Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

The Taiwan National Health Insurance (NHI) program was implemented in 1995. The coverage rate of the program has increased from 92.41% in 1995 to more than 96.16% in 2000. Coverage further increased to 98% after the inclusion of active military forces in 2001. At the beginning of 2004, NHI data related to medical care, such as the leading causes of death, were reclassified and reprocessed in relation to smaller units or areas (e.g., precincts or townships rather than the country as a whole). Regional data from the statistical analysis system (SAS) program are announced publicly by the NHI in regular annual reports (e.g., NHI, 2006) [1]. These reports provide an accurate and reliable data source to help investigators explore health care issues in Taiwan.

In the study of spatially-related objects or characteristics, one first describes the regional characteristics that differentiate areas one from another, and then proceeds with the analysis of spatial interrelations [2]. Common spatial techniques used in health research include disease mapping, clustering techniques, diffusion studies, identification of risk factors through map comparisons and regression analysis [3]. Spatial clustering techniques are important for statistical consideration, and form the beginning steps in the development of models for predicting disease risk sites. Disease risk sites are, specifically, areas located close to one another that tend to share similar disease risk factors, because they share similar environments and are also often connected by the spread of communicable disease via vectors or host dispersal [4].

Cuzick and Edwards (1990) proposed three general methodological approaches that can be utilized for the detection of clustering: the first is based on cell counts; the second on autocorrelative adjacencies of cells with high counts; and the third based on determining the distance between events [5]. Numerical methods have been extensively adopted for spatial cluster detection in health research and epidemiology, especially for the processing of areal data. The analytical approaches include the following: join-count statistics [6]; Ohno statistics [7]; Poisson statistics [8]; Global Moran's I [9–11]; Global Geary's C [9–11]; General Getis-Ord's G [12]; Local Moran's I [13]; and Local Gi(d) and Gi*(d) [12–14]. Spatial autocorrelation statistics such as the Moran's I and Geary's C methods are global, in the sense that they estimate the overall degree of spatial autocorrelation in a dataset. The possibility of spatial heterogeneity suggests that the estimated degree of autocorrelation may vary significantly across geo-space. Local spatial autocorrelation statistics provide estimates which are disaggregated to the level of the spatial analysis units, allowing assessment of dependency relationships in different areas. Local Gi(d) and Gi*(d) statistics can be used to make autocorrelation comparisons in different neighborhoods. A global average is used to help identify local regions of strong autocorrelation. Local version of the Moran's I and Geary's C statistics are also available.

In this study, we develop a method for ascertaining the spatial clustering associated with the 20 leading health care events, based on medical care data collected by the Taiwan NHI agency. Furthermore we also investigate potential spatial risks which contribute to these health care events and redefine epidemiologic and spatially referenced data.

Nearby locations are likely to possess similar attributes. In other words, everything is related to everything else, and nearby things are more closely related to nearby things than to distant things [25]. In epidemiology, a cluster becomes apparent when a number of health events occur which are situated close together in space and/or time. The evaluation of spatial distributions as a measure of disease risk may provide etiological insights [26]. Spatial autocorrelation is defined as the relation between the values of a single variable. This relation is attributable to the geographic arrangement of areal units on a map and can be used to identify the degree of spatial clustering [27, 28]. In this study, the local G-statistic is used to measure the degree of spatial clustering and map the geographic patterns of the areal units. Spatial clustering of the leading cause of death (also called a hot spot) is defined as when we obtain z-score values larger than 1.96. In epidemiology, hot spots are considered interesting because of their correlation to etiology. For this reason, we indicate the hot spots of 20 leading cause of death, as obtained from our analysis, and identify their spatial locations. Information about spatial location is useful for detecting risk factors from a spatial viewpoint. A more detailed survey of these identified hot spots may reveal important clues as to risk factors for these diseases.

To appropriately use public health data aggregated according to irregular administrative districts it is important to decide on the local measures of spatial autocorrelation for the specification of local neighborhood (as defined by the spatial weights matrix). In general, the spatial autocorrelation may be the strongest between the nearest neighbors. As the neighborhoods increase in number, this autocorrelation weakens [29]. A formal guidance for choosing a proper spatial weight matrix has not yet been developed [30, 31]. Therefore, the proper spatial weight matrix is chosen after a comparison of the connectivity distributions of neighbors obtained with the distance-based contiguity and the first order Queen's contiguity methods. However, an evaluation of the sensitivity of the results to the different spatial weight matrices still needs to be developed and assessed for further studies.

