ORCID Profile
0000-0002-5876-165X
Current Organisations
University of Queensland
,
Universidad de Santander
,
Queensland University of Technology
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Publisher: Springer Science and Business Media LLC
Date: 20-07-2023
DOI: 10.1186/S12889-023-16179-5
Abstract: Acute respiratory infections (ARI) in Cúcuta -Colombia, have a comparatively high burden of disease associated with high public health costs. However, little is known about the epidemiology of these diseases in the city and its distribution within suburban areas. This study addresses this gap by estimating and mapping the risk of ARI in Cúcuta and identifying the most relevant risk factors. A spatial epidemiological analysis was designed to investigate the association of sociodemographic and environmental risk factors with the rate of ambulatory consultations of ARI in urban sections of Cúcuta, 2018. The ARI rate was calculated using a method for spatial estimation of disease rates. A Bayesian spatial model was implemented using the Integrated Nested Laplace Approximation approach and the Besag-York-Mollié specification. The risk of ARI per urban section and the hotspots of higher risk were also estimated and mapped. A higher risk of IRA was found in central, south, north and west areas of Cúcuta after adjusting for sociodemographic and environmental factors, and taking into consideration the spatial distribution of the city’s urban sections. An increase of one unit in the percentage of population younger than 15 years the Index of Multidimensional Poverty and the rate of ARI in the migrant population was associated with a 1.08 (1.06—1.1) 1.04 (1.01—1.08) and 1.25 (1.22—1.27) increase of the ARI rate, respectively. Twenty-four urban sections were identified as hotspots of risk in central, south, north and west areas in Cucuta. Sociodemographic factors and their spatial patterns are determinants of acute respiratory infections in Cúcuta. Bayesian spatial hierarchical models can be used to estimate and map the risk of these infections in suburban areas of large cities in Colombia. The methods of this study can be used globally to identify suburban areas and or specific communities at risk to support the implementation of prevention strategies and decision-making in the public and private health sectors.
Publisher: Elsevier BV
Date: 04-2021
Publisher: Ubiquity Press, Ltd.
Date: 2022
DOI: 10.5334/AOGH.3770
Publisher: Springer Science and Business Media LLC
Date: 11-06-2018
Publisher: Springer Science and Business Media LLC
Date: 20-01-2023
DOI: 10.1007/S10708-022-10822-1
Abstract: Morbidity statistics can be reported as grouped data for health services rather than for in idual residence area, especially in low-middle income countries. Although such reports can support some evidence-based decisions, these are of limited use if the geographical distribution of morbidity cannot be identified. This study estimates the spatial rate of Acute respiratory infections (ARI) in census districts in Cúcuta -Colombia, using an analysis of the spatial distribution of health services providers. The spatial scope (geographical area of influence) of each health service was established from their spatial distribution and the population covered. Three levels of spatial aggregation were established considering the spatial scope of primary, intermediate and tertiary health services providers. The ARI cases per census district were then calculated and mapped using the distribution of cases per health services provider and the proportion of population per district in each level respectively. Hotspots of risk were identified using the Local Moran’s I statistic. There were 98 health services providers that attended 8994, 18,450 and 91,025 ARI cases in spatial levels 1, 2 and 3, respectively. Higher spatial rates of ARI were found in districts in central south northwest and northeast and southwest Cúcuta with hotspots of risk found in central and central south and west and northwest Cucuta. The method used allowed overcoming the limitations of health data lacking area of residence information to implementing epidemiological analyses to identify at risk communities. This methodology can be used in socioeconomic contexts where geographic identifiers are not attached to health statistics.
Publisher: Walter de Gruyter GmbH
Date: 11-10-2019
Abstract: The potential impacts of coal mining on health have been addressed by the application of impact assessment methodologies that use the results of qualitative and quantitative analyses to support their conclusions and recommendations. Although human epidemiological analyses can provide the most relevant measures of risk of health outcomes in populations exposed to coal mining by-products, this kind of studies are seldom implemented as part of the impact assessment methods. To review the use of human epidemiological analyses in the methods used to assess the impacts of coal mining, a systematic search in the peer review literature was implemented following the PRISMA protocol. A synthesis analysis identified the methods and the measures used in the selected publications to develop a thematic review and discussion. The major methodological approaches to assess the impacts of coal mining are environmental impact assessment (EIA), health impact assessment (HIA), social impact assessment (SIA) and environmental health impact assessment (EHIA). The measures used to assess the impacts of coal mining on health were classified as the estimates from non-human-based studies such as health risk assessment (HRA) and the measures of risk from human epidemiological analyses. The inclusion of human epidemiological estimates of the populations exposed, especially the general populations in the vicinity of the mining activities, is seldom found in impact assessment applications for coal mining. These methods rather incorporate HRA measures or other sources of evidence such as qualitative analyses and surveys. The implementation of impact assessment methods without estimates of the risk of health outcomes relevant to the potentially exposed populations affects their reliability to address the environmental and health impacts of coal mining. This is particularly important for EIA applications because these are incorporated in regulatory frameworks globally. The effective characterization of the impacts of coal mining on health requires quantitative estimates of the risk, including the risk measures from epidemiological analyses of relevant human health data.
