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Health and Development presents a broad and detailed description of the multifaceted aspects of health and development across the globe. People are living longer, their lifestyles are changing and so too are the diseases from which they suffer. Recession in the North and debt servicing in the South have reduced public expenditure on health and welfare. The links between regional, economic and environmental factors and the health of a population are becoming clearer. Does development mean a longer life of lower quality? Always alive to both the global and the local implications, the authors focus in particular on the critical issues surrounding environmental impact, the interaction of poverty and health, socio-cultural factors in HIV/AIDS transmission, the use of traditional and community health care resources and women's health.

Health care systems in low- and middle-income countries (LMICs) can greatly benefit from artificial intelligence (AI) interventions in various use cases such as diagnostics, treatment, and public health monitoring but face significant challenges in sharing data for developing and deploying AI in health care. This study aimed to identify barriers and enablers to data sharing for AI in health care in LMICs and to test the relevance of these in a local context. First, we conducted a systematic literature search using PubMed, SCOPUS, Embase, Web of Science, and ACM using controlled vocabulary. Primary research studies, perspectives, policy landscape analyses, and commentaries performed in or involving an LMIC context were included. Studies that lacked a clear connection to health information exchange systems or were not reported in English were excluded from the review. Two reviewers independently screened titles and abstracts of the included articles and critically appraised each study. All identified barriers and enablers were classified according to 7 categories as per the predefined framework-technical, motivational, economic, political, legal and policy, ethical, social, organisational, and managerial. Second, we tested the local relevance of barriers and enablers in Thailand through stakeholder interviews with 15 academic experts, technology developers, regulators, policy makers, and health care providers. The interviewers took notes and analyzed data using framework analysis. Coding procedures were standardized to enhance the reliability of our approach. Coded data were reverified and themes were readjusted where necessary to avoid researcher bias. We identified 22 studies, the majority of which were conducted across Africa (n=12, 55%) and Asia (n=6, 27%). The most important data-sharing challenges were unreliable internet connectivity, lack of equipment, poor staff and management motivation, uneven resource distribution, and ethical concerns. Possible solutions included improving IT infrastructure, enhancing funding, introducing user-friendly software, and incentivizing health care organizations and personnel to share data for AI-related tools. In Thailand, inconsistent data systems, limited staff time, low health data literacy, complex and unclear policies, and cybersecurity issues were important data-sharing challenges. Key solutions included building a conducive digital ecosystem-having shared data input platforms for health facilities to ensure data uniformity and to develop easy-to-understand consent forms, having standardized guidelines for data sharing, and having compensation policies for data breach victims. Although AI in LMICs has the potential to overcome health inequalities, these countries face technical, political, legal, policy, and organizational barriers to sharing data, which impede effective AI development and deployment. When tested in a local context, most of these barriers were relevant. Although our findings might not be generalizable to other contexts, this study can be used by LMICs as a framework to identify barriers and strengths within their health care systems and devise localized solutions for enhanced data sharing. PROSPERO CRD42022360644; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360644.

Over the last 25 years, digital health interventions in low- and middle-income countries have undergone substantial transformations propelled by technological advancements, increased internet accessibility, and a deeper appreciation of the benefits of digital tools in enhancing health care availability. This study aims to examine the evolution, impact, and prospects of digital health interventions in low- and middle-income countries, highlighting their role in improving health care accessibility and equity. A retrospective analysis of digital health initiatives scanning the past two and a half decades focused on the progression from basic SMS platforms to sophisticated mobile health apps and other health digital interventions. Relevant literature and case studies were reviewed to elucidate key milestones, successes, challenges, and opportunities in advancing digital health initiatives in low- and middle-income regions. Digital health initiatives in low- and middle-income countries initially targeted specific health concerns, such as malaria diagnosis and treatment, through text-based platforms, demonstrating their efficacy in reaching remote and marginalized communities. With the proliferation of mobile phone ownership and internet access, these interventions evolved into comprehensive mobile health apps, facilitating self-care support, patient education, chronic disease monitoring, and remote consultations. The COVID-19 pandemic further accelerated the adoption of digital health interventions, particularly in disseminating health information, supporting contact tracing efforts, and enabling virtual consultations to alleviate strain on health care systems. The future of digital health interventions in low- and middle-income countries holds immense promise, fueled by emerging technologies such as artificial intelligence, machine learning, and blockchain. However, challenges persist in ensuring equitable access to digital health technologies, addressing disparities in digital literacy, and establishing robust health care infrastructure. Collaboration among governments, health care providers, technology innovators, and communities is essential to overcome these challenges and harness the full potential of digital health to improve health care outcomes in low- and middle-income countries.

