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

EXTENDING LKN CLIMATE REGIONALIZATION WITH SPATIAL REGULARIZATION: AN APPLICATION TO EPIDEMIOLOGICAL RESEARCH

Regional climate is a critical factor in public health research, adaptation studies, climate change burden analysis, and decision support frameworks. Existing climate regionalization schemes are not well suited for these tasks as they rarely take population density into account. In this work, we are extending our recently developed method for automated climate regionalization (LKN-method) to incorporate the spatial features of target population. The LKN method consists of the data limiting step (L-step) to reduce dimensionality by applying principal component analysis, a classification step (K-step) to produce hierarchical candidate regions using k-means unsupervised classification algorithm, and a nomination step (N-step) to determine the number of candidate climate regions using cluster validity indexes. LKN method uses a comprehensive set of multiple satellite data streams, arranged as time series, and allows us to define homogeneous climate regions. The proposed approach extends the LKN method to include regularization terms reflecting the spatial distribution of target population. Such tailoring allows us to determine the optimal number and spatial distribution of climate regions and thus, to ensure more uniform population coverage across selected climate categories. We demonstrate how the extended LKN method produces climate regionalization can be better tailored to epidemiological research in the context of decision support framework

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

LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys