Adeline Adwoa Boatin

UNSTRUCTURED Digital health innnovations have potential to improve health care, particularly in low-and middle income countries, yet few progress from concepts to implementation at scale. We present the Digital Intervetions Idea to Impact (DI3) roadmap, as a practical roadmap that maps the interconnected steps require to move digital health tools and interventions from initial concept through development, evaluation, health system integration and scale. Developed by a multi-disciplianry, cross-sector group with experience advancing digital health innovations across Africa, DI3, is intended to help diverse stakeholders anticipate requirements beyond their individual areas of expertise, identify gaps that may impede progress and coordinate more effectively toward sustainable and equitable health impact.

Background: Prior cesarean delivery (CD) is a leading indication for repeat CD and a key driver of rising cesarean rates in Sub‐Saharan Africa. Evidence on associations between prior CD and adverse outcomes in low‐resource settings remains limited. Objective: We examined the association between one prior CD and adverse maternal and perinatal outcomes and examined current delivery mode as a mediator of this association among women delivering at Mbarara Regional Referral Hospital (MRRH), Uganda. Methods: We conducted a secondary analysis of four prospective studies at MRRH (2014–2024). The exposure was having one prior CD, and outcomes included composite adverse maternal outcomes (postpartum hemorrhage, infection, blood transfusion, prolonged hospital stay, and others) and composite adverse perinatal outcomes (stillbirth, neonatal death, preterm birth, low birthweight, low Apgar score, and neonatal intensive care unit admission). We compared the odds of adverse outcomes for women with one prior CD to those with none using multivariable logistic regression. Causal mediation analysis was used to assess whether current CD mediated the effect of prior CD on adverse maternal outcomes. Results: Of 2222 women with at least one prior delivery, 611 (27.5%) had one prior CD. Women with one prior CD more often delivered by cesarean (73.7% vs. 26.4%) and experienced more adverse maternal outcomes (24.5% vs. 15.4%, P < 0.001), including prolonged hospital stay (21.6% vs. 12.7%, P < 0.001). Prior CD was associated with increased odds of adverse maternal outcomes (adjusted odds ratio [aOR]: 1.99, 95% confidence interval [CI]: 1.54–2.57), with 88.9% of this effect mediated through current delivery mode (indirect odds ratio [OR]: 1.78, 95% CI: 1.41–2.25; direct OR: 1.09, 95% CI: 0.79–1.51, P = 0.586). Adverse perinatal outcomes (19.1%) did not differ by prior CD status ( P = 0.28). HIV‐positive serostatus (aOR: 1.29), referral (aOR: 1.69), and low antenatal care (ANC) attendance (aOR: 1.66) independently predicted perinatal outcomes. Conclusion: While prior CD was associated with higher odds of poor maternal outcomes due to repeat cesarean, HIV‐positive serostatus, facility referral, and low ANC attendance (but not prior CD) independently predicted adverse perinatal outcomes. Individualized delivery planning and improving surgical safety for women with prior CD and strengthened antenatal surveillance are critical to reducing maternal and perinatal morbidity at referral facilities in low‐resource settings.

Purpose Surgical site infections (SSIs) are a major postoperative complication, particularly in resource-limited settings where routine follow-up is challenging. Our study explores the integration of multi-day imaging in deep learning models to improve the detection accuracy for SSIs. Materials and Methods We conducted a retrospective analysis of postoperative wound images from 733 women who underwent cesarean delivery at Kirehe District Hospital, Rwanda (Sept 2019–Mar 2020). Images were captured on postoperative day 3 (POD3) and day 11 (POD11). SSI diagnoses were made by trained general practitioners using CDC criteria. We compared five CNN-based models: (1) POD3 alone, (2) POD11 alone, (3) feature summation across days, (4) feature delta between days, and (5) direct integration via a Siamese network. Models were trained for 50 epochs with SMOTE, class weighting, and 5-fold cross-validation. Performance was evaluated by AUC, sensitivity, specificity, and bootstrapped 95% CIs. Results Our POD3-alone model had the lowest mean AUC (0.64), while the POD11-alone model achieved better performance (AUC: 0.85). Among the multi-day integration methods, summation of features (AUC: 0.76) and delta calculation (AUC: 0.67) underperformed compared to POD11-alone. Direct integration using a Siamese network achieved the highest AUC (0.89) and specificity (0.82). Conclusion Multi-day imaging integration enhances detection of SSIs by capturing progressive wound changes, offering advantages over single-day models. While direct integration provides the most accurate results, its computational intensity poses challenges for real-world deployment. Future work should focus on external validation, integration with thermal image data, ensemble strategies, and computational optimizations for deployment in low-resource settings.

