The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.Accepted manuscrip

Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments.

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches.Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables.Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables.The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure

Agreement among Four Prevalence Metrics for Urogenital Schistosomiasis in the Eastern Region of Ghana

Few studies assess agreement among Schistosoma haematobium eggs, measured hematuria, and self-reported metrics. We assessed agreement among four metrics at a single time point and analyzed the stability of infection across two time points with a single metric. We used data from the Eastern Region of Ghana and constructed logistic regression models. Girls reporting macrohematuria were 4.1 times more likely to have measured hematuria than girls not reporting macrohematuria (CI95%: 2.1–7.9); girls who swim were 3.6 times more likely to have measured hematuria than nonswimmers (CI95%: 1.6–7.9). For boys, neither self-reported metric was predictive. Girls with measured hematuria in 2010 were 3.3 times more likely to be positive in 2012 (CI95%: 1.01–10.5), but boys showed no association. Boys with measured hematuria in 2008 were 6.0 times more likely to have measured hematuria in 2009 (CI95%: 1.5–23.9) and those with eggs in urine in 2008 were 4.8 times more likely to have eggs in urine in 2009 (CI95%: 1.2–18.8). For girls, measured hematuria in 2008 predicted a positive test in 2009 (OR = 2.8; CI95%: 1.1–6.8), but egg status did not. Agreement between dipstick results and eggs suggests continued dipstick used is appropriate. Self-reported swimming should be further examined. For effective disease monitoring, we recommend annual dipstick testing

Assessment of urogenital schistosomiasis knowledge among primary and junior high school students in the Eastern Region of Ghana: A cross-sectional study.

BackgroundKnowledge of urogenital schistosomiasis can empower individuals to limit surface water contact and participate in mass drug administration campaigns, but nothing is currently known about the schistosomiasis knowledge that schoolchildren have in Ghana. We developed and implemented a survey tool aiming to assess the knowledge of urogenital schistosomiasis (treatment, transmission, prevention, symptoms) among science teaches and primary and junior high school students in the Eastern Region of Ghana.MethodsWe developed a 22-question knowledge survey tool and administered it to 875 primary and 938 junior high school students from 74 schools in 37 communities in the Eastern Region of Ghana. Teachers (n = 57) answered 20 questions matched to student questions. We compared knowledge scores (as percent of correct answers) across topics, gender, and class year and assessed associations with teacher's knowledge scores using t-tests, chi-squared tests, univariate, and multivariate linear regression, respectively.ResultsStudents performed best when asked about symptoms (mean±SD: 76±21% correct) and prevention (mean±SD: 69±25% correct) compared with transmission (mean±SD: 50±15% correct) and treatment (mean±SD: 44±23% correct) (pConclusionsOur survey parsed four components of student and teacher knowledge. We found strong knowledge in several realms, as well as knowledge gaps, especially on transmission and treatment. Addressing relevant gaps among students and science teachers in UGS-endemic areas may help high-risk groups recognize risky water contact activities, improve participation in mass drug administration, and spark interest in science by making it practical

Impact of a teen club model on HIV outcomes among adolescents in rural Neno district, Malawi: a retrospective cohort study

Objective To compare the impact of a teen club model to the standard care model on HIV treatment outcomes among adolescents (10–19 years of age).Design Retrospective cohort study.Setting HIV clinics in Neno district, Malawi.Participants Adolescents living with HIV enrolled in teen clubs (n=235) and matched participants in standard HIV care (n=297).Outcome measures Attrition from HIV care, defined as a combination of treatment outcomes ‘died’, ‘defaulted’ and ‘transferred out’.Results Over a 4-year follow-up period, adolescents who participated in the teen club had a significantly higher likelihood of remaining in care than those who did not (HR=2.80; 95% CI: 1.46 to 5.34). Teen clubs also increased the probability of having a recent measured viral load (VL) and BMI, but did not change the probability of VL suppression. The age at antiretroviral treatment initiation below 15 years (aHR=0.37; 95% CI: 0.17 to 0.82) reduced the risk of attrition from HIV care, while underweight status (aHR=3.18; 95% CI: 1.71 to 5.92) increased the risk of attrition, after controlling for sex, WHO HIV staging and teen club participation.Conclusions The teen club model has the potential to improve treatment outcomes among adolescents in rural Neno district. However, in addition to retaining adolescents in HIV care, greater attention is needed to treatment adherence and viral suppression in this special population. Further understanding of the contextual factors and barriers that adolescents in rural areas face could further improve the teen club model to ensure high-quality HIV care and quality of life

Non-communicable disease care in Sierra Leone: a mixed-methods study of the drivers and barriers to retention in care for hypertension

Objective To retrospectively analyse routinely collected data on the drivers and barriers to retention in chronic care for patients with hypertension in the Kono District of Sierra Leone.Design Convergent mixed-methods study.Setting Koidu Government Hospital, a secondary-level hospital in Kono District.Participants We conducted a descriptive analysis of key variables for 1628 patients with hypertension attending the non-communicable disease (NCD) clinic between February 2018 and August 2019 and qualitative interviews with 21 patients and 7 staff to assess factors shaping patients’ retention in care at the clinic.Outcomes Three mutually exclusive outcomes were defined for the study period: adherence to the treatment protocol (attending &gt;80% of scheduled visits); loss-to-follow-up (LTFU) (consecutive 6 months of missed appointments) and engaged in (but not fully adherent) with treatment (&lt;80% attendance).Results 57% of patients were adherent, 20% were engaged in treatment and 22% were LTFU. At enrolment, in the unadjusted variables, patients with higher systolic and diastolic blood pressures had better adherence than those with lower blood pressures (OR 1.005, 95% CI 1.002 to 1.009, p=0.004 and OR 1.008, 95% CI 1.004 to 1.012, p&lt;0.001, respectively). After adjustment, there were 14% lower odds of adherence to appointments associated with a 1 month increase in duration in care (OR 0.862, 95% CI 0.801 to 0.927, p&lt;0.001). Qualitative findings highlighted the following drivers for retention in care: high-quality education sessions, free medications and good interpersonal interactions. Challenges to seeking care included long wait times, transport costs and misunderstanding of the long-term requirement for hypertension care.Conclusion Free medications, high-quality services and health education may be effective ways of helping NCD patients stay engaged in care. Facility and socioeconomic factors can pose challenges to retention in care