Disease map reconstruction

Systematic Assessment of COVID-19 Pandemic in Bangladesh: Effectiveness of Preparedness in the First Wave

Background: To develop an effective countermeasure and determine our susceptibilities to the outbreak of COVID-19 is challenging for a densely populated developing country like Bangladesh and a systematic review of the disease on a continuous basis is necessary. Methods: Publicly available and globally acclaimed datasets (4 March 2020-30 September 2020) from IEDCR, Bangladesh, JHU, and ECDC database are used for this study. Visual exploratory data analysis is used and we fitted a polynomial model for the number of deaths. A comparison of Bangladesh scenario over different time points as well as with global perspectives is made. Results: In Bangladesh, the number of active cases had decreased, after reaching a peak, with a constant pattern of death rate at from July to the end of September, 2020. Seventy-one percent of the cases and 77% of the deceased were males. People aged between 21 and 40 years were most vulnerable to the coronavirus and most of the fatalities (51.49%) were in the 60+ population. A strong positive correlation (0.93) between the number of tests and confirmed cases and a constant incidence rate (around 21%) from June 1 to August 31, 2020 was observed. The case fatality ratio was between 1 and 2. The number of cases and the number of deaths in Bangladesh were much lower compared to other countries. Conclusions: This study will help to understand the patterns of spread and transition in Bangladesh, possible measures, effectiveness of the preparedness, implementation gaps, and their consequences to gather vital information and prevent future pandemics.

"We only have the one": Mapping the prevalence of people with high body mass to aid regional emergency management planning in aotearoa New Zealand

INTRODUCTION

People have been left behind in disasters directly associated with their size, shape, and weight and are disproportionately impacted in pandemics. Despite alignment with known vulnerabilities such as poverty, age, and disability, the literature is inaudible on body mass. Emergency managers report little or no information on body mass prevalence. This exploratory study aimed to illustrate population prevalence of high body mass for emergency planning.

METHODS

Cross-sectional data from the New Zealand Health Survey were pooled for the years 2013/14-2017/18 (n = 68 053 adults aged ≥15 years). Height and weight were measured and used to calculate body mass index. The prevalence of high body mass were mapped to emergency management boundary shapefiles. The resulting maps were piloted with emergency managers.

RESULTS

Maps highlight the population prevalence of high body mass across emergency management regions, providing a visual tool. A pilot with 14 emergency managers assessed the utility of such mapping. On the basis of the visual information, the tool prompted 12 emergency managers to consider such groups in regional planning and to discuss needs.

CONCLUSIONS

Visual mapping is a useful tool to highlight population prevalence of groups likely to be at higher risk in disasters. This is believed to be the first study to map high body mass for the purposes of emergency planning. Future research is required to identify prevalence at a finer geographical scale. More features in the local context such as physical location features, risk and vulnerability features could also be included in future research.

An analysis of hurricane impact across multiple cancers: Accessing spatio-temporal variation in cancer-specific survival with Hurricane Katrina and Louisiana SEER data

Considering the impact of events such as natural disasters on disease risk is important. For this study, we examined temporal trends in multiple cancers available via Louisiana SEER data to understand how event impacts differ in timing and strength by cancer type. The specific event of interest for these Louisiana residents diagnosed with lung and bronchus, prostate, breast, colorectal, leukemia, or ovarian cancer in during the years 2000-2013 was Hurricane Katrina (August 2005). The results across multiple cancers showed similarities among trends, both spatial and temporal. With these results in mind, direct action could be made with the aim of improving survival after detrimental events or in detected Louisiana parishes with worse than average survival.

Identification of cholera hotspots in Zambia: A spatiotemporal analysis of cholera data from 2008 to 2017

The global burden of cholera is increasing, with the majority (60%) of the cases occurring in sub-Saharan Africa. In Zambia, widespread cholera outbreaks have occurred since 1977, predominantly in the capital city of Lusaka. During both the 2016 and 2018 outbreaks, the Ministry of Health implemented cholera vaccination in addition to other preventative and control measures, to stop the spread and control the outbreak. Given the limitations in vaccine availability and the logistical support required for vaccination, oral cholera vaccine (OCV) is now recommended for use in the high risk areas ("hotspots") for cholera. Hence, the aim of this study was to identify areas with an increased risk of cholera in Zambia. Retrospective cholera case data from 2008 to 2017 was obtained from the Ministry of Health, Department of Public Health and Disease Surveillance. The Zambian Central Statistical Office provided district-level population data, socioeconomic and water, sanitation and hygiene (WaSH) indicators. To identify districts at high risk, we performed a discrete Poisson-based space-time scan statistic to account for variations in cholera risk across both space and time over a 10-year study period. A zero-inflated negative binomial regression model was employed to identify the district level risk factors for cholera. The risk map was generated by classifying the relative risk of cholera in each district, as obtained from the space-scan test statistic. In total, 34,950 cases of cholera were reported in Zambia between 2008 and 2017. Cholera cases varied spatially by year. During the study period, Lusaka District had the highest burden of cholera, with 29,080 reported cases. The space-time scan statistic identified 16 districts to be at a significantly higher risk of having cholera. The relative risk of having cholera in these districts was significantly higher and ranged from 1.25 to 78.87 times higher when compared to elsewhere in the country. Proximity to waterbodies was the only factor associated with the increased risk for cholera (P<0.05). This study provides a basis for the cholera elimination program in Zambia. Outside Lusaka, the majority of high risk districts identified were near the border with the DRC, Tanzania, Mozambique, and Zimbabwe. This suggests that cholera in Zambia may be linked to movement of people from neighboring areas of cholera endemicity. A collaborative intervention program implemented in concert with neighboring countries could be an effective strategy for elimination of cholera in Zambia, while also reducing rates at a regional level.

Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All

BACKGROUND

Colorectal cancer (CRC) is common worldwide, with 140,250 diagnoses and 50,630 deaths estimated for the United States in 2018. Guidelines current to the most recent individuals in our analysis suggested regular screenings beginning at age 50 have reduced the incidence of CRC. However, the incidence continues to rise among those under 50. Less is known about survival following CRC diagnosis, but research has suggested that younger cases may also have worse survival. However, we hypothesize that younger individuals are generally healthier with fewer comorbidities, leading to the potential for better survival following diagnosis.

MATERIALS AND METHODS

We utilized the Surveillance, Epidemiology, and End Results data to estimate and assess both spatial and temporal variation in age-specific colorectal cancer incidence and survival in Iowa.

RESULTS

Both overall and older-onset colorectal cancer incidence began to decline in the early 2000s, whereas younger-onset incidences decreased until the late 1980s but then increased steeply through the 2000s. The risk for those younger than 50 years of age first exceeded the risk for those 50 years or older in 2007. Survival times did increase for overall CRC, older-onset CRC, and young-onset CRC throughout the study period, with young-onset CRC increasing at a higher rate. The spatial variation assessment indicated that the survival was positively associated with several variables of interest, most notably disparities including better access to healthcare and higher sociodemographic status.

CONCLUSION

In conclusion, results suggest that regular colorectal screenings could reduce incidence and mortality in people under 50.

Spatial and population drivers of persistent cholera transmission in rural Bangladesh: Implications for vaccine and intervention targeting

We identify high risk clusters and measure their persistence in time and analyze spatial and population drivers of small area incidence over time. The geographically linked population and cholera surveillance data in Matlab, Bangladesh for a 10-year period were used. Individual level data were aggregated by local 250 × 250 m communities. A retrospective space-time scan statistic was applied to detect high risk clusters. Generalized estimating equations were used to identify risk factors for cholera. We identified 10 high risk clusters, the largest of which was in the southern part of the study area where a smaller river flows into a large river. There is persistence of local spatial patterns of cholera and the patterns are related to both the population composition and ongoing spatial diffusion from nearby areas over time. This information suggests that targeting interventions to high risk areas would help eliminate locally persistent endemic areas.

A, Naumova EN. Hospitalization records as a tool for evaluating performance of food- and water-borne disease surveillance systems: a Massachusetts case study

We outline a framework for evaluating food- and water-borne surveillance systems using hospitalization records, and demonstrate the approach using data on salmonellosis, campylobacteriosis and giardiasis in persons aged ≥65 years in Massachusetts. For each infection, and for each reporting jurisdiction, we generated smoothed standardized morbidity ratios (SMR) and surveillance to hospitalization ratios (SHR) by comparing observed surveillance counts with expected values or the number of hospitalized cases, respectively. We examined the spatial distribution of SHR and related this to the mean for the entire state. Through this approach municipalities that deviated from the typical experience were identified and suspected of under-reporting. Regression analysis revealed that SHR was a significant predictor of SMR, after adjusting for population age-structure. This confirms that the spatial “signal” depicted by surveillance is in part influenced by inconsistent testing and reporting practices since municipalities that reported fewer cases relative to the number of hospitalizations had a lower relative risk (as estimated by SMR). Periodic assessment of SHR has potential in assessing the performance of surveillance systems.

Spatial relationship quantification between environmental, socioeconomic and health data at different geographic levels

Spatial health inequalities have often been analyzed in terms of socioeconomic and environmental factors. The present study aimed to evaluate spatial relationships between spatial data collected at different spatial scales. The approach was illustrated using health outcomes (mortality attributable to cancer) initially aggregated to the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. Geographically weighted regression (GWR) was used to quantify spatial relationships. The strongest associations were found when low deprivation was associated with lower lip, oral cavity and pharynx cancer mortality and when low environmental pollution was associated with low pleural cancer mortality. However, applying this approach to other areas or to other causes of death or with other indicators requires continuous exploratory analysis to assess the role of the modifiable areal unit problem (MAUP) and downscaling the health data on the study of the relationship, which will allow decision-makers to develop interventions where they are most needed.