Background: Mobile health (mHealth) apps are increasingly leveraged to support community health workers (CHWs) in delivering high-quality care, particularly in low- and middle-income countries. However, despite the proliferation of mHealth tools, few have been implemented at scale, partly due to limited attention to usability and acceptability among end users. In sub-Saharan Africa, mHealth tools designed for CHWs often lack systematic evaluation using validated instruments tailored to local contexts. Without such assessments, it is difficult to ensure that these tools can be integrated effectively into CHW workflows and scaled sustainably. Objective: This study aimed to adapt and validate existing mHealth usability and acceptability assessment tools to be contextually appropriate for CHWs in Rwanda. Specifically, we sought to ensure contextual appropriateness for CHWs supporting postoperative home follow-up for women after cesarean delivery. The resulting tool was designed for use in an implementation study of a novel CHW-led mHealth app. Methods: This study was conducted in the Kirehe district, Rwanda, from October 2022 to March 2023. We adapted 2 established tools—the mHealth App Usability Questionnaire and selected items from the Practitioner Opinion (Acceptability) Scale—and added new items that reflect core functions of the CHW-focused mHealth app. All items were translated into Kinyarwanda and simplified to align with CHWs’ educational levels. We conducted a three-stage validation that consisted of (1) content validity testing with 8 local and international experts using a recommended content validity index threshold of >0.78; (2) face validity testing with 10 CHWs using a recommended face validity index threshold of ≥0.60; and (3) reliability testing using responses from 30 CHWs, with a Cronbach α coefficient of ≥0.70 indicating acceptable internal consistency. Results: Of the 25 items assessed, 22 (88%) achieved a content validity index score of >0.78 for both clarity and relevance. The face validity index across all 22 items was 0.991, indicating strong comprehensibility and relevance to CHWs. Internal consistency was high: the Cronbach α was 0.86 for the mHealth App Usability Questionnaire items, 0.73 for the Practitioner Opinion (Acceptability) Scale items, and 0.87 for the newly developed questions. The final tool—named the Community Health Worker mHealth Usability and Acceptability Assessment Tool—included 22 items with strong content validity, face validity, and internal reliability. Conclusions: This study presents a rigorously adapted and validated tool for assessing mHealth usability and acceptability among CHWs in Rwanda. The Community Health Worker mHealth Usability and Acceptability Assessment Tool can guide future evaluations of mHealth interventions in similar contexts and serve as a model for localizing mHealth assessment tools in low- and middle-income country settings to ensure fit-for-purpose implementation.

Digital innovations hold immense potential to transform health care delivery, particularly in sub-Saharan Africa, where financial, geographical, and infrastructural constraints continue to hinder progress toward universal health care delivery. Although a growing health tech sector offers creative solutions, few digital health interventions reach scaled implementation. In this paper, we present the digital fit/viability model—an adapted determinant framework to describe facilitators and barriers to moving from digital tools to integrated digital health implementation. We then use this model to describe the specific challenges and recommended solutions when developing digital health tools for health systems in sub-Saharan Africa.

