• Journal of Preventive Medicine and Public Health
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• v.55 no.5
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• pp.485-491
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• 2022

Objectives: Tuberculosis (TB) is a major public health concern worldwide. Social contact patterns can affect the epidemiology and risk of airborne diseases such as TB. This study was designed to investigate the social contact patterns associated with TB. Methods: In this case-control study, groups of participants with and without TB were matched by age and sex. Participants reported the nature, location, frequency, and average duration of social contacts over 1 month. The duration and number of social and spatial contacts were compared between groups using the chi-square test and the t-test. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to quantify the relationship between social contact time and TB status. Data were analyzed using Stata version 11 statistical software. A p-value of <0.05 was considered to indicate statistical significance. Results: In this study, 80 patients with TB and 172 control participants were included, and a total of 3545 social contacts were registered. Social contact with family members (OR, 1.72; 95% CI, 1.10 to 2.40), contact with a person with TB (OR, 1.53; 95% CI, 1.16 to 2.01), and contact at the participant's home (OR, 1.42; 95% CI, 1.19 to 1.82) were significantly associated with TB status. Conclusions: The duration of long-term social contact, rather than the number of contacts, may be the main contact-related factor associated with TB transmission in this population. The focus of contact-tracing efforts should be on finding and treating both family members and long-term contacts in non-household settings.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.455-460
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• 2014

Background: The purpose of the present study was to determine geographic clustering of breast cancer incidence in Kanagawa Prefecture, using cancer registry data. The study also aimed at examining the association between socio-economic factors and any identified cluster. Materials and Methods: Incidence data were collected for women who were first diagnosed with breast cancer during the period from January to December 2006 in Kanagawa. The data consisted of 2,326 incidence cases extracted from the total of 34,323 Kanagawa Cancer Registration data issued in 2011. To adjust for differences in age distribution, the standardized mortality ratio (SMR) and the standardized incidence ratio (SIR) of breast cancer were calculated for each of 56 municipalities (e.g., city, special ward, town, and village) in Kanagawa by an indirect method using Kanagawa female population data. Spatial scan statistics were used to detect any area of elevated risk as a cluster for breast cancer deaths and/or incidences. The Student t-test was performed to examine differences in socio-economic variables, viz, persons per household, total fertility rate, age at first marriage for women, and marriage rate, between cluster and other regions. Results: There was a statistically significant cluster of breast cancer incidence (p=0.001) composed of 11 municipalities in southeastern area of Kanagawa Prefecture, whose SIR was 35 percent higher than that of the remainder of Kanagawa Prefecture. In this cluster, average value of age at first-marriage for women was significantly higher than in the rest of Kanagawa (p=0.017). No statistically significant clusters of breast cancer deaths were detected (p=0.53). Conclusions: There was a statistically significant cluster of high breast cancer incidence in southeastern area of Kanagawa Prefecture. It was suggested that the cluster region was related to the tendency to marry later. This study methodology will be helpful in the analysis of geographical disparities in cancer deaths and incidence.

• Journal of the Korean Geographical Society
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• v.48 no.5
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• pp.768-785
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• 2013

Count data aggregated into areal units such as administrative boundaries are the most important sources of information for geographic research. Despite of ongoing research on spatial cluster analysis of count data, it has received relatively little attention and besides, it is difficult to comprehend research trends as well as major outcomes and challenges. This study aims to review the research literature conducted during the last two decades, to examine methodological characteristics, and finally to discuss some issues and challenges. Methods for indentifying spatial clusters have been used in various fields including geography, criminology, and epidemiology. However, their methodological features are not only quite distinct from each other, but there are issues related to the statistical reliability. Therefore, these have to be taken into account carefully when particular methods are used, and further empirical research about methodological issues and the development of analysis tools is needed.

• Environmental Analysis Health and Toxicology
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• v.28
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• pp.5.1-5.7
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• 2013

Objectives Despite increasing availability of global positioning system (GPS), no research has been conducted to analyze GPS data for exposure opportunities associated with time at indoor and outdoor microenvironments. We developed location-based and time-weighted spatial measures that incorporate indoor and outdoor time-location data collected by GPS. Methods Time-location data were drawn from 38 female subjects in California who wore a GPS device for seven days. Ambient standard deviational ellipse was determined based on outdoor locations and time duration, while indoor time weighted standard deviational ellipse (SDE) was developed to incorporate indoor and outdoor times and locations data into the ellipse measure. Results Our findings indicated that there was considerable difference in the sizes of exposure potential measures when indoor time was taken into consideration, and that they were associated with day type (weekday/weekend) and employment status. Conclusions This study provides evidence that time-location weighted measure may provide better accuracy in assessing exposure opportunities at different microenvironments. The use of GPS likely improves the geographical details and accuracy of time-location data, and further development of such location-time weighted spatial measure is encouraged.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.325-334
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• 2015

Elderly suicide rates tend to be high in Korea. Suicide by the elderly is no longer a personal problem; consequently, further research on risk and regional factors is necessary. Disease mapping in epidemiology estimates spatial patterns for disease risk over a geographical region. In this study, we use a simultaneous conditional autoregressive model for spatial correlations between neighboring areas to estimate standard mortality ratios and mapping. The method is illustrated with cause of death data from 2006 and 2010 to analyze regional patterns of elderly suicide in Korea. By considering spatial correlations, the Bayesian spatial models, mean educational attainment and percentage of the elderly who live alone was the significant regional characteristic for elderly suicide. Gibbs sampling and grid method are used for computation.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.17-29
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• 2007

The major purpose of this article is the comparison of estimation method with Newton-Raphson, Kalman-filter, and prediction method with Kalman prediction. Conditional expectation in space time bilinear(STBL) model, which is a very powerful and parsimonious nonlinear time-series model for the space time series data can be viewed as a set of time series collected simultaneously at a number of spatial locations and time points, and which have appeared in a important applications areas: geography, geology, natural resources, ecology, epidemiology, etc.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.10
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• pp.4587-4590
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• 2016

Background: In many countries gastric cancer has the highest incidence among the gastrointestinal cancers and is the second most common cancer in Iran. The aim of this study was to identify and map high risk gastric cancer regions at the county-level in Iran. Methods: In this study we analyzed gastric cancer data for Iran in the years 2003-2010. Area-to-area Poisson kriging and Besag, York and Mollie (BYM) spatial models were applied to smoothing the standardized incidence ratios of gastric cancer for the 373 counties surveyed in this study. The two methods were compared in term of accuracy and precision in identifying high risk regions. Result: The highest smoothed standardized incidence rate (SIR) according to area-to-area Poisson kriging was in Meshkinshahr county in Ardabil province in north-western Iran (2.4,SD=0.05), while the highest smoothed standardized incidence rate (SIR) according to the BYM model was in Ardabil, the capital of that province (2.9,SD=0.09). Conclusion: Both methods of mapping, ATA Poisson kriging and BYM, showed the gastric cancer incidence rate to be highest in north and north-west Iran. However, area-to-area Poisson kriging was more precise than the BYM model and required less smoothing. According to the results obtained, preventive measures and treatment programs should be focused on particular counties of Iran.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.11-20
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• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.413-421
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• 2010

In this study, we considered bivariate conditional auto-regressive model taking into account spatial association as well as correlation between the two dependent variables, which are the counts of murder and burglary. We conducted likelihood ratio test for checking over-dispersion issues prior to applying spatial poisson models. For the real application, we used the annual counts of violent crimes at 25 districts of Seoul in 2007. The statistical results are visually illustrated by geographical information system.