Ming-Daw Su

Geographic Disparity in Chronic Obstructive Pulmonary Disease (COPD) Mortality Rates among the Taiwan Population

Chronic obstructive pulmonary disease (COPD) causes a high disease burden among the elderly worldwide. In Taiwan, the long-term temporal trend of COPD mortality is declining, but the geographical disparity of the disease is not yet known. Nationwide COPD age-adjusted mortality at the township level during 1999–2007 is used for elucidating the geographical distribution of the disease. With an ordinary least squares (OLS) model and geographically weighted regression (GWR), the ecologic risk factors such as smoking rate, area deprivation index, tuberculosis exposure, percentage of aborigines, density of health care facilities, air pollution and altitude are all considered in both models to evaluate their effects on mortality. Global and local Moran’s I are used for examining their spatial autocorrelation and identifying clusters. During the study period, the COPD age-adjusted mortality rates in males declined from 26.83 to 19.67 per 100,000 population, and those in females declined from 8.98 to 5.70 per 100,000 population. Overall, males’ COPD mortality rate was around three times higher than females’. In the results of GWR, the median coefficients of smoking rate, the percentage of aborigines, PM10 and the altitude are positively correlated with COPD mortality in males and females. The median value of density of health care facilities is negatively correlated with COPD mortality. The overall adjusted R-squares are about 20% higher in the GWR model than in the OLS model. The local Moran’s I of the GWR’s residuals reflected the consistent high-high cluster in southern Taiwan. The findings indicate that geographical disparities in COPD mortality exist. Future epidemiological investigation is required to understand the specific risk factors within the clustering areas.

A Multivariate Model for Coastal Water Quality Mapping Using Satellite Remote Sensing Images.

This study demonstrates the feasibility of coastal water quality mapping using satellite remote sensing images. Water quality sampling campaigns were conducted over a coastal area in northern Taiwan for measurements of three water quality variables including Secchi disk depth, turbidity, and total suspended solids. SPOT satellite images nearly concurrent with the water quality sampling campaigns were also acquired. A spectral reflectance estimation scheme proposed in this study was applied to SPOT multispectral images for estimation of the sea surface reflectance. Two models, univariate and multivariate, for water quality estimation using the sea surface reflectance derived from SPOT images were established. The multivariate model takes into consideration the wavelength-dependent combined effect of individual seawater constituents on the sea surface reflectance and is superior over the univariate model. Finally, quantitative coastal water quality mapping was accomplished by substituting the pixel-specific spectral reflectance into the multivariate water quality estimation model.

Framework for Application of Geographic Information System to the Monitoring of Dengue Vectors

In a successful management program of dengue vectors, not only health education, source reduction or insecticide application should be conducted, but all basic information should also be manipulated properly and efficiently. This information includes the surveys of species, dispersal and dynamics of vectors, as well as the detection of breeding sources, and the records of dengue cases and epidemic periods. Most of the above information expressed as regionalized variables always varies spatially and/or temporally. However, due to the deficiency of topological information, the conventional database management system cannot efficiently analyze those dengue related data. Thus, we have applied the geographic information system (GIS) to the monitoring of dengue vectors. The purpose of this report is to introduce the basic concepts of GIS, to describe the framework of the prototype dengue vector monitoring system which was built using data collected from the Sanmin area, Kaoshiung city, Taiwan, and to indicate the possibility of using this system to manipulate spatially correlated data and support decision making in the control of dengue disease.

Addressing health disparities in chronic kidney disease.

According to the official health statistics, Taiwan has the highest prevalence of end stage renal disease (ESRD) in the world. Each year, around 60,000 ESRD patients in Taiwan consume 6% of the national insurance budget for dialysis treatment. The prevalence of chronic kidney disease (CKD) has been climbing during 2008–2012. However, the spatial disparities and clustering of CKD at the public health level have rarely been discussed. The aims of this study are to explore the possible population level risk factors and identify any clusters of CKD, using the national health insurance database. The results show that the ESRD prevalence in females is higher than that in males. ESRD medical expenditure constitutes 87% of total CKD medical expenditure. Pre-CKD and pre-ESRD disease management might slow the progression from CKD to ESRD. After applying ordinary least-squares regression, the percentages of high education status and the elderly in the townships are positively correlated with CKD prevalence. Geographically weighted regression and Local Moran’s I are used for identifying the clusters in southern Taiwan. The findings can be important evidence for earlier and targeted community interventions and reducing the health disparities of CKD.

A GIS-based framework of regional irrigation water demand assessment

Although a lot of research has been performed on estimating irrigation water demand at the on-farm level, far less has been done on irrigation water demand for a region-wide basis, such as for a river basin or an area covering multiple river basins. The capture and management of the spatial variations in related data such as soil, climate, crops, and canal networks is the key to effective and efficient regional irrigation water demand estimations. The Geographic Information System (GIS), with its powerful spatial data management and analysis capabilities is used in this study to extend the scope of on-farm irrigation water estimation into a regional estimation. A command area covering several river basins in southern Taiwan was used to build a model prototype. The model framework shows the capability of the system to estimate regional irrigation water demand with most of the spatial variations preserved. The model also shows the capability for quickly reflecting changes in irrigation water demand in response to changes in cropping patterns, a feature that may be a necessary for regional water resource planning.

