Costs of surgical site infections in orthopaedic and trauma surgery: a systematic review.

Global interest rates, US dollar, and global risk

Economic value, affordability, and scale-up of adjuvant immunotherapies in lung cancer treatment: From cost-effectiveness decision to budget impact analysis.

In this paper, the researcher examines how the BRICS can break the global hegemony of the United States Dollar (USD) and the future of establishing a common currency. The paper is concerned with economic and political reasons behind the initiative, the viability of such a project, and its larger implications in the world context. The study applies a qualitative, comparative and analytical design to use secondary materials such as scholarly research, policy documents and foreign financial information. The Eurozone and de-dollarization in Russia and China are also the case studies that may be viewed as providing comparative information. Results indicate that dissatisfaction with dollar hegemony is increasing especially among emerging economies that are considering it as an instrument of vulnerability to external shocks and sanctions. The BRICS members also seek to cut on the use of USD by encouraging the local currencies trade, creating systems of payment and discussing a common currency. Nonetheless, the project has major challenges such as economic heterogeneity, leadership asymmetry, poor institutions, and lack of credibility. This paper concludes that although a BRICS common currency is still a distant dream, current efforts already portend a slow transition to financial multipolarity. This adds to the world discussions of currency power and brings about the possibility of a more pluralistic world monetary system.

Background: Patients with HER2-positive (HER2+) breast cancer (BC) who have residual invasive disease after neoadjuvant therapy remain at a significantly increased risk of recurrence. Updated results from the KATHERINE trial demonstrated the clinical benefit of trastuzumab-emtansine (T-DM1), showing a significant improvement in event-free survival. However, in budget-constrained settings, assessing the cost-effectiveness of TDM-1 is essential to guide sustainable adoption. Objective: To evaluate the cost-effectiveness analysis of adjuvant T-DM1 compared with trastuzumab in HER2+ early BC patients and residual disease, following neoadjuvant treatment, from the Colombian Health System perspective. Methods: We conducted a Markov model with a lifetime horizon to evaluate the cost-effectiveness of T-DM1 using the updated results of the KATHERINE trial (8.4-year follow-up). The model comprises 6 health states: patients with residual invasive disease on adjuvant treatment, non-metastatic recurrence, remission after non-metastatic recurrence, metastatic disease (first-line [1LmBC] and subsequent lines [2LmBC]), and death. Direct medical costs were included. The primary outcome was quality-adjusted life-years, with both costs and effects discounted annually at a 5% rate. Model assumptions were based on current guidelines and validated by local experts. Sensitivity and scenario analysis were performed to confirm the reliability of the results. Prices are shown in 2024 US dollars (4071.35 Colombian pesos = US $1) Results: Under a willingness-to-pay threshold of 86% of the 2024 gross domestic product per capita (US $6831), T-DM1 was dominant over trastuzumab. Cost savings were mainly driven by a reduction in recurrences (>50%), particularly in high-cost settings: 1LmBC (US $−30 510) and 2LmBC (US $−6317). The probabilistic sensitivity analysis confirmed T-DM1 dominance in 81.2% of the 1000 Monte Carlo simulations, demonstrating consistency across settings. Conclusion: T-DM1 is a cost-effective and dominant strategy for the adjuvant treatment of residual invasive HER2+ early BC in Colombia, driven by cost savings from reduced high-cost recurrences. These findings support the adoption of T-DM1 to improve outcomes while ensuring efficient use of healthcare resources.

Aims: This study aims to reconceptualize de-dollarization by moving beyond aggregate currency shares and examining the distinct monetary functions performed by the US dollar, in order to clarify the structural limits and risks associated with substitution away from the dollar. Study Design: This is a theoretical and analytical study grounded in international monetary economics, macro-financial stability theory, and international political economy. Methodology: The paper develops a formal analytical framework that decomposes international currency use into four functionally distinct roles: unit of account, medium of exchange, store of value, and financial anchoring. De-dollarization is modeled as functional bypass rather than generalized substitution, with feasibility constrained by balance-sheet risk, safe-asset scarcity, settlement efficiency, and sudden-stop dynamics. A Functional De-Dollarization Index (FDI) is introduced to summarize effective monetary sovereignty net of macro-financial stability costs. Results: The analysis shows that substitution away from the US dollar is relatively feasible in pricing and payment functions but sharply constrained in store-of-value and financial-anchoring roles, particularly in financially shallow economies. Premature bypass in safety- and finance-critical functions generates nonlinear instability through currency mismatch, financing contractions, and settlement fragmentation. As a result, partial dollarization emerges as a second-best equilibrium that stabilizes output and capital flows under global risk shocks. Conclusion: This paper reconceptualizes de-dollarization not as hegemonic decline, but as a function-specific reallocation of monetary roles shaped by infrastructure scarcity and constrained by macro-financial risk. International monetary power arises not from currency share dominance per se, but from the ability to provide stability-critical monetary infrastructure under stress. At present, the currency that can comprehensively fulfill this role is, in practice, limited to the US dollar.