The effect of spatial aggregation on performance when mapping a risk of disease

Background

Spatial data on cases are available either in point form (e.g. longitude/latitude), or aggregated by an administrative region (e.g. zip code or census tract). Statistical methods for spatial data may accommodate either form of data, however the spatial aggregation can affect their performance. Previous work has studied the effect of spatial aggregation on cluster detection methods. Here we consider geographic health data at different levels of spatial resolution, to study the effect of spatial aggregation on disease mapping performance in locating subregions of increased disease risk.

Methods

We implemented a non-parametric disease distance-based mapping (DBM) method to produce a smooth map from spatially aggregated childhood leukaemia data. We then simulated spatial data under controlled conditions to study the effect of spatial aggregation on its performance. We used an evaluation method based on ROC curves to compare performance of DBM across different geographic scales.

Results

Application of DBM to the leukaemia data illustrates the method as a useful visualization tool. Spatial aggregation produced expected degradation of disease mapping performance. Characteristics of this degradation, however, varied depending on the interaction between the geographic extent of the higher risk area and the level of aggregation. For example, higher risk areas dispersed across several units did not suffer as greatly from aggregation. The choice of centroids also had an impact on the resulting mapping.

Conclusions

DBM can be implemented for continuous and discrete spatial data, but the resulting mapping can lose accuracy in the second setting. Investigation of the simulations suggests a complex relationship between performance loss, geographic extent of spatial disturbances and centroid locations. Aggregation of spatial data destroys information and thus impedes efforts to monitor these data for spatial disturbances. The effect of spatial aggregation on cluster detection, disease mapping, and other useful methods in spatial epidemiology is complex and deserves further study.

Risk map of cholera infection for vaccine deployment: the eastern Kolkata case

BACKGROUND

Despite advancement of our knowledge, cholera remains a public health concern. During March-April 2010, a large cholera outbreak afflicted the eastern part of Kolkata, India. The quantification of importance of socio-environmental factors in the risk of cholera, and the calculation of the risk is fundamental for deploying vaccination strategies. Here we investigate socio-environmental characteristics between high and low risk areas as well as the potential impact of vaccination on the spatial occurrence of the disease.

METHODS AND FINDINGS

The study area comprised three wards of Kolkata Municipal Corporation. A mass cholera vaccination campaign was conducted in mid-2006 as the part of a clinical trial. Cholera cases and data of the trial to identify high risk areas for cholera were analyzed. We used a generalized additive model (GAM) to detect risk areas, and to evaluate the importance of socio-environmental characteristics between high and low risk areas. During the one-year pre-vaccination and two-year post-vaccination periods, 95 and 183 cholera cases were detected in 111,882 and 121,827 study participants, respectively. The GAM model predicts that high risk areas in the west part of the study area where the outbreak largely occurred. High risk areas in both periods were characterized by poor people, use of unsafe water, and proximity to canals used as the main drainage for rain and waste water. Cholera vaccine uptake was significantly lower in the high risk areas compared to low risk areas.

CONCLUSION

The study shows that even a parsimonious model like GAM predicts high risk areas where cholera outbreaks largely occurred. This is useful for indicating where interventions would be effective in controlling the disease risk. Data showed that vaccination decreased the risk of infection. Overall, the GAM-based risk map is useful for policymakers, especially those from countries where cholera remains to be endemic with periodic outbreaks.

Combining area-based and individual-level data in the geostatistical mapping of late-stage cancer incidence

This paper presents a geostatistical approach to incorporate individual-level data (e.g. patient residences) and area-based data (e.g. rates recorded at census tract level) into the mapping of late-stage cancer incidence, with an application to breast cancer in three Michigan counties. Spatial trends in cancer incidence are first estimated from census data using area-to-point binomial kriging. This prior model is then updated using indicator kriging and individual-level data. Simulation studies demonstrate the benefits of this two-step approach over methods (kernel density estimation and indicator kriging) that process only residence data.

Geographical mapping and Bayesian spatial modeling of malaria incidence in Sistan and Baluchistan province, Iran

OBJECTIVE

To present the geographical map of malaria and identify some of the important environmental factors of this disease in Sistan and Baluchistan province, Iran.

METHODS

We used the registered malaria data to compute the standard incidence rates (SIRs) of malaria in different areas of Sistan and Baluchistan province for a nine-year period (from 2001 to 2009). Statistical analyses consisted of two different parts: geographical mapping of malaria incidence rates, and modeling the environmental factors. The empirical Bayesian estimates of malaria SIRs were utilized for geographical mapping of malaria and a Poisson random effects model was used for assessing the effect of environmental factors on malaria SIRs.

RESULTS

In general, 64,926 new cases of malaria were registered in Sistan and Baluchistan Province from 2001 to 2009. Among them, 42,695 patients (65.8%) were male and 22,231 patients (34.2%) were female. Modeling the environmental factors showed that malaria incidence rates had positive relationship with humidity, elevation, average minimum temperature and average maximum temperature, while rainfall had negative effect on malaria SIRs in this province.

CONCLUSIONS

The results of the present study reveals that malaria is still a serious health problem in Sistan and Baluchistan province, Iran. Geographical map and related environmental factors of malaria can help the health policy makers to intervene in high risk areas more efficiently and allocate the resources in a proper manner.

