A Comparison of Ukrainian Hospital Services and Functions Before and During the Russia-Ukraine War

Importance

Since the full-scale Russian invasion, hospitals in Ukraine have been compelled to close or operate at reduced capacity due to inadequate supplies, damage, or destruction caused by war.

Objective

To analyze hospital services in Ukraine during the period before and after the Russian invasion.

Design, Setting, and Participants

Of the 450 hospitals currently functioning in Ukraine, a cross-sectional survey was carried out with the participation of 74 hospitals from 12 oblasts. Hospital administrators responded to an online survey with questions on the use of hospital services. Data were abstracted from hospital databases for the prewar period (before February 23, 2022) and during the war (February 23, 2022, to May 30, 2023).

Main Outcomes and Measures

Hospital services (including emergency services, preventive services, screenings, laboratory tests, obstetrics, telehealth, pharmacy, and rehabilitation services) were compared during the prewar and war periods.

Results

Of 450 Ukrainian hospitals in operation, 74 hospitals (16.0%) across 12 oblasts provided data for the current analyses. During the war, daily emergency admissions increased to 2830, compared with 2773 before the war. At the same time, hospitals reported reduced laboratory testing (72 [97%] vs 63 [85%]), tobacco education (52 [70%] vs 36 [49%]), cancer screening (49 [66%] vs 37 [50%]), gynecological services (43 [58%] vs 32 [43%]), rehabilitation services (37 [50%] vs 27 [36%]), pharmacy services (36 [49%] vs 27 [36%]), and telehealth programs (33 [45%] vs 21 [28%]). Hospitals reported additional difficulties during the war, including disruptions in the supply chain for essential equipment and pharmaceuticals, shortages of laboratory test kits, delays in the delivery of crucial medications, and problems around appropriate medication storage due to power outages.

Conclusions and Relevance

The ongoing war has inflicted profound devastation on Ukraine's hospitals. The findings of this cross-sectional survey offer valuable insights into the formidable challenges that hospitals confront in war-affected regions and underscore the pressing necessity for bolstering support to sustain and enhance hospital services during wartime.

A Bayesian spatiotemporal approach to modelling arboviral diseases in Mexico

BACKGROUND

The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico.

METHODS

To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models.

RESULTS

We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases.

CONCLUSION

The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.

SARS-CoV-2 Variants of Concern and Clinical Severity in the Mexican Pediatric Population

The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VOCs) presents global heterogeneity, and their relative effect on pediatric severity is still limited. In this study, we associate VOCs with pediatric clinical severity outcomes in Mexico. Bioinformatics methods were used to characterize VOCs and single amino acid (aa) mutations in 75,348 SARS-CoV-2 genetic sequences from February 2020 to October 2022. High-predominance VOCs groups were calculated and subsequently associated with 372,989 COVID-19 clinical pediatric outcomes. We identified 21 high-frequency mutations related to Omicron lineages with an increased prevalence in pediatric sequences compared to adults. Alpha and the other lineages had a significant increase in case fatality rate (CFR), intensive critical unit (ICU) admission, and automated mechanical ventilation (AMV). Furthermore, a logistic model with age-adjusted variables estimated an increased risk of hospitalization, ICU/AMV, and death in Gamma and Alpha, in contrast to the other lineages. We found that, regardless of the VOCs lineage, infant patients presented the worst severity prognoses. Our findings improve the understanding of the impact of VOCs on pediatric patients across time, regions, and clinical outcomes. Enhanced understanding of the pediatric severity for VOCs would enable the development and improvement of public health strategies worldwide.

Direct and indirect effects of age on dengue severity: The mediating role of secondary infection

Severe dengue occurrence has been attributed to increasing age and different dengue virus (DENV) serotypes that cause secondary infections and immune-enhancing phenomena. Therefore, we examined if the effect of age on dengue severity was mediated by infectivity status while controlling for sex and region. Further, we assessed the spatial clustering of dengue severity for individuals with primary and secondary infection across Mexican municipalities. Health data from 2012 to 2017 was retrieved from Mexico's Ministry of Health. A mediation analysis was performed using multiple logistic regression models based on a directed acyclic graph. The models were explored for the direct effect of age on dengue severity and its indirect impact through secondary infection. In addition, severe dengue clusters were determined in some Northeastern and Southeastern municipalities through spatial analysis. We observed a nonlinear trend between age and severe dengue. There was a downward trend of severe dengue for individuals between 0 and 10 years and an upward trend above 10 years. The effect of age on dengue severity was no longer significant for individuals between 10 and 60 years after introducing infectivity status into the model. The mediating role of infectivity status in the causal model was 17%. Clustering of severe dengue among individuals with primary infection in the Northeastern region may point to the high prevalence of DENV-3 in the region. Public health efforts may prevent secondary infection among infants and the aged. In addition, there should be a further investigation into the effect of DENV-3 in individuals with primary disease.

Indoor resting behavior of Aedes aegypti (Diptera: Culicidae) in northeastern Thailand

BACKGROUND

Aedes aegypti is a vector of several arboviruses, notably dengue virus (DENV), which causes dengue fever and is often found resting indoors. Culex spp. are largely nuisance mosquitoes but can include species that are vectors of zoonotic pathogens. Vector control is currently the main method to control dengue outbreaks. Indoor residual spraying can be part of an effective vector control strategy but requires an understanding of the resting behavior. Here we focus on the indoor-resting behavior of Ae. aegypti and Culex spp. in northeastern Thailand.

METHODS

Mosquitoes were collected in 240 houses in rural and urban settings from May to August 2019 at two collection times (morning/afternoon), in four room types (bedroom, bathroom, living room and kitchen) in each house and at three wall heights (< 0.75 m, 0.75-1.5 m, > 1.5 m) using a battery-driven aspirator and sticky traps. Household characteristics were ascertained. Mosquitoes were identified as Ae. aegypti, Aedes albopictus and Culex spp. Dengue virus was detected in Ae. aegypti. Association analyses between urban/rural and within-house location (wall height, room), household variables, geckos and mosquito abundance were performed.

RESULTS

A total of 2874 mosquitoes were collected using aspirators and 1830 using sticky traps. Aedes aegypti and Culex spp. accounted for 44.78% and 53.17% of the specimens, respectively. Only 2.05% were Ae. albopictus. Aedes aegypti and Culex spp. rested most abundantly at intermediate and low heights in bedrooms or bathrooms (96.6% and 85.2% for each taxon of the total, respectively). Clothes hanging at intermediate heights were associated with higher mean numbers of Ae. aegypti in rural settings (0.81 [SEM: 0.08] vs. low: 0.61 [0.08] and high: 0.32 [0.09]). Use of larval control was associated with lower numbers of Ae. aegypti (yes: 0.61 [0.08]; no: 0.70 [0.07]). All DENV-positive Ae. aegypti (1.7%, 5 of 422) were collected in the rural areas and included specimens with single, double and even triple serotype infections.

CONCLUSIONS

Knowledge of the indoor resting behavior of adult mosquitoes and associated environmental factors can guide the choice of the most appropriate and effective vector control method. Our work suggests that vector control using targeted indoor residual spraying and/or potentially spatial repellents focusing on walls at heights lower than 1.5 m in bedrooms and bathrooms could be part of an integrated effective strategy for dengue vector control.

