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Introduction: Malaria is a life-threatening disease ocuring mainly in developing countries. Almost half of the world's population was at risk of malaria in 2020. Children under five years age are among the population groups at considerably higher risk of contracting malaria and developing severe disease. Most countries use Demographic and Health Survey (DHS) data for health programs and evaluation. However, malaria elimination strategies require a real-time, locally-tailored response based on malaria risk estimates at the lowest administrative levels. In this paper, we propose a two-step modeling framework using survey and routine data to improve estimates of malaria risk incidence in small areas and enable quantifying malaria trends., Methods: To improve estimates, we suggest an alternative approach to modeling malaria relative risk by combining information from survey and routine data through Bayesian spatio-temporal models. We model malaria risk using two steps: (1) fitting a binomial model to the survey data, and (2) extracting fitted values and using them in the Poison model as nonlinear effects in the routine data. We modeled malaria relative risk among under-five-year old children in Rwanda., Results: The estimation of malaria prevalence among children who are under five years old using Rwanda demographic and health survey data for the years 2019-2020 alone showed a higher prevalence in the southwest, central, and northeast of Rwanda than the rest of the country. Combining with routine health facility data, we detected clusters that were undetected based on the survey data alone. The proposed approach enabled spatial and temporal trend effect estimation of relative risk in local/small areas in Rwanda., Conclusions: The findings of this analysis suggest that using DHS combined with routine health services data for active malaria surveillance may provide provide more precise estimates of the malaria burden, which can be used toward malaria elimination targets. We compared findings from geostatistical modeling of malaria prevalence among under-five-year old children using DHS 2019-2020 and findings from malaria relative risk spatio-temporal modeling using both DHS survey 2019-2020 and health facility routine data. The strength of routinely collected data at small scales and high-quality data from the survey contributed to a better understanding of the malaria relative risk at the subnational level in Rwanda.

Purpose: This study assesses the potential acute effects of heatwaves on human morbidities in primary care settings., Methods: We performed a time-stratified case-crossover study to assess the acute effects of heatwaves on selected morbidities in primary care settings in Flanders, Belgium, between 2000 and 2015. We used conditional logistic regression models. We assessed the effect of heatwaves on the day of the event (lag 0) and X days earlier (lags 1 to X). The associations are presented as Incidence Density Ratios (IDR)., Results: We included 22,344 events. Heatwaves are associated with increased heat-related morbidities such as heat stroke IDR 3.93 [2.94-5.26] at lag 0, dehydration IDR 3.93 [2.94-5.26] at lag 1, and orthostatic hypotension IDR 2.06 [1.37-3.10] at lag 1. For cardiovascular morbidities studied, there was only an increased risk of stroke at lag 3 IDR 1.45 [1.04-2.03]. There is no significant association with myocardial ischemia/infarction or arrhythmia. Heatwaves are associated with decreased respiratory infection risk. The IDR for upper respiratory infections is 0.82 [0.78-0.87] lag 1 and lower respiratory infections (LRI) is 0.82 [0.74-0.91] at lag 1. There was no significant effect modification by age or premorbid chronic disease (diabetes, hypertesnsion)., Conclusion: Heatwaves are associated with increased heat-related morbidities and decreased respiratory infection risk. The study of heatwaves' effects in primary care settings helps evaluate the impact of heatwaves on the general population. Primary care settings might be not suitable to study acute life-threatening morbidities.

There are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. We investigate the time from symptom onset to diagnosis and hospitalization or the length of stay (LoS) in the hospital, and whether there are differences in the population. Sciensano collected information on 14,618 hospitalized patients with COVID-19 admissions from 114 Belgian hospitals between 14 March and 12 June 2020. The distributions of different event times for different patient groups are estimated accounting for interval censoring and right truncation of the time intervals. The time between symptom onset and hospitalization or diagnosis are similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient (longest delay in age group 20-60 years) and whether or not the patient lives in a nursing home (additional 2 days for patients from nursing home). The median LoS in hospital varies between 3 and 10.4 days, with the LoS increasing with age. The hospital LoS for patients that recover is shorter for patients living in a nursing home, but the time to death is longer for these patients. Over the course of the first wave, the LoS has decreased.

Mozambique has a high burden of HIV and is currently ranked sixth worldwide for adult prevalence. In Mozambique, HIV prevalence is not uniformly distributed geographically and throughout the population. We investigated the spatial distribution of HIV infection among adolescents and young people in Mozambique using the 2009 AIDS Indicator Survey (AIS). Generalized geoadditive modeling, combining kriging and additive modeling, was used to study the geographical variability of HIV risk among young people. The nonlinear spatial effect was assessed through radial basis splines. The estimation process was done using two-stage iterative penalized quasi-likelihood within the framework of a mixed-effects model. Our estimation procedure is an extension of the approach by Vandendijck et al., estimating the range (spatial decay) parameter in a binary context. The results revealed the presence of spatial patterns of HIV infection. After controlling for important covariates, the results showed a greater burden of HIV/AIDS in the central and northern regions of the country. Several socio-demographic, biological, and behavioral factors were found to be significantly associated with HIV infection among young people. The findings are important, as they can help health officials and policy makers to design targeted interventions for responding to the HIV epidemic.

Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at a lower level are not included in the sample due to financial or logistical reasons. In this paper, we investigated (1) the effect of these unsampled areas on a variety of design-based and hierarchical model-based estimates and (2) the benefits of using auxiliary information in the estimation process by means of an extensive simulation study. The results showed the benefits of hierarchical spatial smoothing models towards obtaining more reliable estimates for areas at the lowest geographical level in case a spatial trend is present in the data. Furthermore, the importance of auxiliary information was highlighted, especially for geographical areas that were not included in the sample. Methods are illustrated on the 2008 Mozambique Poverty and Social Impact Analysis survey, with interest in the district-specific prevalence of school attendance.

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.