Methods and tools for geographical mapping and analysis in primary health care

AIM

The purpose of this paper is to review methods and tools for mapping, visualising and exploring geographic information to aid in primary health care (PHC) research and development.

BACKGROUND

Mapping and spatial analysis of indicators of locality health profiles and healthcare needs assessment are well-established facets of health services research and development. However, while there are a range of different methods and tools used for these purposes, non-specialists responsible for managing the use of such information systems may find knowing where to start and what can be done a relatively steep learning curve. In this review, health and sociodemographic datasets are used to illustrate some key methods, tools and organisational issues, and builds upon two recent reviews in this journal, respectively, focusing on geographic data sources and geographic concepts. Those familiar with mapping and spatial analysis should find this a useful review of current matters.

METHOD

A thematic review is presented with illustrative case studies relevant to PHC. It begins with a section on visualising and interpreting geographic information. This is followed by a section critiquing analytical methods. Consideration is given to software and deployment issues in a third section. Content is based on domain knowledge of the authors as a team of geographic information scientists and a public health practitioner working in tandem, with its scope restricted to routine applications of mapping and analysis. Advanced techniques such as spatio-temporal modelling are not considered, neither are methodological technicalities, although guidance on further reading is provided.

SUMMARY

Geographical perspectives are now playing a significant role in PHC delivery, and for those engaged in informatics and/or managing population-level care, understanding key geographic information systems methods and terminologies are important as is gaining greater familiarity with institutional aspects of implementation.

Explaining loss caused by tomato spotted wilt virus on tobacco with boreal winter weather: a Bayesian approach

In North Carolina, Tomato spotted wilt virus (TSWV) has regularly been reported since 1997, with incidence being the highest in 2002. At the end of each season, a questionnaire is sent to the county agents to report disease losses. TSWV reported losses in 1993 to 2007 from 58 counties were available. A county-year combination was considered a case and, in total, 494 cases were analyzed. The winter months' temperature and precipitation significantly explained the reported TSWV loss (R(2) = 0.82). Specifically, the monthly average air temperature for December to February had a positive association with TSWV loss (P < 0.0001) whereas the total precipitation for the same months had a negative effect (P < 0.0001). Bayesian hierarchical models were implemented to include spatial and nonspatial random effects to investigate if there were significant spatial correlations or unexplained variability, respectively, and, thus, other significant variables that were ignored in the model development. The spatial random effects were not significant but the nonspatial random effects were significant in 36 cases. The importance of spring weather to dispersal of thrips and TSWV has been previously identified. Winter weather also may be a good indicator of potential available TSWV inoculum for the upcoming season.

Spatial patterns of natural hazards mortality in the United States

BACKGROUND

Studies on natural hazard mortality are most often hazard-specific (e.g. floods, earthquakes, heat), event specific (e.g. Hurricane Katrina), or lack adequate temporal or geographic coverage. This makes it difficult to assess mortality from natural hazards in any systematic way. This paper examines the spatial patterns of natural hazard mortality at the county-level for the U.S. from 1970-2004 using a combination of geographical and epidemiological methods.

RESULTS

Chronic everyday hazards such as severe weather (summer and winter) and heat account for the majority of natural hazard fatalities. The regions most prone to deaths from natural hazards are the South and intermountain west, but sub-regional county-level mortality patterns show more variability. There is a distinct urban/rural component to the county patterns as well as a coastal trend. Significant clusters of high mortality are in the lower Mississippi Valley, upper Great Plains, and Mountain West, with additional areas in west Texas, and the panhandle of Florida, Significant clusters of low mortality are in the Midwest and urbanized Northeast.

CONCLUSION

There is no consistent source of hazard mortality data, yet improvements in existing databases can produce quality data that can be incorporated into spatial epidemiological studies as demonstrated in this paper. It is important to view natural hazard mortality through a geographic lens so as to better inform the public living in such hazard prone areas, but more importantly to inform local emergency practitioners who must plan for and respond to disasters in their community.

Spatial risk for gender-specific adult mortality in an area of southern China

Background

Although economic reforms have brought significant benefits, including improved health care to many Chinese people, accessibility to improved care has not been distributed evenly throughout Chinese society. Also, the effects of the uneven distribution of improved healthcare are not clearly understood. Evidence suggests that mortality is an indicator for evaluating accessibility to improved health care services. We constructed spatially smoothed risk maps for gender-specific adult mortality in an area of southern China comprising both urban and rural areas and identified ecological factors of gender-specific mortality across societies.

Results

The study analyzed the data of the Hechi Prefecture in southern in China. An average of 124,204 people lived in the area during the study period (2002–2004). Individual level data for 2002–2004 were grouped using identical rectangular cells (regular lattice) of 0.25 km². Poisson regression was fitted to the group level data to identify gender-specific ecological factors of adult (ages 15–<45 years) mortality. Adult male mortality was more than two-fold higher than adult female mortality. Adults were likely to die of injury, poisoning, or trauma. Significantly more deaths were observed in poor areas than in areas with higher incomes. Specifically, higher spatial risk for adult male mortality was clustered in two rural study areas, which did not overlap with neighborhoods with higher risk for adult female mortality. One high-risk neighborhood for adult female mortality was in a poor urban area.