Introduction. The issue of compensation for damage and losses caused by the Russian Federation’s military aggression against our state, as well as future compensation mechanisms, currently raises more questions than unambiguous answers. The Russian Federation’s military aggression against Ukraine has been going on for 12 years, and the number of victims and victims is increasing daily. Therefore, it is important to resolve controversial issues and find effective legal mechanisms for real compensation for damage and losses. The purpose of the paper is to study existing mechanisms for compensation for damage and losses caused to individuals and legal entities as a result of the Russian Federation’s armed aggression. Results. According to various expert estimates, over 1 trillion US dollars have been caused to the Ukrainian economy during the 12 years of armed aggression by the Russian Federation. Civilian casualties in the form of dead and wounded number in the tens of thousands of civilians. In recent years, the Russian armed forces have been deliberately and systematically attacking Ukraine’s energy infrastructure, which has resulted in a critical reduction in electricity generation capacity. The secondary consequences of such attacks have affected water supply, sewage and sanitation systems, heating and hot water supply, healthcare, education and the general state of the economy, and people’s mental health. The damage to the environment is also inexhaustible, as Ukraine is one of the countries with the highest levels of contamination by explosive devices and mines, which poses a long-term threat to the civilian population. Compensation to victims of damage caused as a result of Russian war crimes is one of the key aspects in the protection of human rights. Conclusions. When developing a global compensation mechanism for damages caused to Ukraine by the Russian aggressor, one should take into account the scientific developments of domestic researchers and real proposals of practitioners who are already working in the field of restoring justice. The main tasks should be the activity of the Ukrainian legal community, the processing of existing problematic issues and the preparation of proposals for their resolution.

Artificial intelligence (AI) scribes are being rapidly adopted in oncology, yet their real-world impact on physician productivity, workflow, and workplace satisfaction is understudied. We evaluated the quantitative and qualitative effects of an AI scribe with electronic medical record (EMR) integration in a multisite community oncology practice. This single-center, multisite study enrolled 22 medical oncologists and three primary care physicians randomly assigned to receiving training and exposure to the Knowtex AI scribe in an initial phase or control phase. Physician billing data were collected for 6 months before the onboarding of intervention arm physicians and for 16 weeks afterward-within the 2024 fiscal year. Surveys administered at baseline and week 8 postexposure assessed documentation burden, quality, EMR experience, and overall documentation satisfaction. All enrolled providers adopted the Knowtex AI scribe during their respective study phase. Intervention arm physicians exhibited an increase in mean visits (β = 3.35, SE = 1.08, P = .010) and mean total billing per working day (β = $433.61 in US dollars, SE = $134.82, P = .007). There was no change in the number of diagnostic codes per visit. Physicians' perceptions of EMR challenges markedly decreased after implementing the AI scribe (P < .001) with no significant change in the quality of documentation. Physicians reported higher satisfaction with documentation workflow, reduced in-clinic documentation hours, and increased time with patients. Adoption of an AI scribe with EMR integration in oncology clinics may enhance certain billing metrics and positively shift physician perceptions of documentation processes and EMR challenges. These findings highlight potential benefits and limitations of AI scribes in improving physician productivity and satisfaction.

To evaluate caregivers' perspectives, challenges, and difficulties associated with at-home pupil dilation (AHPD) during the coronavirus disease 2019 (COVID-19) pandemic. In this retrospective questionnaire study, a pediatric ophthalmologist determined eligibility for AHPD in children younger than 6 years. Cyclopentolate 1% eye drops were prescribed. Age was recorded and divided into three age groups. Of 264 respondents, 73.9% (n = 195) completed AHPD, with half having undergone eye dilation in the past (53.7%, n = 116). The mean task difficulty across all groups was 3.36 ± 1.44 (range: 1 to 5). There were no statistical differences in difficulty between groups. Most caregivers stated that they would consider AHPD in the future (65.8%), and 63% reported that this practice saved time and was worth the cost and difficulty associated with AHPD. For more than 80% of responders, the cost was less than 20 United States Dollars, and 41.8% did not need to pay for their drops. The common reasons for not performing AHPD were difficulty (34.8%, n = 24), cost of the drops (30.4%, n = 21), and unclear instructions (8.7%, n = 6). AHPD can be a useful and cost-effective tool, can offer convenience, and may reduce anxiety in the office. The decision to use this approach should be individualized, considering the child's specific needs, parental comfort and ability, and the nature of the assessed ophthalmologic condition.

