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Healthcare challenges in low and middle income countries (LMICs) have been the focus of many digital initiatives that have aimed to improve both access to healthcare and the quality of healthcare delivery. Moving beyond the initial phase of piloting and experimentation, these initiatives are now more clearly focused on the need for effective scaling and integration to provide sustainable benefit to healthcare systems. Based on real-life case studies of scaling digital health in LMICs, five key focus areas have been identified as being critical for success. Firstly, the intrinsic characteristics of the programme or initiative must offer tangible benefits to address an unmet need, with end-user input from the outset. Secondly, all stakeholders must be engaged, trained and motivated to implement a new initiative, and thirdly, the technical profile of the initiative should be driven by simplicity, interoperability and adaptability. The fourth focus area is the policy environment in which the digital healthcare initiative is intended to function, where alignment with broader healthcare policy is essential, as is sustainable funding that will support long-term growth, including private sector funding where appropriate. Finally, the extrinsic ecosystem should be considered, including the presence of the appropriate infrastructure to support the use of digital initiatives at scale. At the global level, collaborative efforts towards a less-siloed approach to scaling and integrating digital health may provide the necessary leadership to enable innovative solutions to reach healthcare workers and patients in LMICs. This review provides insights into best practice for scaling digital health initiatives in LMICs derived from practical experience in real-life case studies, discussing how these may influence the development and implementation of health programmes in the future.

Prelacteal feeding (PLF)—giving infants food or liquid other than breastmilk within the first 3 days of life—remains common and hinders optimal breastfeeding in Bangladesh. This study assessed changes in PLF practices in rural Bangladesh from 2004 to 2019 and examined associate household, maternal, and infant factors. We analyzed data from two cluster-randomized trials in rural northwest Bangladesh (n = 16,551; n = 4,401). Trained staff collected sociodemographic and birth data through household visits. We used multivariable logistic regression to examine associations between household, maternal, and infant characteristics and PLF and a non-linear approximation of the Oaxaca-Blinder regression decomposition to understand the factors associated with the changing prevalence of PLF. The prevalence of PLF declined from 89% in 2004 to 24% in 2019. Factors associated with PLF shifted over time, particularly household wealth, infant sex, and birth weight. Institutional delivery (OR=0.27; 95% CI 0.22, 0.32 in 2004; OR=0.78; 95% CI 0.61, 1.00 in 2019) and multigravida status (OR=0.68; 95% CI 0.58, 0.79 in 2004; OR=0.73; 95% CI 0.58, 0.93 in 2019) were consistently associated with reduced odds of PLF across cohorts in the multivariable analysis. The decomposition analysis based on the two trials indicated that changes in prevalence of the covariates explained 15% of the decrease in prevalence of PLF, primarily accounted for by increases in health facility deliveries (86%), increases in infant birth weight (13%), and increasing gravidity (12%). 85% of the change remains unexplained by the measured variables. The prevalence of PLF declined considerably in rural Bangladesh over the 15-year period. There are shifts in factors associated with PLF overtime. Improvements in socio-demographic factors played a modest but meaningful role in reducing PLF. However the majority of the reduction remains unexplained by the measured variables. Further research is needed to identify other potential drivers for changes in the prevalence of PLF.

Background Measurement of antenatal care (ANC) service coverage is often limited to the number of contacts or type of providers, reflecting a gap in the assessment of quality as well as cost estimations and health impact. The study aims to determine service subcomponents and provider and patient costs of ANC services and compares them between community (i.e. satellite clinics) and facility care (i.e. primary and secondary health centers) settings in rural Bangladesh. Methods Service contents and cost data were collected by one researcher and four interviewers in various community and facility health care settings in Gaibandha district between September and December 2016. We conducted structured interviews with organization managers, observational studies of ANC service provision ( n  = 70) for service contents and provider costs (service and drug costs) and exit interviews with pregnant women ( n  = 70) for patient costs (direct and indirect costs) in health clinics at community and facility levels. Fisher’s exact tests were used to determine any different patient characteristics between community and facility settings. ANC service contents were assessed by 63 subitems categorized into 11 groups and compared within and across community and facility settings. Provider and patient costs were collected in Bangladesh taka and analyzed as 2016 US Dollars (0.013 exchange rate). Results We found generally similar provider and patient characteristics between the community and facility settings except in clients’ gestational age. High compliance (> 50%) of service subcomponents were observed in blood pressure monitoring, weight measurement, iron and folate supplementation given, and tetanus vaccine, while lower compliance of service subcomponents (< 50%) were observed in some physical examinations such as edema and ultrasonogram and routine tests such as blood test and urine test. Average unit costs of ANC service provision were about double at the facility level ($2.75) compared with community-based care ($1.62). ANC patient costs at facilities ($2.66) were about three times higher than in the community ($0.78). Conclusion The study reveals a delay in pregnant women’s initial ANC care seeking, gaps in compliance of ANC subcomponents and difference of provider and patient costs between facility and community settings.

