic areas of Southwestern and Northeastern Taiwan, as well as to locations in the Tainan urban area (for females) and clusters in Changhua County and Yunlin County (for males). The high-density populations in urban areas showed carcinogen clusters in Taiwan’s 3 main urban centers (i.e., Taipei, Taichung, and Kaohsiung) for female neoplasms. Conclusion: Cluster mapping helped clarify issues such as the spatial aspects of both the internal and external correlations for leading health care events. This information greatly assists in assessing spatial risk factors, which facilitates the planning of the most advantageous types of health care policies, as well as the implementation of effective health care services.

Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.