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Abstract Background The United Nations through universal health coverage, including sexual and reproductive health (SRH), pledges to include all people, leaving no one behind. However, people with disabilities continue to experience multiple barriers in accessing SRH services. Studies analysing the impacts of disability in conjunction with other social identities and health determinants reveal a complex pattern in SRH service use. Framed within a larger mixed methods study conducted in Uganda, we examined how disability, among other key social determinants of health (SDH), was associated with the use of SRH services. Methods We analysed data from repeated cross-sectional national surveys, the Uganda Demographic and Health Surveys (DHS) of 2006, 2011, and 2016. The three outcomes of interest were antenatal care visits, HIV testing, and modern contraception use. Our main exposure of interest was the type of disability, classified according to six functional dimensions: seeing, hearing, walking/climbing steps, remembering/concentrating, communicating, and self-care. We performed descriptive and multivariable logistic regression analyses, which controlled for covariates such as survey year, sex, age, place of residence, education, and wealth index. Interaction terms between disability and other factors such as sex, education, and wealth index were explored. Regression analyses were informed by an intersectionality framework to highlight social and health disparities within groups. Results From 2006 to 2016, 15.5-18.5% of study participants lived with some form of disability. Over the same period, the overall prevalence of at least four antenatal care visits increased from 48.3 to 61.0%, while overall HIV testing prevalence rose from 30.8 to 92.4% and the overall prevalence of modern contraception use increased from 18.6 to 34.2%. The DHS year, highest education level attained, and wealth index were the most consistent determinants of SRH service utilisation. People with different types of disabilities did not have the same SRH use patterns. Interactions between disability type and wealth index were associated with neither HIV testing nor the use of modern contraception. Women who were wealthy with hearing difficulty (Odds Ratio (OR) = 0.15, 95%CI 0.03 – 0.87) or with communication difficulty (OR = 0.17, 95%CI 0.03 – 0.82) had lower odds of having had optimal antenatal care visits compared to women without disabilities who were poorer. Conclusion This study provided evidence that SRH service use prevalence increased over time in Uganda and highlights the importance of studying SRH and the different disability types when examining SDH. The SDH are pivotal to the attainment of universal health coverage, including SRH services, for all people irrespective of their social identities.

BackgroundColombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015.Methodology/principal findingsWe analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff's scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika's spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department.Conclusions/significanceThe results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions.

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.

Introduction We previous developed an informatics platform to: 1) generate large numbers of indicators of chronic conditions and determinants from heterogeneous sources, 2) present indicators in context of known causal relationships. However, the causality was defined by expert-consensus and only concerning direction. Quantitative estimates of causal effects are needed to drive public health decision-making. Objectives and Approach The objective of this work is to quantify the strength of the relationships between chronic disease indicators through empirical analysis of data for a defined population. Eight chronic diseases were explored and the individual data were obtained from linked administrative data for one million randomly sampled Montréal residents. We use Bayesian networks (BN) with our causal model based on expert consensus as a prior for the structure of the BN. In addition, we compare two networks estimated separately from individual-level data and data aggregated at the regional level, the latter being most commonly available to public health agencies. Results BNs were developed using constraint-based and score-based algorithms for structure learning, and maximum likelihood for parameter estimation. We found that the BN structures and parameters learned from individual-level data differed from the one estimated from data aggregated by community health centers. Specifically, the BN structure learned from individual data contained 9 more arcs between indicators and tened to fit the data better (the Bayesian factor between two network structures was 25.55), however, the results from the aggregated data matched our prior understanding of epidemiological knowledge more closely. Conclusion/Implications Conclusion: We compared BNs built using different resolutions of data as means to describe patterns among indicators for a defined population. This strategy for interpreting indicators combines prior domain knowledge with data and represents an initial step towards an intelligent decision-support tool for public health practitioners.

Introduction Linked population health data have the potential to inform evidence-based actions targeting serious public health concerns. However, large-scale data integration efforts can produce hundreds of population health indicators, which can overwhelm the ability of decision-makers to synthesize and interpret the information. Objectives and Approach Our research uses an existing semantic web application for population health surveillance, the Population Health Record (PopHR). PopHR automates a computational pipeline for linking data sources, building timely population health indicators, and uses artificial intelligence to organize indicators along a determinants of health framework. To assist users in interpreting the thousands of indicators, we developed computational algorithms combining values of multiple indicators across chronic diseases, to prioritize conditions within each region. This analytic approach can assist regional decision-makers in identifying their region’s priority conditions by facilitating the integration and analysis of multiple types of indicators (e.g. disease burden, temporal patterns). Results A pilot implementation of the regional prioritization algorithm focused on indicators defined in the Public Health Agency of Canada’s Chronic Disease Indicators Framework. Within this subset of diseases, we developed a computational algorithm to integrate into a priority index regional estimates of incidence, mortality, and prevalence taking into account the relative importance of each indicators’ outlier status and statistical significance of temporal trends. Our results allowed for the development of region-specific data visualizations dashboards, emphasizing the different factors driving the rankings of indicators within and across regions. For example, regions with higher socioeconomic status having generally lower disease burden are presented with visualizations emphasizing temporal trends and other statistically compelling patterns rather than simple indicators of magnitude. Conclusion/Implications This ranking approach represents initial stages ongoing research, expanding our methods to use machine learning strategies and additional expert knowledge. Current and future prioritization analyses within the PopHR platform offer the potential for public health to gain insights from an otherwise challenging complexity and richness of linked data.