Investigating seasonal patterns in enteric infections: a systematic review of time series methods

Foodborne and waterborne gastrointestinal infections and their associated outbreaks are preventable, yet still result in significant morbidity, mortality, and revenue loss. Many enteric infections demonstrate seasonality, or annual systematic periodic fluctuations in incidence, associated with climatic and environmental factors. Public health professionals use statistical methods and time series models to describe, compare, explain, and predict seasonal patterns. However, descriptions and estimates of seasonal features, such as peak timing, depend on how researchers define seasonality for research purposes and how they apply time series methods. In this review, we outline the advantages and limitations of common methods for estimating seasonal peak timing. We provide recommendations improving reporting requirements for disease surveillance systems. Greater attention to how seasonality is defined, modeled, interpreted, and reported is necessary to promote reproducible research and strengthen proactive and targeted public health policies, intervention strategies, and preparedness plans to dampen the intensity and impacts of seasonal illnesses. © 2022 Cambridge University Press. All rights reserved

How do disease control measures impact spatial predictions of schistosomiasis and hookworm? The example of predicting school-based prevalence before and after preventive chemotherapy in Ghana

BACKGROUND: Schistosomiasis and soil-transmitted helminth infections are among the neglected tropical diseases (NTDs) affecting primarily marginalized communities in low- and middle-income countries. Surveillance data for NTDs are typically sparse, and hence, geospatial predictive modeling based on remotely sensed (RS) environmental data is widely used to characterize disease transmission and treatment needs. However, as large-scale preventive chemotherapy has become a widespread practice, resulting in reduced prevalence and intensity of infection, the validity and relevance of these models should be re-assessed. METHODOLOGY: We employed two nationally representative school-based prevalence surveys of Schistosoma haematobium and hookworm infections from Ghana conducted before (2008) and after (2015) the introduction of large-scale preventive chemotherapy. We derived environmental variables from fine-resolution RS data (Landsat 8) and examined a variable distance radius (1-5 km) for aggregating these variables around point-prevalence locations in a non-parametric random forest modeling approach. We used partial dependence and individual conditional expectation plots to improve interpretability. PRINCIPAL FINDINGS: The average school-level S. haematobium prevalence decreased from 23.8% to 3.6% and that of hookworm from 8.6% to 3.1% between 2008 and 2015. However, hotspots of high-prevalence locations persisted for both diseases. The models with environmental data extracted from a buffer radius of 2-3 km around the school location where prevalence was measured had the best performance. Model performance (according to the R2 value) was already low and declined further from approximately 0.4 in 2008 to 0.1 in 2015 for S. haematobium and from approximately 0.3 to 0.2 for hookworm. According to the 2008 models, land surface temperature (LST), modified normalized difference water index (MNDWI), elevation, slope, and streams variables were associated with S. haematobium prevalence. LST, slope, and improved water coverage were associated with hookworm prevalence. Associations with the environment in 2015 could not be evaluated due to low model performance. CONCLUSIONS/SIGNIFICANCE: Our study showed that in the era of preventive chemotherapy, associations between S. haematobium and hookworm infections and the environment weakened, and thus predictive power of environmental models declined. In light of these observations, it is timely to develop new cost-effective passive surveillance methods for NTDs as an alternative to costly surveys, and to focus on persisting hotspots of infection with additional interventions to reduce reinfection. We further question the broad application of RS-based modeling for environmental diseases for which large-scale pharmaceutical interventions are in place

A roadmap for using DHIS2 data to track progress in key health indicators in the Global South: experience from sub-Saharan Africa

High quality health data as collected by health management information systems (HMIS) is an important building block of national health systems. District Health Information System 2 (DHIS2) software is an innovation in data management and monitoring for strengthening HMIS that has been widely implemented in low and middle-income countries in the last decade. However, analysts and decision-makers still face significant challenges in fully utilizing the capabilities of DHIS2 data to pursue national and international health agendas. We aimed to (i) identify the most relevant health indicators captured by DHIS2 for tracking progress towards the Sustainable Development goals in sub-Saharan African countries and (ii) present a clear roadmap for improving DHIS2 data quality and consistency, with a special focus on immediately actionable solutions. We identified that key indicators in child and maternal health (e.g. vaccine coverage, maternal deaths) are currently being tracked in the DHIS2 of most countries, while other indicators (e.g. HIV/AIDS) would benefit from streamlining the number of indicators collected and standardizing case definitions. Common data issues included unreliable denominators for calculation of incidence, differences in reporting among health facilities, and programmatic differences in data quality. We proposed solutions for many common data pitfalls at the analysis level, including standardized data cleaning pipelines, k-means clustering to identify high performing health facilities in terms of data quality, and imputation methods. While we focus on immediately actionable solutions for DHIS2 analysts, improvements at the point of data collection are the most rigorous. By investing in improving data quality and monitoring, countries can leverage the current global attention on health data to strengthen HMIS and progress towards national and international health priorities

Longitudinal borehole functionality in 15 rural Ghanaian towns from three groundwater quality clusters

