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Background and Aims Dental caries is an infectious disease affecting virtually all nations, including Rwanda. In Rwanda, the burden of dental caries is an issue of public health concern. To ensure the progressive eradication of the current dental caries burden in Rwanda through an evidence‐based approach, it is imperative to have an overview of the scientific research landscape of dental caries in the country. This study—a scoping review—aims to review the available evidence and gaps on dental caries in Rwanda. Methods This scoping review was reported based on the Preferred Reporting Items for systematic reviews and meta‐analyses extension for Scoping Reviews checklist. A systematic search of 11 databases was done to scoop out all literature relevant to the topic. Based on the review's selection criteria, a total of eight peer‐reviewed journal articles were included in the review. The extracted data were collated, summarized, and presented as results. Results The analysis of the data extracted from the included articles revealed a high prevalence of dental caries (ranging from 42.42% to 71.5%) in Rwanda. Also, the major pathogens causing dental caries in Rwanda as well as the impact of dental caries on the physical health and quality of life of Rwandans were identified in this review. Furthermore, the reported operative treatment options for dental caries in Rwanda were predominantly nonconservative. Also, no intervention study has been conducted on dental caries in Rwanda. Conclusion The findings in this review identify the need for massive public health interventions on dental caries in Rwanda.

Among the 17 United Nations Sustainable Development Goals set for achievement by 2030 is the objective of reducing inequality within and among countries. However, six years to the targeted year, widespread income inequality remains a reality and a concern to governments and development partners. This paper investigates the impact of public expenditures in achieving income inequality in low and lower-middle-income economies in the Sub-Saharan African region. To achieve this objective, the study uses annual data on 40 such economies for the period 2000−2022. A range of pre-estimation techniques was utilized in the study, and the Driscoll-Kraay technique was applied to account for the issue of cross-sectional dependence. All six explanatory variables employed in the study, including public expenditure components on health, education, and debt service, are found to have a statistically significant impact on income inequality. However, only public expenditure on education exhibited an income inequality-reduction impact. In contrast to many panel-oriented studies that assume uniform structural characteristics for low-income and lower-middle-income countries within a region, this study explicitly addresses the issue of heterogeneity.

Purpose>Rejecting building information modeling (BIM) can negatively impact the architectural, engineering and construction (AEC) industries. While BIM is trending globally, its implementation in post-conflict low-income economies is still limited. The purpose of this paper is to identify the critical factors for implementing BIM in a post-conflict low-income economy, using Afghanistan as a case study.Design/methodology/approach>This study identifies potential affecting factors for BIM implementation through reviewing existing literature and interviewing AEC professionals in Afghanistan. Then, the factors are inserted into a questionnaire survey and disseminated with Afghanistan’s AEC practitioners. The collected data was analyzed to determine the critical factors. Also, the underlying relationships between the critical factors were established through factor analysis.Findings>A total of 11 critical factors are affecting BIM implementation in Afghanistan. From those, nine factors can be grouped into the following three components: technological, environmental and organizational. Two factors, “cost-benefit of implementing BIM” and “market demand for BIM,” are recurring in low- and middle-income economies. Conversely, the “presence of appropriate projects to implement BIM” is the unique critical factor for Afghanistan that might affect other post-conflict low-income economies.Originality/value>This study focuses on affecting factors for BIM implementation in post-conflict low-income economies, using Afghanistan as a reference rather than other types of economies that have been widely studied.

The quest to achieve economic development worldwide has increased carbon dioxide (CO2) emissions, which could vary in high- and low-income economies due to differences in economic activities. Using empirical evidence from the panel data for the period 1960–2018 obtained from the World Bank, we investigate differences in the impact of population, gross domestic product (GDP), and renewable energy on CO2 emissions in high- and low-income economies. For that purpose, we applied the Pesaran cross-sectional dependence test (for cross-sectional dependence), Levin-Lin-Chu unit root test (for Unit roots), Granger causality Wald test (for the possibility of Granger causality among the variables), fixed-effects and random-effects regressions. We established that population, GDP and energy consumption considerably influence CO2 emissions. Results of the Granger causality Wald test, fixed-effects and random-effects regressions clearly demonstrated that growth in population and GDP directly correlates with CO2 emissions in high- and low-income economies, while renewable energy consumption has an indirect correlation. While there are no differences in terms of directions, we revealed differences in the magnitude in high- and low-income economies. The impact of population and renewable energy consumption on CO2 emissions in low-income economies is greater than that of high-income economies. The impact of GDP on CO2 emissions is greater in high-income economies than in low-income economies. Thus, to reduce CO2 emissions, policy makers should promote low carbon emission economic activities and implement population control measures

This article presents a novel methodology to assess the financial risk to crops in highly weather-volatile regions. We use data-driven methodologies that use singular value decomposition techniques in a low-income economy. The risk measure is first derived by applying data-driven frameworks, a Principal Component Analysis (PCA), and Singular Spectrum Analysis (SSA) to productive coffee crops in Colombia (163 weather stations) during 2010–2019. The objective is to understand the future implications that index insurance tools will have on strategic economic crops in the country. The first stage includes the identification of the PCA components at the country level. The risk measure, payouts-in-exceedance ratio, or POER, is derived from an analysis of the most volatile-weather-producing regions. It is obtained from a linear index insurance model applied to the extracted singular-decomposed tendencies through SSA on first-component data. The financial risk measure due to weather volatilities serves to predict the future implications of the payouts-in-exceedance in both seasons—wet and dry. The results show that the first PCA component contributes to forty percent of the total variance. The seasonal forecast analysis for the next 24 months shows increasing additional payouts (PO), especially during the wet season. This is caused by the increasing average precipitation tendency component with POERs of 18 and 60 percent in the first and second years. The findings provide important insights into designing agricultural hedging insurance instruments in low-income economies that are reliant on the export of strategic crops, as is the case of Colombian coffee.