A spatial aggregation index for effective fallow decision in paddy irrigation demand planning

As irrigation demands usually take the largest share of water supply, paddy fallow is considered as a drought relieving measure in some Asian paddy growing countries by transferring the water saved to the municipal and industrial sectors. But the relationship between fallow area and irrigation demand reduction is not necessarily linear, there may be more than dozens combinations of fallow farm that can meet the same amount of irrigation demand reduction requirement. The Joint Count Statistics (JCS), an index commonly used in spatial analysis to measure the spatial coherence among cells was modified as a spatial aggregation index for evaluating the irrigation demand reduction effectiveness from a spatial perspective. This Modified JCS is supposed to identify the degree of spatial aggregation by taking underlying irrigation network into considerations. The modified JCS was proved to be effective to identify better fallow pattern through a case study in Taiwan.

A Spatial-Temporal Approach to Differentiate Epidemic Risk Patterns

The purpose of disease mapping is to find spatial clustering and identify risk areas and potential epidemic initiators. Rather than relying on plotting either the case number or incidence rate, this chapter proposes three temporal risk indices: the probability of case occurrence (how often did uneven cases occur), the duration of an epidemic (how long did cases persist), and the intensity of a transmission (were the case of chronological significance). By integrating the three indicators using the local indicator of spatial autocorrelation (LISA) statistic, this chapter intends to develop a novel approach for evaluating spatial-temporal relationships with different risk patterns in the 2002 dengue epidemic, the worst outbreak in the past sixty years. With this approach, not only are hypotheses generated through the mapping processes in furthering investigation, but also procedures provided to identify spatial health risk levels with temporal characteristics.

Spatial Decision Support System for Irrigation Demand Planning

As it is more difficult nowadays to develop new water supply sources, a demand side planning approach becomes more practical for regional water resource managements and planning. Water demand estimation is the key to the demand side water management. Estimating regional irrigation demand involves huge amount of spatial data, and estimating models for crop consumption. Different model used leads to different results. It may need to study different scenarios and to switch demand-estimating model from one to another for comparison. Geographic Information System (GIS) is the most efficient tool for spatial data management and utilization to better capture the spatial divergence. And the Decision Support System (DSS) is a tool that enables the decision makers to investigate the influence of factors or of decision alternatives. A spatial decision support system (SDSS) for agricultural demand planning framework was proposed in this paper. With all related data and models built in, it will be an efficient tool for better demand planning by setting up different scenarios and revealing the impacts under those scenarios. For demonstration of model framework implementation, the command area of Chi-Nan Irrigation Association was used as pilot study area for building a prototype.

Evaluation of municipal water supply operating rules using stochastic dominance

A procedure for evaluating and selecting among alternative rules for operating a municipal water supply system is outlined in this study. It is assumed that monthly water demands and supplies are random. The total cost, however, is affected by both current month and future water allocation decisions with respect to the operation of facilities. A perfect foresight model using mixed integer programming is developed and applied to 36 years of historical demand and supply data. Using the solutions to this model, several simple operating rules are derived. These rules are applied to the historical data to simulate system operation, and cumulative distribution of net revenue for each rule is derived. Based on first- and second-degree stochastic dominance criteria, the performance of alternative rules are evaluated. The procedure is also repeated with a set of generated data sequences to check the consistency of the solutions. Average reductions of up to 11% in annual net revenues from those of a perfect foresight model are observed, for various operating rules. Using stochastically dominant rules, annual revenues can be increased by 5% on the average from a commonly used rule based on unit cost.

A flow-based statistical model integrating spatial and nonspatial dimensions to measure healthcare access

ABSTRACT Assessing access to healthcare for an entire healthcare system involves accounting for demand, supply, and geographic variation. In order to capture the interaction between healthcare services and populations, various measures of healthcare access have been utilized, including the popular two‐step floating catchment area (2SFCA) method. However, despite the many advantages of 2SFCA, the problems, such as inappropriate assumption of healthcare demand and failure to capture cascading effects across the system have not been satisfactorily addressed. In this paper, a statistical model for evaluating flows of individuals was added to the 2SFCA method (hereafter we refer to it as F2SFCA) in order to overcome limitations associated with its current restriction. The proposed F2SFCA model can incorporate both spatial and nonspatial dimensions and thus synthesizes them into one framework. Moreover, the proposed F2SFCA model can be easily adapted to measure access for different types of individuals, over different service provider types, or with capacity constraints in a healthcare system. We implemented the proposed model in a case study assessing access to healthcare for the elderly in Taipei City, Taiwan, and compared the weaknesses and strengths to the 2SFCA method and its variations. HighlightsThis paper aims to produce an integrated model which can give rise to a more realistic representation of healthcare access.A flow‐based model named F2SFCA which incorporated both spatial and nonspatial dimensions into one framework.F2SFCA applied spatial interaction model to estimate spatial access probability from an individual to healthcare service.The cost‐benefit structure was added in F2SFCA to address the competition by service providers.F2SFCA can be adapted for different types of demands and supplies, or capacity constraints in a healthcare system.