Moderation effects of serotype on dengue severity across pregnancy status in Mexico

BACKGROUND

Pregnancy increases a woman's risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico.

METHOD

Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status.

RESULTS

Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype.

CONCLUSION

The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.

Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention

Background

The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies.

Methods

This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015.

Results

The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions.

Conclusions

The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies.

Malaria is an infectious disease that is widespread in many African countries. Malaria transmission within a country can vary between regions, so tailored interventions for malaria control and elimination targeted to different regions are necessary. To achieve this, it is important to measure and monitor the frequency of malaria infections, its risk, and trends at different geographic administrative scales. This study analysed over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. The results showed an increasing national trend in malaria risk and malaria infection frequency and identified differences between health facility and district trends. These findings support a flexible approach when implementing and expanding national malaria monitoring, control and elimination strategies, especially in areas bordering countries where malaria is widespread, cross-border movement is common, and cross-border initiatives could be beneficial.

Lubinda et al. analyse over 32 million health-facility reported malaria cases in Zambia (2009–15) to examine spatially-structured temporal trends. They observe overall increasing trends in risk and rates and highlight the potential benefits of using an adaptive scaling approach in national malaria strategies, and a need for cross-border initiatives.

Country-Level Governance Indicators as Predictors of COVID-19 Morbidity, Mortality, and Vaccination Coverage: An Exploratory Global Analysis

As the COVID-19 pandemic continues to affect all countries across the globe, this study seeks to investigate the relationship between nations' governance, COVID-19 national data, and nation-level COVID-19 vaccination coverage. National-level governance indicators (corruption index, voice and accountability, political stability, and absence of violence/terrorism), officially reported COVID-19 national data (cases, death, and tests per one million population), and COVID-19 vaccination coverage was considered for this study to predict COVID-19 morbidity and mortality. Results indicate a strong relationship between nations' governance and officially reported COVID-19 data. Countries were grouped into three clusters using only the governance data: politically stable countries, average countries or "less corrupt countries," and corrupt countries or "more corrupt countries." The clusters were then tested for significant differences in reporting various aspects of the COVID-19 data. According to multinomial regression, countries in the cluster of politically stable nations reported significantly more deaths, tests per one million, total cases per one million, and higher vaccination coverage compared with nations both in the clusters of corrupt countries and average countries. The countries in the cluster of average nations reported more tests per one million and higher vaccination coverage than countries in the cluster of corrupt nations. Countries included in the corrupt cluster reported a lower death rate and morbidity, particularly compared with the politically stable nations cluster, a trend that can be attributed to poor governance and inaccurate COVID-19 data reporting. The epidemic evaluation indices of the COVID-19 cases demonstrate that the pandemic is still evolving on a global level.

Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico

Background

The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico.

Methods

We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters.

Results

DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence.

Conclusions

Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.

The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days

BACKGROUND

We examined the human toll and subsequent humanitarian crisis resulting from the Russian invasion of Ukraine, which began on 24 February 2022.

METHOD

We extracted and analysed data resulting from Russian military attacks on Ukrainians between 24 February and 4 August 2022. The data tracked direct deaths and injuries, damage to healthcare infrastructure and the impact on health, the destruction of residences, infrastructure, communication systems, and utility services - all of which disrupted the lives of Ukrainians.

RESULTS

As of 4 August 2022, 5552 civilians were killed outright and 8513 injured in Ukraine as a result of Russian attacks. Local officials estimate as many as 24 328 people were also killed in mass atrocities, with Mariupol being the largest (n=22 000) such example. Aside from wide swaths of homes, schools, roads, and bridges destroyed, hospitals and health facilities from 21 cities across Ukraine came under attack. The disruption to water, gas, electricity, and internet services also extended to affect supplies of medications and other supplies owing to destroyed facilities or production that ceased due to the war. The data also show that Ukraine saw an increase in cases of HIV/AIDS, tuberculosis, and Coronavirus (COVID-19).

CONCLUSIONS

The 2022 Russia-Ukraine War not only resulted in deaths and injuries but also impacted the lives and safety of Ukrainians through destruction of healthcare facilities and disrupted delivery of healthcare and supplies. The war is an ongoing humanitarian crisis given the continuing destruction of infrastructure and services that directly impact the well-being of human lives. The devastation, trauma and human cost of war will impact generations of Ukrainians to come.

Community acceptability of dengue fever surveillance using unmanned aerial vehicles: A cross-sectional study in Malaysia, Mexico, and Turkey

Surveillance is a critical component of any dengue prevention and control program. There is an increasing effort to use drones in mosquito control surveillance. Due to the novelty of drones, data are scarce on the impact and acceptance of their use in the communities to collect health-related data. The use of drones raises concerns about the protection of human privacy. Here, we show how willingness to be trained and acceptance of drone use in tech-savvy communities can help further discussions in mosquito surveillance. A cross-sectional study was conducted in Malaysia, Mexico, and Turkey to assess knowledge of diseases caused by Aedes mosquitoes, perceptions about drone use for data collection, and acceptance of drones for Aedes mosquito surveillance around homes. Compared with people living in Turkey, Mexicans had 14.3 (p < 0.0001) times higher odds and Malaysians had 4.0 (p = 0.7030) times the odds of being willing to download a mosquito surveillance app. Compared to urban dwellers, rural dwellers had 1.56 times the odds of being willing to be trained. There is widespread community support for drone use in mosquito surveillance and this community buy-in suggests a potential for success in mosquito surveillance using drones. A successful surveillance and community engagement system may be used to monitor a variety of mosquito spp. Future research should include qualitative interview data to add context to these findings.

The Disproportionate Impact of COVID-19 among Undocumented Immigrants and Racial Minorities in the US

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts. Population characteristics strongly related to United States (US) county-level data on COVID-19 cases and deaths during all stages of the pandemic were identified from the onset of the epidemic and included county-level socio-demographic and comorbidities data, as well as daily meteorological modeled observation data from the North American Regional Reanalysis (NARR), and the NARR high spatial resolution model to assess the environment. Advanced machine learning (ML) approaches were used to identify outbreaks (geographic clusters of COVID-19) and included spatiotemporal risk factors and COVID-19 vaccination efforts, especially among vulnerable and underserved communities. COVID-19 outcomes were found to be negatively associated with the number of people vaccinated and positively associated with age, the prevalence of cardiovascular disease, diabetes, and the minority population. There was also a strong positive correlation between unauthorized immigrants and the prevalence of COVID-19 cases and deaths. Meteorological variables were also investigated, but correlations with COVID-19 were relatively weak. Our findings suggest that COVID-19 has had a disproportionate impact across the US population among vulnerable and minority communities. Findings also emphasize the importance of vaccinations and tailored public health initiatives (e.g., mask mandates, vaccination) to reduce the spread of COVID-19 and the number of COVID-19 related deaths across all populations.

Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach

BACKGROUND

Mapping the spatial distribution of the dengue vector Aedes (Ae.) aegypti and accurately predicting its abundance are crucial for designing effective vector control strategies and early warning tools for dengue epidemic prevention. Socio-ecological and landscape factors influence Ae. aegypti abundance. Therefore, we aimed to map the spatial distribution of female adult Ae. aegypti and predict its abundance in northeastern Thailand based on socioeconomic, climate change, and dengue knowledge, attitude and practices (KAP) and/or landscape factors using machine learning (ML)-based system.