Conclusion

We found a disparity in mortality rates between rural and urban areas in the study area in southern China, especially for adult men. There were also differences in mortality rates between poorer and wealthy populations in both rural and urban areas, which may in part reflect differences in health care quality. Spatial influences upon adult male versus adult female mortality difference underscore the need for more research on gender-related influences on adult mortality in China.

Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary

We provide a relatively non-technical glossary of terms and a description of the tools used in spatial or geographical epidemiology and associated geographical information systems. Statistical topics included cover adjustment and standardisation to allow for demographic and other background differences, data structures, data smoothing, spatial autocorrelation and spatial regression. We also discuss the rationale for geographical epidemiology and specific techniques such as disease clustering, disease mapping, ecological analyses, geographical information systems and global positioning systems.

Bayesian statistics in medicine: a 25 year review

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Application of Poisson kriging to the mapping of cholera and dysentery incidence in an endemic area of Bangladesh

Background

Disease maps can serve to display incidence rates geographically, to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Poisson kriging was recently introduced to filter the noise attached to rates recorded over sparsely populated administrative units. Its benefit over simple population-weighted averages and empirical Bayesian smoothers was demonstrated by simulation studies using county-level cancer mortality rates. This paper presents the first application of Poisson kriging to the spatial interpolation of local disease rates, resulting in continuous maps of disease rate estimates and the associated prediction variance. The methodology is illustrated using cholera and dysentery data collected in a cholera endemic area (Matlab) of Bangladesh.

Results

The spatial analysis was confined to patrilineally-related clusters of households, known as baris, located within 9 kilometers from the Matlab hospital to avoid underestimating the risk of disease incidence, since patients far away from the medical facilities are less likely to travel. Semivariogram models reveal a range of autocorrelation of 1.1 km for dysentery and 0.37 km for cholera. This result translates into a cholera risk map that is patchier than the dysentery map that shows a large zone of high incidence in the south-central part of the study area, which is quasi-urban. On both maps, lower risk values are found in the Northern part of the study area, which is also the most distant from the Matlab hospital. The weaker spatial continuity of cholera versus dysentery incidence rates resulted in larger kriging variance across the study area.

Conclusion

The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of incidence rates into the mapping of risk values and the quantification of the associated uncertainty. Differences in spatial patterns, in particular the range of spatial autocorrelation, reflect differences in the mode of transmission of cholera and dysentery. Our risk maps for cholera and dysentery incidences should help identifying putative factors of increased disease incidence, leading to more effective prevention and remedial actions in endemic areas.

Modeling the relation between socioeconomic status and mortality in a mixture of majority and minority ethnic groups

Ethnic variation in mortality and whether this variation can be explained by socioeconomic status are of substantive interest to social epidemiologists. The authors consider the analysis of mortality data for a mixture of majority and minority ethnic groups. Such data are likely to be coarsely cross-classified by age and socioeconomic status and yet, even then, in some cells of this cross-classification the observed mortality rate will be an imprecise estimate of the underlying rate. The authors illustrate conventional and Bayesian approaches to analysis with data from the 1996 census used by the New Zealand Census-Mortality Study. A conventional approach is exploratory data analysis first followed by Poisson regression. The authors use spline smoothing within a generalized additive model framework as an exploratory data analysis, following a strategy of adding just enough model structure to gain a sensible picture. A Bayesian approach is modeling first and then a description of posterior estimates using exploratory data analysis techniques. The authors use hierarchical Poisson regression and then illustrate their posterior estimates of the mortality rate using the same spline smoothing as before. The advantage of the hierarchical Bayesian approach is that it assesses uncertainty about a Poisson regression model proposed a priori; the conventional approach assumes that the fitted Poisson regression model is correct. All analyses use software that is available at no cost.

Geographical clustering of pancreatic cancers in the Northeast Nile Delta region of Egypt

The northeast Nile Delta, Egypt's most polluted region, appears to have a high incidence of pancreatic cancer. We sought to determine whether there is any geographic clustering of pancreatic cancers there and, if so, whether such clustering might be associated with environmental pollution. Using data from the medical records of the Gastrointestinal Surgical Center of Mansoura University in the Dakahleia Province of Egypt and detailed geographical maps of the northeast Nile Delta region, we plotted the residences of all 373 patients who had pancreatic cancer diagnosed between 1995 and 2000. The study region has 15 administrative districts, whose centroid coordinates, population, and number of pancreatic cancer patients were determined for this study. Monte Carlo simulation identified statistically significant clustering of pancreatic cancer in five subdivisions located near the Nile River and Delta plains. This clustering was independent of population size and formed two larger clusters. When data were analyzed by sex, clustering of pancreatic cancer was observed in the same five subdivisions for men but only two subdivisions showed clustering for women. Together, our data suggest that there is clustering of pancreatic cancer cases in the northeast Nile delta region and that this clustering may be related to water pollution. Our data also warrant future studies of the association between water pollution and pancreatic cancer in the region.

