Vol.3, No.12, 712731 (2011) doi:10.4236/health.2011.312120 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ Health Spatial autocorrelation analysis of 13 leading malignant neoplasms in Taiwan: a comparison between the 19951998 and 20052008 periods PuiJen Tsai1*, ChengHwang Perng2 1Center for General Education, Aletheia University, New Taipei, Taiwan; *Corresponding Author: puijentsai@gmail.com 2Department of Statistics and Actuarial Science, Aletheia University, New Taipei, Taiwan. Received 23 September 2011; revised 10 November 2011; accepted 21 November 2011. ABSTRACT Spatial autocorrelation methodologies, includ ing Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A lo gistic regression fit model was also used to identify similar characteristics over time. Two time periods (19951998 and 20052008) were compared in an attempt to formulate common spatiotemporal risks. Spatial cluster patterns were identified using local spatial autocorrela tion analysis. We found a significant spatio temporal variation between the leading malig nant neoplasms and welldocumented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clus tered in locations in central Taiwan, with distinct differences between the two time periods. Sto mach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differ ence s between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of spacetime patterns with discon tinuous data. Spatiotemporal mapping com parison helps to clarify issues such as the spa tial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatiotemporal risk factors, and to as certain 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. Keywords: Spatial Autocorrelation Analy sis; Global Moran’s I Statistic; Local Indicators of Spatial Association Statistic; Logi stic R egression; Malignant Neoplasm; Taiwan 1. INTRODUCTION Spatial analytical techniques and models can identify spatial anomalies in the epidemiology of diseases, iden tify “hot spots” and locate spatiotemporal patterns. Cluster mapping clarifies issues of internal and external correlations, while logistic regression is a useful ap proach for the differentiation of spatial distribution pat terns over time. Common spatial techniques for health research include: disease mapping, clustering techniques, diffusion studies, identification of risk factors through comparisons, and regression analyses [1]. All of these methods are useful when assessing risk factors. They also facilitate the planning of health care policies and support the implementation of effective health care ser vices. Cuzick and Edwards (1990) [2] proposed three gen eral methodologies for the detection of clustering. Spa tial autocorrelation statistics, such as Moran’s I [36] an d Geary’s C [35] are global methods used to estimate the overall degree of spatial autocorrelation in a dataset. However, the possibility of spatial heterogeneity sug gests that the estimated degree of autocorrelation may vary significantly. Local spatial autocorrelation statistics provide estimates disaggregated to the unit level, allow ing the assessment of dependency relationships in dif ferent areas. LISA detect local spatial autocorrelation in aggregated data by dividing Moran’s I statistic into con tributions for each area within a study region. These in dicators can detect clusters of similar or dissimilar dis ease frequency values around a given observation [7]. Unlike Moran’s I statistic, which measures the correla tion between attribute values in adjacent areas, the Gi(d)
P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ 713713 local statistic is an indicator of local clustering that measures the “concentration” of a spatially distributed attribute variable [8,9]. The analysis of spatiotemporal change is a major concern in geographical research. Analytical approaches include: the Knox test [10], Mantel’s Z statistic [11], the Jacquez k nearest neighbor test [12], Kulldorff’s spatial scan statistic [1315] and Bayesian spatial scan statistic [16]. Herein, we are primarily interested in detecting clusters that emerge over time, and our goal is to detect emerging clusters as early as possible. For example, in the public health domain, our goal is to detect emerging clusters of disease indicative of naturally occurring dis ease outbreaks (such as influenza), bioterrorist attacks (such as anthrax release), or environmental hazards (such as a radiation leak). Clearly, the early detection of such clusters would contribute to a more rapid response, leading to lives being saved. Cancer is one chronic disease with a multistage pro gression. Many studies examine cancer incidence at dif ferent times, under different environmental exposures and in different ethnic groups. Cancer incidence changes