The modifiable areal unit problem (MAUP) is a phenomenon whereby different results are obtained from analysis of the same data, grouped into different sets of areal units. The MAUP can be subdivided into two separate effects that usually occur simultaneously during the analysis of aggregated data. The scale effect causes variation in statistical results given different levels of aggregation. In other words, association between variables depends on the size of the areal units for which data are reported. Generally, correlation increases as the size of the areal unit increases. The zone effect describes variation in correlation statistics caused by the regrouping of data into different configurations but with the same scale. These effects occur because spatial processes generating the observed data may exist at scales and for particular areal units that may be reflected more or less accurately by the boundaries in use [32]. Studies of the MAUP based on empirical data provide only limited insight of the inability to control relationships between multiple spatial variables. Data simulation is necessary to control over various properties of individual level data. Simulation studies, such as those by Swift et al. (2008), have demonstrated that the spatial support of variables can affect the magnitude of the ecological bias caused by spatial data aggregation [33]. Manley et al. (2006) concluded that MAUP is not really a problem but in constrast, a resource. Data at different scales can help us identify processes operating at different scales. It is clear that it is not possible to define an ideal single census geography that captures all the processes for all variables [32]. Furthermore, the internal composition of the areal units may not be homogeneous, particularly for disease distribution. Further to this, Matisziw et al. (2008) suggested that downscaling the spatial structure of polygonal units sould provide valuable information on the spatial distribution of disease [34].

This is the first study of the spatial distribution of the 20 leading health problems in Taiwan. There have been few previous ecological studies related to health care issues and their correlation to risk factors in Taiwan, although malignant neoplasms and tuberculosis have been documented and are discussed briefly below. We hope that this study of the spatial clustering of Taiwan's leading health issues can provide help for the study of spatial epidemiology.

Residents along the southwestern and northeastern coasts of Taiwan drank well water contaminated with a high concentration of arsenic before the establishment of the public water system [35]. Residents in these areas were found to have an increased risk of malignant neoplasms, including cancers of the liver, nasal cavity, lung, skin, bladder and kidney, for both males and females, as well as prostate cancer in males [36, 37]. Although well water was no longer used for drinking or cooking after the mid-1970s, there was still significantly increased risks of urinary cancers [38, 39] and lung cancer [39, 40] in the arseniasis-endemic areas of southwestern and northeastern Taiwan. Our results, showed clusters for malignant neoplasms in these arseniasis-endemic areas, but also did reveal a new carcinogen clustering (for females) in the northen coastal region of Taiwan. This is worthy of more investigation in the future.

According to data from the Center for Disease Control in Taiwan, there is a four-fold higher incidence of tuberculosis in aboriginal portions of the population than in people of Han ethnicity (Hans) [41]. Environmental factors such as hygiene, income, and social behavior (e.g., alcoholism) have been blamed for the prevalence of tuberculosis in aboriginal populations. Genetic variations in NRAMP 1 may also affect susceptibility to and increase the risk of tuberculosis in Taiwanese aboriginals [42]. Here we calculate tuberculosis clusters for males and females by utilizing the local G-statistic. The results show clear spatial clustering in Taiwanese aboriginal townships. Thus, our observations support the results obtained in previous studies. In addition, the hypertensive disease cluster, also possibly closely correlated to mountainous and aboriginal townships, is also worthy of attention. The strength of the relationship between aboriginal populations and hypertensive disease clusters needs further study to clarify. A more detailed survey of hypertensive disease may reveal valuable findings in terms of the risk factors between populations (four main populations are distributed in Taiwan) and hypertensive disease.

The z-scores for the local G-statistic are calculated using the logistic regression model. The results for various leading health problems and gender are compared. The test results show statistically significant differences for five health care problems in Taiwan in the year 2006, but another fifteen cases which are not, on the other hand. In other words, the null hypothesis is accepted. The accepted null hypothesis results indicate that the common spatial factor(s) may interact with both sexes.

Spatial autocorrelation calculation is useful for cluster mapping of regional health care problems. Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations of leading health care events. This helps planners to assess spatial risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.