METHOD

A total of 1066 females adult Ae. aegypti were collected from four villages in northeastern Thailand during January-December 2019. Information on household socioeconomics, KAP regarding climate change and dengue, and satellite-based landscape data were also acquired. Geographic information systems (GIS) were used to map the household-based spatial distribution of female adult Ae. aegypti abundance (high/low). Five popular supervised learning models, logistic regression (LR), support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and random forest (RF), were used to predict females adult Ae. aegypti abundance (high/low). The predictive accuracy of each modeling technique was calculated and evaluated. Important variables for predicting female adult Ae. aegypti abundance were also identified using the best-fitted model.

RESULTS

Urban areas had higher abundance of female adult Ae. aegypti compared to rural areas. Overall, study respondents in both urban and rural areas had inadequate KAP regarding climate change and dengue. The average landscape factors per household in urban areas were rice crop (47.4%), natural tree cover (17.8%), built-up area (13.2%), permanent wetlands (21.2%), and rubber plantation (0%), and the corresponding figures for rural areas were 12.1, 2.0, 38.7, 40.1 and 0.1% respectively. Among all assessed models, RF showed the best prediction performance (socioeconomics: area under curve, AUC = 0.93, classification accuracy, CA = 0.86, F1 score = 0.85; KAP: AUC = 0.95, CA = 0.92, F1 = 0.90; landscape: AUC = 0.96, CA = 0.89, F1 = 0.87) for female adult Ae. aegypti abundance. The combined influences of all factors further improved the predictive accuracy in RF model (socioeconomics + KAP + landscape: AUC = 0.99, CA = 0.96 and F1 = 0.95). Dengue prevention practices were shown to be the most important predictor in the RF model for female adult Ae. aegypti abundance in northeastern Thailand.

CONCLUSION

The RF model is more suitable for the prediction of Ae. aegypti abundance in northeastern Thailand. Our study exemplifies that the application of GIS and machine learning systems has significant potential for understanding the spatial distribution of dengue vectors and predicting its abundance. The study findings might help optimize vector control strategies, future mosquito suppression, prediction and control strategies of epidemic arboviral diseases (dengue, chikungunya, and Zika). Such strategies can be incorporated into One Health approaches applying transdisciplinary approaches considering human-vector and agro-environmental interrelationships.

COVID-19 Vaccine Hesitancy and Emerging Variants: Evidence from Six Countries

As the world tries to cope with the devastating effects of the COVID-19 pandemic and emerging variants of the virus, COVID-19 vaccination has become an even more critical tool toward normalcy. The effectiveness of the vaccination program and specifically vaccine uptake and coverage, however, is a function of an individual's knowledge and individual opinion about the disease and available vaccines. This study investigated the knowledge, attitudes, and resulting community practice(s) associated with the new COVID-19 variants and vaccines in Bangladesh, Colombia, India, Malaysia, Zimbabwe, and the USA. A cross-sectional web-based Knowledge, Attitudes, and Practices (KAP) survey was administered to respondents living in six different countries using a structured and multi-item questionnaire. Survey questions were translated into English, Spanish, and Malay to accommodate the local language in each country. Associations between KAP and a range of explanatory variables were assessed using univariate and multiple logistic regression. A total of 781 responses were included in the final analysis. The Knowledge score mean was 24 (out of 46), Attitude score 28.9 (out of 55), and Practice score 7.3 (out of 11). Almost 65% of the respondents reported being knowledgeable about COVID-19 variants and vaccination, 55% reported a positive attitude toward available COVID-19 vaccines, and 85% reported engaging in practices that supported COVID-19 vaccination. From the multiple logistic models, we found post-graduate education (AOR = 1.83, 95% CI: 1.23-2.74) and an age range 45-54 years (AOR = 5.81, 95% CI: 2.30-14.69) to be significantly associated with reported COVID-19 knowledge. In addition, positive Attitude scores were associated with respondents living in Zimbabwe (AOR = 4.49, 95% CI: 2.04-9.90) and positive Practice scores were found to be associated with people from India (AOR = 3.68, 95% CI: 1.15-11.74) and high school education (AOR = 2.16, 95% CI: 1.07-4.38). This study contributes to the identification of socio-demographic factors associated with poor knowledge, attitudes, and practices relating to COVID-19 variants and vaccines. It presents an opportunity for collaboration with diverse communities to address COVID-19 misinformation and common sources of vaccine hesitancy (i.e., knowledge, attitudes, and practices).

Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand

Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon's Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson's correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4-40%) of districts in Laos (mean r = 0.5) and 27% (15-53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12-40%) of districts in Laos and in 13% (3-38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0-28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.

Acute Inflammatory Mediators in Young Adult Patients with COVID-19 in Mexico

Young adults (18–40 years old) are an active population with high risk of infection and transmission of COVID-19. They are considered a low-risk population due to its low 1.0% case fatality rate (CFR). Despite their high clinical usefulness to prevent fatal cases, inflammatory and coagulation biomarkers studies are limited. For this reason, we performed a retrospective cohort study with COVID-19 patients in Hermosillo, Mexico, to assess inflammation, coagulopathy profile, and severity outcomes in young adults. We analyzed blood samples to determine the neutrophil/lymphocyte ratio (NLR), neutrophil/monocyte ratio (NMR), lymphocyte/monocyte ratio (LMR), platelet/lymphocyte ratio (PLR), and C-reactive protein (C-RP). We included epidemiological features and comorbidities, and compared them to the severity status. Only 359 COVID-19-confirmed young adults were included in the ambulatory (44.8%), hospitalized (42.9%), and death (12%) severity groups. Laboratory results showed an increase in NMR, LMR, and C-RP associated with the aggravated patients. Additionally, obesity, arterial hypertension, and type-2 diabetes mellitus (T2DM) were associated with the COVID-19 severity outcome. We found that 9.1% and 30.3% of young adults presented the novel COVID-19-associated coagulopathy (CAC) and the risk of CAC, respectively. These parameters can be considered independent biomarkers reflecting an enhanced inflammatory process related to the COVID-19 prognosis.

Impact of Environmental Indicators on the COVID-19 Pandemic in Delhi, India

Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman's rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = -0.75), explaining the variability of about 20-30% in the lockdown period and 18-19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India.

Appraising the historical and projected spatiotemporal changes in the heat index in Bangladesh

Climate change-derived extreme heat phenomena are one of the major concerns across the globe, including Bangladesh. The appraisal of historical spatiotemporal changes and possible future changes in heat index (HI) is essential for developing heat stress mitigation strategies. However, the climate-health nexus studies in Bangladesh are very limited. This study was intended to appraise the historical and projected changes in HI in Bangladesh. The HI was computed from daily dry bulb temperature and relative humidity. The modified Mann-Kendal (MMK) test and linear regression were used to detect trends in HI for the observed period (1985-2015). The future change in HI was projected for the mid-century (2041-2070) for three Representative Concentration Pathway (RCP) scenarios, RCP 2.6, 4.5, and 8.5 using the Canadian Earth System Model Second Generation (CanESM2). The results revealed a monotonic rise in the HI and extreme caution conditions, especially in the humid summer season for most parts of Bangladesh for the observed period (1985-2015). Future projections revealed a continuous rise in HI in the forthcoming period (2041-2070). A higher and remarkable increase in the HI was projected in the northern, northeastern, and south-central regions. Among the three scenarios, the RCP 8.5 showed a higher projection of HI both in hot and humid summer compared to the other scenarios. Therefore, Bangladesh should take region-specific adaptation strategies to mitigate the impacts of HI.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00704-021-03705-x.