over time for people of different ages, which may be due to variations in lifestyle, changing environmental expo sure, etc. Cancer incidence also varies in different geo graphic locations [1720]. Again, this may have various explanations with environmental impact being a strong possibility. The detection of spatiotemporal clustering generally requires continuous data. Discontinuous data, with dif ferent durations of disease surveillance at the same loca tion, present a challenge. This study focuses on the use of a set of discontinuo us data to detect ch anges in spatio temporal clustering. We propose herein a method for ascertaining spatial clustering associated with the 13 leading malignant neoplasms, based on medicalcare data collected by the Taiwan National Health Insurance and Taiwan Cancer Registry agencies. To test this ap proach, we have compared local clusters between two periods (19951998 and 20052008) looking for simi larities. We have also investigated potential spatial risks that could contribute to these health care events, rede fining epidemiologic and spatially referenced data. 2. MATERIALS AND METHODS 2.1. Study Area The study area included the main island of Taiwan (excluding all surrounding islets) which, in the year 2000, comprised more than 22 million inhabitants living in an area of 36 ,0 00 k m2. A total of 350 local administra tive government areas, including five main urban areas, two secondary urban areas, 162 rural townships, and 54 aboriginal townships on the plain and in mountainous regions, were assessed (Figure 1). According to a 2002 Ministry of Interior report, urban areas are classified as regions having at least one metropo litan centre, and they can include neighboring cities and townships that share socioeconomic activities. Main urban areas are defined as those with a population larger than one million , speci fically, TaipeiK eelun g, Kaohsiung , Taich ung Changh u a, JhongliTaoyuan and Tainan. Secondary urban areas are defined as those with a residential population ranging from 0.3 to 1 million (e.g. Hsinchu and Chiayi). 2.2. Data Collection and Management The Taiwan Nation al Health In suran ce (NHI) prog ram was initiated in 1995. The coverage rate of the program increased from 92.4% in 1995 to more than 96.2% in 2000, increasing to 98% after the inclusion of those ac tive in the military forces in 2001. Once the NHI medi cal care data were properly collected and analyzed, a complete picture of population behaviors according to disease could be used for reference in the calculation of prevalence and incidence of various diseases. At the beginning of 2004, NHI data that was available relative to medical care, such as the leading causes of death, were reclassified and reprocessed in relation to smaller units or areas (for example, precincts or town ships rather than the country as a whole). In addition, regional data from the statistical analysis system (SAS) program are now announced publicly by the NHI in Figure 1. Map of urban areas and aboriginal townships in the study area. Map of the study area divided into 350 administra tive districts including seven urban areas and an integrated area of 54 plains and mountain aboriginal townships. regular annual reports (for example, NHI, 20052008 [2124]). These reports provide an accurate and reliable
P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ 714 data source for the investigation of health care issues in Taiwan. Data were collected from contractual medicalcare in stitutions, where th e NHI covers the costs of prescription medicines and treatment at outpatient clinics. Such fa cilities accumulate detailed databases on medical costs for inpatient care. The number of outpatient cases were classified in relation to disease codes, as defined in the 1975 edition of “The International Classification of Dis eases, 9th Revision, Clinical Modification” (ICD 9 CM). Patients suffering from diseases that were difficult to classify into a given code or had mismatched ID num bers were not included in the final statistical data set. Disease codes were classified according to gender and age. Cases with the same ID numbers, but which exhib ited different diseases, were counted as different in stances. Medical care data obtained from the 20052008 NHI reports were examined, and the morbidity rates of the 13 leading causes of death were calculated. Disease classi fications (according to the ICD 9 CM) included the fol lowing (indicated within parentheses): trachea, bronchus, and lung cancer (ICD 162); liver and intrahepatic bile ducts cancer (ICD 155); colon and rectum cancer (ICD 153, 154); stomach cancer (ICD 151); oral cavity cancer (ICD 140, 141, 143146, 148, 149); oesophagus cancer (ICD 150); pancreas cancer (ICD 157); nonHodgkin’s lymphoma (ICD 200, 202, 203); gallbladder and extra hepatic bile ducts cancer (ICD 156); leukaemia (ICD 204208); female breast cancer (ICD 174); cervix uteri cancer (ICD 179, 180); and prostate cancer (ICD 185). Demographic information was provided by the Minis try of Interior [25]. The smallest administrative units coded for examination of the various diseases cases or health care events were precincts and townships. Age adjusted standard morbidity rates, adjusted using the Segi (“world”) population in 1976 as the standard [26], were then calculated prov iding results giving the leading causes of death for males and females in each township. During the period from 1995 to 1998, data on age adjusted malignancies by precinct and township were obtained from the Atlas of Cancer Mortality and Inci dence in Taiwan, officially published by the Bureau of Health Promotion, Department of Health [27]. 2.3. Statistics The global Moran’s I spatial autocorrelation was used to assess the correlation among neighbouring observa tions and to identify patterns and levels of spatial clus tering in neighbouring districts [28]. The Moran’s I sta tistic, similar to the Pearson correlation coefficient [29], was calculated by the following formula: 2 ij ij ij Oi i xx x N Iw Sxx (1) where N is the number of districts, wij the element in the spatial weight matrix corresponding to the observation pair i, j and xi and xj observations for the areas i and j with the mean and: O ij Sij w (2) Since the weights were rowstandardized (1 ij w ), the first step in the spatial autocorrelation analysis was to construct a spatial weight matrix that contained in formation about the neighbourhood structure for each location. Adjacency was defined as immediately neigh boring administrative districts, including the district it self. Nonneighbouring administrative districts were assigned the we i ght of zero . Spatial contiguity for polygons is defined as the prop erty of sharing a common boundary or vertex. Contigu ity analysis is an importan t method fo r assessing unu sual features in connectivity distribution [4,30]. The Queen’s measure of contiguity can be utilized to make up for spatial contiguity by incorporating both the Rook and Bishop relationships into a single measure [30]. The administrative districts considered in this study were highly irregular in both shape and size. Tsai et al. (20 09) demonstrated that the most appropriate method is the first order queen polygon contiguity method for quanti fying the spatial weights matrix for the analysis of con nectivity. Based on this approach, the spatial weight/ connectivity matrices were determined and utilized in conjunction with the global Moran’s I statistic and fol lowing LISA calculations [6]. Moran’s I va lues may range from –1 (dispersed) to +1 (clustered). A Moran’s I value of 0 suggests complete spatial randomness. A random permutation procedure recalculates a statistic many times by reshuffling the data values among the map units to generate a reference dis tribution. The obtained calculated statistic based on the observed spatial pattern is then compared to this refer ence distribution an d a pseu do significance level (pseudo pvalue) computed. To verify that the value of Moran’s I was significantly different from the expected value, we applied a Monte Carlo randomisation test with 999 per mutations to achieve highly significant values. Data values were reassigned among the N locations, providing a randomised distribution against which one may judge the observed value. If the observed value of I was within the tails of this distribution, there was significant spatial autocorrelation in the data, a pseudo pvalue smaller than 0.05, and the assumption of independence among the observations could be rejected [31]. LISA statistic provides information related to the lo
P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ 715715 cation of spatial clusters and outliers and the types of spatial correlation. Local statistics are important becau se the magnitude of spatial autocorrelation is not necessar ily uniform over the study area [7,32]. LISA allowed us to divide the study area into small locations, thus ena bling the assessment of significant local spatial cluster ing around an individual location. In addition to the de gree of spatial clustering, detailed variations of cluster ing in the locally defined geospace were identified as well as the locations of the spatial clusters. The local version of Moran’s I at location i is given by: 21i i j i i xx ijj wx x xx n (3) where n indicates the total number of locations (350 townships used in the years 19951998 and 349 town ships in 20052009); xi denotes the value of the variable of