Knowledge, attitudes, and practices regarding dengue, chikungunya, and Zika in Cali, Colombia

Zika virus knowledge, attitudes and prevention behaviors among pregnant women in the ZEN cohort study, Colombia, 2017-2018

BACKGROUND

Zika virus (ZIKV) infection during pregnancy can cause severe birth defects in the fetus and is associated with neurodevelopmental abnormalities in childhood. Our objective was to describe ZIKV knowledge and attitudes among pregnant women in Colombia while ZIKV was circulating and whether they predicted the adoption of behaviors to prevent ZIKV mosquito-borne and sexual transmission.

METHODS

We used self-reported data from Zika en Embarazadas y Niños (ZEN), a cohort study of women in early pregnancy across three regions of Colombia during 2017-2018. We used Poisson regression to estimate associations between knowledge, attitudes and previous experience with mosquito-borne infection and preventative behaviors.

RESULTS

Among 1519 women, knowledge of mosquito-borne transmission was high (1480; 97.8%) and 1275 (85.5%) participants were worried about ZIKV infection during pregnancy. The most common preventive behavior was wearing long pants (1355; 89.4%). Regular mosquito repellent use was uncommon (257; 17.0%). While ZIKV knowledge and attitudes were not associated with the adoption of ZIKV prevention behaviors, previous mosquito-borne infection was associated with increased condom use (prevalence ratio 1.4, 95% CI 1.1 to 1.7).

CONCLUSIONS

Participants were well informed about ZIKV transmission and its health consequences. However, whether this knowledge resulted in behavior change is less certain.

Identification of Hazard and Socio-Demographic Patterns of Dengue Infections in a Colombian Subtropical Region from 2015 to 2020: Cox Regression Models and Statistical Analysis

Dengue is a disease of high interest for public health in the affected localities. Dengue virus is transmitted by Aedes species and presents hyperendemic behaviors in tropical and subtropical regions. Colombia is one of the countries most affected by the dengue virus in the Americas. Its central-west region is a hot spot in dengue transmission, especially the Department of Antioquia, which has suffered from multiple dengue outbreaks in recent years (2015-2016 and 2019-2020). In this article, we perform a retrospective analysis of the confirmed dengue cases in Antioquia, discriminating by both subregions and dengue severity from 2015 to 2020. First, we conduct an exploratory analysis of the epidemic data, and then a statistical survival analysis is carried out using a Cox regression model. Our findings allow the identification of the hazard and socio-demographic patterns of dengue infections in the Colombian subtropical region of Antioquia from 2015 to 2020.

Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Factors influencing healthcare seeking in patients with dengue: systematic review

OBJECTIVE

Delays in seeking healthcare for dengue are associated with poor health outcomes. Despite this, the factors influencing such delays remain unclear, rendering interventions to improve healthcare seeking for dengue ineffective. This systematic review aimed to synthesise the factors influencing healthcare seeking of patients with dengue and form a comprehensive framework.

METHODS

This review included both qualitative and quantitative studies. Studies were obtained by searching five databases, contacting field experts and performing backward reference searches. The best-fit meta-synthesis approach was used during data synthesis, where extracted data were fitted into the social-ecological model. Sub-analyses were conducted to identify the commonly reported factors and their level of statistical significance.

RESULTS

Twenty studies were selected for meta-synthesis. Eighteen factors influencing healthcare seeking in dengue were identified and categorised under four domains: individual (11 factors), interpersonal (one factor), organisational (four factors) and community (two factors). The most reported factors were knowledge of dengue, access to healthcare, quality of health service and resource availability. Overall, more barriers to dengue health seeking than facilitators were found. History of dengue infection and having knowledge of dengue were found to be ambiguous as they both facilitated and hindered dengue healthcare seeking. Contrary to common belief, women were less likely to seek help for dengue than men.

CONCLUSIONS

The factors affecting dengue healthcare-seeking behaviour are diverse, can be ambiguous and are found across multiple social-ecological levels. Understanding these complexities is essential for the development of effective interventions to improve dengue healthcare-seeking behaviour.

Dengue, Severity Paradox, and Socioeconomic Distribution Among Afro-Colombians

BACKGROUND

The clinical presentation of dengue ranges from self-limited mild illness to severe forms, including death. African ancestry is often described as protective against dengue severity. However, in the Latin American context, African ancestry has been associated with increased mortality. This "severity paradox" has been hypothesized as resulting from confounding or heterogeneity by socioeconomic status (SES). However, few systematic analyses have been conducted to investigate the presence and nature of the disparity paradox.

METHODS

We fit Bayesian hierarchical spatiotemporal models using individual-level surveillance data from Cali, Colombia (2012-2017), to assess the overall morbidity and severity burden of notified dengue. We fitted overall and ethnic-specific models to assess the presence of heterogeneity by SES across and within ethnic groups (Afro-Colombian vs. non-Afro-Colombians), conducting sensitivity analyses to account for potential underreporting.

RESULTS

Our study included 65,402 dengue cases and 13,732 (21%) hospitalizations. Overall notified dengue incidence rates did not vary across ethnic groups. Severity risk was higher among Afro-Colombians (risk ratio [RR] = 1.16; 95% Credible Interval [95% CrI] = 1.08, 1.24) but after accounting for underreporting by ethnicity this association was nearly null (RR = 1.02; 95% CrI = 0.97, 1.07). Subsidized health insurance and low-SES were associated with increased overall dengue rates and severity.

CONCLUSION

The paradoxically increased severity among Afro-Colombians can be attributed to differential health-seeking behaviors and reporting among Afro-Colombians. Such differential reporting can be understood as a type of intersectionality between SES, insurance scheme, and ethnicity that requires a quantitative assessment in future studies.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis

BACKGROUND

Dengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia.

METHODS

Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1^st, 2010 to December 31^st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.

RESULTS

In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data.

CONCLUSIONS

Climate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

Public Perception of the First Major SARS-Cov-2 Outbreak in the Suceava County, Romania

The first months of 2020 were marked by the rapid spread of the acute respiratory disease, which swiftly reached the proportions of a pandemic. The city and county of Suceava, Romania, faced an unprecedented crisis in March and April 2020, triggered not only by the highest number of infections nationwide but also by the highest number of infected health professionals (47.1% of the infected medical staff nationwide, in April 2020). Why did Suceava reach the peak number of COVID-19 cases in Romania? What were the vulnerability factors that led to the outbreak, the closure of the city of Suceava and neighboring localities, and the impossibility of managing the crisis with local resources? What is the relationship between the population's lack of confidence in the authorities' ability to solve the crisis, and their attitude towards the imposed measures? The present article aims to provide answers to the above questions by examining the attitudes of the public towards the causes that have led to the outbreak of an epidemiological crisis, systemic health problems, and the capacity of decision makers to intervene both at local and national level. The research is based on an online survey, conducted between April and May 2020, resulting in a sample of 1231 people from Suceava County. The results highlight that the development of the largest COVID-19 outbreak in Romania is, without a doubt, the result of a combination of factors, related to the medical field, decision makers, and the particularities of the population's behavior.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level—where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space–time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

Comparing different spatio-temporal modeling methods in dengue fever data analysis in Colombia during 2012-2015

In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.