Human rhinovirus spatial-temporal epidemiology in rural coastal Kenya, 2015-2016, observed through outpatient surveillance

Background Human rhinovirus (HRV) is the predominant cause of upper respiratory tract infections, resulting in a significant public health burden. The virus circulates as many different types (~160), each generating strong homologous, but weak heterotypic, immunity. The influence of these features on transmission patterns of HRV in the community is understudied. Methods Nasopharyngeal swabs were collected from patients with symptoms of acute respiratory infection (ARI) at nine out-patient facilities across a Health and Demographic Surveillance System between December 2015 and November 2016. HRV was diagnosed by real-time RT-PCR, and the VP4/VP2 genomic region of the positive samples sequenced. Phylogenetic analysis was used to determine the HRV types. Classification models and G-test statistic were used to investigate HRV type spatial distribution. Demographic characteristics and clinical features of ARI were also compared. Results Of 5,744 NPS samples collected, HRV was detected in 1057 (18.4%), of which 817 (77.3%) were successfully sequenced. HRV species A, B and C were identified in 360 (44.1%), 67 (8.2%) and 390 (47.7%) samples, respectively. In total, 87 types were determined: 39, 10 and 38 occurred within species A, B and C, respectively. HRV types presented heterogeneous temporal patterns of persistence. Spatially, identical types occurred over a wide distance at similar times, but there was statistically significant evidence for clustering of types between health facilities in close proximity or linked by major road networks. Conclusion This study records a high prevalence of HRV in out-patient presentations exhibiting high type diversity. Patterns of occurrence suggest frequent and independent community invasion of different types. Temporal differences of persistence between types may reflect variation in type-specific population immunity. Spatial patterns suggest either rapid spread or multiple invasions of the same type, but evidence of similar types amongst close health facilities, or along road systems, indicate type partitioning structured by local spread

Descriptive and spatial epidemiology of bovine cysticercosis in North-Eastern Spain (Catalonia).

From March 2005 to December 2007, 284 animals from 67 cattle farms (24 dairy and 43 beef) affected by bovine cysticercosis were detected in the region of Catalonia (North-Eastern Spain). Dairy farms were almost twice more likely to be affected than beef farms (OR=1.79, 95% CI=1.08-2.96, p<0.05), and infected premises have a statistically significant (p<0.05) larger number of animals when compared to uninfected farms in Catalonia. The geographical distribution of the infected farms was evaluated and two statistically significant clusters were identified. The most likely cluster was located in the western part of the study region, with 8 out of 10 farms infected. Epidemiological investigations revealed that the 8 farms belonged to the same company. The secondary cluster was located in Eastern Catalonia with 12 infected farms out of 167 cattle farms. No epidemiological links were found among the 12 infected premises. A questionnaire, based on the EFSA risk assessment, was used to assess the most likely route of introduction into each affected farm. Water supply for animals was the route with the highest score in 41.8% of the cases

Spatial epidemiology: New approaches to old questions

Spatial Epidemiology is used to describe, quantify and explain the geographical variations of diseases, to evaluate the association between the incidence of diseases and potential risk factors and to identify spatial clusters of diseases. This article goes through the main aspects of spatial epidemiology, starting with an explanation of the importance of mapping health data, an historical perspective of the development of the discipline, a description of spatial data types, some methods of spatial statistics, and the importance of Geographical Information Systems (GIS) in the analysis of spatially-referenced data. Some applications of GIS regarding oral health are presented.info:eu-repo/semantics/publishedVersio