interest, X, at location I; xj denotes the observation at neighboring location s j; and is th e sample average of X. wij is the spatial weight matrix, which defines spatial interaction across study regions. In general, wij = 1 if location i and location j are neighboring, (share a com mon boundary); otherwise, wij = 0. In this study, spatial contiguity was assessed as the first order queen’s conti guity which defines spatial n eighbors as those areas with shared borders and vertexes. Significance was tested by comparison to a reference distribution obtained by random permutations [7]. This analysis used 999 permutations to determine differences between spatial un its. A positive value for the local Mo ran’s I index (i ) indicates that a feature has neighboring features with similarly high or low attribute values and is therefore part of a cluster. A negative value for (i ) in dicates that a feature has neighboring features with dis similar values; this feature is an outlier. In either instance, the pvalue for the feature must be small enough for the cluster or outlier to be considered statistically significant. LISA enables distinguishment between a statistically significant (0.05 level) cluster of high values (HH), a cluster of low values (LL), an outlier in which a high value is surrounded primarily by low values (HL), and an outlier in which a low value is surrounded primarily by high va lues (LH). In add itio n to the va lue of a zscore larger than +1.96, the outcomes are defined as clusters with both HH and LL. In th e case of a value of a zscore less than –1.96, the outlier is considered as clusters with (HL) and (LH). We consider that outliers may not be stablily and precisely displayed the outcomes of spatio temporal pattern comparison, because it is difficult to distinguish between outliers how strength with or with out disease risks. Therefore, only hot and cold spots are mapped on local Moran’s maps. In addition to mapping, similarities between spatial distribution patterns for the two periods (19951998 and 20052008) were determined using logistic regression analysis. The binary response indicates whether there is significant autocorrelation between administrative dis tricts or areas. The correlation is better (higher) if the value of the zscore of the local Moran’s I statistic is larger than +1.96 (clusters with hot spots and cold spots), otherwise it is deemed to be low. The model is exp ressed as: 01 PrHigher correlation log Period PrLower correlation (4) where the Period is considered an explanatory variable in the logistic regression model and the two β valu es the logistic regression coefficients of the model. Pr (Higher correlation) and Pr(Lower correlation) denote the “Higher” and “Lower” correlation probabilities, respec tively. In this study, two distinct precincts, the central and west precincts in Tainan city, merged into one single unified administrative unit in 2004. These unpaired data were omitted and the total data from 348 townships were tested using logistic regression. Modeling of the logistic regression was performed using SPSS 12. Global Moran’s I statistic and local Mo ran’s I statistic was calculated using Geoda (http://www. geoda.uiuc.edu/), an open source spatial analysis system, and visualized on LISA cluster maps using ArcMap 9.3. 3. RESULTS Figure 2 displays the spatial clusters (hot spots and clod spots) as obtained using LISA statistic for the top 13 leading malignant neoplasms for both males and fe males in Taiwan during two time periods (19951998 and 20052008). Tab le 1 summarizes the results from global autocor relation statistics for the top 13 leading malignant neo plasms according to gender and in the two time periods (19951998 and 20052008) in Taiwan. The results of the global Moran’s I tests for most cases related to the leading malignant neoplasms are statistically significant, having a pseudo pvalue smaller than 0.05, and indicated spatial heterogeneity. However, opposing results (a pseu do pvalue larger than 0.05) emerged in nine cases of which are pancreas cancer for males (19951998), non Hodgkin’s lymphoma for males (19951998) and fe males (19951998 and 20052008), gallbladder and ex trahepatic bile ducts cancer for males (19951998) and females (19951998 and 20052008), and leukemia for males (20052008) and females (19951998), respec tively. Table 2 summarizes the typology patterns, as calcu