Publisher: MDPI AG
Date: 21-01-2022
Abstract: The populations in the vicinity of surface coal mining activities have a higher risk of morbidity due to diseases, such as cardiovascular, respiratory and hypertensive diseases, as well as cancer and diabetes mellitus. Despite the large and historical volume of coal production in Queensland, the main Australian coal mining state, there is little research on the association of coal mining exposures with morbidity in non-occupational populations in this region. This study explored the association of coal production (Gross Raw Output—GRO) with hospitalisations due to six disease groups in Queensland using a Bayesian spatial hierarchical analysis and considering the spatial distribution of the Local Government Areas (LGAs). There is a positive association of GRO with hospitalisations due to circulatory diseases (1.022, 99% CI: 1.002–1.043) and respiratory diseases (1.031, 95% CI: 1.001–1.062) for the whole of Queensland. A higher risk of circulatory, respiratory and chronic lower respiratory diseases is found in LGAs in northwest and central Queensland and a higher risk of hypertensive diseases, diabetes mellitus and lung cancer is found in LGAs in north, west, and north and southeast Queensland, respectively. These findings can be used to support public health strategies to protect communities at risk. Further research is needed to identify the causal links between coal mining and morbidity in non-occupational populations in Queensland.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Cold Spring Harbor Laboratory
Date: 19-04-2022
DOI: 10.1101/2022.04.18.22274002
Abstract: Morbidity statistics can be reported as grouped data for health services rather than for in idual residence area, especially in low-middle income countries. Although such reports can support some evidence-based decisions, these are of limited use if the geographical distribution of morbidity cannot be identified. This study estimates the spatial rate of Acute respiratory infections (ARI) in census districts in Cúcuta -Colombia, using an analysis of the spatial distribution of health services providers. The spatial scope (geographical area of influence) of each health service was established from their spatial distribution and the population covered. Three levels of spatial aggregation were established considering the spatial scope of primary, intermediate and tertiary health services providers. The ARI cases per census district were then calculated and mapped using the distribution of cases per health services provider and the proportion of population per district in each level respectively. Hotspots of risk were identified using the Local Moran’s I statistic. There were 98 health services providers that attended 8994, 18450 and 91025 ARI cases in spatial levels 1, 2 and 3, respectively. Higher spatial rates of ARI were found in districts in central south northwest and northeast and southwest Cúcuta with hotspots of risk found in central and central south and west and northwest Cucuta. The method used allowed overcoming the limitations of health data lacking area of residence information to implementing epidemiological analyses to identify at risk communities. This methodology can be used in socioeconomic contexts where geographic identifiers are not attached to health statistics.
Publisher: Universidade de Sao Paulo, Agencia USP de Gestao da Informacao Academica (AGUIA)
Date: 10-07-2020
DOI: 10.11606/S1518-8787.2020054002481
Abstract: The World Health Organization has emphasized that one of the most important questions to address regarding the covid-19 pandemic is to understand risk factors for disease severity. We conducted a brief review that synthesizes the available evidence and provides a judgment on the consistency of the association between risk factors and a composite end-point of severe-fatal covid-19. Additionally, we also conducted a comparability analysis of risk factors across 17 studies. We found evidence supporting a total of 60 predictors for disease severity, of which seven were deemed of high consistency, 40 of medium and 13 of low. Among the factors with high consistency of association, we found age, C-reactive protein, D-dimer, albumin, body temperature, SOFA score and diabetes. The results suggest that diabetes might be the most consistent comorbidity predicting disease severity and that future research should carefully consider the comparability of reporting cases, factors, and outcomes along the different stages of the natural history of covid-19.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 08-11-2019
Publisher: University of Queensland Library
Date: 2019
Publisher: Elsevier BV
Date: 02-2022
Publisher: MDPI AG
Date: 25-06-2022
Abstract: Electronic waste management is a global rising concern that is primarily being handled by informal recycling practices. These release a mix of potentially hazardous chemicals, which is an important public health concern. These chemicals include polybrominated diphenyl ethers (PBDEs), used as flame retardants in electronic parts, which are persistent in nature and show bioaccumulative characteristics. Although PBDEs are suspected endocrine disruptors, particularly targeting thyroid and reproductive hormone functions, the relationship of PBDEs with these health effects are not well established. We used the Navigation Guide methodology to conduct a systematic review of studies in populations exposed to e-waste to better understand the relationships of these persistent flame retardants with hormonal and reproductive health. We assessed nineteen studies that fit our pre-determined inclusion criteria for risk of bias, indirectness, inconsistency, imprecision, and other criteria that helped rate the overall evidence for its quality and strength of evidence. The studies suggest PBDEs may have an adverse effect on thyroid hormones, reproductive hormones, semen quality, and neonatal health. However, more research is required to establish a relationship of these effects in the e-waste-exposed population. We identified the limitations of the data available and made recommendations for future scientific work.
No related grants have been discovered for Javier Cortes-Ramirez.