Editorial

3 pages, 191 KiB

Open AccessEditorial

Geospatial Health (GeoHealth): Current Trends, Methods, and Applications

by Frank Badu Osei and Santanu Sasidharan

Trop. Med. Infect. Dis. 2023, 8(7), 366; https://doi.org/10.3390/tropicalmed8070366 - 17 Jul 2023

Viewed by 1436

As an emerging field, Geospatial Health (GeoHealth) integrates geospatial technologies, (spatial) epidemiology, and health services/resource allocations (health accessibility), with a focus to fight the burden of diseases [...] Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

Research

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8 pages, 989 KiB

Open AccessCommunication

A Practical Approach in Refining Binary Outcome for Treatment Effect of COVID-19 According to Geographical Diversity

by I-Shiang Tzeng

Trop. Med. Infect. Dis. 2023, 8(2), 83; https://doi.org/10.3390/tropicalmed8020083 - 26 Jan 2023

Cited by 4 | Viewed by 958

Abstract

The recent COVID-19 pandemic has drawn attention to health and economics worldwide. Initially, diseases only ravage local populations, while a pandemic could aggravate global economic burdens. Lopinavir/Ritonavir is an anti-HIV drug that was used on small scale patients during SARS, but its effectiveness [...] Read more.

The recent COVID-19 pandemic has drawn attention to health and economics worldwide. Initially, diseases only ravage local populations, while a pandemic could aggravate global economic burdens. Lopinavir/Ritonavir is an anti-HIV drug that was used on small scale patients during SARS, but its effectiveness for COVID-19 treatment is still unclear. Previous studies or meta-analysis have retrieved clinical data of subgroup analysis to evaluate the efficacy and safety of Lopinavir/Ritonavir for the treatment of COVID-19 in a few affected regions. However, geographical diversity and small number of studies bias correction were not achieved in such subgroup analysis of published meta-analysis. The present study demonstrates a practical approach in refining the binary outcome for COVID-19 treatment of Lopinavir/Ritonavir according to geographical location diversity and small number of studies (less than or equal to five) for subgroup analysis. After performing practical approach, the risk of adverse event with LPV/RTV for treatment of COVID-19 becomes nonsignificant compared to previous meta-analysis. Furthermore, we also notice heterogeneity of random effect of meta-analysis may be declined after proposed adjustment. In conclusion, proposed practical approach is recommend for performing a subgroup analysis to avoid concentration in a single geographical location and small number of studies bias. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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12 pages, 748 KiB

Open AccessArticle

Spatio-Temporal Pattern and Meteo-Climatic Determinants of Visceral Leishmaniasis in Italy

by Giovenale Moirano, Marta Ellena, Paola Mercogliano, Lorenzo Richiardi and Milena Maule

Trop. Med. Infect. Dis. 2022, 7(11), 337; https://doi.org/10.3390/tropicalmed7110337 - 29 Oct 2022

Cited by 11 | Viewed by 1382

Abstract

Historically, visceral leishmaniasis (VL) in Italy was constrained to Mediterranean areas. However, in the last 20 years, sand fly vectors and human cases of VL have been detected in northern Italy, traditionally classified as a cold area unsuitable for sand fly survival. We [...] Read more.

Historically, visceral leishmaniasis (VL) in Italy was constrained to Mediterranean areas. However, in the last 20 years, sand fly vectors and human cases of VL have been detected in northern Italy, traditionally classified as a cold area unsuitable for sand fly survival. We aim to study the spatio-temporal pattern and climatic determinants of VL incidence in Italy. National Hospital Discharge Register records were used to identify incident cases of VL between 2009 and 2016. Incident rates were computed for each year (N = 8) and for each province (N = 110). Data on mean temperature and cumulative precipitation were obtained from the ERA5-Land re-analysis. Age- and sex-standardized incidence rates were modeled with Bayesian spatial and spatio-temporal conditional autoregressive Poisson models in relation to the meteo-climatic parameters. Statistical inference was based on Monte Carlo–Markov chains. We identified 1123 VL cases (incidence rate: 2.4 cases/1,000,000 person-years). The highest incidence rates were observed in southern Italy, even though some areas of northern Italy experienced high incidence rates. Overall, in the spatial analysis, VL incidence rates were positively associated with average air temperatures (

β

for 1 °C increase in average mean average temperature: 0.14; 95% credible intervals (CrI): 0.01, 0.27) and inversely associated with average precipitation (