Mapping health on the internet: a new tool for environmental justice and public health research

This paper examines the prospects for integrating Internet platform GIS or 'web-GIS' into environmental justice and related public health research. Specifically, we document the development of a web-GIS created for investigating relationships between health, air quality and socioeconomic factors in Hamilton, Canada. After development of the web-GIS site, we assembled a focus group of public health professionals to test functionality and render opinions about the potential of the site and geographic information in their program implementation. Results show overwhelming support for the further integration of GIS into public health practice. The results also underscore the potential of web-GIS to alleviate concerns of cost and data availability that often limit the use of GIS in community debates centred on environmental justice issues.

Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

Background

Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided.

Results

The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothing techniques. Because of the small weight assigned to the rate observed over the entity being smoothed (kernel weight), the population-weighted average leads to risk maps that show little variability. Other techniques assign larger and similar kernel weights but they use a different piece of auxiliary information in the prediction: global or local means for global or local empirical Bayes smoothers, and spatial combination of surrounding rates for the geostatistical estimator. Simulation studies indicated that Poisson kriging outperforms other approaches for most scenarios, with a clear benefit when the risk values are spatially correlated. Global empirical Bayes smoothers provide more accurate predictions under the least frequent scenario of spatially random risk.

Conclusion

The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of mortality rates into the mapping of risk values and the quantification of the associated uncertainty, while being easier to implement than a full Bayesian model. The availability of a public-domain executable makes the geostatistical analysis of health data, and its comparison to traditional smoothers, more accessible to common users. In future papers this methodology will be generalized to the simulation of the spatial distribution of risk values and the propagation of the uncertainty attached to predicted risks in local cluster analysis.

The health status of southern children: a neglected regional disparity

PURPOSE

Great variations exist in child health outcomes among states in the United States, with southern states consistently ranked among the lowest in the country. Investigation of the geographical distribution of children's health status and the regional factors contributing to these outcomes has been neglected. We attempted to identify the degree to which region of residence may be linked to health outcomes for children with the specific aim of determining whether living in the southern region of the United States is adversely associated with children's health status.

METHODS

A child health index (CHI) that ranked each state in the United States was computed by using state-specific composite scores generated from outcome measures for a number of indicators of child health. Five indicators for physical health were chosen (percent low birth weight infants, infant mortality rate, child death rate, teen death rate, and teen birth rates) based on their historic and routine use to define health outcomes in children. Indicators were calculated as rates or percentages. Standard scores were calculated for each state for each health indicator by subtracting the mean of the measures for all states from the observed measure for each state. Indicators related to social and economic status were considered to be variables that impact physical health, as opposed to indicators of physical health, and therefore were not used to generate the composite child health score. These variables were subsequently examined in this study as potential confounding variables. Mapping was used to redefine regional groupings of states, and parametric tests (2-sample t test, analysis of means, and analysis-of-variance F tests) were used to compare the means of the CHI scores for the regional groupings and test for statistical significance. Multiple-regression analysis computed the relationship of region, social and economic indicators, and race to the CHI. Simple linear-regression analyses were used to assess the individual effect of each indicator.

RESULTS

A geographic region of contiguous states, characterized by their poor child health outcomes relative to other states and regions of the United States, exists within the "Deep South" (Mississippi, Louisiana, Arkansas, Tennessee, Alabama, Georgia, North Carolina, South Carolina, and Florida). This Deep-South region is statistically different in CHI scores from the US Census Bureau-defined grouping of states in the South. The mean of CHI scores for the Deep-South region was >1 SD below the mean of CHI scores for all states. In contrast, the CHI score means for each of the other 3 regions were all above the overall mean of CHI scores for all states. Regression analysis showed that living in the Deep-South region is a stronger predictor of poor child health outcomes than other consistently collected and reported variables commonly used to predict children's health.

CONCLUSIONS

The findings of this study indicate that region of residence in the United States is statistically related to important measures of children's health and may be among the most powerful predictors of child health outcomes and disparities. This clarification of the poorer health status of children living in the Deep South through spatial analysis is an essential first step for developing a better understanding of variations in the health of children. Similar to early epidemiology work linking geographic boundaries to disease, discovering the mechanisms/pathways/causes by which region influences health outcomes is a critical step in addressing disparities and inequities in child health and one that is an important and fertile area for future research. The reasons for these disparities may be complex and synergistically related to various economic, political, social, cultural, and perhaps even environmental (physical) factors in the region. This research will require the use and development of new approaches and applications of spatial analysis to develop insights into the societal, environmental, and historical determinants of child health that have been neglected in previous child health outcomes and policy research. The public policy implications of the findings in this study are substantial. Few, if any, policies identify these children as a high-risk group on the basis of their region of residence. A better understanding of the depth and breadth of disparities in health, education, and other social outcomes among and within regions of the United States is necessary for the generation of policies that enable policy makers to address and mitigate the factors that influence these disparities. Defining and clarifying the regional boundaries is also necessary to better inform public policy decisions related to resource allocation and the prevention and/or mitigation of the effects of region on child health. The identification of the Deep South as a clearly defined subregion of the Census Bureau's regional definition of the South suggests the need to use more culturally and socially relevant boundaries than the Census Bureau regions when analyzing regional data for policy development.

