Evaluating the Adoption of mHealth Technologies by Community Health Workers to Improve the Use of Maternal Health Services in Sub-Saharan Africa: Systematic Review.

BACKGROUND: Limited information exists on the impact of mobile health (mHealth) use by community health workers (CHWs) on improving the use of maternal health services in sub-Saharan Africa (SSA). OBJECTIVE: This systematic review addresses 2 objectives: evaluating the impact of mHealth use by CHWs on antenatal care (ANC) use, facility-based births, and postnatal care (PNC) use in SSA; and identifying facilitators and barriers to mHealth use by CHWs in programs designed to increase ANC use, facility-based births, and PNC use in SSA using a sociotechnical system approach. METHODS: We searched for articles in 6 databases (MEDLINE, CINAHL, Web of Science, Embase, Scopus, and Africa Index Medicus) from inception up to September 2022, with additional articles identified from Google Scholar. After article selection, 2 independent reviewers performed title and abstract screening, full-text screening, and data extraction using Covidence software (Veritas Health Innovation Ltd). In addition, we manually screened the references lists of the included articles. Finally, we performed a narrative synthesis of the outcomes. RESULTS: Among the 2594 records retrieved, 10 (0.39%) studies (n=22, 0.85% articles) met the inclusion criteria and underwent data extraction. The studies were published between 2012 and 2022 in 6 countries. Of the studies reporting on ANC outcomes, 43% (3/7) reported that mHealth use by CHWs increased ANC use. Similarly, of the studies reporting on facility-based births, 89% (8/9) demonstrated an increase due to mHealth use by CHWs. In addition, in the PNC studies, 75% (3/4) showed increased PNC use associated with mHealth use by CHWs. Many of the studies reported on the importance of addressing factors related to the social environment of mHealth-enabled CHWs, including the perception of CHWs by the community, trust, relationships, digital literacy, training, mentorship and supervision, skills, CHW program ownership, and the provision of incentives. Very few studies reported on how program goals and culture influenced mHealth use by CHWs. Providing free equipment, accessories, and internet connectivity while addressing ongoing challenges with connectivity, power, the ease of using mHealth software, and equipment maintenance support allowed mHealth-enabled CHW programs to thrive. CONCLUSIONS: mHealth use by CHWs was associated with an increase in ANC use, facility-based births, and PNC use in SSA. Identifying and addressing social and technical barriers to the use of mHealth is essential to ensure the success of mHealth programs. TRIAL REGISTRATION: PROSPERO CRD42022346364; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346364.

Prevalence and predictors of residual antibiotics in children's blood in community settings in Tanzania.

OBJECTIVES: Children account for a significant proportion of antibiotic consumption in low- and middle-income countries, with overuse occurring in formal and informal health sectors. This study assessed the prevalence and predictors of residual antibiotics in the blood of children in the Mbeya and Morogoro regions of Tanzania. METHODS: The cross-sectional community-based survey used two-stage cluster sampling to include children aged under 15 years. For each child, information on recent illness, healthcare-seeking behaviour, and use of antibiotics, as well as a dried blood spot sample, were collected. The samples underwent tandem mass spectrometry analysis to quantify the concentrations of 15 common antibiotics. Associations between survey variables and the presence of residual antibiotics were assessed using mixed-effects logistic regression. RESULTS: In total, 1742 children were surveyed, and 1699 analysed. The overall prevalence of residual antibiotics in the blood samples was 17.4% (296/1699), the highest among children under the age of 5 years. The most frequently detected antibiotics were trimethoprim (144/1699; 8.5%), sulfamethoxazole (102/1699; 6.0%), metronidazole (61/1699; 3.6%), and amoxicillin (43/1699; 2.5%). The strongest predictors of residual antibiotics in the blood were observed presence of antibiotics at home (adjusted odds ratio [aOR] = 2.9; 95% CI, 2.0-4.1) and reported consumption of antibiotics in the last 2 weeks (aOR = 2.5; 95% CI, 1.6-3.9). However, half (145/296) of the children who had residual antibiotics in their blood, some with multiple antibiotics, had no reported history of illness or antibiotic consumption in the last 2 weeks, and antibiotics were not found at home. DISCUSSION: This study demonstrated a high prevalence of antibiotic exposure among children in Tanzanian communities, albeit likely underestimated, especially for compounds with short half-lives. A significant proportion of antibiotic exposure was unexplained and may have been due to unreported self-medication or environmental pathways. Incorporating biomonitoring into surveillance strategies can help better understand exposure patterns and design antibiotic stewardship interventions.