BACKGROUND Mobile health (mHealth) apps are increasingly leveraged to support community health workers (CHWs) in delivering high-quality care, particularly in low- and middle-income countries. However, despite the proliferation of mHealth tools, few have been implemented at scale, partly due to limited attention to usability and acceptability among end users. In sub-Saharan Africa, mHealth tools designed for CHWs often lack systematic evaluation using validated instruments tailored to local contexts. Without such assessments, it is difficult to ensure that these tools can be integrated effectively into CHW workflows and scaled sustainably. OBJECTIVE This study aimed to adapt and validate existing mHealth usability and acceptability assessment tools to be contextually appropriate for CHWs in Rwanda. Specifically, we sought to ensure contextual appropriateness for CHWs supporting postoperative home follow-up for women after cesarean delivery. The resulting tool was designed for use in an implementation study of a novel CHW-led mHealth app. METHODS This study was conducted in the Kirehe district, Rwanda, from October 2022 to March 2023. We adapted 2 established tools—the mHealth App Usability Questionnaire and selected items from the Practitioner Opinion (Acceptability) Scale—and added new items that reflect core functions of the CHW-focused mHealth app. All items were translated into Kinyarwanda and simplified to align with CHWs’ educational levels. We conducted a three-stage validation that consisted of (1) content validity testing with 8 local and international experts using a recommended content validity index threshold of >0.78; (2) face validity testing with 10 CHWs using a recommended face validity index threshold of ≥0.60; and (3) reliability testing using responses from 30 CHWs, with a Cronbach α coefficient of ≥0.70 indicating acceptable internal consistency. RESULTS Of the 25 items assessed, 22 (88%) achieved a content validity index score of >0.78 for both clarity and relevance. The face validity index across all 22 items was 0.991, indicating strong comprehensibility and relevance to CHWs. Internal consistency was high: the Cronbach α was 0.86 for the mHealth App Usability Questionnaire items, 0.73 for the Practitioner Opinion (Acceptability) Scale items, and 0.87 for the newly developed questions. The final tool—named the Community Health Worker mHealth Usability and Acceptability Assessment Tool—included 22 items with strong content validity, face validity, and internal reliability. CONCLUSIONS This study presents a rigorously adapted and validated tool for assessing mHealth usability and acceptability among CHWs in Rwanda. The Community Health Worker mHealth Usability and Acceptability Assessment Tool can guide future evaluations of mHealth interventions in similar contexts and serve as a model for localizing mHealth assessment tools in low- and middle-income country settings to ensure fit-for-purpose implementation.

Objective: The objective of this study is to determine the proportion of women undergoing trial of labour after caesarean (TOLAC) and vaginal birth after caesarean (VBAC) in Sub‐Saharan Africa (SSA), and to estimate associated adverse events.Methods: We searched PubMed, MEDLINE, CAB, EMBASE and African‐specific databases from 1966 to July 2023, including SSA studies reporting on women with previous caesarean section (CS). We extracted data on study design, planned (TOLAC versus elective repeat CS (ERCS)) and actual delivery mode, and adverse outcomes. We calculated mean TOLAC and VBAC proportions, and pooled proportion of adverse events, comparing between TOLAC and ERCS where available. We assessed bias using the methodological index for nonrandomized studies.Results: From 62 studies with 36 611 births, the estimated proportion undergoing TOLAC and achieving VBAC, adjusted for study variability, were 79% (95% confidence interval [95% CI]: 74%, 85%) and 47% (95% CI: 42%, 51%), respectively, for all births. Mean estimated uterine rupture proportion and maternal mortality were 2.1% (95% CI: 1.1%, 3.2%) and 0.1% (95% CI: 0.02%, 0.2%) respectively. We found no differences between TOLAC and ERCS for uterine rupture (1.2% vs. 0.2%, pooled odds ratio [OR]: 1.54; 95% CI: 0.63, 3.8) or maternal mortality (0.3% vs. <0.1%, pooled OR: 0.77; 95% CI: 0.30, 2.0), respectively. However perinatal mortality was higher with TOLAC compared to ERCS (5% vs. 1%; pooled OR: 3.3; 95% CI: 1.5, 6.9).Discussion: We found high proportions of women undergoing TOLAC and moderate proportions of successful VBAC across SSA, with a higher perinatal mortality associated with TOLAC. Few studies reported on adverse outcomes and used inconsistent definitions. Further research is needed to understand outcomes in this population of women in these settings.Registration: The study protocol was registered with PROSPERO (CRD42020175434).