The ecological association between suicide rates and indices of deprivation in English local authorities

INTRODUCTION

There are several published studies that have been focused on the ecological association between suicide rates in different areas with indices of deprivation or fragmentation. Most of these studies, however, have used census-based indices of deprivation or fragmentation. In the present study the newly developed Indices of Deprivation have been used, taking into account the results of the spatial autocorrelation tests.

METHODS

Data on all deaths for which suicide or an open verdict was returned during 1996-1998 in England were subjects of this study. These data were provided by the National Confidential Inquiry into Suicide and Homicide by People with Mental Illness. The indices of deprivation and the population counts were provided by the Department of the Environment, Transport and the Region (DETR) and Office for National Statistics (ONS), respectively.

RESULTS

The results show that, in England as a whole, the rates of suicide in young and middle-aged males were strongly associated with the indices of deprivation. However, the rates of suicide in females and in older age groups were less influenced by the indices of deprivation. In the present study all the indices of deprivation tended to show a similar pattern in which a better socio-economic status of local authorities was associated with a lower rate of suicide.

CONCLUSION

These findings suggest that at the local authority level, the "hot spots" index of deprivation may represent the same level of magnitude in predicting the rates of suicide as the number of unemployed or income-deprived people. However, more studies using multilevel modelling are needed to shed more light on the ecological associations between suicide rates and socio-economic and social cohesion status.

Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence

In recent years, there has been a renewed interest in applying statistical ranking criteria to identify sites on a road network, which potentially present high traffic crash risks or are over-represented in certain type of crashes, for further engineering evaluation and safety improvement. This requires that good estimates of ranks of crash risks be obtained at individual intersections or road segments, or some analysis zones. The nature of this site ranking problem in roadway safety is related to two well-established statistical problems known as the small area (or domain) estimation problem and the disease mapping problem. The former arises in the context of providing estimates using sample survey data for a small geographical area or a small socio-demographic group in a large area, while the latter stems from estimating rare disease incidences for typically small geographical areas. The statistical problem is such that direct estimates of certain parameters associated with a site (or a group of sites) with adequate precision cannot be produced, due to a small available sample size, the rareness of the event of interest, and/or a small exposed population or sub-population in question. Model based approaches have offered several advantages to these estimation problems, including increased precision by "borrowing strengths" across the various sites based on available auxiliary variables, including their relative locations in space. Within the model based approach, generalized linear mixed models (GLMM) have played key roles in addressing these problems for many years. The objective of the study, on which this paper is based, was to explore some of the issues raised in recent roadway safety studies regarding ranking methodologies in light of the recent statistical development in space-time GLMM. First, general ranking approaches are reviewed, which include naïve or raw crash-risk ranking, scan based ranking, and model based ranking. Through simulations, the limitation of using the naïve approach in ranking is illustrated. Second, following the model based approach, the choice of decision parameters and consideration of treatability are discussed. Third, several statistical ranking criteria that have been used in biomedical, health, and other scientific studies are presented from a Bayesian perspective. Their applications in roadway safety are then demonstrated using two data sets: one for individual urban intersections and one for rural two-lane roads at the county level. As part of the demonstration, it is shown how multivariate spatial GLMM can be used to model traffic crashes of several injury severity types simultaneously and how the model can be used within a Bayesian framework to rank sites by crash cost per vehicle-mile traveled (instead of by crash frequency rate). Finally, the significant impact of spatial effects on the overall model goodness-of-fit and site ranking performances are discussed for the two data sets examined. The paper is concluded with a discussion on possible directions in which the study can be extended.

Neighborhood size and local geographic variation of health and social determinants

BACKGROUND: Spatial filtering using a geographic information system (GIS) is often used to smooth health and ecological data. Smoothing disease data can help us understand local (neighborhood) geographic variation and ecological risk of diseases. Analyses that use small neighborhood sizes yield individualistic patterns and large sizes reveal the global structure of data where local variation is obscured. Therefore, choosing an optimal neighborhood size is important for understanding ecological associations with diseases. This paper uses Hartley's test of homogeneity of variance (Fmax) as a methodological solution for selecting optimal neighborhood sizes. The data from a study area in Vietnam are used to test the suitability of this method. RESULTS: The Hartley's Fmax test was applied to spatial variables for two enteric diseases and two socioeconomic determinants. Various neighbourhood sizes were tested by using a two step process to implement the Fmaxtest. First the variance of each neighborhood was compared to the highest neighborhood variance (upper, Fmax1) and then they were compared with the lowest neighborhood variance (lower, Fmax2). A significant value of Fmax1 indicates that the neighborhood does not reveal the global structure of data, and in contrast, a significant value in Fmax2 implies that the neighborhood data are not individualistic. The neighborhoods that are between the lower and the upper limits are the optimal neighbourhood sizes. CONCLUSION: The results of tests provide different neighbourhood sizes for different variables suggesting that optimal neighbourhood size is data dependent. In ecology, it is well known that observation scales may influence ecological inference. Therefore, selecting optimal neigborhood size is essential for understanding disease ecologies. The optimal neighbourhood selection method that is tested in this paper can be useful in health and ecological studies.