β

for 20 mm increase in average summer cumulative precipitation: −0.28, 95% CrI: −0.42, −0.13). In the spatio-temporal analysis, no association between VL cases and season-year specific temperature and precipitation anomalies was detected. Our findings indicate that VL is endemic in the whole Italian peninsula and that climatic factors, such as air temperature and precipitation, might play a relevant role in shaping the geographical distribution of VL cases. These results support that climate change might affect leishmaniasis distribution in the future. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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Graphical abstract

36 pages, 50702 KiB

Open AccessArticle

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

by Yong Zhou, Kaixu Zhao, Junling Han, Sidong Zhao and Jingyuan Cao

Trop. Med. Infect. Dis. 2022, 7(10), 292; https://doi.org/10.3390/tropicalmed7100292 - 10 Oct 2022

Cited by 9 | Viewed by 1987

Abstract

(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) [...] Read more.

(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) Methods: Based on the competitive state model and spatial mismatch index, this paper uses GIS and Geodetector spatial analysis methods and three typical indicators of hospitals, doctors, and beds to conduct an empirical study on the evolutionary characteristics and degree of mismatch in the geographic pattern of health resources in China from 2010 to 2020 (the data are from official publications issued by the National Bureau of statistics in China), in two dimensions of resource supply (economic carrying capacity) and demand (potential demand or need of residents). (3) Results: The spatial pattern of health resources at the provincial level in China has been firmly established for a long time, and the children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30–40% of the factor pairs are in a nonlinear enhancement relationship. Hospital, doctor, and bed evolution trends and the magnitude and speed of their changes vary widely in spatial differentiation, but all are characterized by a high level of geographic agglomeration, heterogeneity, and gradient. Dynamic matching is the mainstream of development, while the geographical distribution of negative and positive mismatch shows strong spatial agglomeration and weak spatial autocorrelation. The cold and hot spots with evolution trend and space mismatch are highly clustered, shaping a center-periphery or gradient-varying spatial structure. (4) Conclusions: Despite the variability in the results of the analyses by different dimensions and indicators, the mismatch of health resources in China should not be ignored. According to the mismatch types and change trend, and following the geographic differentiation and spatial agglomeration patterns, this paper constructs a policy design framework of “regionalized governance-classified management”, in line with the concept of spatial adaptation and spatial justice, in order to provide a decision making basis for the government to optimize the allocation of health resources and carry out health spatial planning. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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15 pages, 444 KiB

Open AccessArticle

Factors Influencing False-Negative Results of QuantiFERON-TB Gold In-Tube (QFT-GIT) in Active Tuberculosis and the Desirability of Resetting Cutoffs for Different Populations: A Retrospective Study

by Yuanyuan Yu, Yidian Liu, Lan Yao, Yanheng Shen, Qin Sun and Wei Sha

Trop. Med. Infect. Dis. 2022, 7(10), 278; https://doi.org/10.3390/tropicalmed7100278 - 30 Sep 2022

Cited by 4 | Viewed by 2145

Abstract

Objectives The value of QuantiFERON-TB Gold In-Tube (QFT-GIT) in the diagnosis of TB varies by population, comorbidities, and other factors. In this study, we aimed to investigate factors that influence false-negative results of QFT-GIT test in the diagnosis of TB as well as [...] Read more.

Objectives The value of QuantiFERON-TB Gold In-Tube (QFT-GIT) in the diagnosis of TB varies by population, comorbidities, and other factors. In this study, we aimed to investigate factors that influence false-negative results of QFT-GIT test in the diagnosis of TB as well as the impact of different cutoffs on the diagnostic value. Methods A total of 3562 patients who underwent QFT-GIT tests at Shanghai Pulmonary Hospital were enrolled retrospectively between May 2016 and May 2017. False-negative and false-positive results were analyzed using different clinical stratifications. The optimal cutoff values were established under different clinical conditions. Results Positive QFT-GIT results greatly shortened the time taken to diagnose smear-negative TB. The factors of age, smear and culture results, site of TB, comorbidity with tumors, white blood cell count, neutrophil count, and CD4/CD8 ratio were significantly correlated with false-negative QFT-GIT results (p < 0.05). Personalized cutoff values were established according to different influencing factors. The results showed high consistency between the smear-negative and total populations. Conclusion QFT-GIT can facilitate the early diagnosis of smear-negative TB. The diagnostic performance of the QFT-GIT test in the diagnosis of active TB was shown to be affected by many clinical factors. Personalized cutoff values may have superior value in the identification of active tuberculosis under different conditions. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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17 pages, 3378 KiB

