Health Outcomes Among Garment Workers in Low-Middle Income Countries: A Scoping Review

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Syahidatul Khafizah Mohd Hajaraih ◽  
Shelby P. Gordon ◽  
Karen M. Tabb
Keyword(s):  
Health Outcomes ◽  
Scoping Review ◽  
Middle Income ◽  
Garment Workers ◽  
Middle Income Countries ◽  
Low Middle Income Countries

scholarly journals Exploration of recovery of people living with severe mental illness (SMI) in low/middle-income countries (LMICs): a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e045005
Author(s):  
Fadia Gamieldien ◽  
Roshan Galvaan ◽  
Bronwyn Myers ◽  
Zarina Syed ◽  
Katherine Sorsdahl
Keyword(s):  
Mental Illness ◽  
Severe Mental Illness ◽  
Scoping Review ◽  
Data Extraction ◽  
Flow Diagram ◽  
Personal Recovery ◽  
Middle Income ◽  
Middle Income Countries ◽  
Provider Perspectives ◽  
Low Middle Income Countries

ObjectiveTo examine the literature on how recovery of people with severe mental illness (SMI) is conceptualised in low/middle-income countries (LMICs), and in particular what factors are thought to facilitate recovery.DesignScoping review.Data sources and eligibilityWe searched 14 electronic databases, hand searched citations and consulted with experts during the period May–December 2019. Eligible studies were independently screened for inclusion and exclusion by two reviewers. Unresolved discrepancies were referred to a third reviewer.Data extraction and synthesisAll bibliographical data and study characteristics were extracted using a data charting form. Selected studies were analysed through a thematic analysis emerging from extracted data.ResultsThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram offers a summary of the results: 4201 titles, 1530 abstracts and 109 full-text articles were screened. Ten articles were selected for inclusion: two from Turkey, two from India, and one each from China, Swaziland, Indonesia, Egypt, South Africa and Vietnam. Although most studies used qualitative methods, data collection and sampling methods were heterogeneous. One study reported on service provider perspectives while the rest provided perspectives from a combination of service users and caregivers. Three themes emerged from the data analysis. First, studies frame recovery as a personal journey occurring along a continuum. Second, there was an emphasis on social relationships as a facilitator of recovery. Third, spirituality emerged as both a facilitator and an indicator of recovery. These themes were not mutually exclusive and some overlap exists.ConclusionAlthough there were commonalities with how high-income countries describe recovery, we also found differences in conceptualisation. These differences in how recovery was understood reflect the importance of framing the personal recovery concept in relation to local needs and contextual issues found in LMICs. This review highlighted the current sparse evidence base and the need to better understand recovery from SMI in LMICs.

scholarly journals Implementation of colorectal cancer screening interventions in low-income and middle-income countries: a scoping review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e037520
Author(s):  
Désirée Schliemann ◽  
Nicholas Matovu ◽  
Kogila Ramanathan ◽  
Paloma Muñoz-Aguirre ◽  
Ciaran O'Neill ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Scoping Review ◽  
Low Income ◽  
Middle Income ◽  
Crc Screening ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Scoping Reviews

IntroductionColorectal cancer (CRC) imposes a significant global burden of disease. CRC survival rates are much lower in low-income and middle-income countries (LMICs). Screening tends to lead to an improvement in cancer detection and the uptake of available treatments and, in turn, to better chances of cancer survival. Most evidence on CRC screening interventions comes from high-income countries. The objective of this scoping review is to map the available literature on the implementation of CRC screening interventions in LMICs.Methods and analysisWe will conduct a scoping review according to the framework proposed by Arksey and O’Malley (2005). We will search MEDLINE, EMBASE, Web of Science and Google Scholar using a combination of terms such as “colorectal cancer”, “screening” and “low-middle-income countries”. Studies of CRC screening interventions/programmes conducted in the general adult population in LMICs as well as policy reviews (of interventions in LMICs) and commentaries on challenges and opportunities of delivering CRC screening in LMICs, published in the English language before February 2020 will be included in this review. The title and abstract screen will be conducted by one reviewer and two reviewers will screen full-texts and extract data from included papers, independently, into a data charting template that will include criteria from an adapted template for intervention description and replication checklist and implementation considerations. The presentation of the scoping review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidance.Ethics and disseminationThere are no ethical concerns. The results will be used to inform colorectal screening interventions in LMICs. We will publish the findings in a peer-reviewed journal and present them at relevant conferences.