Assessment of Knowledge, Attitudes, and Practices Regarding Dengue among Physicians: A Web-Based Cross-Sectional Survey

Dengue fever is one of the most important viral infections transmitted by Aedes mosquitoes and a major cause of morbidity and mortality globally. Accurate identification of cases and treatment of dengue patients at the early stages can reduce medical complications and dengue mortality rate. This survey aims to determine the knowledge, attitude, and practices (KAP) among physicians in dengue diagnosis and treatment. This study was conducted among physicians in Turkey as one nonendemic country and Bangladesh, India, and Malaysia as three dengue-endemic countries. The dosing frequencies, maximum doses, and contraindications in dengue fever were examined. The results found that physicians from Bangladesh, India, and Malaysia have higher KAP scores in dengue diagnosis and treatment compared to physicians in Turkey. This may be due to a lack of physician's exposure to a dengue patient as Turkey is considered a nonendemic country. This assessment may help establish a guideline for intervention strategies among physicians to have successful treatment outcomes and reduce dengue mortality.

Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media-based cross-sectional survey

BACKGROUND

Bangladesh experienced its worst dengue fever (DF) outbreak in 2019. This study investigated the knowledge, attitudes and practices (KAP) among university students in Bangladesh and significant factors associated with their prevention practices related to climate change and DF.

METHODS

A social media-based (Facebook) cross-sectional KAP survey was conducted and secondary data of reported DF cases in 2019 extracted. Logistic regression and spatial analysis were run to examine the data.

RESULTS

Of 1500 respondents, 76% believed that climate change can affect DF transmission. However, participants reported good climate change knowledge (76.7%), attitudes (87.9%) and practices (39.1%). The corresponding figures for DF were knowledge (47.9%), attitudes (80.3%) and practices (25.9%). Good knowledge and attitudes were significantly associated with good climate change adaptation or mitigation practices (p<0.05). Good knowledge, attitudes and previous DF experiences were also found to be significantly associated with good DF prevention practices (p<0.001). There was no significant positive correlation between climate change and DF KAP scores and the number of DF cases.

CONCLUSIONS

Findings from this study provide baseline data that can be used to promote educational campaigns and intervention programs focusing on climate change adaptation and mitigation and effective DF prevention strategies among various communities in Bangladesh and similar dengue-endemic countries.

Short-term forecasting of the COVID-19 outbreak in India

As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.

Climate change and the dynamics of age-related malaria incidence in Southern Africa

In the last decade, many malaria-endemic countries, like Zambia, have achieved significant reductions in malaria incidence among children <5 years old but face ongoing challenges in achieving similar progress against malaria in older age groups. In parts of Zambia, changing climatic and environmental factors are among those suspectedly behind high malaria incidence. Changes and variations in these factors potentially interfere with intervention program effectiveness and alter the distribution and incidence patterns of malaria differentially between young children and the rest of the population. We used parametric and non-parametric statistics to model the effects of climatic and socio-demographic variables on age-specific malaria incidence vis-à-vis control interventions. Linear regressions, mixed models, and Mann-Kendall tests were implemented to explore trends, changes in trends, and regress malaria incidence against environmental and intervention variables. Our study shows that while climate parameters affect the whole population, their impacts are felt most by people aged ≥5 years. Climate variables influenced malaria substantially more than mosquito nets and indoor residual spraying interventions. We establish that climate parameters negatively impact malaria control efforts by exacerbating the transmission conditions via more conducive temperature and rainfall environments, which are augmented by cultural and socioeconomic exposure mechanisms. We argue that an intensified communications and education intervention strategy for behavioural change specifically targeted at ≥5 aged population where incidence rates are increasing, is urgently required and call for further malaria stratification among the ≥5 age groups in the routine collection, analysis and reporting of malaria mortality and incidence data.

POS0267-HPR IDENTIFYING MEANINGFUL AND DETECTABLE CHANGE FROM THE PATIENT PERSPECTIVE ACROSS COMMON FATIGUE MEASURES IN RHEUMATOID ARTHRITIS

Background:

Different tools are commonly used to assess fatigue in RA including single items (visual analogue (VAS), numeric rating (NRS), and Likert scales), SF-36 Vitality, and PROMIS. Evidence is needed to identify scores that reflect meaningful change to patients to interpret fatigue results in trials and for clinical care.

Objectives:

To use symptom-specific and RA change anchors to estimate meaningful and detectable change scores from the patient perspective across several fatigue measures.

Methods:

Stable RA patients (ACR/EULAR 2010 criteria) were recruited from 3 academic clinical practices across the Southern, Mid-Atlantic, and Northeastern United States. Scales were administered at 2 consecutive visits to capture PROs including fatigue. We also asked a fatigue-specific change question (How would you rate your fatigue since the last visit?). Response options were a lot better, a little better, the same, a little worse, and a lot worse. An RA disease activity change question followed a similar format. We compared mean change between visits across fatigue and RA change categories for the Fatigue NRS, Fatigue 5-point Likert (none to very severe), SF36 Vitality, and PROMIS Fatigue 4a, 7a, and 8a.

Results:

The sample included 282 patients with stable RA who completed questionnaires 4.6 (SD 2.4) months apart. Patients were mostly white (78%) women (82%) with RA duration of 13 (11) years. At V1, most were in CDAI LDA (57%) or MDA (30%) with 5% in REM and 8% in HDA states. Using the Fatigue change anchor, 6% were a lot better, with mean change ranging from |1-11|points across scales (Table 1); among 13% a little better, smaller changes |0.4 to 5|were reported. Across all measures score changes for meaningful and minimal improvement were numerically larger for improvement than worsening.

Fatigue Change CategoriesLot Better 6%Little Better 13%Same 49%Little Worse 22%Lot Worse 10%NMeanSDNMeanSDNMeanSDNMeanSDNMeanSDFatigue 11 point NRS19-2.22.437-0.72.3137-0.51.9610.41.9261.32.1Fatigue 5-point Likert19-0.81.236-0.41.21370.00.9600.20.9270.61.2SF36 Vitality (0-100)1911.216.1364.913.71370.310.659-3.215.326-11.416.6PROMIS Fatigue 7a19-5.98.537-2.67.51380.06.462-0.36.5274.38.3PROMIS Fatigue 8a19-6.110.237-2.77.1138-0.37.1621.36.9275.611.0PROMIS Fatigue 4a19-6.89.937-3.38.2138-0.47.4601.27.2275.211.5

Using the RA change categories, more people rated their RA as at least a little better compared with fatigue (28% vs. 19%, respectively) at the second visit (Table 2). Similar patterns were evident across RA change categories, though score changes associated with improvement and worsening were about half those observed using fatigue change anchors.

