Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation

A survey of surveys: an evaluation of the quality of published surveys in neurosurgery

PURPOSE

Surveys generate valuable data in epidemiologic and qualitative clinical research. The quality of a survey depends on its design, the number of responses it receives, and the reporting of the results. In this study, we aimed to assess the quality of surveys in neurosurgery.

METHODS

Neurosurgical surveys published between 2000 and 2020 (inclusive) were identified from PubMed. Various datapoints regarding the surveys were collated. The number of citations received by the papers was determined from Google Scholar. A 6-dimensional quality assessment tool was applied to the surveys. Parameters from this tool were combined with the number of responses received to create the survey quality score (SQS).

RESULTS

A total of 618 surveys were included for analysis. The target sample size correlated with the number of responses received. The response rate correlated positively with the target sample size and the number of reminders sent and negatively with the number of questions in the survey. The median number of authors on neurosurgery survey papers was 6. The number of authors correlated with the SQS and the number of citations received by published survey papers. The median normalized SQS for neurosurgical surveys was 65%. The nSQS independently predicted the citations received per year by surveys.

CONCLUSIONS

The modifiable factors that correlated with improvements in survey design were optimizing the number of questions, maximizing the target sample size, and incorporating reminders in the survey design. Increasing the number of contributing authors led to improvements in survey quality. The SQS was validated and correlated well with the citations received by surveys.

Implementation of REDCap mobile app in an oral HIV clinical study

BACKGROUND

In Peru, HIV cases are highly concentrated among men who have sex with men (MSM). Despite the availability of anti-retroviral therapy, people living with HIV (PWH) have higher levels of oral diseases. Alcohol use disorder (AUD) is significantly present among PWH. Our overarching goal was to generate foundational evidence on the association of AUD and oral health in MSM with HIV and enhance research capacity for future intersectional research on AUD, oral health and HIV. Our specific aim was to implement an on-site electronic data collection system through the use of a REDCap Mobile App in a low-middle income country (LMIC) setting.

METHODS

Five validated surveys were utilized to gather data on demographics, medical history, HIV status, alcohol use, HIV stigma, perceived oral health status, and dietary supplement use. These surveys were developed in REDCap and deployed with the REDCap Mobile App, which was installed on ten iPads across two medical HIV clinics in Lima, Peru. REDCap app as well as the protocol for data collection were calibrated with feedback from trial participants and clinical research staff to improve clinical efficiency and participant experience.

RESULTS

The mean age of participants (n = 398) was 35.94 ± 9.13y, of which 98.5% identified as male, and 85.7% identified as homosexual. 78.1% of participants binge drank, and 12.3% reported being heavy drinkers. After pilot testing, significant modifications to the structure and layout of the surveys were performed to improve efficiency and flow. The app was successfully deployed to replace cumbersome paper records and collected data was directly stored in a REDCap database.

CONCLUSIONS

The REDCap Mobile App was successfully used due to its ability to: (a) capture and store data offline, (b) timely translate between multiple languages on the mobile app interface, and (c) provide user-friendly interface with low associated costs and ample support.

TRIAL REGISTRATION

1R56DE029639-01.

Scoping review of health information technology usability methods leveraged in Africa

OBJECTIVE

The aim of this study was to explore the state of health information technology (HIT) usability evaluation in Africa.

MATERIALS AND METHODS

We searched three electronic databases: PubMed, Embase, and Association for Computing Machinery. We categorized the stage of evaluations, the type of interactions assessed, and methods applied using Stead's System Development Life Cycle (SDLC) and Bennett and Shackel's usability models.

RESULTS

Analysis of 73 of 1002 articles that met inclusion criteria reveals that HIT usability evaluations in Africa have increased in recent years and mainly focused on later SDLC stage (stages 4 and 5) evaluations in sub-Saharan Africa. Forty percent of the articles examined system-user-task-environment (type 4) interactions. Most articles used mixed methods to measure usability. Interviews and surveys were often used at each development stage, while other methods, such as quality-adjusted life year analysis, were only found at stage 5. Sixty percent of articles did not include a theoretical model or framework.

DISCUSSION

The use of multistage evaluation and mixed methods approaches to obtain a comprehensive understanding HIT usability is critical to ensure that HIT meets user needs.

CONCLUSIONS

Developing and enhancing usable HIT is critical to promoting equitable health service delivery and high-quality care in Africa. Early-stage evaluations (stages 1 and 2) and interactions (types 0 and 1) should receive special attention to ensure HIT usability prior to implementing HIT in the field.

Healthcare professionals' knowledge, attitude and its associated factors toward electronic personal health record system in a resource-limited setting: A cross-sectional study

Introduction

Electronic personal health record (e-PHR) system enables individuals to access their health information and manage it themselves. It helps patient engagement management of health information that is accessed and shared with their healthcare providers using the platform. This improves individual healthcare through the exchange of health information between patients and healthcare providers. However, less is known about e-PHRs among healthcare professionals.

Objective

Therefore, this study aimed to assess Health professionals' Knowledge and attitude and its associated factors toward e-PHR at the teaching hospital in northwest Ethiopia.

Methods

An institution-based cross-sectional study design was used to determine healthcare professionals' knowledge and attitude and their associated factors toward e-PHR systems in teaching hospitals of Amhara regional state, Ethiopia, from 20 July to 20 August 2022. Pretested structured self-administered questionnaires were used to collect the data. Descriptive statistic was computed based on sociodemographic and other variables presented in the form of table graphs and texts. Bivariable and multivariable logistic analyses were performed with an adjusted odds ratio (AOR) and 95% CI to identify predictor variables.

Result

Of the total study participants, 57% were males and nearly half of the respondents had a bachelor's degree. Out of 402 participants, ~65.7% [61–70%] and 55.5% [50–60%] had good knowledge and favorable attitude toward e-PHR systems, respectively. Having a social media account 4.3 [AOR = 4.3, 95% CI (2.3–7.9)], having a smartphone 4.4 [AOR = 4.4, 95% CI (2.2–8.6)], digital literacy 8.8 [(AOR = 8.8, 95% CI (4.6–15.9)], being male 2.7 [AOR = 2.7, 95% CI (1.4–5.0)], and perceived usefulness 4.5 [(AOR = 4.5, 95% CI (2.5–8.5)] were positively associated with knowledge toward e-PHR systems. Similarly, having a personal computer 1.9 [AOR = 1.9, 95% CI (1.1–3.5)], computer training 3.9 [AOR = 3.9, 95% CI (1.8–8.3)], computer skill 19.8 [AOR = 19.8, 95% CI (10.7–36.9)], and Internet access 6.0 [AOR = 6.0, 95% CI (3.0–12.0)] were predictors for attitude toward e-PHR systems.

Conclusion

The findings from the study showed that healthcare professionals have good knowledge and a favorable attitude toward e-PHRs. Providing comprehensive basic computer training to improve healthcare professionals' expectation on the usefulness of e-PHR systems has a paramount contribution to the advancement of their knowledge and attitude toward successfully implementing e-PHRs.

Factors Affecting the Transition from Paper to Digital Data Collection for Mobile Tuberculosis Active Case Finding in Low Internet Access Settings in Pakistan

Between September 2020 and March 2021, Mercy Corps piloted hybrid digital (CAPI) and paper-based (PAPI) data collection as part of its tuberculosis (TB) active case finding strategy. Data were collected using CAPI and PAPI at 140 TB chest camps in low Internet access areas of Punjab and Khyber Pakhtunkhwa provinces in Pakistan. PAPI data collection was performed primarily during the camp and entered using a tailor-performed CAPI tool after camps. To assess the feasibility of this hybrid approach, quality of digital records were measured against the paper “gold standard”, and user acceptance was evaluated through focus group discussions. Completeness of digital data varied by indicator, van screening team, and month of implementation: chest camp attendees and pulmonary TB cases showed the highest CAPI/PAPI completeness ratios (1.01 and 0.96 respectively), and among them, all forms of TB diagnosis and treatment initiation were lowest (0.63 and 0.64 respectively). Vans entering CAPI data with high levels of completeness generally did so for all indicators, and significant differences in mean indicator completeness rates between PAPI and CAPI were observed between vans. User feedback suggested that although the CAPI tool required practice to gain proficiency, the technology was appreciated and will be better perceived once double entry in CAPI and PAPI can transition to CAPI only. CAPI data collection enables data to be entered in a more timely fashion in low-Internet-access settings, which will enable more rapid, evidence-based program steering. The current system in which double data entry is conducted to ensure data quality is an added burden for staff with many activities. Transitioning to a fully digital data collection system for TB case finding in low-Internet-access settings requires substantial investments in M&E support, shifts in data reporting accountability, and technology to link records of patients who pass through separate data collection stages during chest camp events.

REDCap mobile data collection: Using implementation science to explore the potential and pitfalls of a digital health tool in routine voluntary medical male circumcision outreach settings in Zimbabwe

Background

Digital data collection tools improve data quality but are limited by connectivity. ZAZIC, a Zimbabwean consortium focused on scaling up male circumcision (MC) services, provides MC in outreach settings where both data quality and connectivity is poor. ZAZIC implemented REDCap Mobile app for data collection among roving ZAZIC MC nurses. To inform continued scale-up or discontinuation, this paper details if, how, and for whom REDCap improved data quality using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

Methods

Data were collected for this retrospective, cross-sectional study for nine months, from July 2019 to March 2020, before COVID-19 paused MC services. Data completeness was compared between paper- and REDCap-based tools and between two ZAZIC partners using two sample, one-tailed t-tests.

Results

REDCap reached all roving nurses who reported 26,904 MCs from 1773 submissions. REDCap effectiveness, as measured by data completeness, decreased from 89.2% in paper to 76.6% in REDCap app for Partner 1 (p < 0.001, 95% CI: −0.24, −0.12) but increased modestly from 86.2% to 90.3% in REDCap for Partner 2 (p = 0.05, 95% CI: -.007, 0.12). Adoption of REDCap was 100%; paper-based reporting concluded in October 2019. Implementation varied by partner and user. Maintenance appeared high.

Conclusion

Although initial transition from paper to REDCap showed mixed effectiveness, post-hoc analysis from service resumption found increased REDCap data completeness across partners, suggesting locally-led momentum for REDCap-based data collection. Staff training, consistent mentoring, and continued technical support appear critical for continued use of digital health tools for quality data collection in rural Zimbabwe and similar low connectivity settings.

Going digital: added value of electronic data collection in 2018 Afghanistan Health Survey

BACKGROUND

Through a nationally representative household survey in Afghanistan, we conducted an operational study in two relatively secure provinces comparing effectiveness of computer-aided personal interviewing (CAPI) with paper-and-pencil interviewing (PAPI).

METHODS

In Panjshir and Parwan provinces, household survey data were collected using paper questionnaires in 15 clusters, and OpenDataKit (ODK) software on electronic tablets in 15 other clusters. Added value was evaluated from three perspectives: efficient implementation, data quality, and acceptability. Efficiency was measured through financial expenditures and time stamped data. Data quality was measured by examining completeness. Acceptability was studied through focus group discussions with survey staff.

RESULTS

Survey costs were 68% more expensive in CAPI clusters compared to PAPI clusters, due primarily to the upfront one-time investment for survey programming. Enumerators spent significantly less time administering surveys in CAPI cluster households (248 min survey time) compared to PAPI (289 min), for an average savings of 41 min per household (95% CI 25-55). CAPI offered a savings of 87 days for data management over PAPI. Among 49 tracer variables (meaning responses were required from all respondents), small differences were observed between PAPI and CAPI. 2.2% of the cleaned dataset's tracer data points were missing in CAPI surveys (1216/ 56,073 data points), compared to 3.2% in PAPI surveys (1953/ 60,675 data points). In pre-cleaned datasets, 3.9% of tracer data points were missing in CAPI surveys (2151/ 55,092 data points) compared to 3.2% in PAPI surveys (1924/ 60,113 data points). Enumerators from Panjsher and Parwan preferred CAPI over PAPI due to time savings, user-friendliness, improved data security, and less conspicuity when traveling; however approximately half of enumerators trained from all 34 provinces reported feeling unsafe due to Taliban presence. Community and household respondent skepticism could be resolved by enumerator reassurance. Enumerators shared that in the future, they prefer collecting data using CAPI when possible.

