Linkages between FAO agroclimatic data resources and the development of GIS models for control of vector-borne diseases

Bernardi, Michele

doi:10.1016/s0001-706x(01)00100-0

Cited by 23 publications

(12 citation statements)

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“…Spatial analysis, the linking of diseases to geographic areas, is a fundamental epidemiologic tool dating back to the earliest days of epidemiology when John Snow linked a London cholera epidemic to a contaminated well. [21][22][23] It has continued to be used as an essential tool in defining the epidemiology of a wide range of infectious diseases [24][25][26][27][28] and is evolving as an important approach to environmental and other areas of epidemiology. [29][30][31][32][33][34][35][36][37] Its relevance to a broad range of health issues such as diabetes, 38 childhood lead poisoning, 39 pediatric burn injuries, 40 fertility, 41 cancer screening, 42 general chronic disease prevention, 43 and health services research 44 is also unfolding.…”

Section: Spatial Analysismentioning

confidence: 99%

The Health Status of Southern Children: A Neglected Regional Disparity

et al. 2005

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ABSTRACT. 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 statespecific 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. Multipleregression 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 DeepSouth 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 imp...

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Section: Spatial Analysismentioning

confidence: 99%

The Health Status of Southern Children: A Neglected Regional Disparity

et al. 2005

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“…Geographical information system (GIS) and remote sensing (RS) have been used to define the epidemiology of schistosomiasis in many parts of the world [ 5 - 10 ]. Many organizations such as Food and Agriculture Organization (FAO) and United Nations are making RS datasets from earth-observing satellites freely available for researchers through the Internet following increasing recognition that these datasets facilitate the development of powerful tools for disease control [ 11 - 13 ].…”

Section: Introductionmentioning

confidence: 99%

Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

et al. 2008

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Background: The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria. Methods:Infection data used were derived from carefully validated morbidity questionnaires among primary school children in [2001][2002], in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state.Results: Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST) (B = 0.308, p = 0.013). While LST (B = -0.478, p = 0.035), rainfall (B = -0.006, p = 0.0005), ferric luvisols (B = 0.539, p = 0.274), dystric nitosols (B = 0.133, p = 0.769) and pellic vertisols (B = 1.386, p = 0.008) soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs) in State. The high-risk areas (≥ 50% prevalence) however, are confined to scatter foci in the north western part of the State. The model also estimated that 98.99% of schools aged children (5-14 years) are living in areas suitable for urinary schistosomiasis transmission and are at risk of infection. Conclusion:The risk maps developed will hopefully be useful to the state health officials, by providing them with detailed distribution of urinary schistosomiasis, help to delineate areas for intervention, assesses population at risk thereby helping in optimizing scarce resources.

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“…These models can also be important when attempting to predict distribution shifts due to climate change. Aragon and Lobo [ 69 ] used a multidimensional envelope model customized for DVV while Bernardi et al [ 70 ] used a geographic information system and agroclimate data to estimate possible European distributions. Eitsinger et al [ 71 ] expanded this to include soil temperature and ground cover data to estimate lower developmental temperature thresholds.…”

Section: Models Of Diabrotica Geographic Spreadmentioning

confidence: 99%

Models of Diabrotica Populations: Demography, Population Genetics, Geographic Spread, and Management

Onstad

Caprio

Pan

2020

Insects

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Both Diabrotica virgifera virgifera LeConte and D. barberi Smith and Lawrence are among the most damaging insects impacting corn in North America. D. virgifera virgifera has also invaded Europe and has become an important pest in that region. Computer models have become an important tool for understanding the impact and spread of these important pests. Over the past 30 years, over 40 models have been published related to these pests. The focus of these models range from occupancy models (particularly for Europe), impact of climate change, range expansion, economics of pest management, phenology, to the evolution of resistance to toxins and crop rotation. All of these models share characteristics. We elaborate on the methods in which modelers have incorporated the biology of these pests, including density-dependence, movement, fecundity and overwintering mortality. We discuss the utility of both spatially-explicit, complex models and spatially-implicit, generational models and where each might be appropriate. We review resistance models that either explain past evolution to crop rotation, insecticides or insecticidal traits or attempt to predict the consequences of resistance management strategies.

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Linkages between FAO agroclimatic data resources and the development of GIS models for control of vector-borne diseases

Cited by 23 publications

References 0 publications

The Health Status of Southern Children: A Neglected Regional Disparity

The Health Status of Southern Children: A Neglected Regional Disparity

Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

Models of Diabrotica Populations: Demography, Population Genetics, Geographic Spread, and Management

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