National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure–the Healthcare Quality and Access (HAQ) Index–on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r=0·88), an index of 11 universal health coverage interventions (r=0·83), and human resources for health per 1000 (r=0·77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28·6 to 94·6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40·7 (95% uncertainty interval, 39·0–42·8) in 1990 to 53·7 (52·2–55·4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21·2 in 1990 to 20·1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73·8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-system characteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Bill & Melinda Gates Foundation.

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.

While World Health Organization member countries embraced the concept of universal coverage as early as 2005, few low-income countries have yet achieved the objective. This is mainly due to numerous barriers that hamper access to needed health services. In this paper we provide an overview of the various dimensions of barriers to access to health care in low-income countries (geographical access, availability, affordability and acceptability) and outline existing interventions designed to overcome these barriers. These barriers and consequent interventions are arranged in an analytical framework, which is then applied to two case studies from Cambodia. The aim is to illustrate the use of the framework in identifying the dimensions of access barriers that have been tackled by the interventions. The findings suggest that a combination of interventions is required to tackle specific access barriers but that their effectiveness can be influenced by contextual factors. It is also necessary to address demand-side and supply-side barriers concurrently. The framework can be used both to identify interventions that effectively address particular access barriers and to analyse why certain interventions fail to tackle specific barriers.

This paper complements the other papers in the Lancet Series on midwifery by documenting the experience of low-income and middle-income countries that deployed midwives as one of the core constituents of their strategy to improve maternal and newborn health. It examines the constellation of various diverse health-system strengthening interventions deployed by Burkina Faso, Cambodia, Indonesia, and Morocco, among which the scaling up of the pre-service education of midwives was only one element. Efforts in health system strengthening in these countries have been characterised by: expansion of the network of health facilities with increased uptake of facility birthing, scaling up of the production of midwives, reduction of financial barriers, and late attention for improving the quality of care. Overmedicalisation and respectful woman-centred care have received little or no attention.

Background Low-income and middle-income countries (LMICs) have difficulties achieving universal financial protection, which is primordial for universal health coverage. A promising avenue to provide universal financial protection for the informal sector and the rural populace is community-based health insurance (CBHI). We systematically assessed and synthesised factors associated with CBHI enrolment in LMICs. Methods We searched PubMed, Scopus, ERIC, PsychInfo, Africa-Wide Information, Academic Search Premier, Business Source Premier, WHOLIS, CINAHL, Cochrane Library, conference proceedings, and reference lists for eligible studies available by 31 October 2013; regardless of publication status. We included both quantitative and qualitative studies in the review. Results Both quantitative and qualitative studies demonstrated low levels of income and lack of financial resources as major factors affecting enrolment. Also, poor healthcare quality (including stock-outs of drugs and medical supplies, poor healthcare worker attitudes, and long waiting times) was found to be associated with low CBHI coverage. Trust in both the CBHI scheme and healthcare providers were also found to affect enrolment. Educational attainment (less educated are willing to pay less than highly educated), sex (men are willing to pay more than women), age (younger are willing to pay more than older individuals), and household size (larger households are willing to pay more than households with fewer members) also influenced CBHI enrolment. Conclusion In LMICs, while CBHI schemes may be helpful in the short term to address the issue of improving the rural population and informal workers’ access to health services, they still face challenges. Lack of funds, poor quality of care, and lack of trust are major reasons for low CBHI coverage in LMICs. If CBHI schemes are to serve as a means to providing access to health services, at least in the short term, then attention should be paid to the issues that militate against their success.