CONCLUSIONS

CAPI offers clear gains in efficiency over PAPI for data collection and management time, although costs are relatively comparable even without the programming investment. However, serious field staff concerns around Taliban threats and general insecurity mean that CAPI should only be conducted in relatively secure areas.

Data Quality of Longitudinally Collected Patient-Reported Outcomes After Thoracic Surgery: Comparison of Paper- and Web-Based Assessments

Background

High-frequency patient-reported outcome (PRO) assessments are used to measure patients' symptoms after surgery for surgical research; however, the quality of those longitudinal PRO data has seldom been discussed.

Objective

The aim of this study was to determine data quality-influencing factors and to profile error trajectories of data longitudinally collected via paper-and-pencil (P&P) or web-based assessment (electronic PRO [ePRO]) after thoracic surgery.

Methods

We extracted longitudinal PRO data with 678 patients scheduled for lung surgery from an observational study (n=512) and a randomized clinical trial (n=166) on the evaluation of different perioperative care strategies. PROs were assessed by the MD Anderson Symptom Inventory Lung Cancer Module and single-item Quality of Life Scale before surgery and then daily after surgery until discharge or up to 14 days of hospitalization. Patient compliance and data error were identified and compared between P&P and ePRO. Generalized estimating equations model and 2-piecewise model were used to describe trajectories of error incidence over time and to identify the risk factors.

Results

Among 678 patients, 629 with at least 2 PRO assessments, 440 completed 3347 P&P assessments and 189 completed 1291 ePRO assessments. In total, 49.4% of patients had at least one error, including (1) missing items (64.69%, 1070/1654), (2) modifications without signatures (27.99%, 463/1654), (3) selection of multiple options (3.02%, 50/1654), (4) missing patient signatures (2.54%, 42/1654), (5) missing researcher signatures (1.45%, 24/1654), and (6) missing completion dates (0.30%, 5/1654). Patients who completed ePRO had fewer errors than those who completed P&P assessments (ePRO: 30.2% [57/189] vs. P&P: 57.7% [254/440]; P<.001). Compared with ePRO patients, those using P&P were older, less educated, and sicker. Common risk factors of having errors were a lower education level (P&P: odds ratio [OR] 1.39, 95% CI 1.20-1.62; P<.001; ePRO: OR 1.82, 95% CI 1.22-2.72; P=.003), treated in a provincial hospital (P&P: OR 3.34, 95% CI 2.10-5.33; P<.001; ePRO: OR 4.73, 95% CI 2.18-10.25; P<.001), and with severe disease (P&P: OR 1.63, 95% CI 1.33-1.99; P<.001; ePRO: OR 2.70, 95% CI 1.53-4.75; P<.001). Errors peaked on postoperative day (POD) 1 for P&P, and on POD 2 for ePRO.

Conclusions

It is possible to improve data quality of longitudinally collected PRO through ePRO, compared with P&P. However, ePRO-related sampling bias needs to be considered when designing clinical research using longitudinal PROs as major outcomes.

The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia

The World Health Organization (WHO) recently put forth a Global Strategy on Digital Health 2020–2025 with several countries having already achieved key milestones. We aimed to understand whether and how digital health technologies (DHTs) are absorbed in Africa, tracking Ethiopia as a key node. We conducted a systematic review, searching PubMed-MEDLINE, Embase, ScienceDirect, African Journals Online, Cochrane Central Registry of Controlled Trials, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform databases from inception to 02 February 2021 for studies of any design that investigated the potential of DHTs in clinical or public health practices in Ethiopia. This review was registered with PROSPERO (CRD42021240645) and it was designed to inform our ongoing DHT-enabled randomized controlled trial (RCT) (ClinicalTrials.gov ID: NCT04216420). We found 27,493 potentially relevant citations, among which 52 studies met the inclusion criteria, comprising a total of 596,128 patients, healthy individuals, and healthcare professionals. The studies involved six DHTs: mHealth (29 studies, 574,649 participants); electronic health records (13 studies, 4534 participants); telemedicine (4 studies, 465 participants); cloud-based application (2 studies, 2382 participants); information communication technology (3 studies, 681 participants), and artificial intelligence (1 study, 13,417 participants). The studies targeted six health conditions: maternal and child health (15), infectious diseases (14), non-communicable diseases (3), dermatitis (1), surgery (4), and general health conditions (15). The outcomes of interest were feasibility, usability, willingness or readiness, effectiveness, quality improvement, and knowledge or attitude toward DHTs. Five studies involved RCTs. The analysis showed that although DHTs are a relatively recent phenomenon in Ethiopia, their potential harnessing clinical and public health practices are highly visible. Their adoption and implementation in full capacity require more training, access to better devices such as smartphones, and infrastructure. DHTs hold much promise tackling major clinical and public health backlogs and strengthening the healthcare ecosystem in Ethiopia. More RCTs are needed on emerging DHTs including artificial intelligence, big data, cloud, cybersecurity, telemedicine, and wearable devices to provide robust evidence of their potential use in such settings and to materialize the WHO’s Global Strategy on Digital Health.