RA Disease Activity Change CategoriesLot Better13%Little Better15%Same42%Little Worse21%Lot Worse9%NMeanSDNMeanSDNMeanSDNMeanSDNMeanSDFatigue 11 point NRS36-0.82.343-0.62.7117-0.21.958-0.21.9260.72.2Fatigue 5-point Likert35-0.31.242-0.31.11180.11.0580.10.8260.31.3SF36 Vitality (0-100)356.417.4433.813.0117-0.612.957-3.711.325-6.017.0PROMIS Fatigue 7a37-1.69.543-1.57.4118-0.37.0590.55.6261.37.5PROMIS Fatigue 8a37-2.19.543-1.79.01180.17.6590.74.8263.011.4PROMIS Fatigue 4a36-2.79.643-2.39.01180.18.1590.45.6252.612.0

Conclusion:

The score change associated with meaningful improvement and worsening between visits on commonly used fatigue scales was much larger with fatigue vs. RA change categories. More people rated their RA improved as compared with fatigue at the 2nd visit. Symptom-specific anchors are likely to offer more relevant change scores associated with meaningful improvement and worsening than RA change anchors. These estimates offer new information about meaningful and detectable improvement and worsening on common measures for trialists, researchers, and clinicians monitoring fatigue in people with RA.

Acknowledgements:

The primary research data included within this report were acquired through funding in part from a PCORI Methods Award (SC14-1402-10818). This work was also supported by the NIH through the Rheumatic Diseases Resource-based Core Center (P30-AR053503 Core D, and P30-AR070254, Core B), the Camille Julia Morgan Arthritis Research and Education Fund, and the Rheumatoid Arthritis Discovery Fund.

Disclosure of Interests:

None declared

Climate change and its effect on the vulnerability to zoonotic cutaneous leishmaniasis in Iran

Zoonotic cutaneous leishmaniasis (ZCL) is an important vector-borne disease with an incidence of 15.8 cases per 100,000 people in Iran in 2019. Despite all efforts to control the disease, ZCL has expanded into new areas during the last decades. The aim of this study was to predict the best ecological niches for both vectors and reservoirs of ZCL under climate change scenarios in Iran. Several online scientific databases were searched. In this study, various scientific sources (Google Scholar, PubMed, SID, Ovid Medline, Web of Science, Irandoc, Magiran) were searched. The inclusion criteria for this study included all records with spatial information about vectors and reservoirs of ZCL which were published between 1980 and 2019. The bioclimatic data were downloaded from online databases. MaxEnt model was used to predict the ecological niches for each species under two climate change scenarios in two periods: the 2030s and 2050s. The results obtained from the model were analysed in ArcMap to find the vulnerability of different provinces for the establishment of ZCL foci. The area under the curve (AUC) for all models was >0.8, which suggests the models are able to make an accurate prediction. The distribution of all studied species in different climatic conditions showed changes. The variables affecting each of the studied species are introduced in the article. The predicted maps show that by 2050 there will be more suitable areas for the co-occurrence of vector and reservoir(s) of ZCL in Iran compared to the current climate condition and RCP2.6 scenario. An area in the northwest of Iran is predicted to have suitable environmental conditions for both vectors and reservoirs of ZCL, although the disease has not yet been reported in this area. These areas should be considered for field studies to confirm these results and to prevent the establishment of new ZCL foci in Iran.

Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden

While mortality from malaria continues to decline globally, incidence rates in many countries are rising. Within countries, spatial and temporal patterns of malaria vary across communities due to many different physical and social environmental factors. To identify those areas most suitable for malaria elimination or targeted control interventions, we used Bayesian models to estimate the spatiotemporal variation of malaria risk, rates, and trends to determine areas of high or low malaria burden compared to their geographical neighbours. We present a methodology using Bayesian hierarchical models with a Markov Chain Monte Carlo (MCMC) based inference to fit a generalised linear mixed model with a conditional autoregressive structure. We modelled clusters of similar spatiotemporal trends in malaria risk, using trend functions with constrained shapes and visualised high and low burden districts using a multi-criterion index derived by combining spatiotemporal risk, rates and trends of districts in Zambia. Our results indicate that over 3 million people in Zambia live in high-burden districts with either high mortality burden or high incidence burden coupled with an increasing trend over 16 years (2000 to 2015) for all age, under-five and over-five cohorts. Approximately 1.6 million people live in high-incidence burden areas alone. Using our method, we have developed a platform that can enable malaria programs in countries like Zambia to target those high-burden areas with intensive control measures while at the same time pursue malaria elimination efforts in all other areas. Our method enhances conventional approaches and measures to identify those districts which had higher rates and increasing trends and risk. This study provides a method and a means that can help policy makers evaluate intervention impact over time and adopt appropriate geographically targeted strategies that address the issues of both high-burden areas, through intensive control approaches, and low-burden areas, via specific elimination programs.

Correction: Doum, D., et al. Dengue Seroprevalence and Seroconversion in Urban and Rural Populations in Northeastern Thailand and Southern Laos. Int. J. Environ. Res. Public Health 2020, 17, 9134

There was an error in the original article [...].

Knowledge, attitudes, and practices on climate change and dengue in Lao People's Democratic Republic and Thailand

BACKGROUND

Dengue is linked with climate change in tropical and sub-tropical countries including the Lao People's Democratic Republic (Laos) and Thailand. Knowledge about these issues and preventive measures can affect the incidence and outbreak risk of dengue. Therefore, the present study was conducted to determine the knowledge, attitudes, and practices (KAP) among urban and rural communities and government officials about climate change and dengue in Laos and Thailand.

METHODS

A cross-sectional KAP survey about climate change and dengue were conducted in 360 households in Laos (180 urban and 180 rural), 359 households in Thailand (179 urban and 180 rural), and 20 government officials (10 in each country) using structured questionnaires. Data analysis was undertaken using descriptive methods, principal component analysis (PCA), Chi-square test or Fisher's exact test (as appropriate), and logistic regression.

RESULTS

Significant differences among the selected communities in both countries were found in terms of household participant's age, level of education, socioeconomic status, attitude level of climate change and KAP level of dengue (P < 0.05; 95% CI). Overall, participants' KAP about climate change and dengue were low except the attitude level for dengue in both countries. The level of awareness among government officials regarding the climatic relationship with dengue was also low. In Lao households, participants' knowledge about climate change and dengue was significantly associated with the level of education and socioeconomic status (SES) (P < 0.01). Their attitudes towards climate change and dengue were associated with educational level and internet use (P < 0.05). Householders' climate change related practices were associated with SES (P < 0.01) and dengue related practices were associated with educational level, SES, previous dengue experience and internet use (P < 0.01). In Thailand, participants' knowledge about climate change was associated with the level of education and SES (P < 0.01). Their attitudes towards climate change were associated with residence status (urban/rural) and internet use (P < 0.05); climate change related practices were associated with educational level and SES (P < 0.05). Dengue related knowledge of participants was associated with SES and previous dengue experience (P < 0.05); participants' dengue related attitudes and practices were associated with educational level (P < 0.01).

CONCLUSION

The findings call for urgently needed integrated awareness programs to increase KAP levels regarding climate change adaptation, mitigation and dengue prevention to improve the health and welfare of people in these two countries, and similar dengue-endemic countries.