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P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. http://www.scirp.org/journal/HEALTH/ 727727 Figure 2. Spatial clusters of the 13 leading malignant neoplasms in Taiwan. Maps showing the spatial clusters of the 13 leading ma lignant neoplasms in Taiwan: A indicates trachea, bronchus, and lung cancer; B, liver and intrahepatic bile ducts cancer; C, colon and rectum cancer; D, stomach cancer; E, oral cavity cancer; F, oesophagus cancer; G, pancreas cancer; H, nonHodgkin’s ly mphoma; I, gallbladder and extrahepatic bile ducts cancer; J, leukaemia; K, female breast cancer; L, cervix uteri cancer; M, prostate cancer. 1 indicates males within the period from 1995 to 1998 years; 2, males within the period from 2005 to 2008 years; 3, females within the period from 1995 to 1998; 4, females within the period from 2005 to 2008. lated using LISA statistic, categorized as clusters or non clusters at a zscore larger than +1.96. It also compares the top 13 leading malignant neoplasms during the two time periods (19951998 and 20052008). Dissimilarities between the spatial distribution pat terns during the two periods (199519 98 and 20052 008) are not statistically significant (pvalue > 0.05) in males for six out of eleven spatial clusters, and in females for ten of twelve spatial clusters. In males, there are dis similarities for stomach cancer, oral cavity cancer, pan creas cancer, nonHodgkin’s lymphoma, and prostate cancer. In females, colon and rectum cancer, and pan creas cancer are dissimilar. Ta b l e 2 presents these find ings. 4. DISCUSSION Locations in close proximity tend to share similar attributes. According to Tobler (1979), “everything is related to everything else, and nearby things are more closely related to nearby things than to distant things” [33]. 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 [34]. Spatial autocorrelation is the relation between the values of a single variable attribut able to the geographic arrangement of areal units on a map and can be used to determine the degree of spatial clustering [35,36]. In this study, local Moran’s I statistic was 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 hot spots and cold spots) was identified by a zscore value arger than +1.96. In epidemiology, “hot spots” are l Openly accessible at
P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ 728 Table 1. Global autocorrelation analysis of data for the 13 leading malignant neoplasms in Taiwan, according to gender, during 1995 1998 and 20052008. Moran’s I Leading malignant neoplasms (ICD code) Male Female 19951998 20052008 19951998 20052008 Trachea, bronchus, and lung cancer (ICD 162) 0.38* 0.46* 0.17* 0.17* Liver and intrahepatic bile ducts cancer (ICD 155) 0.45* 0.59* 0.34* 0.42* Colon and rectum cancer (ICD 153, 154) 0.40* 0.52* 0.40* 0.49* Stomach cancer (ICD 151) 0.34* 0.37* 0.22* 0.35* Oral cavity cancer (ICD 140, 141, 143 146, 148, 149) 0.43* 0.68* 0.09* 0.68* Oesophagus cancer (ICD 150) 0.24* 0.22* 0.07* 0.25* Pancreas cancer (ICD 157) 0.05 0.18* 0.07* 0.22* NonHodgkin’s lymphoma (ICD 200, 202, 203) 0.02 0.07* 0.05 0.05 Gallbladder and extrahepa t i c b i l e ducts cancer (ICD 156) 0.06 0.14* 0.05 0.04 Leukaemia ( ICD 204208) 0.08* 0.04 0.01 0.08* Female breast cancer (ICD 174) n.d. n.d. 0.52* 0.53* Cervix uteri cancer (ICD 179, 180) n.d. n.d. 0.24 * 0.26* Prostate cancer (ICD 185) 0.12* 0.60* n.d. n.d. n.d.: no detection. *: A pseudo pvalue smaller than 0.05. Table 2. Logistic regression model comparisons of the 13 leading malignant neoplasms in Taiwan, during 19951998 and 20052008. Male Female Leading malignant neoplasm s (I CD code) pvalue description pvalue description Trachea, bro n c h u s , and lung cancer (ICD 162) 0.245 similaritya 0.21 similaritya Liver and intrahepatic bile duc t s c anc er (ICD 155) 0.505 similaritya 0.412 similaritya Colon and rectum cancer (ICD 153, 154) 0.492 similaritya 0.019 dissimilaritya Stomach cancer (ICD 151) 0.034 dissimilaritya 0.053 similaritya Oral cavity cancer (ICD 1 4 0, 141, 143146, 148, 149) 0.007 dissimilaritya 0.229 similaritya Oesophagus cancer (ICD 150) 0.844 similaritya 0.266 similaritya Pancreas cancer (ICD 157) 0.029 dissimilarity 0.047 dissimilaritya NonHodgkin’s lymphoma (ICD 200, 202, 203) 0.006 dissimilarity 0.179 similarity Gallbladder and extrahepatic b i le d u ct s c a nc e r (ICD 156) 0.409 similarity 0.197 similarity Leukaemia ( ICD 204208) 0.137 similarity 0 .781 similarity Female breast cancer (ICD 174) n.d. 0.182 similaritya Cervix uteri cancer (I C D 179 , 180) n.d. 0.84 similaritya Prostate cancer (ICD 185) 0.007 dissimilaritya n.d. n.d.: no detection. a: A comparison of the two periods during which all of Moran’s test results are clusters (results based on Table 1). considered interesting because of their correlation to aetiology. This study, therefore, focuses on the spatial locations of 13 leading malignant neoplasms. Information about spatial location is useful for detecting risk from a spatial point of view. A more detailed survey of these identified “hot spots” may provide