Assessment of the Chad guinea worm surveillance information system: A pivotal foundation for eradication

Background

In the absence of a vaccine or pharmacological treatment, prevention and control of Guinea worm disease is dependent on timely identification and containment of cases to interrupt transmission. The Chad Guinea Worm Eradication Program (CGWEP) surveillance system detects and monitors Guinea worm disease in both humans and animals. Although Guinea worm cases in humans has declined, the discovery of canine infections in dogs in Chad has posed a significant challenge to eradication efforts. A foundational information system that supports the surveillance activities with modern data management practices is needed to support continued program efficacy.

Methods

We sought to assess the current CGWEP surveillance and information system to identify gaps and redundancies and propose system improvements. We reviewed documentation, consulted with subject matter experts and stakeholders, inventoried datasets to map data elements and information flow, and mapped data management processes. We used the Information Value Cycle (IVC) and Data-Information System-Context (DISC) frameworks to help understand the information generated and identify gaps.

Results

Findings from this study identified areas for improvement, including the need for consolidation of forms that capture the same demographic variables, which could be accomplished with an electronic data capture system. Further, the mental models (conceptual frameworks) IVC and DISC highlighted the need for more detailed, standardized workflows specifically related to information management.

Conclusions

Based on these findings, we proposed a four-phased roadmap for centralizing data systems and transitioning to an electronic data capture system. These included: development of a data governance plan, transition to electronic data entry and centralized data storage, transition to a relational database, and cloud-based integration. The method and outcome of this assessment could be used by other neglected tropical disease programs looking to transition to modern electronic data capture systems.

Guinea worm disease has no pharmacological treatment or vaccines, and therefore existing prevention and control strategies (e.g., case containment, health education, chemical treatment of water bodies) are critically dependent on timely, accurate, and actionable data. We conducted informant interviews, used conceptual frameworks, and mapped data flow to evaluate the Chad Guinea Worm Eradication Program’s current information system. We identified areas for improvement including the need to consolidate variables across data collection forms and the need to develop streamlined workflows. We proposed a four-phased roadmap for transitioning to an electronic data capture system and centralizing data storage. Our approach and proposed roadmap could be adopted by other neglected tropical disease control programs looking to modernize data collection and storage procedures.

Electronic data collection in a multi-site population-based survey: EN-INDEPTH study

BACKGROUND

Electronic data collection is increasingly used for household surveys, but factors influencing design and implementation have not been widely studied. The Every Newborn-INDEPTH (EN-INDEPTH) study was a multi-site survey using electronic data collection in five INDEPTH health and demographic surveillance system sites.

METHODS

We described experiences and learning involved in the design and implementation of the EN-INDEPTH survey, and undertook six focus group discussions with field and research team to explore their experiences. Thematic analyses were conducted in NVivo12 using an iterative process guided by a priori themes.

