Explore the vast range of titles available.

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.

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. 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. 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. 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.

Digital clinical decision support algorithms (CDSAs) that guide healthcare workers during consultations can enhance adherence to guidelines and the resulting quality of care. However, this improvement depends on the accuracy of inputs (symptoms and signs) entered by healthcare workers into the digital tool, which relies mainly on their clinical skills, often limited, especially in resource-constrained primary care settings. This study aimed to identify and characterize potential clinical skill gaps based on CDSA data patterns and clinical observations. We retrospectively analyzed data from 20,085 pediatric consultations conducted using an IMCI-based CDSA in 16 primary health centers in Rwanda. We focused on clinical signs with numerical values: temperature, mid-upper arm circumference (MUAC), weight, height, z-scores (MUAC for age, weight for age, and weight for height), heart rate, respiratory rate and blood oxygen saturation. Statistical summary measures (frequency of skipped measurements, plausible and implausible values) and their variation in individual health centers compared to the overall average were used to identify 10 health centers with irregular data patterns signaling potential clinical skill gaps. We subsequently observed 188 consultations in these health centers and interviewed healthcare workers to understand potential error causes. Observations indicated basic measurements not being assessed correctly in most children; weight (70%), MUAC (69%), temperature (67%), height (54%). These measures were predominantly conducted by minimally trained non-clinical staff in the registration area. More complex measures, done by healthcare workers in the consultation room, were often skipped: respiratory rate (43%), heart rate (37%), blood oxygen saturation (33%). This was linked to underestimating the importance of these signs in child management, especially in context of high patient loads at primary care level. Addressing clinical skill gaps through in-person training, eLearning and regular personalized mentoring tailored to specific health center needs is imperative to improve quality of care and enhance the benefits of CDSAs.

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 A cluster randomized trial in Tanzania showed that the implementation of a decision support algorithm decreased antibiotic prescriptions to children considerably, without impacting clinical outcomes.

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, modelled, 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.

Maternal mortality in Malawi is high, with low coverage of maternity care being a contributing factor. To improve maternal health coverage, an Android-based, integrated mobile health (mHealth) app called YendaNafe was introduced to community health workers (CHWs) in the Neno district, rural Malawi. This study evaluates the impact of this app on the uptake of antenatal care (ANC), facility-based births, and postnatal care (PNC), compared to a reference period where CHWs provided the same services without mHealth, using the interrupted time series analysis. Using aggregated monthly data and segmented quasi-Poisson regression models, we compared the effects of mHealth on selected maternal health outcomes. The models were adjusted for the COVID-19 pandemic, the occurrence of cyclones, and a cholera epidemic. We analyzed data from six eligible health facilities and their respective catchment areas in which CHWs were using YendaNafe, and compared 12 months before and 12 months after its introduction. The use of YendaNafe was associated with a 22% immediate increase in facility-based births (aIRR 1.22, 95% CI 1.12-1.33, p<0.001) but not an immediate increase in new ANC visits (aIRR 1.02,95% CI 0.90-1.14, p=0.77), ANC in the first trimester (aIRR 1.17, 95% CI 0.95-1.45 p=0.13), or PNC visits (aIRR 1.03, 95% CI 0.79-1.36, p=0.81). For long-term effect, YendaNafe was associated with an increase in new ANC visits (aIRR 1.04, 95% CI 1.01-1.07, p <0.01) and ANC in the first trimester (aIRR 1.03,95% CI 1.00-1.07 p=0.046), but not facility-based births (aIRR 1.01, 95% CI 0.99-1.03, p=0.46) or PNC (aIRR 0.97 95% CI 0.93-1.01, p=0.14). mHealth shows potential of increasing utilization of new ANC visits, ANC in the first trimester and facility-based births. Further research is needed to understand why mHealth did not have an effect on PNC.

Timely access to quality data is a key aspect of global governance and accountability. Data on development and health indicators are important for policymakers, public health experts and donors. With the endorsement of Transforming our world, the 2030 agenda for sustainable development, with its 17 sustainable development goals (SDGs) and their 232 indicators, the demand for data at all levels has increased. This demand is placing pressure on national monitoring and reporting systems, particularly in low- and middle-income countries. The final assessments of country-level progress in global health achieved between 2000 and 2015 were often based on sparse or outdated data, leading to misplaced confidence in the results. Some of these data were collected five years ago or more, leading to a considerable potential for incorrect conclusions and thus ineffective policy decisions. Therefore, the long-term solution to adequately track progress towards the SDGs is investment in the production of empirical data through national health management information systems, instead of reliance on out-dated estimates. An adequate health management information system that allows close monitoring of population health through the systematic collection of data from health facilities nationwide is a key building block of national health system planning and decision-making.

ObjectiveTo compare the impact of a teen club model to the standard care model on HIV treatment outcomes among adolescents (10–19 years of age).DesignRetrospective cohort study.SettingHIV clinics in Neno district, Malawi.ParticipantsAdolescents living with HIV enrolled in teen clubs (n=235) and matched participants in standard HIV care (n=297).Outcome measuresAttrition from HIV care, defined as a combination of treatment outcomes ‘died’, ‘defaulted’ and ‘transferred out’.ResultsOver 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.ConclusionsThe 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.

Chiyembekezo Kachimanga,1,2 Wingston Felix Ng’ambi,3 Doctor Kazinga,1 Enoch Ndarama,4 Mercy Ambogo Amulele,5 Fabien Munyaneza,1 Ibukun-Oluwa O Abejirinde,6,7 Thomas van den Akker,2,8 Alexandra V Kulinkina1,9,101Partners in Health Malawi, Neno, Malawi; 2Athena Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; 3Health Economics and Policy Unit, Department of Health Systems and Policy, Kamuzu University of Health Sciences, Lilongwe, Malawi; 4Ministry of Health, Neno, Malawi; 5Medic, Nairobi, Kenya; 6Women College Hospital Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada; 7Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; 8Department of Obstetrics and Gynaecology, Leiden University Medical Center, Leiden, Netherlands; 9Swiss Tropical and Public Health Institute, Allschwil, Switzerland; 10University of Basel, Basel, SwitzerlandCorrespondence: Chiyembekezo Kachimanga, Partners in Health Malawi, Post Office Box 56, Neno, Malawi, Email [email protected]View the original paper by Dr Kachimanga and colleaguesThis is in response to the Letter to the Editor

ObjectiveTo 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.DesignConvergent mixed-methods study.SettingKoidu Government Hospital, a secondary-level hospital in Kono District.ParticipantsWe 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.OutcomesThree 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).Results57% 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.ConclusionFree 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.