Agricola Odoi

The incidence risk, clustering, and clinical presentation of La Crosse virus infections in the eastern United States, 2003-2007.

Background Although La Crosse virus (LACV) is one of the most common causes of pediatric arboviral infections in the United States, little has been done to assess its geographic distribution, identify areas of higher risk of disease, and to provide a national picture of its clinical presentation. Therefore, the objective of this study was to investigate the geographic distribution of LACV infections reported in the United States, to identify hot-spots of infection, and to present its clinical picture. Methods and Findings Descriptive and cluster analyses were performed on probable and confirmed cases of LACV infections reported to the Centers for Disease Control and Prevention from 2003–2007. A total of 282 patients had reported confirmed LACV infections during the study period. Of these cases the majority (81 percent) presented during the summer, occurred in children 15 years and younger (83.3 percent), and were found in male children (64.9 percent). Clinically, the infections presented as meningioencephalitis (56.3 percent), encephalitis (20.7 percent), meningitis (17.2 percent), or uncomplicated fever (5 percent). Deaths occurred in 1.9 percent of confirmed cases, and in 8.6 percent of patients suffering from encephalitis. The majority of these deaths were in patients 15 years and younger. The county-level incidence risk among counties (n = 136) reporting both probable and confirmed cases for children 15 years and younger (n = 355) ranged from 0.2 to 228.7 per 100,000 persons. The southern United States experienced a significantly higher (p<0.05) incidence risk during the months of June, July, August, and October then the northern United States. There was significant (p<0.05) clustering of high risk in several geographic regions with three deaths attributed to complications from LAC encephalitis occurring in two of these hot-spots of infections. Conclusions Both the incidence risk and case fatality rates were found to be higher than previously reported. We detected clustering in four geographic regions, a shift from the prior geographic distributions, and developed maps identifying high-risk areas. These findings are useful for raising awareness among health care providers regarding areas at a high risk of infections and for guiding targeted multifaceted interventions by public health officials.

Geographical and temporal distribution of human giardiasis in Ontario, Canada.

BackgroundGiardia is the most frequently identified intestinal parasite in North America. Although information on geographical distribution of giardiasis is critical in identifying communities at high risk, little has been done in this area. Therefore, the objective of this study was to investigate the geographical and temporal distribution of human giardiasis in Ontario in order to identify possible high risk areas and seasons. Two spatial scales of analyses and two disease measures were used with a view to identifying the best of each in assessing geographical patterns of giardiasis in Ontario. Global Morans I and Moran Local Indicators of Spatial Associations were used to test for evidence of global and local spatial clustering, respectively.ResultsThere were seasonal patterns with summer peaks and a significant (P < 0.001) decreasing temporal trend. Significant (P < 0.05) global spatial clustering of high rates was observed at the Census Sub-division spatial scale but not at the Census Division scale. The Census Sub-division scale was a better scale of analyses but required spatial empirical Bayesian smoothing of the rates. A number of areas with significant local clustering of giardiasis rates were identified.ConclusionsThe study identified spatial and temporal patterns in giardiasis distribution. This information is important in guiding decisions on disease control strategies. The study also showed that there is benefit in performing spatial analyses at more than one spatial scale to assess geographical patterns in disease distribution and that smoothing of disease rates for mapping in small areas enhances visualization of spatial patterns.

Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

BackgroundStroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.MethodsStroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.ResultsThere were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.ConclusionsThese methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.

Green tea catechin intervention of reactive oxygen species-mediated ERK pathway activation and chronically induced breast cell carcinogenesis

Long-term exposure to low doses of environmental carcinogens contributes to sporadic human breast cancers. Epidemiologic and experimental studies indicate that green tea catechins (GTCs) may intervene with breast cancer development. We have been developing a chronically induced breast cell carcinogenesis model wherein we repeatedly expose non-cancerous, human breast epithelial MCF10A cells to bioachievable picomolar concentrations of environmental carcinogens, such as 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and benzo[a]pyrene (B[a]P), to progressively induce cellular acquisition of cancer-associated properties, as measurable end points. The model is then used as a target to identify non-cytotoxic preventive agents effective in suppression of cellular carcinogenesis. Here, we demonstrate, for the first time, a two-step strategy that initially used end points that were transiently induced by short-term exposure to NNK and B[a]P as targets to detect GTCs capable of blocking the acquisition of cancer-associated properties and subsequently used end points constantly induced by long-term exposure to carcinogens as targets to verify GTCs capable of suppressing carcinogenesis. We detected that short-term exposure to NNK and B[a]P resulted in elevation of reactive oxygen species (ROS), leading to Raf-independent extracellular signal-regulated kinase (ERK) pathway activation and subsequent induction of cell proliferation and DNA damage. These GTCs, at non-cytotoxic levels, were able to suppress chronically induced cellular carcinogenesis by blocking carcinogen-induced ROS elevation, ERK activation, cell proliferation and DNA damage in each exposure cycle. Our model may help accelerate the identification of preventive agents to intervene in carcinogenesis induced by long-term exposure to environmental carcinogens, thereby safely and effectively reducing the health risk of sporadic breast cancer.