RESULTS

Five steps of the process of selecting, adapting and implementing electronic data collection in the EN-INDEPTH study are described. Firstly, we reviewed possible electronic data collection platforms, and selected the World Bank's Survey Solutions® as the most suited for the EN-INDEPTH study. Secondly, the survey questionnaire was coded and translated into local languages, and further context-specific adaptations were made. Thirdly, data collectors were selected and trained using standardised manual. Training varied between 4.5 and 10 days. Fourthly, instruments were piloted in the field and the questionnaires finalised. During data collection, data collectors appreciated the built-in skip patterns and error messages. Internet connection unreliability was a challenge, especially for data synchronisation. For the fifth and final step, data management and analyses, it was considered that data quality was higher and less time was spent on data cleaning. The possibility to use paradata to analyse survey timing and corrections was valued. Synchronisation and data transfer should be given special consideration.

CONCLUSION

We synthesised experiences using electronic data collection in a multi-site household survey, including perceived advantages and challenges. Our recommendations for others considering electronic data collection include ensuring adaptations of tools to local context, piloting/refining the questionnaire in one site first, buying power banks to mitigate against power interruption and paying attention to issues such as GPS tracking and synchronisation, particularly in settings with poor internet connectivity.

Routine health management information system data in Ethiopia: consistency, trends, and challenges

Background: Ethiopia is investing in the routine Health Management Information System. Improved routine data are needed for decision-making in the health sector. Objective: To analyse the quality of the routine Health Management Information System data and triangulate with other sources, such as the Demographic and Health Surveys. Methods: We analysed national Health Management Information System data on 19 indicators of maternal health, neonatal survival, immunization, child nutrition, malaria, and tuberculosis over the 2012-2018 time period. The analyses were conducted by 38 analysts from the Ministry of Health, Ethiopia, and two government agencies who participated in the Operational Research and Coaching for Analysts (ORCA) project between June 2018 and June 2020. Using a World Health Organization Data Quality Review toolkit, we assessed indicator definitions, completeness, internal consistency over time and between related indicators, and external consistency compared with other data sources. Results: Several services reported coverage of above 100%. For many indicators, denominators were based on poor-quality population data estimates. Data on individual vaccinations had relatively good internal consistency. In contrast, there was low external consistency for data on fully vaccinated children, with the routine Health Management Information System showing 89% coverage but the Demographic and Health Survey estimate at 39%. Maternal health indicators displayed increasing coverage over time. Indicators on child nutrition, malaria, and tuberculosis were less consistent. Data on neonatal mortality were incomplete and operationalised as mortality on day 0-6. Our comparisons with survey and population projections indicated that one in eight early neonatal deaths were reported in the routine Health Management Information System. Data quality varied between regions. Conclusions: The quality of routine data gathered in the health system needs further attention. We suggest regular triangulation with data from other sources. We recommend addressing the denominator issues, reducing the complexity of indicators, and aligning indicators to international definitions.

Setting up a maternal and newborn registry applying electronic platform: an experience from the Bangladesh site of the global network for women's and children's health

BACKGROUND

The Global Network for Women's and Children's Health Research (Global Network, GN) has established the Maternal Newborn Health Registry (MNHR) to assess MNH outcomes over time. Bangladesh is the newest country in the GN and has implemented a full electronic MNH registry system, from married women surveillance to pregnancy enrollment and subsequent follow ups.

METHOD

Like other GN sites, the Bangladesh MNHR is a prospective, population-based observational study that tracks pregnancies and MNH outcomes. The MNHR site is in the Ghatail and Kalihati sub-districts of the Tangail district. The study area consists of 12 registry clusters each of ~ 18,000-19,000 population. All pregnant women identified through a two-monthly house-to-house surveillance are enrolled in the registry upon consenting and followed up on scheduled visits until 42 days after pregnancy outcome. A comprehensive automated registry data capture system has been developed that allows for married women surveillance, pregnancy enrollment, and data collection during follow-up visits using a web-linked tablet-PC-based system.

RESULT

During March-May 2019, a total of 56,064 households located were listed in the Bangladesh MNH registry site. Of the total 221,462 population covered, 49,269 were currently married women in reproductive age (CMWRA). About 13% CMWRA were less susceptible to pregnancy. Large variability was observed in selected contraceptive usage across clusters. Overall, 5% of the listed CMWRAs were reported as currently pregnant.