Open AccessEditor’s ChoiceArticle

Healthcare Management of Human African Trypanosomiasis Cases in the Eastern, Muchinga and Lusaka Provinces of Zambia

by Allan Mayaba Mwiinde, Martin Simuunza, Boniface Namangala, Chitalu Miriam Chama-Chiliba, Noreen Machila, Neil E. Anderson, Peter M. Atkinson and Susan C. Welburn

Trop. Med. Infect. Dis. 2022, 7(10), 270; https://doi.org/10.3390/tropicalmed7100270 - 27 Sep 2022

Cited by 1 | Viewed by 2095

Abstract

Human African trypanosomiasis (HAT) is a neglected tropical disease that has not received much attention in Zambia and most of the countries in which it occurs. In this study, we assessed the adequacy of the healthcare delivery system in diagnosis and management of [...] Read more.

Human African trypanosomiasis (HAT) is a neglected tropical disease that has not received much attention in Zambia and most of the countries in which it occurs. In this study, we assessed the adequacy of the healthcare delivery system in diagnosis and management of rHAT cases, the environmental factors associated with transmission, the population at risk and the geographical location of rHAT cases. Structured questionnaires, focus group discussions and key informant interviews were conducted among the affected communities and health workers. The study identified 64 cases of rHAT, of which 26 were identified through active surveillance and 38 through passive surveillance. We identified a significant association between knowledge of the vector for rHAT and knowledge of rHAT transmission (p < 0.028). In all four districts, late or poor diagnosis occurred due to a lack of qualified laboratory technicians and diagnostic equipment. This study reveals that the current Zambian healthcare system is not able to adequately handle rHAT cases. Targeted policies to improve staff training in rHAT disease detection and management are needed to ensure that sustainable elimination of this public health problem is achieved in line with global targets. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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14 pages, 1610 KiB

Open AccessArticle

The Use of Spatial Video to Map Dynamic and Challenging Environments: A Case Study of Cholera Risk in the Mujoga Relief Camp, D.R.C.

by Andrew J. Curtis, Felicien Maisha, Jayakrishnan Ajayakumar, Sandra Bempah, Afsar Ali and J. Glenn Morris, Jr.

Trop. Med. Infect. Dis. 2022, 7(10), 257; https://doi.org/10.3390/tropicalmed7100257 - 22 Sep 2022

Cited by 1 | Viewed by 1609

Abstract

In this paper, we provide an overview of how spatial video data collection enriched with contextual mapping can be used as a universal tool to investigate sub-neighborhood scale health risks, including cholera, in challenging environments. To illustrate the method’s flexibility, we consider the [...] Read more.

In this paper, we provide an overview of how spatial video data collection enriched with contextual mapping can be used as a universal tool to investigate sub-neighborhood scale health risks, including cholera, in challenging environments. To illustrate the method’s flexibility, we consider the life cycle of the Mujoga relief camp set up after the Nyiragongo volcanic eruption in the Democratic Republic of Congo on 22 May 2021. More specifically we investigate how these methods have captured the deteriorating conditions in a camp which is also experiencing lab-confirmed cholera cases. Spatial video data are collected every month from June 2021 to March 2022. These coordinate-tagged images are used to make monthly camp maps, which are then returned to the field teams for added contextual insights. At the same time, a zoom-based geonarrative is used to discuss the camp’s changes, including the cessation of free water supplies and the visible deterioration of toilet facilities. The paper concludes by highlighting the next data science advances to be made with SV mapping, including machine learning to automatically identify and map risks, and how these are already being applied in Mujoga. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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14 pages, 2889 KiB

Open AccessArticle

Using Routinely Collected Health Records to Identify the Fine-Resolution Spatial Patterns of Soil-Transmitted Helminth Infections in Rwanda

by Elias Nyandwi, Tom Veldkamp, Sherif Amer, Eugene Ruberanziza, Nadine Rujeni and Ireneé Umulisa

Trop. Med. Infect. Dis. 2022, 7(8), 202; https://doi.org/10.3390/tropicalmed7080202 - 22 Aug 2022

Cited by 2 | Viewed by 1634

Abstract

Background. Soil-transmitted helminths (STH) are parasitic diseases with significant public health impact. Analysis is generally based on cross-sectional prevalence surveys; outcomes are mostly aggregated to larger spatial units. However, recent research demonstrates that infection levels and spatial patterns differ between STH species and [...] Read more.