Increased mobile phone subscribership in low- and middle-income countries (LMICs) provides novel opportunities to track population health. The objective of this study was to examine reliability of data in comparing participant responses collected using two mobile phone survey (MPS) delivery modalities, computer assisted telephone interviews (CATI) and interactive voice response (IVR) in Bangladesh (BGD) and Tanzania (TZA). Using a cross-over design, we used random digit dialing (RDD) to call randomly generated mobile phone numbers and recruit survey participants to receive either a CATI or IVR survey on non-communicable disease (NCD) risk factors, followed 7 days later by the survey mode not received during first contact; either IVR or CATI. Respondents who received the first survey were designated as first contact (FC) and those who consented to being called a second time and subsequently answered the call were designated as follow-up (FU). We used the same questionnaire for both contacts, with response options modified to suit the delivery mode. Reliability of responses was analyzed using the Cohen's kappa statistic for percent agreement between two modes. Self-reported data on demographic characteristics and NCD behavioral risk factors were collected from 482 (CATI-FC) and 653 (IVR-FC) age-eligible and consenting respondents in BGD, and from 387 (CATI-FC) and 674 (IVR-FC) respondents in TZA respectively. Survey follow-up rates were 30.7% (n = 482) for IVR-FU and 53.8% (n = 653) for CATI-FU in BGD; and 42.4% (n = 387) for IVR-FU and 49.9% (n = 674) for CATI-FU in TZA respectively. Overall, there was high consistency between delivery modalities for alcohol consumption in the past 30 days in both countries (kappa = 0.64 for CATI→IVR (BGD), kappa = 0.54 for IVR→CATI (BGD); kappa = 0.66 for CATI→IVR (TZA), kappa = 0.76 for IVR→CATI (TZA)), and current smoking (kappa = 0.68 for CATI→IVR (BGD), kappa = 0.69 for IVR→CATI (BGD); kappa = 0.39 for CATI→IVR (TZA), kappa = 0.50 for IVR→CATI (TZA)). There was moderate to substantial consistency in both countries for history of checking for hypertension and diabetes with kappa statistics ranging from 0.43 to 0.67. There was generally lower consistency in both countries for physical activity (vigorous and moderate) with kappa statistics ranging from 0.10 to 0.41, weekly fruit and vegetable with kappa ranging from 0.08 to 0.45, consumption of foods high in salt and efforts to limit salt with kappa generally below 0.3. The study found that when respondents are re-interviewed, the reliability of answers to most demographic and NCD variables is similar whether starting with CATI or IVR. The study underscores the need for caution when selecting questions for mobile phone surveys. Careful design can help ensure clarity of questions to minimize cognitive burden for respondents, many of whom may not have prior experience in taking automated surveys. Further research should explore possible differences and determinants of survey reliability between delivery modes and ideally compare both IVR and CATI surveys to in-person face-to-face interviews. In addition, research is needed to better understand factors that influence survey cooperation, completion, refusal and attrition rates across populations and contexts.

Background Hepatitis E Virus (HEV) infection is a newly recognized serious threat to global public health and Africa is suspected to be among the most severely affected regions in the world. Understanding HEV epidemiology in Africa will expedite the implementation of evidence-based control policies aimed at preventing the spread of HEV including policies for the use of available resources such as HEV vaccines. Methods Here we present a comprehensive review of HEV epidemiology in Africa based on published data. We searched for articles on HEV epidemiology in Africa from online databases such as PubMed, Scopus, and ISI Web of Science and critically reviewed appropriate publications to extract consistent findings, identify knowledge gaps, and suggest future studies. Results Taking a particularly high toll in pregnant women and their fetuses, HEV has infected human populations in 28 of 56 African countries. Since 1979, 17 HEV outbreaks have been reported about once every other year from Africa causing a reported 35,300 cases with 650 deaths. Conclusions In Africa, HEV infection is not new, is widespread, and the number of reported outbreaks are likely a significant underestimate. The authors suggest that this is a continent-wide public health problem that deserves the attention of local, regional and international agencies to implement control policies that can save numerous lives, especially those of pregnant women and their fetuses.