A Computational Modeling Study of COVID-19 in Bangladesh

The COVID-19 pandemic has spread globally. Only three cases in Bangladesh were reported on March 8, 2020. Here, we aim to predict the epidemic progression for 1 year under different scenarios in Bangladesh. We extracted the number of daily confirmed cases from March 8 to July 20, 2020. We considered the suspected-infected-removed (SIR) model and performed a maximum likelihood-based grid search to determine the removal rate (ɣ). The transmission was modeled as a stochastic random walk process, and sequential Monte Carlo simulation was run 100 times with bootstrap fits to infer the transmission rate (β) and R _t. According to the simulation, the (real) peak daily incidence of 3,600 would be followed by a steady decline, reaching below 1,000 in late January 2021. Thus, the model predicted that there would still be more than 300 cases/day even after a year. However, with proper interventions, a much steeper decline would be achieved following the peak. If we apply a combined (0.8β, 1.2ɣ) intervention, there would be less than 100 cases by mid-October, only around five odd cases at the beginning of the year 2021, and zero cases in early March 2021. The predicted total number of deaths (in status quo) after 1 year would be 8,533 which would reduce to 3,577 if combined (0.8β, 1.2ɣ) intervention is applied. We have also predicted the ideal number of tests that Bangladesh should perform and based on that redid the whole simulation. The outcome, though worse, would be manageable with interventions according to the simulation.

Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques

Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.

COVID-19 in China: Risk Factors and R0 Revisited

The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R₀ is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R₀ among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices

As other nations around the world, Bangladesh is facing enormous challenges with the novel coronavirus (COVID-19) epidemic. To design a prevention and control strategy for this new infectious disease, it is essential to first understand people's knowledge, attitudes, and practices (KAP) regarding COVID-19. This study sought to determine KAP among rural and urban residents as well as predictors of preventive practices associated with COVID-19 in Bangladesh. A social media-based (Facebook) cross-sectional survey was conducted to explore these variables among Bangladeshi adults. Of 1520 respondents who completed the questionnaire, low level of good or sufficient knowledge of COVID-19 (70.8%) and practices associated with COVID-19 (73.8%) were found. Despite the low level of knowledge and practices, respondents' attitude (78.9%) towards COVID-19 was relatively high. Results suggest that compared to urban, rural residents are at a particularly high risk of COVID-19 because they were found to have significantly lower knowledge (p = 0.001) and practice levels (p = 0.002) than were urban residents. Multivariable logistic regression analysis identified gender, education, knowledge of COVID-19 transmission, signs and symptoms, and sources of information as factors significantly associated with preventive practices against COVID-19. Further attention and effort should be directed toward increasing both knowledge and practices targeting the general population in Bangladesh, particularly the rural and less educated residents. Findings from this study provide baseline data that can be used to promote integrated awareness of and effective health education programs about COVID-19 prevention and control strategies in Bangladesh, and similar COVID-19 endemic countries.

The impact of COVID-19 on globalization

Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted.

The effect of globalization were operationalized in terms of mobility, economy, and healthcare systems. The mobility of individuals and its magnitude was assessed using airline and seaport trade data and travel information. The economic impact was measured based on the workforce, event cancellations, food and agriculture, academic institutions, and supply chain. The healthcare capacity was assessed by considering healthcare system indicators and preparedness of countries. Utilizing a technique for order of preference by similarity to ideal solution (TOPSIS), we calculated a pandemic vulnerability index (PVI) by creating a quantitative measure of the potential global health. The pandemic has placed an unprecedented burden on the world economy, healthcare, and globalization through travel, events cancellation, employment workforce, food chain, academia, and healthcare capacity. Based on PVI results, certain countries were more vulnerable than others. In Africa, more vulnerable countries included South Africa and Egypt; in Europe, they were Russia, Germany, and Italy; in Asia and Oceania, they were India, Iran, Pakistan, Saudi Arabia, and Turkey; and for the Americas, they were Brazil, USA, Chile, Mexico, and Peru. The impact on mobility, economy, and healthcare systems has only started to manifest. The findings of this study may help in the planning and implementation of strategies at the country level to help ease this emerging burden.

The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?

Objectives

To provide an overview of the three major deadly coronaviruses and identify areas for improvement of future preparedness plans, as well as provide a critical assessment of the risk factors and actionable items for stopping their spread, utilizing lessons learned from the first two deadly coronavirus outbreaks, as well as initial reports from the current novel coronavirus (COVID-19) epidemic in Wuhan, China.

Methods

Utilizing the Centers for Disease Control and Prevention (CDC, USA) website, and a comprehensive review of PubMed literature, we obtained information regarding clinical signs and symptoms, treatment and diagnosis, transmission methods, protection methods and risk factors for Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and COVID-19. Comparisons between the viruses were made.

Results

Inadequate risk assessment regarding the urgency of the situation, and limited reporting on the virus within China has, in part, led to the rapid spread of COVID-19 throughout mainland China and into proximal and distant countries. Compared with SARS and MERS, COVID-19 has spread more rapidly, due in part to increased globalization and the focus of the epidemic. Wuhan, China is a large hub connecting the North, South, East and West of China via railways and a major international airport. The availability of connecting flights, the timing of the outbreak during the Chinese (Lunar) New Year, and the massive rail transit hub located in Wuhan has enabled the virus to perforate throughout China, and eventually, globally.

Conclusions

We conclude that we did not learn from the two prior epidemics of coronavirus and were ill-prepared to deal with the challenges the COVID-19 epidemic has posed. Future research should attempt to address the uses and implications of internet of things (IoT) technologies for mapping the spread of infection.

A magnetic immunoconjugate nanoplatform for easy colorimetric detection of the NS1 protein of dengue virus in infected serum

In this work, as a proof of principle, the design and performance evaluation of a simple, cheap and efficient colorimetric test for the detection of the NS1 protein of dengue virus, assisted by an immunoconjugate of magnetite (Fe₃O₄) nanoparticles coupled to anti-NS1 antibodies is reported. A monoclonal antibody against the NS1 antigen was covalently immobilized on the surface of superparamagnetic iron oxide nanoparticles (SPIONs ∼ 20 nm) and used for the immunodetection of this protein. When the magnetic immuno-nanoplatform is added into infected serum, it conjugates with the NS1 protein and can then be easily separated using an external magnetic field; then, the recovered immunoconjugate is transferred into a well containing a second immobilized NS1-antibody to form an ELISA-type system. When the NS1 protein is present, a color change to blue is induced by reaction with the Perls reagent, which is consistent with the formation of a SPION-antibody-NS1 antigen-antibody conjugate that confirms infection. No false positives were found when NS1 was not present or a different antibody and the NS1 protein were added into the system. The experimental findings could be extrapolated and scaled up to lead to future developments of simple, quick, and inexpensive, in situ biomolecular diagnostic tests for emergent viral infections.

The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?

OBJECTIVES

To provide an overview of the three major deadly coronaviruses and identify areas for improvement of future preparedness plans, as well as provide a critical assessment of the risk factors and actionable items for stopping their spread, utilizing lessons learned from the first two deadly coronavirus outbreaks, as well as initial reports from the current novel coronavirus (COVID-19) epidemic in Wuhan, China.

METHODS

Utilizing the Centers for Disease Control and Prevention (CDC, USA) website, and a comprehensive review of PubMed literature, we obtained information regarding clinical signs and symptoms, treatment and diagnosis, transmission methods, protection methods and risk factors for Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS) and COVID-19. Comparisons between the viruses were made.

RESULTS

Inadequate risk assessment regarding the urgency of the situation, and limited reporting on the virus within China has, in part, led to the rapid spread of COVID-19 throughout mainland China and into proximal and distant countries. Compared with SARS and MERS, COVID-19 has spread more rapidly, due in part to increased globalization and the focus of the epidemic. Wuhan, China is a large hub connecting the North, South, East and West of China via railways and a major international airport. The availability of connecting flights, the timing of the outbreak during the Chinese (Lunar) New Year, and the massive rail transit hub located in Wuhan has enabled the virus to perforate throughout China, and eventually, globally.

CONCLUSIONS

We conclude that we did not learn from the two prior epidemics of coronavirus and were ill-prepared to deal with the challenges the COVID-19 epidemic has posed. Future research should attempt to address the uses and implications of internet of things (IoT) technologies for mapping the spread of infection.

Defending against the Novel Coronavirus (COVID-19) outbreak: How can the Internet of Things (IoT) help to save the world?

•IoT within infectious disease epidemiology is an emerging field of research, however the ubiquitous availability of smart technologies, as well as increased risks of infectious disease spread through the globalization and interconnectedness of the world necessitates its use for predicting, preventing and controlling emerging infectious diseases.•Considering the present situation in China, IoT based smart disease surveillance systems have the potential to be a major breakthrough in efforts to control the current pandemic. With much of the infrastructure itself in place already (i.e. smartphones, wearable technologies, internet access) the role this technology can have in limiting the spread of the pandemic involves only the collection and analysis of data already gathered.•More research must be carried out for the development of automated and effective alert systems to provide early and timely detection of outbreaks of such diseases in order to reduce morbidity mortality and prevent global spread.

Malaria epidemics in India: Role of climatic condition and control measures

Malaria is a major public health problem in India, which is the second most populous country in the world. This study aimed to investigate the impact of climatic parameters and malaria control efforts implemented by the Indian national malaria control program on malaria epidemics between January of 2009 and December of 2015. A chi-squared test was used to study the correlation of all implemented control methods with occurrence of epidemics within 30, 45, 60 and 90 days and in the same district, 50, 100 and 200 km distance radiuses. The effect of each control method on probability of epidemics was also measured, and the effects of district population, season, and incidence of malaria parasite types were evaluated using logistic regression models. Fever survey was found to be effective for decreasing the odds of epidemics within 45, 60 and 90 days in 100 km. Anti-larval activity was also effective within 30, 45 and 60 days in 200 km. Winter had negative effects on odds ratio while summer and fall were more likely to trigger epidemics. These results contribute to understanding the role of climate variability and control efforts performed in India.

Retrospective data analyses of social and environmental determinants of malaria control for elimination prospects in Eritrea

Background

The present study focuses on both long- and short-term malaria transmission in Eritrea and investigates the risk factors. Annual aggregates of information on malaria cases, deaths, diagnostics and control interventions from 2001 to 2008 and monthly reported data from 2009 to 2017 were obtained from the National Malaria Control Programme. We used a generalized linear regression model to examine the associations among total malaria cases, death, insecticide-treated net coverage, indoor residual spraying and climatic parameters.

Results

Reduction in malaria mortality is demonstrated by the milestone margins of over 97% by the end of 2017. Malaria incidence likewise declined during the period (from 33 to 5 per 1000 population), representing a reduction of about 86% (R² = 0.3) slightly less than the decline in mortality. The distribution of insecticide treated nets generally declined between 2001 and 2014 (R² = 0.16) before increasing from 2015 to 2017, while the number of people protected by indoor residual spraying slightly increased (R² = 0.27). Higher rainfall was significantly associated with an increased number of malaria cases. The covariates rainfall and temperature are a better pair than IRS and LLIN to predict incidences. On the other hand, IRS and LLIN is a more significant pair to predict mortality cases.

Conclusions

While Eritrea has made significant progress towards malaria elimination, this progress should be maintained and further improved. Distribution, coverage and utilization of malaria control and elimination tools should be optimized and sustained to safeguard the gains made. Additionally, consistent annual performance evaluation of malaria indicators would ensure a continuous learning process from gains/threats of epidemics and resurgence in regions already earmarked for elimination.

An investigation of the Plasmodium falciparum malaria epidemic in Kavango and Zambezi regions of Namibia in 2016

Background

Namibia is one of the countries among the eight that are targeting malaria elimination in southern Africa. However, the country has encountered malaria epidemics in recent years. The objective of this study was to investigate malaria epidemics and to contribute to strengthening malaria surveillance and control in an effort to move Namibia toward eliminating malaria.

Method

Malaria epidemiology data for 2014-2015 were collected from the weekly surveillance system. All consenting household members within a 100-m radius of index households were screened in 2016 using a Carestart malaria HRP2/pLDH combined rapid diagnostic test after epidemics. All houses within this radius were sprayed in 2016 with the pyrethroid deltamethrin and K-Othrine WG 250. Anopheles mosquito-positive breeding sites were identified and treated with the organophosphate larvicide temephos. Insecticide susceptibility and bioassay tests were conducted.

Results

During the epidemic response period in 2016, 56 parasitologically confirmed Plasmodium falciparum malaria cases in the Zambezi region were detected from active screening. The majority of those cases (83%) were asymptomatic infections. In the Kavango region, the malaria epidemic persisted, with 228 P. falciparum malaria cases recorded, but only 97 were investigated. In Namibia, malaria vector susceptibility was detected to 4% dichlorodiphenyltrichloroethane. Indoor residual spraying was conducted in 377 (90%) of the targeted households along with community awareness through health education of 1499 people and distribution of more than 2000 information, education and communication materials. The P. falciparum malaria cases in the Zambezi decreased from 122 in week 9 to 97 after week 15.

Conclusions

Malaria epidemics along with the persistence of asymptomatic reservoir infections pose a serious challenge in Namibia's elimination effort. The country needs to ensure sustainable interventions to target asymptomatic reservoir infections and prevent epidemics in order to successfully achieve its goal of eliminating malaria.

The human cost of global warming: Deadly landslides and their triggers (1995-2014)

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

Environmental suitability for Aedes aegypti and Aedes albopictus and the spatial distribution of major arboviral infections in Mexico

Background

This paper discusses a comparative geographic distribution of Aedes aegypti and Aedes albopictus mosquitoes in Mexico, using environmental suitability modeling and reported cases of arboviral infections.

Methods

Using presence-only records, we modeled mosquito niches to show how much they influenced the distribution of Ae. aegypti and Ae. albopictus based on mosquito records collected at the municipality level. Mosquito surveillance data were used to create models regarding the predicted suitability of Ae. albopictus and Ae. aegypti mosquitos in Mexico.