CONCLUSION

In comparison to paper-pen capturing system electronic data capturing system (EDC) has advantages of less error-prone data collection, real-time data collection progress monitoring, data quality check and sharing. But the implementation of EDC in a resource-poor setting depends on technical infrastructure, skilled staff, software development, community acceptance and a data security system. Our experience of pregnancy registration, intervention coverage, and outcome tracking provides important contextualized considerations for both design and implementation of individual-level health information capturing and sharing systems.

Digital Health Innovations, Tools, and Resources to Support Helping Babies Survive Programs

The Helping Babies Survive (HBS) initiative features a suite of evidence-based curricula and simulation-based training programs designed to provide health workers in low- and middle-income countries (LMICs) with the knowledge, skills, and competencies to prevent, recognize, and manage leading causes of newborn morbidity and mortality. Global scale-up of HBS initiatives has been rapid. As HBS initiatives rolled out across LMIC settings, numerous bottlenecks, gaps, and barriers to the effective, consistent dissemination and implementation of the programs, across both the pre- and in-service continuums, emerged. Within the first decade of expansive scale-up of HBS programs, mobile phone ownership and access to cellular networks have also concomitantly surged in LMICs. In this article, we describe a number of HBS digital health innovations and resources that have been developed from 2010 to 2020 to support education and training, data collection for monitoring and evaluation, clinical decision support, and quality improvement. Helping Babies Survive partners and stakeholders can potentially integrate the described digital tools with HBS dissemination and implementation efforts in a myriad of ways to support low-dose high-frequency skills practice, in-person refresher courses, continuing medical and nursing education, on-the-job training, or peer-to-peer learning, and strengthen data collection for key newborn care and quality improvement indicators and outcomes. Thoughtful integration of purpose-built digital health tools, innovations, and resources may assist HBS practitioners to more effectively disseminate and implement newborn care programs in LMICs, and facilitate progress toward the achievement of Sustainable Development Goal health goals, targets, and objectives.

An in-depth report of quality control on Kato-Katz and data entry in four clinical trials evaluating the efficacy of albendazole against soil-transmitted helminth infections

BACKGROUND

Efforts to control soil-transmitted helminth (STH) infections have intensified over the past decade. Field-survey data on STH prevalence, infection intensity and drug efficacy is necessary to guide the implementation of control programs and should be of the best possible quality.

METHODOLOGY

During four clinical trials designed to evaluate the efficacy of albendazole against STHs in Brazil, Ethiopia, Lao PDR and Tanzania, quality control (QC) was performed on the duplicate Kato-Katz thick smears and the data entry. We analyzed datasets following QC on both fecal egg counts (FECs) and data entry, and compared the prevalence of any STH infection and moderate-to-heavy intensity (MHI) infections and the drug efficacy against STH infections.

RESULTS

Across the four study sites, a total of 450 out of 4,830 (9.3%) Kato-Katz thick smears were re-examined. Discrepancies in FECs varied from ~3% (hookworms) to ~6.5% (Ascaris lumbricoides and Trichuris trichiura). The difference in STH prevalence and prevalence of MHI infections using the datasets with and without QC of the FECs did not exceed 0.3%, except for hookworm infections in Tanzania, where we noted a 2.2 percentage point increase in MHI infections (pre-QC: 1.6% vs. post-QC: 3.8%). There was a 100% agreement in the classification of drug efficacy of albendazole against STH between the two datasets. In total, 201 of the 28,980 (0.65%) data entries that were made to digitize the FECs were different between both data-entry clerks. Nevertheless, the overall prevalence of STH, the prevalence of MHI infections and the classification of drug efficacy remained largely unaffected.

CONCLUSION/SIGNIFICANCE

In these trials, where staff was informed that QC would take place, minimal changes in study outcomes were reported following QC on FECs or data entry. Nevertheless, imposing QC did reduce the number of errors. Therefore, application of QC together with proper training of the personnel and the availability of clear standard operating procedures is expected to support higher data quality.

Health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis

BACKGROUND

Mobile health (mHealth), refers to healthcare practices supported by mobile devices, such as mobile phones and tablets. Within primary care, health workers often use mobile devices to register clients, track their health, and make decisions about care, as well as to communicate with clients and other health workers. An understanding of how health workers relate to, and experience mHealth, can help in its implementation.