Title: Insights on Stillbirths in Africa using the Robson Classification System: Mini CommentaryAdeline A. BoatinDepartment of Obstetrics and Gynecology, Massachusetts General HospitalHarvard Medical School, Boston, USAContact:55 Fruit Street,Dept OB/GYNMassachusetts General HospitalBoston, MA 02114Email: adeline_boatin@mgh.harvard.eduTel: +1 617 726 1320Shortened Running title: Insights from Robson Classification on StillbirthDisclosures: I have no relevant disclosuresContribution to Authorship: AAB conceived, wrote, and edited this manuscript.Globally, stillbirths remain a largely neglected health crisis, with up to 2 million stillbirths annually and minimal progress in reduction.1 Despite several global initiatives targeting perinatal mortality, such as the Every Newborn Action Plan (ENAP) launched in 2014, efforts to decrease stillbirth rates have seen only a modest 2.3% decline over the last 20 years.1This underscores the urgent need for novel approaches to understand and addresse this global challenge.1,2Compounding this issue is a substantial lack of comprehensive data on the trends, timing and causes of stillbirths.1 Many classification systems for categorizing stillbirths have been developed to address this data gap.3 To be useful, classification systems should ideally use routinely collected data that does not require substantial input to collect, and that is already readily available. However, most stillbirth classification systems have failed to meet the criteria that allow widespread adoption, particularly in resource-constrained environments where stillbirth rates are the highest.4In this issue of the journal, Hanson et al use the Robson 10-group Classification system to explore stillbirth trends in four countries – Benin, Malawi, Tanzania, and Uganda – using data on over 80,000 births and just over 3000 stillbirths collected from 16 hospitals. Their use of the Robson Classification System across these four countries and hospitals of varying levels of care demonstrates the benefits of the chosen classification system: wide applicability across various settings, objective and easily standardizable classifications, and easy comparability across time and place.5 Although not used in this exploration, this classification system also benefits from being versatile in that it can be applied to understand not only stillbirths, but also cesarean delivery or other intrapartum outcomes of interest, thus gaining an efficiency with data collection.The findings from Hanson et al highlight leading contributors to stillbirth among the studied countries – group 10 (preterm births), group 3 (multipara in spontaneous labor) and births complicated by malpresentation and multiple gestations. While high rates of intrapartum stillbirths among preterm deliveries demand attention, they are not surprising. More surprising and warranting further investigation are the high rates of stillbirth seen in Group 3, which, under normal circumstances, should be at the lowest risk for mortality.Importantly, as pointed out by the authors, analysis using the Robson classification system serves as a starting point to pinpoint areas for focused investigation and quality improvement, while also offering valuable insights for each hospital on performance, guidance on where to concentrate efforts, and potentially where to seek lessons from “good performers.”Globally, we have far to go to combat high rates of stillbirths. Using versatile and standardized approaches, as done by Hanson et al offers the opportunity to facilitate targeted investigations and to promote cross-country learning and continuous improvement for maternal and perinatal care.References1 United Nations Children’s Fund. Never forgotten: The situation of stillbirth around the globe. Report of the United Nations Inter-agency Group for Child Mortality Estimation, 2022. New York, 2023 https://data.unicef.org/resources/never-forgotten-stillbirth-estimates-report/.2 World Health Organization. Every newborn: an action plan to end preventable deaths. 2014.3 Leisher SH, Teoh Z, Reinebrant H, et al. Seeking order amidst chaos: a systematic review of classification systems for causes of stillbirth and neonatal death, 2009–2014.BMC pregnancy and childbirth 2016; 16 : 1–17.4 Mukherjee A, Di Stefano L, Blencowe H, Mee P. Determinants of stillbirths in sub‐Saharan Africa: A systematic review. BJOG: An International Journal of Obstetrics & Gynaecology 2024; 131 : 140–50.5 Robson MS. The 10-Group Classification System-a new way of thinking.American journal of obstetrics and gynecology 2018; 219 : 1–4.

Background: Physiologic and immunologic adaptations in pregnancy may increase the risk of adverse outcomes from respiratory viral infections. However, data are limited on longer-term outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in pregnancy prior to widespread vaccine availability. Methods: Using electronic health record data, we retrospectively compared 6-, 12-, and 18-month outcomes including death and rehospitalization between pregnant and nonpregnant reproductive-aged individuals hospitalized for SARS-CoV-2 infection between 2020 and 2021 at 2 academic referral hospitals. Results: There were 190 nonpregnant and 70 pregnant participants. Mean age was 31 years for pregnant and 34 years for nonpregnant participants. For pregnant patients, mean gestational age at coronavirus disease 2019 (COVID-19) diagnosis was 36 weeks, 54% delivered by cesarean, and 97% delivered a live birth. Compared to pregnant participants, nonpregnant participants had a higher prevalence of baseline comorbidities and a higher proportion received mechanical ventilation (84% vs 55%). Index hospitalization complications (31% vs 17%) and mortality (3% vs 0%) were more common in nonpregnant participants. Over 18 months following index hospitalization, 39 (21%) nonpregnant and 5 (7%) pregnant participants were readmitted, most for infection (28/44 [64%]). Most readmissions occurred within 6 months. There were no posthospitalization deaths in the pregnant group. Conclusions: Pregnant people with severe COVID-19 disease had a low rate of severe adverse outcomes after index hospitalization. The low readmission rate is reassuring that pregnant individuals may not be at higher risk for long-term severe adverse health outcomes after COVID-19 compared to the nonpregnant reproductive-aged population, possibly because any increased risk conferred by pregnancy resolves soon after delivery.