Investigation of Disparities in Geographic Accessibility to Emergency Stroke and Myocardial Infarction Care in East Tennessee Using Geographic Information Systems and Network Analysis

PURPOSE Stroke and myocardial infarction (MI) require timely geographic accessibility to emergency care. Historically, studies used straight line distances as measures of geographic accessibility. Recently, travel time has been recognized as a better indicator of accessibility because travel impedances can be considered. This study used finer grained transportation data and network analysis to investigate neighborhood disparities in travel time to emergency stroke and MI care. METHODS Travel times to stroke and cardiac centers were computed using network analysis, while considering distance, speed limit, road connectivity, and turn impedances. Neighborhoods within 30, 60, or 90 minutes travel were identified. Travel time by air ambulance was calculated and adjusted for flying speed and some delays. RESULTS Approximately 8% and 15% of the study population did not have timely geographic accessibility to emergency stroke and MI care, respectively. Populations with poor access were located in rural areas. The entire study population had timely access by air ambulance. CONCLUSIONS This study identified disparities in geographic accessibility to emergency stroke and MI care in East Tennessee. Use of air ambulance or telemedicine could play a vital role in addressing these disparities. This information is important for evidence-based health planning and resource allocation.

Risk factors of gastrointestinal nematode parasite infections in small ruminants kept in smallholder mixed farms in Kenya

BackgroundHelminth infections in small ruminants are serious problems in the developing world, particularly where nutrition and sanitation are poor. This study investigated the burden and risk factors of gastrointestinal nematode parasite infections in sheep and goats kept in smallholder mixed farms in the Kenyan Central Highlands. Three hundred and seven small ruminants were sampled from 66 smallholder mixed farms in agro-ecological zones 1 (humid) and 3 (semi-humid) in the Kenyan Central highlands. The farms were visited once a month for eight months during which a health and production survey questionnaire was administered. Fecal samples were collected at each visit from each animal. Fecal egg counts (FEC) were performed using the modified McMaster technique. Associations between potential risk factors and FEC were assessed using 3-level Poisson models fit in SAS using GLIMMIX macro. Correlations among repeated observations were adjusted for using three different correlation structures.ResultsA rise in FEC was observed two months after the onset of rains. Farmer education, age category, de-worming during the preceding month and grazing system were significant predictors of FEC. Additionally, there were significant interactions between grazing system and both de-worming and age category implying that the effect of grazing system is dependent on both de-worming status and age category; and that the effect of de-worming depends on the grazing system. The most important predictors of FEC in the study area were grazing system, de-worming status and education of the farmers.ConclusionSince several factors were important predictors of FEC, controlling gastrointestinal helminths of small ruminants in these resource-poor smallholder mixed farms requires a sustainable integrated helminth control strategy that includes adoption of zero-grazing and more farmer education probably through extension services. Achieving improved helminth controls in these resource-poor farming systems offers an opportunity to increase small ruminant productivity and hence has a potential of improving the livelihood of the resource-poor farmers.

Determinants of the geographical distribution of endemic giardiasis in Ontario, Canada: a spatial modelling approach

Giardiasis surveillance data as well as drinking water, socioeconomic and land-use data were used in spatial regression models to investigate determinants of the geographic distribution of endemic giardiasis in southern Ontario. Higher giardiasis rates were observed in areas using surface water [rate ratio (RR) 2.36, 95 % CI 1.38-4.05] and in rural areas (RR 1.79, 95 % CI 1.32-2.37). Lower rates were observed in areas using filtered water (RR 0.55, 95 % CI 0.42-0.94) and in those with high median income (RR 0.62, 95 % CI 0.42-0.92). Chlorination of drinking water, cattle density and intensity of manure application on farmland were not significant determinants. The study shows that waterborne transmission plays an important role in giardiasis distribution in southern Ontario and that well-collected routine surveillance data could be useful for investigation of disease determinants and identification of high-risk communities. This information is useful in guiding decisions on control strategies.