important clues on risk factors for these diseases. The modifiable areal unit problem (MAUP) is a phe nomenon whereby analysis of the same data provides different results, 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 according to different levels of aggre gation. An association between variables, therefore, de pends on the sizes of the areal units of the rep orted data. Generally, correlation increases as the size of the areal unit increases. The zone effect describes variations in correlation statistics caused by the regrouping of data into different configurations, but with the same scale. The MAUP occurs because spatial processes generating the observed data may exist within certain scales, and for particular areal units. These may be reflected more or less accurately by the boundaries in use [37]. Manley et
P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ 729729 al. (2006) concluded that MAUP is not really a problem, but rather, a resource. Data at different scale levels can enable the identification of processes operating within different scales. It is clear that it is not possible to define an ideal single census geography that captures all of the processes for all variables [37]. Furthermore, the internal composition of given areal units may not be homoge neous, particularly for disease distribution. Matisziw et al. (2008) have suggested that downscaling the spatial structure of polygonal units could provide valuable in formation pertaining to the spatial distribution of disease [38]. In this study, administrative government regions are almost similar but not completely consistent in the two periods (19951998 and 20052008). This was to some degree due to the merging of the central and west districts in Tainan city merging into one unit in 2004. The use of only one scale to estimate spatial distribu tion patterns, although still a cluster comparison, would be more convenient; however, bias could be caused by using a nonrealistic spatial boundary. An ideal process would be to calculate the spatial autocorrelation coeffi cients (such as the zscores) based on realistic boundaries (two scales for shape files that represented 350 townships in 19951998 and 349 townships in 20052008, respec tively) and then omit the values of autocorrelation co efficients that were nonpaired data from the comparison of the two periods within the administrative regions. The local spatial autocorrelation coefficients can be tested for statistical significance under two rather dif ferent model assumptions. The first is the classical statis tical assumption of normality, whereb y it is assumed that the observed value of the coefficient is the result of the set of zscore values being independent and identically distributed drawings from a normal distribution, implying that variances are cons tant across the reg ion. The second model is one of randomization, whereby the observed pattern of the set of zscore values is assumed to be just one realization from all possible random permutations of the observed values across all the zones. Both models have important weaknesses. For example, there is an underlying population size variatio n and a lack of homo geneity of probabilities; however these models are widely implemented in software packages to provide estimates of the significance of observed results. In the case of the randomization model, many software pack ages generate a set of N random permutations of the input data, where N is us er specified. For each simulation run, index values are computed and the set of such values are used to provide a pseudoprobability distribu tion for the given problem, against which the observed value can be compared. A ztransform of the coefficients under normality or randomization assumptions is distri buted approximately as N(0, 1); hence, this may be com pared to percentage points of the normal distribution to identify particularly high or low values [39]. In this study, the comparison of databases from the two periods (19951998 and 20052008) was addressed by the Tai wan Cancer Registry and the Taiwan National Health Insurance agencies, respectively. Although the two data bases have a referenced value with high validity and reliability, this case was defined with the same diagnostic criteria (ICD 9 CM) and a world standard population in 1976 to calculate the morbidity rate. However, the esti mated morbidity rates derived from the two databases cannot be directly compared with one another. Our suggested resolution is to change the morbidity rate into a ztransform by using a spatial autocorrelation calcula tion with a randomization of 999 permutations, and this then makes two ztransform comparisons feasible. Bino minal variable logistic regression models were