Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review

The quality of routine data for measuring facility-based maternal mortality in public and private health facilities in Kampala City, Uganda

BACKGROUND

Routine health facility data are an important source of health information in resource-limited settings. Regular quality assessments are necessary to improve the reliability of routine data for different purposes, including estimating facility-based maternal mortality. This study aimed to assess the quality of routine data on deliveries, livebirths and maternal deaths in Kampala City, Uganda.

METHODS

We reviewed routine health facility data from the district health information system (DHIS2) for 2016 to 2021. This time period included an upgrade of DHIS2, resulting in two datasets (2016-2019 and 2020-2021) that were managed separately. We analysed data for all facilities that reported at least one delivery in any of the six years, and for a subset of facilities designated to provide emergency obstetric care (EmOC). We adapted the World Health Organization data quality review framework to assess completeness and internal consistency of the three data elements, using 2019 and 2021 as reference years. Primary data were collected to verify reporting accuracy in four purposively selected EmOC facilities. Data were disaggregated by facility level and ownership.

RESULTS

We included 255 facilities from 2016 to 2019 and 247 from 2020 to 2021; of which 30% were EmOC facilities. The overall completeness of data for deliveries and livebirths ranged between 53% and 55%, while it was < 2% for maternal deaths (98% of monthly values were zero). Among EmOC facilities, completeness was higher for deliveries and livebirths at 80%; and was < 6% for maternal deaths. For the whole sample, the prevalence of outliers for all three data elements was < 2%. Inconsistencies over time were mostly observed for maternal deaths, with the highest difference of 96% occurring in 2021.

CONCLUSIONS

Routine data from childbirth facilities in Kampala were generally suboptimal, but the quality was better in EmOC facilities. Given likely underreporting of maternal deaths, further efforts to verify and count all facility-related maternal deaths are essential to accurately estimate facility-based maternal mortality. Data reliability could be enhanced by improving reporting practices in EmOC facilities and streamlining reporting processes in private-for-profit facilities. Further qualitative studies should identify critical points where data are compromised, and data quality assessments should consider service delivery standards.

Applied artificial intelligence for global child health: Addressing biases and barriers

Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.

Exploring the accessibility of primary health care data in Europe's COVID-19 response: developing key indicators for managing future pandemics (Eurodata study)

BACKGROUND

Primary Health Care (PHC) plays a crucial role in managing the COVID-19 pandemic, with only 8% of cases requiring hospitalization. However, PHC COVID-19 data often goes unnoticed on European government dashboards and in media discussions. This project aims to examine official information on PHC patient care during the COVID-19 pandemic in Europe, with specific objectives: (1) Describe PHC's clinical pathways for acute COVID-19 cases, including long-term care facilities, (2) Describe PHC COVID-19 pandemic indicators, (3) Develop COVID-19 PHC activity indicators, (4) Explain PHC's role in vaccination strategies, and (5) Create a PHC contingency plan for future pandemics.

METHODS

A mixed-method study will employ two online questionnaires to gather retrospective PHC data on COVID-19 management and PHC involvement in vaccination strategies. Validation will occur through focus group discussions with medical and public health (PH) experts. A two-wave Delphi survey will establish a European PHC indicators dashboard for future pandemics. Additionally, a coordinated health system action plan involving PHC, secondary care, and PH will be devised to address future pandemic scenarios.

ANALYSIS

Quantitative data will be analysed using STATA v16.0 for descriptive and multivariate analyses. Qualitative data will be collected through peer-reviewed questionnaires and content analysis of focus group discussions. A Delphi survey and multiple focus groups will be employed to achieve consensus on PHC indicators and a common European health system response plan for future pandemics. The Eurodata research group involving researchers from 28 European countries support the development.

DISCUSSION

While PHC manages most COVID-19 acute cases, data remains limited in many European countries. This study collects data from numerous countries, offering a comprehensive perspective on PHC's role during the pandemic in Europe. It pioneers the development of a PHC dashboard and health system plan for pandemics in Europe. These results may prove invaluable in future pandemics. However, data may have biases due to key informants' involvement and may not fully represent all European GP practices. PHC has a significant role in the management of the COVID-19 pandemic, as most of the cases are mild or moderate and only 8% needed hospitalization. However, PHC COVID-19 activity data is invisible on governments' daily dashboards in Europe, often overlooked in media and public debates.