Inequalities in neighbourhood socioeconomic characteristics: potential evidence-base for neighbourhood health planning

BackgroundPopulation health planning aims to improve the health of the entire population and to reduce health inequities among population groups. Socioeconomic factors are increasingly being recognized as major determinants of many aspects of health and causes of health inequities. Knowledge of socioeconomic characteristics of neighbourhoods is necessary to identify their unique health needs and enhance identification of socioeconomically disadvantaged populations. Careful integration of this knowledge into health planning activities is necessary to ensure that health planning and service provision are tailored to unique neighbourhood population health needs. In this study, we identify unique neighbourhood socioeconomic characteristics and classify the neighbourhoods based on these characteristics. Principal components analysis (PCA) of 18 socioeconomic variables was used to identify the principal components explaining most of the variation in socioeconomic characteristics across the neighbourhoods. Cluster analysis was used to classify neighbourhoods based on their socioeconomic characteristics.ResultsResults of the PCA and cluster analysis were similar but the latter were more objective and easier to interpret. Five neighbourhood types with distinguishing socioeconomic and demographic characteristics were identified. The methodology provides a more complete picture of the neighbourhood socioeconomic characteristics than when a single variable (e.g. income) is used to classify neighbourhoods.ConclusionCluster analysis is useful for generating neighbourhood population socioeconomic and demographic characteristics that can be useful in guiding neighbourhood health planning and service provision. This study is the first of a series of studies designed to investigate health inequalities at the neighbourhood level with a view to providing evidence-base for health planners, service providers and policy makers to help address health inequity issues at the neighbourhood level. Subsequent studies will investigate inequalities in health outcomes both within and across the neighbourhood types identified in the current study.

Does place of residence affect risk of suicide? a spatial epidemiologic investigation in Kentucky from 1999 to 2008

BackgroundApproximately 32,000 people take their own lives every year in the United States. In Kentucky, suicide mortality rates have been steadily increasing since 1999. Few studies in the United States have assessed spatial clustering of suicides. The purpose of this study was to identify high-risk clusters of suicide at the county level in Kentucky and assess the characteristics of those suicide cases within the clusters.MethodsA spatial epidemiological study was undertaken using suicide data for the period January 1, 1999 to December 31, 2008, obtained from the Kentucky Office of Vital Statistics. Descriptive analyses using Pearsons chi-square test and t-test were performed to determine whether differences existed in age, marital status, year, season, and suicide method between males and females, and between cases inside and outside high-risk spatial clusters. Annual age-adjusted cumulative incidence rates were also calculated. Suicide incidence rates were spatially smoothed using the Spatial Empirical Bayesian technique. Kulldorffs spatial scan statistic was applied on all suicide cases at the county level to identify counties with the highest risks of suicide. Temporal cluster analysis was also performed.ResultsThere were a total of 5,551 suicide cases in Kentucky from 1999 to 2008, of which 5,237 (94%) were included in our analyses. The majority of suicide cases were males (82%). The average age of suicide victims was 45.4 years. Two statistically significant (p < 0.05) high-risk spatial clusters, involving 15 counties, were detected. The county level cumulative incidence rate in the most likely high-risk cluster ranged from 12.4 to 21.6 suicides per 100,000 persons. The counties inside both high-risk clusters had relative risks ranging from 1.24 to 1.38.ConclusionsStatistically significant high-risk spatial clusters of suicide were detected at the county level. This study may be useful for guiding future research and intervention efforts. Future studies will need to focus on these high-risk clusters to investigate reasons for these occurrences.

Socioeconomic determinants of geographic disparities in campylobacteriosis risk: a comparison of global and local modeling approaches.

BackgroundSocioeconomic factors play a complex role in determining the risk of campylobacteriosis. Understanding the spatial interplay between these factors and disease risk can guide disease control programs. Historically, Poisson and negative binomial models have been used to investigate determinants of geographic disparities in risk. Spatial regression models, which allow modeling of spatial effects, have been used to improve these modeling efforts. Geographically weighted regression (GWR) takes this a step further by estimating local regression coefficients, thereby allowing estimations of associations that vary in space. These recent approaches increase our understanding of how geography influences the associations between determinants and disease. Therefore the objectives of this study were to: (i) identify socioeconomic determinants of the geographic disparities of campylobacteriosis risk (ii) investigate if regression coefficients for the associations between socioeconomic factors and campylobacteriosis risk demonstrate spatial variability and (iii) compare the performance of four modeling approaches: negative binomial, spatial lag, global and local Poisson GWR.MethodsNegative binomial, spatial lag, global and local Poisson GWR modeling techniques were used to investigate associations between socioeconomic factors and geographic disparities in campylobacteriosis risk. The best fitting models were identified and compared.ResultsTwo competing four variable models (Models 1 & 2) were identified. Significant variables included race, unemployment rate, education attainment, urbanicity, and divorce rate. Local Poisson GWR had the best fit and showed evidence of spatially varying regression coefficients.ConclusionsThe international significance of this work is that it highlights the inadequacy of global regression strategies that estimate one parameter per independent variable, and therefore mask the true relationships between dependent and independent variables. Since local GWR estimate a regression coefficient for each location, it reveals the geographic differences in the associations. This implies that a factor may be an important determinant in some locations and not others. Incorporating this into health planning ensures that a needs-based, rather than a “one-size-fits-all”, approach is used. Thus, adding local GWR to the epidemiologists’ toolbox would allow them to assess how the impacts of different determinants vary by geography. This knowledge is critical for resource allocation in disease control programs.