Chetan Tiwari

Five Essential Properties of Disease Maps

We argue that as the disease map user group grows, disease maps must prioritize several essential properties that support public health uses of disease maps. We identify and describe five important properties of disease maps that will produce maps appropriate for public health purposes: (1) Control the population basis of spatial support for estimating rates, (2) display rates continuously through space, (3) provide maximum geographic detail across the map, (4) consider directly and indirectly age–sex-adjusted rates, and (5) visualize rates within a relevant place context. We present an approach to realize these properties and illustrate it with small-area data from a population-based cancer registry. Users whose interests are in selecting areas for interventions to improve the health of local populations will find maps with these five properties useful. We discuss benefits and limitations of our approach, as well as future logical extensions of this work.

Mapping late testers for HIV in Texas

Understanding the spatial patterns of late testing for HIV infection is critically important for designing and evaluating intervention strategies to reduce the social and economic burdens of HIV/AIDS. Traditional mapping methods that rely on frequency counts or rates in predefined areal units are known to be problematic due to issues of small numbers and visual biases. Additionally, confidentiality requirements associated with health data further restrict the ability to produce cartographic representations at fine geographic scales. While kernel density estimation methods produce stable and geographically detailed patterns of the late testing burden, the resulting pattern depends critically on the definition of the at-risk population. Using three definitions of at risk groups, we examine the cartographic representation of HIV late testers in Texas and show that the resulting spatial patterns and the interpretation of disease burdens are different based on the choice of the at-risk population. Disease mappers should exercise considerable caution in selecting the denominator population for mapping.

A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement

In the presence of naturally occurring and man-made public health threats, the feasibility of regional bio-emergency contingency plans plays a crucial role in the mitigation of such emergencies. While the analysis of in-place response scenarios provides a measure of quality for a given plan, it involves human judgment to identify improvements in plans that are otherwise likely to fail. Since resource constraints and government mandates limit the availability of service provided in case of an emergency, computational techniques can determine optimal locations for providing emergency response assuming that the uniform distribution of demand across homogeneous resources will yield an optimal service outcome. This paper presents an algorithm that recursively partitions the geographic space into subregions while equally distributing the population across the partitions. For this method, we have proven the existence of an upper bound on the deviation from the optimal population size for subregions.

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

Background The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). Conclusions The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Quantifying access disparities in response plans

Effective response planning and preparedness are critical to the health and well-being of communities in the face of biological emergencies. Response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable subpopulations, resulting in access disparities during emergency response. For a response plan to be effective, sufficient mitigation resources must be made accessible to target populations within short, federally-mandated time frames. A major challenge in response plan design is to establish a balance between the allocation of available resources and the provision of equal access to PODs for all individuals in a given geographic region. Limitations on the availability, granularity, and currency of data to identify vulnerable populations further complicate the planning process. To address these challenges and limitations, data driven methods to quantify vulnerabilities in the context of response plans have been developed and are explored in this article.

Vulnerable Places: Prison Locations, Socioeconomic Status, and HIV Infection in Texas

U.S. prisons have higher rates of HIV infection and tend to locate in poor areas. Because the geographic concentration of vulnerable peoples creates an environment of heightened vulnerability to disease, and vulnerable places attract vulnerable people (Oppong and Harold 2009), we should expect higher HIV infection rates in areas immediately adjacent to prison facilities. Using deidentified HIV surveillance data, we explore this hypothesis. The results suggest that areas in close proximity to prison units have lower socioeconomic status and higher HIV rates, with clear distance decay, and should be prioritized for increased intervention to reduce HIV incidence.

The Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER

CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) is the nations primary data repository for health statistics. Before WONDER data are released to the public, data cells with fewer than 10 case counts are suppressed. We showed that maps produced from suppressed data have predictable geographic biases that can be removed by applying population data in the system and an algorithm that uses regional rates to estimate missing data. By using CDC WONDER heart disease mortality data, we demonstrated that effects of suppression could be largely overcome.

RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated.

India: Localized Introduction of GIS in Elite Urban Private Schools and Prospects for Diffusion

The regulations, scope, and specific content of secondary education in India is defined by federally recognized boards of school education that operate at either the state or the national level. While Geographic Information Systems (GIS) is still at a nascent stage in terms of the overall curriculum at the secondary school level, its importance and relevance in higher education and in certain sectors of the job market has significantly grown in the recent past. In this chapter we examine the role of GIS as a specific subject area in three major national boards of education in India. While there is some standardization in the core curriculum at private and public schools associated with these national boards of education, the role of GIS is still limited to its use as a pedagogical tool among private schools that mostly cater to elite populations in major urban centers across India. Despite a growing demand for GIS skills, its inclusion as a core component of secondary education remains unfulfilled due to a variety of reasons. This chapter identifies some of the key challenges that secondary schools across the country face in adopting GIS and its associated technologies as part of their core curriculums. We conclude this chapter by identifying specific opportunities for GIS education at secondary school level in India.

Data Driven Prediction of Dengue Incidence in Thailand

Communicable diseases such as dengue pose a significant threat on public health across the world. Modeling an accurate and efficient prediction of dengue disease will improve public health response planning to outbreaks. However, despite the fact that many researches has focused on dengue prediction, it has been lacking geographical variation of dengue fever taken into account. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. The infection pattern is different from region to region. We developed a model for predicting dengue fever for four provinces of Thailand with geographical variation taken into account. These predictions show slightly varying outcomes across provinces. Support Vector Regression (SVR) was used as the modeling tool. Additionally, we introduced a novel method of assessing regression model in terms of accuracies over Mean Square Error (MSE) which does not capture the behavior of data pattern spatially. This novel method resulted in 71% accuracy of prediction for Kamphaeng Phet province. The proposed model of prediction facilitates administrative bodies to make informed decisions in the context of public health of Thailand.