scholarly journals Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e035983
Author(s):  
Rodrigo M Carrillo-Larco ◽  
Lorainne Tudor Car ◽  
Jonathan Pearson-Stuttard ◽  
Trishan Panch ◽  
J Jaime Miranda ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Scoping Review ◽  
Low Income ◽  
Clinical Medicine ◽  
Scientific Publications ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Health Related

IntroductionMachine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs.Methods and analysisThis scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations.Ethics and disseminationThe review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.

scholarly journals Unravelling ‘low-resource settings’: a systematic scoping review with qualitative content analysis

2021 ◽  
Vol 6 (6) ◽  
pp. e005190
Author(s):  
Chanel van Zyl ◽  
Marelise Badenhorst ◽  
Susan Hanekom ◽  
Martin Heine
Keyword(s):  
Content Analysis ◽  
Scoping Review ◽  
Qualitative Content Analysis ◽  
Middle Income ◽  
Low Resource Settings ◽  
Low Resource ◽  
Middle Income Countries ◽  
Resource Setting ◽  
Low Resource Setting ◽  
Low Middle Income Countries

IntroductionThe effects of healthcare-related inequalities are most evident in low-resource settings. Such settings are often not explicitly defined, and umbrella terms which are easier to operationalise, such as ‘low-to-middle-income countries’ or ‘developing countries’, are often used. Without a deeper understanding of context, such proxies are pregnant with assumptions, insinuate homogeneity that is unsupported and hamper knowledge translation between settings.MethodsA systematic scoping review was undertaken to start unravelling the term ‘low-resource setting’. PubMed, Africa-Wide, Web of Science and Scopus were searched (24 June 2019), dating back ≤5 years, using terms related to ‘low-resource setting’ and ‘rehabilitation’. Rehabilitation was chosen as a methodological vehicle due to its holistic nature (eg, multidisciplinary, relevance across burden of disease, and throughout continuum of care) and expertise within the research team. Qualitative content analysis through an inductive approach was used.ResultsA total of 410 codes were derived from 48 unique articles within the field of rehabilitation, grouped into 63 content categories, and identified nine major themes relating to the term ‘low-resource setting’. Themes that emerged relate to (1) financial pressure, (2) suboptimal healthcare service delivery, (3) underdeveloped infrastructure, (4) paucity of knowledge, (5) research challenges and considerations, (6) restricted social resources, (7) geographical and environmental factors, (8) human resource limitations and (9) the influence of beliefs and practices.ConclusionThe emerging themes may assist with (1) the groundwork needed to unravel ‘low-resource settings’ in health-related research, (2) moving away from assumptive umbrella terms like ‘low-to-middle-income countries’ or ‘low/middle-income countries’ and (3) promoting effective knowledge transfer between settings.

scholarly journals Data Equity to Advance Health and Health Equity in Low- and Middle-Income Countries: A Scoping Review

2021 ◽  
Vol 7 ◽  
pp. 205520762110619
Author(s):  
So O‘Neil ◽  
Sydney Taylor ◽  
Anitha Sivasankaran
Keyword(s):  
Decision Making ◽  
Health Outcomes ◽  
Health Equity ◽  
Scoping Review ◽  
Data Use ◽  
Data Access ◽  
Middle Income ◽  
Middle Income Countries ◽  
Improve Health ◽  
Low And Middle Income

Objective To assess a common hypothesis that data serve as a mechanism to improve health and health equity in low-and middle-income countries (LMICs), we conducted a synthesis of the evidence about the linkage between data capabilities in LMICs and health outcomes. Methods We searched and reviewed peer-reviewed and grey literature published in the past decade that focused on at least one aspect of health data or health equity or provided insights on the relationship between data use and improved health outcomes, decision-making, or both. We supplemented this with expert interviews and convenience-sampled literature. Results Of the 50 included articles, 33 discussed data collection, with 23 stating that poor accuracy, reliability, and completeness hindered data-informed decision-making. Of 27 articles discussing data access, 18 described how lack of interoperability between data systems hampered governments’ and other organizations’ ability to leverage the full value of data available. Of 19 articles discussing data use, 13 discussed how data were not getting to those doing work on the ground. Although key informants postulated a virtuous cycle between data and improved health outcomes, evidence did not support this connection. Conclusions Findings indicate better data might improve health service delivery. However, more work is needed to examine whether improvements in data yield improvements in health outcomes in LMICs. Our conceptual framework of data equity for health and health equity developed through this scoping review helps identify the key components along which to assess improvements in LMICs’ data capabilities.