Empirical Bayes methods for disease mapping

This paper reviews empirical Bayes methods for disease mapping. A distinction is made between spatial models (which take into account the geographical distribution of disease) and nonspatial models. Several estimators are presented, and methods of estimation are described. Empirical Bayes methods are compared with full Bayes methods, and we argue that both have their place.

Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms

This paper presents a geostatistical methodology which accounts for spatially varying population size in the processing of cancer mortality data. The approach proceeds in two steps: (1) spatial patterns are first described and modeled using population-weighted semivariogram estimators, (2) spatial components corresponding to nested structures identified on semivariograms are then estimated and mapped using a variant of factorial kriging. The main benefit over traditional spatial smoothers is that the pattern of spatial variability (i.e. direction-dependent variability, range of correlation, presence of nested scales of variability) is directly incorporated into the computation of weights assigned to surrounding observations. Moreover, besides filtering the noise in the data the procedure allows the decomposition of the structured component into several spatial components (i.e. local versus regional variability) on the basis of semivariogram models. A simulation study demonstrates that maps of spatial components are closer to the underlying risk maps in terms of prediction errors and provide a better visualization of regional patterns than the original maps of mortality rates or the maps smoothed using weighted linear averages. The proposed approach also attenuates the underestimation of the magnitude of the correlation between various cancer rates resulting from noise attached to the data. This methodology has great potential to explore scale-dependent correlation between risks of developing cancers and to detect clusters at various spatial scales, which should lead to a more accurate representation of geographic variation in cancer risk, and ultimately to a better understanding of causative relationships.

Bayesian analysis in plant pathology

ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.

Exploratory disease mapping: kriging the spatial risk function from regional count data

BACKGROUND: There is considerable interest in the literature on disease mapping to interpolate estimates of disease occurrence or risk of disease from a regional database onto a continuous surface. In addition to many interpolation techniques available the geostatistical method of kriging has been used but also criticised. RESULTS: To circumvent these critics one may use kriging along with already smoothed regional estimates, where smoothing is based on empirical Bayes estimates, also known as shrinkage estimates. The empirical Bayes step has the advantage of shrinking the unstable and often extreme estimates to the global or local mean, and also has a stabilising effect on variance by borrowing strength, as well. Negative interpolates are prevented by choice of the appropriate kriging method. The proposed mapping method is applied to the North Carolina SIDS data example as well as to an example data set from veterinary epidemiology. The SIDS data are modelled without spatial trend. And spatial interpolation is based on ordinary kriging. The second example is included to demonstrate the method when the phenomenon under study exhibits a spatial trend and interpolation is based on universal kriging. CONCLUSION: Interpolation of the regional estimates overcomes the areal bias problem and the resulting isopleth maps are easier to read than choropleth maps. The empirical Bayesian estimate for smoothing is related to internal standardization in epidemiology. Therefore, the proposed concept is easily communicable to map users.

Public health, GIS, and spatial analytic tools

We review literature that uses spatial analytic tools in contexts where Geographic Information Systems (GIS) is the organizing system for health data or where the methods discussed will likely be incorporated in GIS-based analyses in the future. We conclude the review with the point of view that this literature is moving toward the development and use of systems of analysis that integrate the information geo-coding and data base functions of GISystems with the geo-information processing functions of GIScience. The rapidity of this projected development will depend on the perceived needs of the public health community for spatial analysis methods to provide decision support. Recent advances in the analysis of disease maps have been influenced by and benefited from the adoption of new practices for georeferencing health data and new ways of linking such data geographically to potential sources of environmental exposures, the locations of health resources and the geodemographic characteristics of populations. This review focuses on these advances.

Bayesian logistic regression of soybean sclerotinia stem rot prevalence in the u.s. North-central region: accounting for uncertainty in parameter estimation

ABSTRACT Bayesian ideas have recently gained considerable ground in several scientific fields mainly due to the rapid progress in computing resources. Nevertheless, in plant epidemiology, Bayesian methodology is not yet commonly discussed or applied. Results of a logistic regression analysis of a 4-year data set collected between 1995 and 1998 on soybean Sclerotinia stem rot (SSR) prevalence in the north-central region of the United States were reexamined with Bayesian methodology. The objective of this study was to use Bayesian methodology to explore the level of uncertainty associated with the parameter estimates derived from the logistic regression analysis of SSR prevalence. Our results suggest that the 4-year data set used in the logistic regression analysis of SSR prevalence in the north-central region of the United States may not be informative enough to produce reliable estimates of the effect of some explanatory variables on SSR prevalence. Such confident estimations are necessary for deriving robust conclusions and high quality predictions.