Background. Soil-transmitted helminths (STH) are parasitic diseases with significant public health impact. Analysis is generally based on cross-sectional prevalence surveys; outcomes are mostly aggregated to larger spatial units. However, recent research demonstrates that infection levels and spatial patterns differ between STH species and tend to be localized. Methods. Incidence data of STHs including roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura) and hookworms per primary health facility for 2008 were linked to spatially delineated primary health center service areas. Prevalence data per district for individual and combined STH infections from the 2008 nationwide survey in Rwanda were also obtained. Results. A comparison of reported prevalence and incidence data indicated significant positive correlations for roundworm (R² = 0.63) and hookworm (R² = 0.27). Weak positive correlations were observed for whipworm (R² = 0.02) and the three STHs combined (R² = 0.10). Incidence of roundworm and whipworm were found to be focalized with significant spatial autocorrelation (Moran’s I > 0: 0.05–0.38 and p ≤ 0.03), with (very) high incidence rates in some focal areas. In contrast, hookworm incidence is ubiquitous and randomly distributed (Moran’s I > 0: 0.006 and p = 0.74) with very low incidence rates. Furthermore, an exploratory regression analysis identified relationships between helminth infection cases and potential environmental and socio-economic risk factors. Conclusions. Findings show that the spatial distribution of STH incidence is significantly associated with soil properties (sand proportion and pH), rainfall, wetlands and their uses, population density and proportion of rural residents. Identified spatial patterns are important for guiding STH prevention and control programs. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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19 pages, 2450 KiB

Open AccessArticle

Optimal Validated Multi-Factorial Climate Change Risk Assessment for Adaptation Planning and Evaluation of Infectious Disease: A Case Study of Dengue Hemorrhagic Fever in Indonesia

by Lia Faridah, Djoko Santoso Abi Suroso, Muhammad Suhardjono Fitriyanto, Clarisa Dity Andari, Isnan Fauzi, Yonatan Kurniawan and Kozo Watanabe

Trop. Med. Infect. Dis. 2022, 7(8), 172; https://doi.org/10.3390/tropicalmed7080172 - 08 Aug 2022

Cited by 2 | Viewed by 2144

Abstract

(1) Background: This paper will present an elaboration of the risk assessment methodology by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), Eurac Research and United Nations University Institute for Environment and Human Security (UNU-EHS) for the assessment of dengue. (2) Methods: We validate [...] Read more.

(1) Background: This paper will present an elaboration of the risk assessment methodology by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), Eurac Research and United Nations University Institute for Environment and Human Security (UNU-EHS) for the assessment of dengue. (2) Methods: We validate the risk assessment model by best-fitting it with the number of dengue cases per province using the least-square fitting method. Seven out of thirty-four provinces in Indonesia were chosen (North Sumatra, Jakarta Capital, West Java, Central Java, East Java, Bali and East Kalimantan). (3) Results: A risk assessment based on the number of dengue cases showed an increased risk in 2010, 2015 and 2016 in which the effects of El Nino and La Nina extreme climates occurred. North Sumatra, Bali, and West Java were more influenced by the vulnerability component, in line with their risk analysis that tends to be lower than the other provinces in 2010, 2015 and 2016 when El Nino and La Nina occurred. (4) Conclusion: Based on data from the last ten years, in Jakarta Capital, Central Java, East Java and East Kalimantan, dengue risks were mainly influenced by the climatic hazard component while North Sumatra, Bali and West Java were more influenced by the vulnerability component. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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14 pages, 3162 KiB

Open AccessArticle

Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic

by Sayambhu Saita, Sasithan Maeakhian and Tassanee Silawan

Trop. Med. Infect. Dis. 2022, 7(8), 171; https://doi.org/10.3390/tropicalmed7080171 - 08 Aug 2022

Cited by 6 | Viewed by 3195

Abstract

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) [...] Read more.