scholarly journals Evidence on Pulmonary Rehabilitation and the Challenges Faced in Low- and Middle-Income Countries: A Systematic Qualitative Review

2021 ◽  
Author(s):  
Rebecca Farah ◽  
Wim Groot ◽  
Milena Pavlova
Keyword(s):  
Systematic Review ◽  
Health Outcomes ◽  
Low Income ◽  
Pulmonary Rehabilitation ◽  
Grey Literature ◽  
Individual Characteristics ◽  
Middle Income ◽  
Middle Income Countries ◽  
Rehabilitation Programs ◽  
Low Middle Income Countries

Abstract Background In low-income countries (LIC) and low-middle-income countries (LMIC), the burden of chronic obstructive pulmonary disease (COPD) has increased due to the lack of prevention and the presence of barriers to enter rehabilitation programs. The aim of this systematic review is to analyze the evidence on pulmonary rehabilitation (PR) in LIC and LMIC. Methods A systematic literature review was conducted. Four electronic databases were searched for qualitative and quantitative studies that documented the presence of PR in LIC and LMIC. We report our findings following the Prisma guidelines. In addition, grey literature was also searched. Articles not in English, presenting a point of view and/or not treating an adult population (< 18 years old) were excluded from the review. Data were extracted by one reviewer and synthesized in the form of tables. Tables present individual characteristics of the PR reported within countries, including country of origin, study design, population attending, intervention (kind of program setting), frequency and duration of a program established (if available), with health outcomes. The PICO framework was used for every country with reported PR to assess population, intervention, comparison and outcomes found. This systematic review is registered on Prospero: CRD42020141655. Results In total, 47 publications were included in the review. In LIC, PR for HIV-infected patients was most frequently reported, while in LMIC, PR for COPD patients was most frequent. Duration and frequency of treatments reported were also different in LIC and LMIC. Health outcomes on cardiopulmonary function were established in all publications. Results found that the implementation of PR in LMIC is ongoing. The most important barriers to access are the lack of funds and know-how among professional healthcare givers. Conclusion Findings suggest that the literature on PR is scarce in LIC and LMIC. Structured or non-structured rehabilitation programs for patients suffering from COPD, HIV and Tuberculosis, are infrequently available. Strategic policy initiatives to diminish barriers and challenges are needed to implement more PR programs in LIC and LMIC.

Mobile Health Literacy to Improve Health Outcomes in Low-Middle Income Countries

2017 ◽  
Vol 6 (4) ◽  
pp. 4-16 ◽  
Author(s):  
Nafisa Fatima Maria Vaz
Keyword(s):  
Health Literacy ◽  
Health Outcomes ◽  
Information And Communication Technologies ◽  
Mobile Phones ◽  
Health Indicators ◽  
Well Being ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Improve Health

Despite improvements in health indicators over time, such as decreased mortality and morbidity, significant challenges remain with regard to the quality in the delivery of healthcare in low and middle-income countries (LMIC's), especially in rural and remote regions of developing countries.In the effort to find feasible solutions to these issues, a lot of importance is given to the information and communication technologies (ICTs) The author reviews the evidence of the role mobile phones facilitating health literacy to contribute to improved health outcomes in the LMIC's. This was done by exploring the results of ten projects. The author examines the extent to which the use of mobile phones could help improve health outcomes in two specific ways: in improving health literacy and promoting health and well-being, thus increasing life expectancy in LMIC's. Analysis of the papers indicates that there is important evidence of mobile phones boosting increased access, promoting education and increased health literacy leads to the better health status of the population.

scholarly journals Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

2020 ◽  
Vol 11 ◽  
pp. 215013272096363
Author(s):  
Maleeha Naseem ◽  
Ramsha Akhund ◽  
Hajra Arshad ◽  
Muhammad Talal Ibrahim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scoping Review ◽  
Protein Structures ◽  
Learning System ◽  
Middle Income ◽  
Current Time ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Scoping Reviews

Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

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