Abstract Using confidential daily data, we examine the Bank of Israel's foreign exchange interventions from 2013 to 2019. We find that a 1 billion U.S. dollars (USD) purchase leads to a 0.82% depreciation of the Israeli Shekel (ILS)–a strong effect compared to other studies. We show that this effectiveness can partially be attributed to the limited risk‐taking capacity of global banks. The interventions also widen the negative deviation from covered interest parity and influence the higher‐order moments of risk‐neutral expectations derived from options prices. We find that USD purchases shift the USD/ILS distribution upward and reduce crash risk. Moreover, the options market anticipates and prices in upcoming interventions.

Accurate exchange rate forecasting is critical for international finance, yet it remains notoriously challenging. This challenge is uniquely framed in the Gulf Cooperation Council (GCC) region, where most currencies operate under formal or de facto pegs to the US dollar. This study investigates the efficacy of two advanced tree-based machine learning algorithms—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—in forecasting the daily US dollar exchange rates of six Gulf currencies: the Omani rial (OMR), Saudi riyal (SAR), UAE dirham (AED), Kuwaiti dinar (KWD), Qatari riyal (QAR), and Bahraini dinar (BHD). Utilizing daily data from January 1, 2010, to January 31, 2025, we train models on an in-sample period (2010-2023) and evaluate their out-of-sample performance (2023-2025) using R², Mean Squared Error (MSE), and Mean Absolute Error (MAE). A key contribution is the inclusion of a naive persistence model as a benchmark to assess the marginal value added by machine learning. Our findings indicate that while the pegged currencies (OMR, SAR, AED, QAR, BHD) exhibit minimal forecastable variation, making sophisticated models only marginally better than the naive benchmark, the Kuwaiti dinar (KWD), with its basket peg, presents a more fruitful case for ML application. The study provides a fully reproducible Python framework, underscoring the importance of model interpretability and economic significance over mere statistical fit in highly stable, policy-constrained financial environments. The results offer practical insights for corporate treasurers, FX risk managers, and policymakers in the GCC.

Accurate exchange rate forecasting is critical for international finance, yet it remains notoriously challenging. This challenge is uniquely framed in the Gulf Cooperation Council (GCC) region, where most currencies operate under formal or de facto pegs to the US dollar. This study investigates the efficacy of two advanced tree-based machine learning algorithms—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—in forecasting the daily US dollar exchange rates of six Gulf currencies: the Omani rial (OMR), Saudi riyal (SAR), UAE dirham (AED), Kuwaiti dinar (KWD), Qatari riyal (QAR), and Bahraini dinar (BHD). Utilizing daily data from January 1, 2010, to January 31, 2025, we train models on an in-sample period (2010-2023) and evaluate their out-of-sample performance (2023-2025) using R², Mean Squared Error (MSE), and Mean Absolute Error (MAE). A key contribution is the inclusion of a naive persistence model as a benchmark to assess the marginal value added by machine learning. Our findings indicate that while the pegged currencies (OMR, SAR, AED, QAR, BHD) exhibit minimal forecastable variation, making sophisticated models only marginally better than the naive benchmark, the Kuwaiti dinar (KWD), with its basket peg, presents a more fruitful case for ML application. The study provides a fully reproducible Python framework, underscoring the importance of model interpretability and economic significance over mere statistical fit in highly stable, policy-constrained financial environments. The results offer practical insights for corporate treasurers, FX risk managers, and policymakers in the GCC.