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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9 pages, 1521 KiB

Open AccessArticle

Human-Altered Landscapes and Climate to Predict Human Infectious Disease Hotspots

by Soushieta Jagadesh, Marine Combe and Rodolphe Elie Gozlan

Trop. Med. Infect. Dis. 2022, 7(7), 124; https://doi.org/10.3390/tropicalmed7070124 - 01 Jul 2022

Cited by 2 | Viewed by 1807

Abstract

Background: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to [...] Read more.

Background: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. Methods: We modelled presence–absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. Results: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. Conclusions: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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12 pages, 1091 KiB

Open AccessArticle

Identifying Hotspots of People Diagnosed of Tuberculosis with Addiction to Alcohol, Tobacco, and Other Drugs through a Geospatial Intelligence Application in Communities from Southern Brazil

by Alessandro Rolim Scholze, Felipe Mendes Delpino, Luana Seles Alves, Josilene Dália Alves, Thaís Zamboni Berra, Antônio Carlos Vieira Ramos, Miguel Fuentealba-Torres, Inês Fronteira and Ricardo Alexandre Arcêncio

Trop. Med. Infect. Dis. 2022, 7(6), 82; https://doi.org/10.3390/tropicalmed7060082 - 24 May 2022

Cited by 2 | Viewed by 2092

Abstract

(1) Background: tuberculosis (TB) is considered one of the leading causes of death worldwide by a single infectious agent. This study aimed to identify hotspots of people diagnosed with tuberculosis and abusive use of alcohol, tobacco, and other drugs in communities through a [...] Read more.

(1) Background: tuberculosis (TB) is considered one of the leading causes of death worldwide by a single infectious agent. This study aimed to identify hotspots of people diagnosed with tuberculosis and abusive use of alcohol, tobacco, and other drugs in communities through a geospatial intelligence application; (2) Methods: an ecological study with a spatio-temporal approach. We considered tuberculosis cases diagnosed and registered in the Notifiable Diseases Information System, which presented information on alcoholism, smoking, and drug abuse. Spatial Variations in Temporal Trends (SVTT) and scan statistics were applied for the identification of Hotspots; (3) Results: between the study period, about 29,499 cases of tuberculosis were reported. When we applied the SVTT for alcoholism, three Hotspots were detected, one of which was protective (RR: 0.08–CI95%: 0.02–0.32) and two at risk (RR: 1.42–CI95%: 1.11–1.73; RR: 1.39–CI95%: 1.28–1.50). Regarding smoking, two risk clusters were identified (RR: 1.15–CI95%: 1.01–1.30; RR: 1.68–CI95%: 1.54–1.83). For other drugs, a risk cluster was found (RR: 1.13–CI95%: 0.99–1.29) and two protections (RR: 0.70–CI95%: 0.63–0.77; RR: 0.76–CI95%: 0.65–0.89); (4) Conclusion: it was evidenced that in the communities being studied, there exists a problem of TB with drug addiction. The disordered use of these substances may harm a person’s brain and behavior and lead to an inability to continue their treatment, putting the community at further risk for TB. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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Other

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6 pages, 216 KiB

Open AccessEditor’s ChoiceOpinion

Community-Led Data Collection: Enhancing Local-Level Scabies Surveillance in Remote Aboriginal Communities in Australia

by Miriam Glennie, Michelle Dowden, Meg Scolyer, Irene O’Meara, Geoffrey Angeles, Hannah Woerle, Patricia T. Campbell and Karen Gardner

Trop. Med. Infect. Dis. 2023, 8(4), 200; https://doi.org/10.3390/tropicalmed8040200 - 29 Mar 2023

Cited by 1 | Viewed by 1439

Abstract

Novel approaches to geohealth data analysis offer major benefits to neglected tropical disease control by identifying how social, economic and environmental elements of place interact to influence disease outcomes. However, a lack of timely and accurate geohealth data poses substantial risks to the [...] Read more.