OBJECTIVES

To synthesise qualitative research evidence on health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services, and to develop hypotheses about why some technologies are more effective than others.

SEARCH METHODS

We searched MEDLINE, Embase, CINAHL, Science Citation Index and Social Sciences Citation Index in January 2018. We searched Global Health in December 2015. We screened the reference lists of included studies and key references and searched seven sources for grey literature (16 February to 5 March 2018). We re-ran the search strategies in February 2020. We screened these records and any studies that we identified as potentially relevant are awaiting classification.

SELECTION CRITERIA

We included studies that used qualitative data collection and analysis methods. We included studies of mHealth programmes that were part of primary healthcare services. These services could be implemented in public or private primary healthcare facilities, community and workplace, or the homes of clients. We included all categories of health workers, as well as those persons who supported the delivery and management of the mHealth programmes. We excluded participants identified as technical staff who developed and maintained the mHealth technology, without otherwise being involved in the programme delivery. We included studies conducted in any country.

DATA COLLECTION AND ANALYSIS

We assessed abstracts, titles and full-text papers according to the inclusion criteria. We found 53 studies that met the inclusion criteria and sampled 43 of these for our analysis. For the 43 sampled studies, we extracted information, such as country, health worker category, and the mHealth technology. We used a thematic analysis process. We used GRADE-CERQual to assess our confidence in the findings.

MAIN RESULTS

Most of the 43 included sample studies were from low- or middle-income countries. In many of the studies, the mobile devices had decision support software loaded onto them, which showed the steps the health workers had to follow when they provided health care. Other uses included in-person and/or text message communication, and recording clients' health information. Almost half of the studies looked at health workers' use of mobile devices for mother, child, and newborn health. We have moderate or high confidence in the following findings. mHealth changed how health workers worked with each other: health workers appreciated being more connected to colleagues, and thought that this improved co-ordination and quality of care. However, some described problems when senior colleagues did not respond or responded in anger. Some preferred face-to-face connection with colleagues. Some believed that mHealth improved their reporting, while others compared it to "big brother watching". mHealth changed how health workers delivered care: health workers appreciated how mHealth let them take on new tasks, work flexibly, and reach clients in difficult-to-reach areas. They appreciated mHealth when it improved feedback, speed and workflow, but not when it was slow or time consuming. Some health workers found decision support software useful; others thought it threatened their clinical skills. Most health workers saw mHealth as better than paper, but some preferred paper. Some health workers saw mHealth as creating more work. mHealth led to new forms of engagement and relationships with clients and communities: health workers felt that communicating with clients by mobile phone improved care and their relationships with clients, but felt that some clients needed face-to-face contact. Health workers were aware of the importance of protecting confidential client information when using mobile devices. Some health workers did not mind being contacted by clients outside working hours, while others wanted boundaries. Health workers described how some community members trusted health workers that used mHealth while others were sceptical. Health workers pointed to problems when clients needed to own their own phones. Health workers' use and perceptions of mHealth could be influenced by factors tied to costs, the health worker, the technology, the health system and society, poor network access, and poor access to electricity: some health workers did not mind covering extra costs. Others complained that phone credit was not delivered on time. Health workers who were accustomed to using mobile phones were sometimes more positive towards mHealth. Others with less experience, were sometimes embarrassed about making mistakes in front of clients or worried about job security. Health workers wanted training, technical support, user-friendly devices, and systems that were integrated into existing electronic health systems. The main challenges health workers experienced were poor network connections, access to electricity, and the cost of recharging phones. Other problems included damaged phones. Factors outside the health system also influenced how health workers experienced mHealth, including language, gender, and poverty issues. Health workers felt that their commitment to clients helped them cope with these challenges.

AUTHORS' CONCLUSIONS

Our findings propose a nuanced view about mHealth programmes. The complexities of healthcare delivery and human interactions defy simplistic conclusions on how health workers will perceive and experience their use of mHealth. Perceptions reflect the interplay between the technology, contexts, and human attributes. Detailed descriptions of the programme, implementation processes and contexts, alongside effectiveness studies, will help to unravel this interplay to formulate hypotheses regarding the effectiveness of mHealth.