Travelling numbers and broken loops: A qualitative systematic review on collecting and reporting maternal and neonatal health data in low-and lower-middle income countries

Data and indicator estimates are considered vital to document persisting challenges in maternal and newborn health and track progress towards global goals. However, prioritization of standardised, comparable quantitative data can preclude the collection of locally relevant information and pose overwhelming burdens in low-resource settings, with negative effects on the provision of quality of care. A growing body of qualitative studies aims to provide a place-based understanding of the complex processes and human experiences behind the generation and use of maternal and neonatal health data. We conducted a qualitative systematic review exploring how national or international requirements to collect and report data on maternal and neonatal health indicators are perceived and experienced at the sub-national and country level in low-income and lower-middle income countries. We systematically searched six electronic databases for qualitative and mixed-methods studies published between January 2000 and March 2023. Following screening of 4084 records by four reviewers, 47 publications were included in the review. Data were analysed thematically and synthesised from a Complex Adaptive Systems (CAS) theoretical perspective. Our findings show maternal and neonatal health data and indicators are not fixed, neutral entities, but rather outcomes of complex processes. Their collection and uptake is influenced by a multitude of system hardware elements (human resources, relevancy and adequacy of tools, infrastructure, and interoperability) and software elements (incentive systems, supervision and feedback, power and social relations, and accountability). When these components are aligned and sufficiently supportive, data and indicators can be used for positive system adaptivity through performance evaluation, prioritization, learning, and advocacy. Yet shortcomings and broken loops between system components can lead to unforeseen emergent behaviors such as blame, fear, and data manipulation. This review highlights the importance of measurement approaches that prioritize local relevance and feasibility, necessitating participatory approaches to define context-specific measurement objectives and strategies.

Insights into COVID-19 data collection and management in Malawi: exploring processes, perceptions, and data discrepancies

Background

The completion of case-based surveillance forms was vital for case identification during COVID-19 surveillance in Malawi. Despite significant efforts, the resulting national data suffered from gaps and inconsistencies which affected its optimal usability. The objectives of this study were to investigate the processes of collecting and reporting COVID-19 data, to explore health workers' perceptions and understanding of the collection tools and processes, and to identify factors contributing to data quality.

Methods

A total of 75 healthcare professionals directly involved in COVID-19 data collection from the Malawi Ministry of Health in Lilongwe and Blantyre participated in Focus Group Discussions and In-Depth Interviews. We collected participants' views on the effectiveness of surveillance forms in collecting the intended data, as well as on the data collection processes and training needs. We used MAXQDA for thematic and document analysis.

Results

Form design significantly influenced data quality and, together with challenges in applying case definitions, formed 44% of all issues raised. Concerns regarding processes used in data collection and training gaps comprised 49% of all the issues raised. Language issues (2%) and privacy, ethical, and cultural considerations (4%), although mentioned less frequently, offered compelling evidence for further review.

Conclusions

Our study highlights the integral connection between data quality and the design and utilization of data collection forms. While the forms were deemed to contain the most relevant fields, deficiencies in format, order of fields, and the absence of an addendum with guidelines, resulted in large gaps and errors. Form design needs to be reviewed so that it appropriately fits into the overall processes and systems that capture surveillance data. This study is the first of its kind in Malawi, offering an in-depth view of the perceptions and experiences of health professionals involved in disease surveillance on the tools and processes they use.

Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review

Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.

Improving data use in decision-making and utilization of maternal healthcare services through a data-informed platform for health approaches in districts of the Gedeo Zone, southern Ethiopia, 2023: a cluster-randomized control trial

Background

In low-resource countries such as Ethiopia, the utilization of local data for planning and decision-making health systems was frequently constrained. In addition, despite several government initiatives, maternal health services were not completely utilized. On the other hand, efforts to effectively utilize the local data available to improve the utilization level of maternal healthcare services were insufficient, necessitating the need for a different approach.

Objective

This implementation study aims to test and validate the effectiveness of a data-informed platform for health (DIPH) strategies on data use for decision-making and utilization of maternal health services in districts of the Gedeo Zone, southern Ethiopia.

Methods

A two-arm parallel group, type II hybrid, cluster-randomized control trial design has been implemented to conduct the study between 1 September 2022 and 29 February 2024. Six woredas/districts have been assigned to the intervention arm and the other six to the control arm. Baseline and end-line data have been collected from 120 eligible health management staff (from both intervention arm and control arm). In the intervention arm, district health management staff have been given specialized training and continuous technical assistance as a package called the DIPH strategy by embedding it with the district's current decision-making platform such as Performance Review Team meetings. The DIPH strategy has mainly focused on five-step approaches such as situational assessment, stakeholder engagement, defining/setting priorities, planning, and follow-up. Health management staff in the control arm have performed their regular daily activities. The χ2 and t-tests have been used to check the effect of the intervention. In addition, difference-in-differences estimates have been calculated because the change may inherently occur over time. A P-value of <0.05 and a 95% confidence interval have been used to declare the significance of the intervention.