Experiences of community health workers on adopting mHealth in rural Malawi: A qualitative study.

Background: The use of mobile health technology (mHealth) by community health workers (CHWs) can strengthen community-based service delivery and improve access to and quality of healthcare. Objective: This qualitative study sought to explore experiences and identify factors influencing the use of an integrated smartphone-based mHealth called YendaNafe by CHWs in rural Malawi. Methods: Using pre-tested interview guides, between August and October 2022, we conducted eight focus group discussions with CHWs (n = 69), four in-depth interviews with CHW supervisors, and eight key informant interviews in Neno District, Malawi. We audio-recorded and transcribed the interviews verbatim and organized them for analysis in Dedoose V9.0.62. We used an inductive analysis technique to analyze the data. We further applied the six domains of the socio-technical system (STS) framework to map factors influencing the use of YendaNafe. Results: User experiences and facilitators and barriers were the two main themes that emerged. mHealth was reported to improve the task efficiency, competence, trust, and perceived professionalism of CHWs. CHWs less frequently referred to cultural factors influencing app uptake. However, for other social systems, they identified relationships and trust with stakeholders, availability of training and programmatic support, and performance monitoring and feedback as influencing the use of YendaNafe. From the STS technical domain, the availability and adequacy of hardware such as phones, mobile connectivity, and usability influenced the use of YendaNafe. Conclusions: Despite the initial discomfort, CHWs found mHealth helpful in supporting their service delivery tasks. Identifying and addressing social and technical factors during mHealth implementation may help improve end users' attitudes and uptake.

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 >80% of scheduled visits); loss-to-follow-up (LTFU) (consecutive 6 months of missed appointments) and engaged in (but not fully adherent) with treatment (<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<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<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.

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.

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.

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.

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 of results. 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 infections. 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, 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.

Adoption of mHealth Technologies by Community Health Workers to Improve the Use of Maternal Health Services in Sub-Saharan Africa: Protocol for a Mixed Method Systematic Review.

BACKGROUND: Studies have shown that mobile health technologies (mHealth) enhance the use of maternal health services. However, there is limited evidence of the impact of mHealth use by community health workers (CHWs) on the use of maternal health services in sub-Saharan Africa. OBJECTIVE: This mixed method systematic review will explore the impact of mHealth use by CHWs on the use of the maternal health continuum of care (antenatal care, intrapartum care, and postnatal care [PNC]), as well as barriers and facilitators of mHealth use by CHWs when supporting maternal health services. METHODS: We will include studies that report the impact of mHealth by CHWs on the use of antenatal care, facility-based births, and PNC visits in sub-Saharan Africa. We will search 6 databases (MEDLINE, CINAHL, Web of Science, Embase, Scopus, and Africa Index Medicus), with additional articles identified from Google Scholar and manual screening of references of the included studies. The included studies will not be limited by language or year of publication. After study selection, 2 independent reviewers will perform title and abstract screening, followed by full-text screening to identify the final papers to be included. Data extraction and risk-of-bias assessment will be performed using Covidence software by 2 independent reviewers. We will use a Mixed Methods Appraisal Tool to perform risk-of-bias assessments on all included studies. Finally, we will perform a narrative synthesis of the outcomes, integrating information about the effect of mHealth on maternal health use and barriers and facilitators of mHealth use. This protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. RESULTS: In September 2022, we conducted an initial search in the eligible databases. After removing duplicates, we identified 1111 studies that were eligible for the title and abstract screening. We will finalize the full-text assessment for eligibility, data extraction, assessment of methodological quality, and narrative synthesis by June 2023. CONCLUSIONS: This systematic review will present new and up-to-date evidence on the use of mHealth by CHWs along the pregnancy, childbirth, and PNC continuum of care. We anticipate the results will inform program implementation and policy by highlighting the potential impacts of mHealth and presenting contextual factors that should be addressed to ensure the success of the programs. TRIAL REGISTRATION: PROSPERO CRD42022346364; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346364. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44066.

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.