used to distinguish spatial distribution patterns that addressed the two periods (1 9951998 and 20052008). Zscores for the LISA method were calculated using the logistic regression model and results for various leading malignant neoplasms during two periods (1995 1998 and 20052008) were compared. However, the constraint condition for spatial clustering comparison (such as global Moran’s tested clusters on both sides) are required to be satisfied before calculating the logistic regression for purposes of comparison. Based on this constraint, the results demonstrate statistically significant differences for stomach cancer (in males), oral cavity cancer (in males), prostate cancer (in males), colon and rectum cancer (in females), and pancreas cancer (in females). Another eleven compared cases were not signi ficantly different. The null hypothesis is, therefore, accepted. The accepted null hypothesis results indicate that the common spatial factor(s) may interact with both periods. Few previous ecological studies relate to malignant neoplasms and their correlation to risk factors in Taiwan, although oral cancer and stomach cancer have been documented and are discussed briefly below. It is hoped that this assessment of the spatial clustering of Taiwan’s leading malignant neoplasms can contribute to the study of spatial epidemiology. Two separate groups identified clusters of areas showing elevated mortality from oral cavity cancer in females in the aboriginal townships in eastern Taiwan. The habits of cigarette smoking, alcohol drinking and betel nut chewing had higher prevalence in aboriginal women in eastern Taiwan than in women in other regions [40,41]. Chiang et al. suggested that highrisk areas of oral cancer incidence in males closely coincided with spatial distribution of heavymetal pollution in soils (such as chromium and nickel) in central Taiwan [42]. In
P.J. Tsai et al. / Health 3 (2011) 712731 Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/HEALTH/ 730 this study, oral cavity cancer clusters for each gender were calculated using the LISA statistic. Results identify clear spatial clustering in central Taiwan, for males, and eastern Taiwan for females, among Taiwanese aboriginal townships. These observations, therefore, support the results described in previous studies. However, according to our results, the two periods (19951988 and 2005 2008), show dissimilarity in the spatial distribution of oral cavity cancer in males. Spatial risks affecting oral cancer morbidity in males reveal spacetime changes. These findings could be interpreted as the changing disease clusters over time, are due to the changes of exposure cond itions to metal pollu tant and leading to the results of a variation of virulence. Further investigation is therefore warranted. Several metaanalyses identified a strong and consis tent association between H. pylori infection and non cardiac gastric cancer [4346]. The ecological study in Taiwan suggests an association between this infection and gastric cancer. H. pylori in fection in early childhood may be a key issue and, it appears, a long indu ction time is required for gastric carcinogenesis. High gastric cancer mortality areas are clustered in the aboriginal townships where the prevalence of H. pylori is high [40, 47]. Our results are similar to these previous studies. Stomach cancer clusters for males and females are located in the Taiwanese aboriginal townships, and a new carcinogen cluster was identified in the northern coastal region of Taiwan. This is worthy of further investigation. However, the two periods (19951988 and 20052008) show dissimilarity in the spatial distribution of gastric cancer in males. Spatial risks affecting gastric cancer morbidity in males reveal spacetime changes. By changing disease clusters over time, a possible reason is due to the changes of prevalence ranges of H. pylori or increased in the interference of other risks in the study area. Further investigation is therefore warranted. 5. CONCLUSIONS A method which combines LISA statistics and log istic regression is an effective tool for the detection of space time patterns with discontinuous data. Similarity is a result of unchangeable condition in disease risks. Con versely, dissimilarity is deemed a significant change of morbidity risks over the studied periods. This enables planners to assess spatial risk factors and to determine the most advantageous types of health care policies for the planning and imple mentation of health care services. These issues can greatly improve the performance and effectiveness of health care services and also provide a clear outline for better understanding of the results in depth. 6. 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