OBJECTIVE: In sub-Saharan Africa, 45% of the rural population uses boreholes (BHs). Despite recent gains in improved water access and coverage, parallel use of unimproved sources persists. Periodic infrastructure disrepair contributes to non-exclusive use of BHs. Our study describes functionality of BHs in 2014, 2015, and 2016 in 15 rural towns in the Eastern Region of Ghana sourced from three groundwater quality clusters (high iron, high salinity, and control). We also assess factors affecting cross-sectional and longitudinal functionality using logistic regression. RESULTS: BH functionality rates ranged between 81 and 87% and were similar across groundwater quality clusters. Of 51 BHs assessed in all three years, 34 (67%) were consistently functional and only 3 (6%) were consistently broken. There was a shift toward proactive payment for water over the course of the study in the control and high-salinity clusters. Payment mechanism, population served, presence of nearby alternative water sources, and groundwater quality cluster were not significant predictors of cross-sectional or longitudinal BH functionality. However, even in the high iron cluster, where water quality is poor and no structured payment mechanism for water exists, BHs are maintained, showing that they are important community resources

A household-based community health worker programme for non-communicable disease, malnutrition, tuberculosis, HIV and maternal health: a stepped-wedge cluster randomised controlled trial in Neno District, Malawi

BACKGROUND: Community health worker (CHW) programmes are a valuable component of primary care in resource-poor settings. The evidence supporting their effectiveness generally shows improvements in disease-specific outcomes relative to the absence of a CHW programme. In this study, we evaluated expanding an existing HIV and tuberculosis (TB) disease-specific CHW programme into a polyvalent, household-based model that subsequently included non-communicable diseases (NCDs), malnutrition and TB screening, as well as family planning and antenatal care (ANC). METHODS: We conducted a stepped-wedge cluster randomised controlled trial in Neno District, Malawi. Six clusters of approximately 20 000 residents were formed from the catchment areas of 11 healthcare facilities. The intervention roll-out was staggered every 3 months over 18 months, with CHWs receiving a 5-day foundational training for their new tasks and assigned 20-40 households for monthly (or more frequent) visits. FINDINGS: The intervention resulted in a decrease of approximately 20% in the rate of patients defaulting from chronic NCD care each month (-0.8 percentage points (pp) (95% credible interval: -2.5 to 0.5)) while maintaining the already low default rates for HIV patients (0.0 pp, 95% CI: -0.6 to 0.5). First trimester ANC attendance increased by approximately 30% (6.5pp (-0.3, 15.8)) and paediatric malnutrition case finding declined by 10% (-0.6 per 1000 (95% CI -2.5 to 0.8)). There were no changes in TB programme outcomes, potentially due to data challenges. INTERPRETATION: CHW programmes can be successfully expanded to more comprehensively address health needs in a population, although programmes should be carefully tailored to CHW and health system capacity

COMBINING REMOTELY SENSED ENVIRONMENTAL CHARACTERISTICS WITH SOCIAL AND BEHAVIORAL CONDITIONS THAT AFFECT SURFACE WATER USE IN SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

Schistosoma haematobium transmission is influenced by environmental conditions that determine the suitability of the parasite and intermediate host snail habitats, as well as by socioeconomic conditions, access to water and sanitation infrastructure, and human behaviors. Remote sensing is a demonstrated valuable tool to characterize environmental conditions that support schistosomiasis transmission. Socioeconomic and behavioral conditions that propagate repeated domestic and recreational surface water contact are more difficult to quantify at large spatial scales. We present a mixed-methods approach that builds on the remotely sensed ecological variables by exploring water and sanitation related community characteristics as independent risk factors of schistosomiasis transmission

Implementation of a non-communicable disease clinic in rural Sierra Leone: early experiences and lessons learned

This study is an evaluation of the first cohort of patients enrolled in an outpatient non-communicable disease clinic in Kono, Sierra Leone. In the first year, the clinic enrolled 916 patients. Eight months after the enrollment of the last patient, 53% were still active in care, 43% had been lost to follow-up (LTFU) and 4% had defaulted. Of the LTFU patients, 47% only came for the initial enrollment visit and never returned. Treatment outcomes of three patient groups [HTN only (n = 720), DM only (n = 51), and HTN/DM (n = 96)] were analyzed through a retrospective chart review. On average, all groups experienced reductions in blood pressure and/or blood glucose of approximately 10% and 20%, respectively. The proportions of patients with their condition controlled also increased. As NCDs remain underfunded and under-prioritized in low-income countries, the integrated program in Kono demonstrates the possibility of improving outpatient NCD care in Sierra Leone and similar settings

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

A digital health algorithm to guide antibiotic prescription in pediatric outpatient care: a cluster randomized controlled trial.

Excessive antibiotic use and antimicrobial resistance are major global public health threats. We developed ePOCT+, a digital clinical decision support algorithm in combination with C-reactive protein test, hemoglobin test, pulse oximeter and mentorship, to guide health-care providers in managing acutely sick children under 15 years old. To evaluate the impact of ePOCT+ compared to usual care, we conducted a cluster randomized controlled trial in Tanzanian primary care facilities. Over 11 months, 23,593 consultations were included from 20 ePOCT+ health facilities and 20,713 from 20 usual care facilities. The use of ePOCT+ in intervention facilities resulted in a reduction in the coprimary outcome of antibiotic prescription compared to usual care (23.2% versus 70.1%, adjusted difference -46.4%, 95% confidence interval (CI) -57.6 to -35.2). The coprimary outcome of day 7 clinical failure was noninferior in ePOCT+ facilities compared to usual care facilities (adjusted relative risk 0.97, 95% CI 0.85 to 1.10). There was no difference in the secondary safety outcomes of death and nonreferred secondary hospitalizations by day 7. Using ePOCT+ could help address the urgent problem of antimicrobial resistance by safely reducing antibiotic prescribing. Clinicaltrials.gov Identifier: NCT05144763