Spatial Epidemiology: Current Approaches and Future Challenges

Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health

Spatial Epidemiology of Prediabetes and Diabetes in Florida

The burden of diabetes and diabetes-related Emergency Department (ED) visits has increased in Florida. However, Diabetes Self-management Education (DSME) Program participation remained considerably low. Little is known about disparities of DSME participation, diabetes complications, and ED use by diabetes patients in Florida and yet this information is important for guiding health programs aimed at reducing diabetes burden. Therefore, the objectives of this study were to investigate: (a) disparities of diabetes prevalence and DSME participation; (b) disparities of diabetes-related ED visit risks; and (c) prevalence and predictors of stroke among persons with prediabetes and diabetes. Behavioral Risk Factor Surveillance System and ED data were obtained from the Florida Department of Health and the Agency for Healthcare Administration, respectively. Data were aggregated to the county level. Temporal changes of diabetes prevalence, DSME participation, and ED visit were investigated. High-risk spatial clusters were identified using Tango’s flexible and Kulldorff’s circular spatial scan statistics. Predictors of DSME participation, ED visit, and stroke were investigated using ordinary least square and logistic regression models. Geographic distribution of significant (p≤0.05) spatial clusters and predictors were displayed on maps. There were significant (p≤0.05) increases in age-adjusted diabetes prevalence, DSME participation rates, and ED visit risks over time. Clusters of high diabetes prevalence and ED visit risks were identified in northern and central Florida, while clusters of high DSME participation rates were observed in central Florida. Rural counties and those with high proportions of Hispanic populations had low DSME participation rates. Counties with high proportions of populations that were Black, current smokers, uninsured, or with diabetes had significantly higher diabetes-related ED visit risks, while counties with high proportions of married individuals had significantly low ED visit risks. Individuals with prediabetes had high odds of strokes if they were ≥45 years old, had hypertension and hypercholesterolemia, while those with diabetes had high odds if they were non-Hispanic Black, hypertensive, and had depression. The identified disparities and predictors of diabetes prevalence, DSME participation, diabetes-related ED visit, and stroke among populations with prediabetes and diabetes are useful in guiding evidence-based health planning and resource allocation in combating the diabetes problem in Florida

Identifying Clusters in Bayesian Disease Mapping

Disease mapping is the field of spatial epidemiology interested in estimating the spatial pattern in disease risk across $n$ areal units. One aim is to identify units exhibiting elevated disease risks, so that public health interventions can be made. Bayesian hierarchical models with a spatially smooth conditional autoregressive prior are used for this purpose, but they cannot identify the spatial extent of high-risk clusters. Therefore we propose a two stage solution to this problem, with the first stage being a spatially adjusted hierarchical agglomerative clustering algorithm. This algorithm is applied to data prior to the study period, and produces $n$ potential cluster structures for the disease data. The second stage fits a separate Poisson log-linear model to the study data for each cluster structure, which allows for step-changes in risk where two clusters meet. The most appropriate cluster structure is chosen by model comparison techniques, specifically by minimising the Deviance Information Criterion. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

Spatial epidemiology of hospital-diagnosed brucellosis in Kampala, Uganda

<p>Abstract</p> <p>Background</p> <p>A retrospective case-control study was undertaken to examine the spatial risk factors for human brucellosis in Kampala, Uganda.</p> <p>Methods</p> <p>Information on age, sex and month of diagnosis was derived from records from plate agglutination tests undertaken at Mulago Hospital, Kampala. Information on Parishes (LC2s) where patients reside was sourced from the outpatient registration book. In-patient fracture cases were selected for use as controls using 1:1 matching based on the age, sex and month of diagnosis. The locations of cases and controls were obtained by calculating Cartesian coordinates of the centroids of Parish level (LC2) polygons and a spatial scan statistic was applied to test for disease clustering. Parishes were classified according to the level of urbanization as urban, peri-urban or rural.</p> <p>Results</p> <p>Significantly more females than males were found to show sero-positivity for brucellosis when compared with the sex ratio of total outpatients, in addition female brucellosis patients were found to be significantly older than the male patients. Spatial clustering of brucellosis cases was observed including around Mulago Hospital (radius = 6.8 km, <it>p </it>= 0.001). The influence of proximity to the hospital that was observed for brucellosis cases was not significantly different from that observed in the controls. The disease cluster was confounded by the different catchment areas between cases and controls. The level of urbanization was not associated with the incidence of brucellosis but living in a slum area was a significant risk factor among urban dwellers (odds ratio 1.97, 95% CI: 1.10-3.61).</p> <p>Conclusions</p> <p>Being female was observed to be a risk factor for brucellosis sero-positvity and among urban dwellers, living in slum areas was also a risk factor although the overall risk was not different among urban, peri-urban and rural areas of the Kampala economic zone.</p