We examined the incremental cost-effectiveness between two mHealth programs, implemented from 2011 to 2015 in rural Bangladesh: (1) Comprehensive mCARE package as an intervention group and (2) Basic mCARE package as a control group. Both programs included a core package of census enumeration and pregnancy surveillance provided by an established cadre of digitally enabled community health workers (CHWs). In the comprehensive mCARE package, short message service (SMS) and home visit reminders were additionally sent to pregnant women (n = 610) and CHWs (n = 70) to promote the pregnant women's care-seeking of essential maternal and newborn care services. Economic costs were assessed from a program perspective inclusive of development, start-up, and implementation phases. Effects were calculated as disability adjusted life years (DALYs) and the number of newborn deaths averted. For comparative purposes, we normalized our evaluation to estimate total costs and total newborn deaths averted per 1 million people in a community for both groups. Uncertainty was assessed using probabilistic sensitivity analyses with Monte Carlo simulation. The addition of SMS and home visit reminders based on a mobile phone-facilitated pregnancy surveillance system was highly cost effective at a cost per DALY averted of $31 (95% uncertainty range: $19-81). The comprehensive mCARE program had at least 88% probability of being highly cost-effective as compared to the basic mCARE program based on the threshold of Bangladesh's GDP per capita. mHealth strategies such as SMS and home visit reminders on a well-established pregnancy surveillance system may improve service utilization and program cost-effectiveness in low-resource settings.

Automated mobile phone surveys (MPS) can be used to collect public health data of various types to inform health policy and programs globally. One challenge in administering MPS is identification of an appropriate and effective participant consent process. This study investigated the impact of different survey consent approaches on participant disposition (response characteristics and understanding of the purpose of the survey) within the context of an MPS that measured noncommunicable disease (NCD) risk factors across Colombia and Uganda. Participants were randomized to one of five consent approaches, with consent modules varying by the consent disclosure and mode of authorization. The control arm consisted of a standard consent disclosure and a combined opt-in/opt-out mode of authorization. The other four arms consist of a modified consent disclosure and one of four different forms of authorization (i.e., opt-in, opt-out, combined opt-in/opt-out, or implied). Data related to respondent disposition and respondent understanding of the survey purpose were analyzed. Among 1889 completed surveys in Colombia, differences in contact, response, refusal, and cooperation rates by study arms were found. About 68% of respondents correctly identified the survey purpose, with no significant difference by study arm. Participants reporting higher levels of education and urban residency were more likely to identify the purpose correctly. Participants were also more likely to accurately identify the survey purpose after completing several survey modules, compared to immediately following the consent disclosure (78.8% vs 54.2% correct, p<0.001). In Uganda, 1890 completed surveys were collected. Though there were differences in contact, refusal, and cooperation rates by study arm, response rates were similar across arms. About 37% of respondents identified the survey purpose correctly, with no difference by arm. Those with higher levels of education and who completed the survey in English were able to more accurately identify the survey purpose. Again, participants were more likely to accurately identify the purpose of the survey after completing several NCD modules, compared to immediately following the consent module (42.0% vs 32.2% correct, p = 0.013). This study contributes to the limited available evidence regarding consent procedures for automated MPS. Future studies should develop and trial additional interventions to enhance consent for automated public health surveys, and measure other dimensions of participant engagement and understanding.

Introduction Concerns about SARS-CoV-2 infection risk in health care settings have resulted in changes in prenatal care and birth plans, such as shifts to in-person visits and increased Cesarean delivery. These changes may affect quality of care and limit opportunities for clinicians to counsel pregnant individuals, who are at higher risk of severe COVID-19 disease and adverse pregnancy outcomes, about prevention and vaccination. Methods We conducted a cross-sectional online survey of United States adults on changes in prenatal care, COVID-19 vaccine willingness, and reasons for unwillingness to receive a vaccine. We summarized changes in access to care and examined differences in vaccine willingness between pregnant and propensity-score matched non-pregnant controls using chi-squared tests and multivariable conditional logistic regression. Results Between December 15–23, 2020, 8481 participants completed the survey, of which 233 were pregnant. Three-quarters of pregnant women ( n  = 186) experienced a change in prenatal care, including format of care ( n  = 84, 35%) and reduced visits ( n  = 69, 24%). Two-thirds experienced a change in birth plans, from a hospital birth to home birth ( n  = 45, 18%) or vaginal birth to a Cesarean delivery ( n  = 42, 17%). Although 40% of pregnant women ( n  = 78) were unwilling to receive COVID-19 vaccination, they had higher, though non-significant, odds of reporting willingness to receive vaccination compared to similar non-pregnant women (aOR 1.38, 95% CI: 0.95, 2.00). Conclusion To support pregnant women through the perinatal care continuum, maternity care teams should develop protocols to foster social support, patient-centered education around infection prevention that focuses on improved risk perception, expected changes in care due to COVID-19, and vaccine effectiveness and safety.

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.