The study presents a simple extension of a Harrodian model, that explores, the relationship between the environment and economic growth in a hypothetical dual low-income economy with relatively low levels of environmental quality. It is supposed that the rise in effective demand increases the flow of negative externalities on the environment, which, in turn, would affect output expansion negatively in the capitalist sector through the occurrence of environmental adjustment costs. From such conflictual dynamics, the model shows that perpetual vicious circles may characterize the pattern of fluctuations in economic activity in this economy.

Dynamics in economic trend and banks’ creditors’ expectation have directed banks to search the innovative means of income generation. It is with this view that this study examines the relationship between the income diversification and financial performance of banks in SSA low income countries. A panel data of 1,280 observations were extracted from the financial profile of 160 commercial banks from 19 purposively selected countries from 2009 to 2016. The findings from the empirical analysis indicate that non-interest income accounts for 95% of operating income in Low Income countries’ commercial banking sector. Also, it was found that income diversification in SSA banks enhanced financial performance as affirmed by the finance theory because both interest and non-interest income sources are indeed blessings as they increase the financial performance significantly. Therefore, low income SSA countries’ commercial banks are urged to strive to ensure proper investment with their income diversification so that better performance of their economies is enhanced.

Background Demand for rapid evidence-based syntheses to inform health policy and systems decision-making has increased worldwide, including in low- and middle-income countries (LMICs). To promote use of rapid syntheses in LMICs, the WHO’s Alliance for Health Policy and Systems Research (AHPSR) created the Embedding Rapid Reviews in Health Systems Decision-Making (ERA) Initiative. Following a call for proposals, four LMICs were selected (Georgia, India, Malaysia and Zimbabwe) and supported for 1 year to embed rapid response platforms within a public institution with a health policy or systems decision-making mandate. Methods While the selected platforms had experience in health policy and systems research and evidence syntheses, platforms were less confident conducting rapid evidence syntheses. A technical assistance centre (TAC) was created from the outset to develop and lead a capacity-strengthening program for rapid syntheses, tailored to the platforms based on their original proposals and needs as assessed in a baseline questionnaire. The program included training in rapid synthesis methods, as well as generating synthesis demand, engaging knowledge users and ensuring knowledge uptake. Modalities included live training webinars, in-country workshops and support through phone, email and an online platform. LMICs provided regular updates on policy-makers’ requests and the rapid products provided, as well as barriers, facilitators and impacts. Post-initiative, platforms were surveyed. Results Platforms provided rapid syntheses across a range of AHPSR themes, and successfully engaged national- and state-level policy-makers. Examples of substantial policy impact were observed, including for COVID-19. Although the post-initiative survey response rate was low, three quarters of those responding felt confident in their ability to conduct a rapid evidence synthesis. Lessons learned coalesced around three themes – the importance of context-specific expertise in conducting reviews, facilitating cross-platform learning, and planning for platform sustainability. Conclusions The ERA initiative successfully established rapid response platforms in four LMICs. The short timeframe limited the number of rapid products produced, but there were examples of substantial impact and growing demand. We emphasize that LMICs can and should be involved not only in identifying and articulating needs but as co-designers in their own capacity-strengthening programs. More time is required to assess whether these platforms will be sustained for the long-term.

The global incidence of blindness has continued to increase, despite the enactment of a Global Eye Health Action Plan by the World Health Assembly. This can be attributed, in part, to an aging population, but also to the limited diagnostic resources within low- and middle-income countries (LMICs). The advent of generative artificial intelligence (AI) within health care could pose a novel solution to combating the prevalence of blindness globally. The objectives of this study are to quantify the effect the addition of a complex prompt has on the diagnostic accuracy of a commercially available LLM, and to assess whether such LLMs are better or worse at diagnosing conditions that are more prevalent in LMICs. Ten clinical vignettes representing globally and LMIC-prevalent ophthalmological conditions were presented to GPT-4-0125-preview using simple and complex prompts. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated. Statistical comparison between prompts was conducted using a chi-square test of independence. The complex prompt achieved a higher diagnostic accuracy (90.1%) compared to the simple prompt (60.4%), with a statistically significant difference (χ2=428.86; P<.001). Sensitivity, specificity, PPV, and NPV were consistently improved for most conditions with the complex prompt. The simple prompt struggled with LMIC-prevalent conditions, diagnosing only 1 of 5 accurately, while the complex prompt successfully diagnosed 4 of 5. The study established that overall, the inclusion of a complex prompt positively affected the diagnostic accuracy of GPT-4-0125-preview, particularly for LMIC-prevalent conditions. This highlights the potential for LLMs, when appropriately tailored, to support clinicians in diverse health care settings. Future research should explore the generalizability of these findings across other models and specialties.