Results

Ae. albopictus had relatively a better predictive performance (area under the curve, AUC = 0.87) to selected bioclimatic variables compared to Ae. aegypti (AUC = 0.81). Ae. aegypti were more suitable for areas with minimum temperature of coldest month (Bio6, permutation importance 28.7%) −6 °C to 21.5 °C, cumulative winter growing degree days (GDD) between 40 and 500, and precipitation of wettest month (Bio13) >8.4 mm. Minimum temperature range of the coldest month (Bio6) was −6.6 °C to 20.5 °C, and average precipitation of the wettest month (Bio13) 8.9 mm ~ 600 mm were more suitable for the existence of Ae. albopictus. However, arboviral infections maps prepared from the 2012–2016 surveillance data showed cases were reported far beyond predicted municipalities.

Conclusions

This study identified the urgent necessity to start surveillance in 925 additional municipalities that reported arbovirus infections but did not report Aedes mosquito.

Threats of Zika virus transmission for Asia and its Hindu-Kush Himalayan region

Asia and its Hindu Kush Himalayan (HKH) region is particularly vulnerable to environmental change, especially climate and land use changes further influenced by rapid population growth, high level of poverty and unsustainable development. Asia has been a hotspot of dengue fever and chikungunya mainly due to its dense human population, unplanned urbanization and poverty. In an urban cycle, dengue virus (DENV) and chikungunya virus (CHIKV) are transmitted by Aedes aegypti and Ae. albopictus mosquitoes which are also competent vectors of Zika virus (ZIKV). Over the last decade, DENV and CHIKV transmissions by Ae. aegypti have extended to the Himalayan countries of Bhutan and Nepal and ZIKV could follow in the footsteps of these viruses in the HKH region. The already established distribution of human-biting Aedes mosquito vectors and a naïve population with lack of immunity against ZIKV places the HKH region at a higher risk of ZIKV. Some of the countries in the HKH region have already reported ZIKV cases. We have documented an increasing threat of ZIKV in Asia and its HKH region because of the high abundance and wide distribution of human-biting mosquito vectors, climate change, poverty, report of indigenous cases in the region, increasing numbers of imported cases and a naïve population with lack of immunity against ZIKV. An outbreak anywhere is potentially a threat everywhere. Therefore, in order to ensure international health security, all efforts to prevent, detect, and respond to ZIKV ought to be intensified now in Asia and its HKH region. To prepare for possible ZIKV outbreaks, Asia and the HKH region can also learn from the success stories and strategies adopted by other regions and countries in preventing ZIKV and associated complications. The future control strategies for DENV, CHIKV and ZIKV should be considered in tandem with the threat to human well-being that is posed by other emerging and re-emerging vector-borne and zoonotic diseases, and by the continuing urgent need to strengthen public primary healthcare systems in the region.

Modeling the Environmental Suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the Contiguous United States

The mosquitoes Aedes (Stegomyia) aegypti (L.)(Diptera:Culicidae) and Ae. (Stegomyia) albopictus (Skuse) (Diptera:Culicidae) transmit dengue, chikungunya, and Zika viruses and represent a growing public health threat in parts of the United States where they are established. To complement existing mosquito presence records based on discontinuous, non-systematic surveillance efforts, we developed county-scale environmental suitability maps for both species using maximum entropy modeling to fit climatic variables to county presence records from 1960-2016 in the contiguous United States. The predictive models for Ae. aegypti and Ae. albopictus had an overall accuracy of 0.84 and 0.85, respectively. Cumulative growing degree days (GDDs) during the winter months, an indicator of overall warmth, was the most important predictive variable for both species and was positively associated with environmental suitability. The number (percentage) of counties classified as environmentally suitable, based on models with 90 or 99% sensitivity, ranged from 1,443 (46%) to 2,209 (71%) for Ae. aegypti and from 1,726 (55%) to 2,329 (75%) for Ae. albopictus. Increasing model sensitivity results in more counties classified as suitable, at least for summer survival, from which there are no mosquito records. We anticipate that Ae. aegypti and Ae. albopictus will be found more commonly in counties classified as suitable based on the lower 90% sensitivity threshold compared with the higher 99% threshold. Counties predicted suitable with 90% sensitivity should therefore be a top priority for expanded mosquito surveillance efforts while still keeping in mind that Ae. aegypti and Ae. albopictus may be introduced, via accidental transport of eggs or immatures, and potentially proliferate during the warmest part of the year anywhere within the geographic areas delineated by the 99% sensitivity model.

Spatial distribution of Zika virus infection in Northeastern Colombia

In this study, we investigated the weekly reported spatio-temporal distribution and topographic risk factors for Zika virus (ZIKV) infection in northeastern Colombia. Weekly reported surveillance data, including clinical, suspected and confirmed cases from the ongoing ZIKV epidemic in the Santander and Norte de Santander departments (Santanderes) in Colombia were used to estimate cumulative incidence rates. Spatial analysis was performed to develop hot spot maps and to identify spatial topographic risk factors for infection. From January 1, 2016 to March 19, 2016, 11,515 cases of ZIKV were reported in Santanderes, with cumulative rates of 316.07 cases/100,000 population for the region (representing 18.5% of the cases of the country). Five municipalities (four in Norte de Santander) reported high incidence of ZIKV infection (>1,000 cases/100,000 pop); these municipalities are close to the border with Venezuela. Most of the cases reported occurred mainly in low altitude areas, and persistent hot spots were observed. Higher infection rates were reported in the Northeastern part of the study area. Use of risk maps can help guide decisions for the prevention and control of ZIKV. Hotspots on the Colombia-Venezuela border can have implications for international spread.

Differences in mortality characteristics in neonates with Down's syndrome

OBJECTIVE

Neonates with Down's syndrome (nDS) may have multiple medical issues that place them at increased risk for mortality during the newborn period. Goal of this study was to determine if there are differences in baseline characteristics, medical complications or procedures performed during hospitalization between nDS who survived versus those who died during initial hospitalization.

STUDY DESIGN

Data from 2000 to 2014 were reviewed using the Pediatric Health Information Systems (PHIS) database on all DS patients admitted to the hospital <30 days postnatal life. Baseline demographics, medical complications, procedures performed and mortality were recorded. Patients were divided into nDS patients who were discharged alive (nDS-a) versus nDS patients who died (nDS-d). Multivariate logistic analysis with odds ratios was performed to determine significant predictors of death. A P<0.05 was considered significant.

RESULTS

A total of 5737 nDS were evaluated. Overall mortality was 7.5% (431/5737). nDS-d were more likely than nDS-a to have a lower birth weight (1.0 (0.9 to 1.0)), presence of a diaphragmatic hernia (6.9 (1.9 to 25.1), or a cardiac diagnosis of a pulmonary venous abnormality (6.8 (1.9 to 24.4)), Ebstein's anomaly (3.2 (1.2 to 8.5)) or left-sided obstructive lesion (2.0 (1.3 to 3.0). nDS-d were more likely to develop hydrops (5.7 (3.5 to 9.5)) and necrotizing enterocolitis (1.7 (1.2 to 2.6)). In addition, nDS-d had significantly higher odds of requiring mechanical ventilation (20.7 (9.9 to 43.1)) or extracorporeal membrane oxygenation (8.7 (4.7 to 16.1)).

CONCLUSIONS

A number of characteristics, specifically certain cardiac diagnosis, place nDS at increased risk for mortality. Furthermore, development of specific medical complications or need for particular procedures increases the odds for mortality in nDS. Caregivers should be cognizant that they are taking care of a high-risk population nDS with an increased risk for mortality if these variables are present.