Discussion

The findings of this study were supposed to give insights into implementation strategies that improve data use in decision-making and utilization of maternal healthcare services at the woreda level and uncover contextual factors that boost the response of these strategies.

Addressing the Need for a Preexposure Prophylaxis Monitoring and Evaluation Implementation Guide: Experience From Zambia

BACKGROUND

The HIV care continuum comprises well-defined steps and indicators. In contrast, indicators along the preexposure prophylaxis (PrEP) cascade are still in the early stages of implementation. Monitoring and evaluation (M&E) of PrEP services is critical to optimizing PrEP uptake and adherence during periods of HIV risk. We provide an overview of global indicators for PrEP, describe the development process and outcomes of Zambia's 2022 National Pre-Exposure Prophylaxis (PrEP) Program Monitoring & Evaluation Implementation Guide, and discuss the guide's implications for other countries in sub-Saharan Africa.

NATIONAL M&E GUIDE DEVELOPMENT PROCESS

During the scale-up of PrEP, the Zambia Ministry of Health (MOH) and the National HIV/AIDS/STI/TB Council recognized the need for a national unified monitoring system to guide the effective implementation of PrEP services. Stakeholders from the MOH, civil society, professional organizations, funding agencies, and implementing partners developed the National Pre-Exposure Prophylaxis (PrEP) Program Monitoring & Evaluation Implementation Guide. This guide is aligned with the existing global indicators from the World Health Organization and the U.S. President's Emergency Plan for AIDS Relief and adapted to the country's needs, context, and health information systems. Zambia's experience in developing the guide has highlighted the importance of strengthening client-level monitoring systems for HIV prevention, ensuring flexibility of the PrEP monitoring system to accommodate PrEP delivery modalities and differentiated service delivery models, and training health workers to enhance PrEP services and deliver care PrEP services along the continuum to prevent HIV acquisition effectively.

CONCLUSIONS

Using a collaborative and consensus-based approach, Zambia developed its first national PrEP M&E implementation guide to provide standardized guidelines for optimizing the delivery, monitoring, and evaluation of PrEP service delivery. Zambia's experience can inform other countries in sub-Saharan Africa as they develop national M&E implementation approaches for PrEP.

Regular measurement is essential but insufficient to improve quality of healthcare

Ambrose Agweyu and colleagues argue that large scale improvements in quality of healthcare require strong change management as well as health information systems that can provide continuous and rapid feedback

Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review

In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.

Methodological reflections on health system oriented assessment of maternity care in 16 hospitals in sub-Saharan Africa: an embedded case study

Health Facility Assessments (HFAs) assessing facilities' readiness to provide services are well-established. However, HFA questionnaires are typically quantitative and lack depth to understand systems in which health facilities operate-crucial to designing context-oriented interventions. We report lessons from a multiple embedded case study exploring the experiences of HFA data collectors in implementing a novel HFA tool developed using systems thinking approach. We assessed 16 hospitals in four countries (Benin, Malawi, Tanzania, and Uganda) as part of a quality improvement implementation research. Our tool was organized in 17 sections and included dimensions of hospital governance, leadership, and financing; maternity care standards and procedures; ongoing quality improvement practices; interactions with communities; and mapping of the areas related to maternal care. Data for this study was collected using in-depth interviews with senior experts who conducted the HFA in the countries one to three months after completion of the HFAs. Data were analyzed using the inductive thematic analysis approach. Our HFA faced challenges in logistics (accessing key hospital-based respondents, high turnover of managerial staff, and difficulty accessing information considered sensitive in the context) and methodology (response bias, lack of data quality, and data entry into an electronic platform). Data elements of governance, leadership, and financing were the most affected. Opportunities and strategies adopted aimed at enhancing data collection (building on prior partnerships, understanding local and institutional bureaucracies) and enhancing data richness (identifying respondents with institutional memory, learning from experience, and conducting observations at various times). Moreover, HFA data collectors conducted abstraction of records and interviews in a flexible and adaptive way to enhance data quality. Lessons and new skills learned from our HFA could be used as inputs to respond to the growing need of integrating the systems thinking approach in HFA to improve contextual understanding of operations and structure.