Novel approaches to geohealth data analysis offer major benefits to neglected tropical disease control by identifying how social, economic and environmental elements of place interact to influence disease outcomes. However, a lack of timely and accurate geohealth data poses substantial risks to the accuracy of risk identification and challenges to the development of suitably targeted disease control programs. Scabies is one of many skin-related NTDs that is nominated as a priority for global disease control by the World Health Organization, but for which there remains a lack of baseline geospatial data on disease distribution. In this opinion paper, we consider lessons on impediments to geohealth data availability for other skin-related NTDs before outlining challenges specific to the collection of scabies-related geohealth data. We illustrate the importance of a community-centred approach in this context using a recent initiative to develop a community-led model of scabies surveillance in remote Aboriginal communities in Australia. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

10 pages, 6752 KiB

Open AccessCase Report

Using Community Engagement and Geographic Information Systems to Address COVID-19 Vaccination Disparities

by Tsu-Yin Wu, Xining Yang, Sarah Lally, Alice Jo Rainville, Olivia Ford, Rachel Bessire and Jessica Donnelly

Trop. Med. Infect. Dis. 2022, 7(8), 177; https://doi.org/10.3390/tropicalmed7080177 - 11 Aug 2022

Cited by 2 | Viewed by 1842

Abstract

The COVID-19 pandemic has exacerbated existing health disparities and had a disproportionate impact on racial and ethnic minority groups in the United States. Limited COVID-19 data for Asian Americans have led to less attention for this population; nevertheless, available statistics have revealed lesser [...] Read more.

The COVID-19 pandemic has exacerbated existing health disparities and had a disproportionate impact on racial and ethnic minority groups in the United States. Limited COVID-19 data for Asian Americans have led to less attention for this population; nevertheless, available statistics have revealed lesser known impacts of COVID-19 on this population. Even with significant increases in vaccine supply and recent increases in COVID-19 vaccination rates, racial and ethnic disparities in vaccine uptake still persist. These disparities are amplified for individuals with limited English proficiency (LEP). The purpose of this paper is to apply community-engaged and geographic information system (GIS) strategies to increase equitable access to COVID-19 vaccination uptake by decreasing the structural barriers to COVID-19 vaccine uptake, with a particular focus on Asian Americans with LEP. Building upon existing community-academic partnerships between the academic unit and community-based organizations, the project team established community-led mobile and pop-up COVID-19 vaccination clinics to reach underserved individuals in their communities, worked with commercial pharmacies and reserved appointments for community-based organizations, used GIS to establish COVID-19 vaccination sites close to communities with the greatest need, and deployed trusted messengers to deliver linguistically and culturally relevant COVID-19 vaccine messages which built vaccine confidence among the community members. The implementation of mobile clinics expanded COVID-19 vaccine access and community-driven, multi-sector partnerships can increase the capacity to enhance efforts and facilitate access to COVID-19 vaccination for hard-to-reach populations. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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16 pages, 4462 KiB

Open AccessSystematic Review

Presence and Multi-Species Spatial Distribution of Oropouche Virus in Brazil within the One Health Framework

by Sofia Sciancalepore, Maria Cristina Schneider, Jisoo Kim, Deise I. Galan and Ana Riviere-Cinnamond

Trop. Med. Infect. Dis. 2022, 7(6), 111; https://doi.org/10.3390/tropicalmed7060111 - 20 Jun 2022

Cited by 5 | Viewed by 2223

Abstract

Oropouche virus (OROV) is an emerging vector-borne arbovirus with high epidemic potential, causing illness in more than 500,000 people. Primarily contracted through its midge and mosquito vectors, OROV remains prevalent in its wild, non-human primate and sloth reservoir hosts as well. This virus [...] Read more.

Oropouche virus (OROV) is an emerging vector-borne arbovirus with high epidemic potential, causing illness in more than 500,000 people. Primarily contracted through its midge and mosquito vectors, OROV remains prevalent in its wild, non-human primate and sloth reservoir hosts as well. This virus is spreading across Latin America; however, the majority of cases occur in Brazil. The aim of this research is to document OROV’s presence in Brazil using the One Health approach and geospatial techniques. A scoping review of the literature (2000 to 2021) was conducted to collect reports of this disease in humans and animal species. Data were then geocoded by first and second subnational levels and species to map OROV’s spread. In total, 14 of 27 states reported OROV presence across 67 municipalities (second subnational level). However, most of the cases were in the northern region, within the tropical and subtropical moist broadleaf forests biome. OROV was identified in humans, four vector species, four genera of non-human primates, one sloth species, and others. Utilizing One Health was important to understand the distribution of OROV across several species and to suggest possible environmental, socioeconomic, and demographic drivers of the virus’s presence. As deforestation, climate change, and migration rates increase, further study into the spillover potential of this disease is needed. Full article

(This article belongs to the Special Issue Geospatial Health (GeoHealth): Current Trends, Methods, and Applications)

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