This study aims to deliver contemporary, population-based estimates of herpes zoster (HZ) incidence, temporal trends, complications, healthcare utilisation and direct costs among general adults and adults with increased-risk conditions. A retrospective cohort study using the Yinzhou Integrated Health Platform (2015-2021; China) was performed. Incident HZ was ascertained after a 1-year washout; increased-risk conditions were pre-specified (immunocompromising/autoimmune). We calculated age-/sex-standardised incidence, assessed trends with joinpoint regression and summarised HZ-related outpatient visits, hospitalisations and direct medical costs [Chinese yuan (¥) and US dollars ($)]. Among 5.42 million person-years, including 790,410 subjects, 25,855 incident HZ events were identified. Overall incidence was 4.77/1000 person-years (PY) [95% confidence interval (CI) 4.71-4.83] and 16.13/1000 PY (95% CI 15.25-17.06) in increased-risk adults [incidence rate ratio (IRR) 3.44 versus in adults without immunocompromising conditions or autoimmune diseases (AIDs)]. Incidence rose with age (peak 70-79years overall; 60-69years increased-risk) and was higher in women. Postherpetic neuralgia (PHN) was the most frequent complication (8.96% overall; 10.88% increased-risk). Standardised incidence increased from 4.67 to 7.51/1000 PY during 2015-2021 [annual percentage change (APC) 7.94%], with a steep rise to 2019 and plateau thereafter. Hospitalisation among incident HZ was 1.35%. Mean direct cost per episode was Chinese yuan (¥)625.52 [US dollars ($)94.35] for outpatients and ¥8854.03 ($1335.45) for inpatients; increased-risk outpatients incurred higher mean costs (¥1205.47, $181.82). Across strata, complications - especially PHN - were associated with more visits and higher expenditure. HZ imposes a rising, age- and risk-concentrated burden in Chinese adults, with PHN being the principal driver of resource use and costs. These real-world estimates support prioritising zoster vaccination for adults ≥ 50years and clinically vulnerable groups, and integrating HZ surveillance and management within chronic-disease programmes.

Despite strong guideline support, single maintenance and reliever therapy (SMART) for asthma is underused in the US. Limited insurance coverage of SMART-compatible inhalers remains a major barrier to its adoption. To compare the annual asthma management costs of SMART vs traditional therapy from a US health care payer perspective. This economic evaluation used a probabilistic decision-tree model with Monte Carlo simulations to compare the total asthma management costs for patients prescribed SMART vs traditional therapy, conducting analyses from September 1, 2024, to March 13, 2025. Input data were extracted through a systematic review of 6 randomized clinical trials as well as current asthma guidelines. SMART vs traditional therapy. The main outcome was annual asthma-related costs to health care payers. Model inputs, including exacerbation rates and expected inhaler utilization, were extracted from prior randomized clinical trials. Morbidity data and medication costs were obtained from national databases and inflated to 2024 US dollars. Analyses used a 1-year time horizon and were repeated with and without quality-adjusted life-years (QALYs) considered. The model includes 11 988 individuals with moderate to severe asthma who participated in the randomized clinical trials. For patients prescribed SMART, the estimated total annual cost of asthma management was $2181 (95% CI, $1606-$2939) per patient compared with $2235 (95% CI, $1595-$3267) for traditional therapy. SMART was associated with an incremental gain of 0.0006 QALYs (95% CI, 0.0003-0.0011 QALYs) per patient. SMART was less costly in 57% of simulations when QALYs were excluded, was more cost-effective in 67% of simulations when QALYs were included, and produced a mean incremental net monetary benefit of $118 (95% CI, -$344 to $663) per patient per year. The findings of this economic analysis suggest that SMART was associated with modest cost savings and improved health outcomes compared with traditional asthma therapy. Given its cost-effectiveness, demonstrated effectiveness, and strong guideline endorsement, expanding insurance coverage of SMART may reduce asthma-related morbidity while lowering costs to US health care payers.

We assessed the availability, price, and affordability of commonly used chemotherapy medications in Cameroon, Ethiopia, Kenya, and Malawi. We also examined the characteristics that could predict chemotherapy medication quality. Samples of seven commonly used chemotherapy medications were collected: cisplatin, cyclophosphamide, doxorubicin, ifosfamide, leucovorin, methotrexate, and oxaliplatin. Stockouts and medicine prices were recorded at public national hospitals and community pharmacies. Using the National Comprehensive Cancer Network's harmonized guidelines for sub-Saharan Africa, we estimated the costs of medications to treat early-stage breast cancer, colorectal cancer (CRC), and head and neck cancer. Every sample was tested for quality using high-performance liquid chromatography against USP standards. We ran logistic regressions to assess medicine characteristics that could predict substandard and falsified chemotherapy medications. Stockouts of chemotherapy medications in public hospitals were observed in three of four countries. Other than in Malawi where medications are free when available in the public sector, chemotherapy medications were unaffordable, costing government worker salary equivalents of 47-242 days, 233-869 days, and 22-196 days to treat early-stage breast cancer, CRC, and head and neck cancer, respectively. On average across the population, $36 US dollars (USD) (95% CI, $50 USD to $134 USD, 9%-25% of medication cost) was spent on poor-quality medicines for treatment of breast cancer, $32 USD (95% CI, $6 USD to $214 USD, 0.5%-17%) for CRC, and $15 USD (95% CI, $4 USD to $49 USD, 2%-21%) for head and neck cancer. Price of the medication, public/private source, and medicine registration status were poor predictors of medicine quality. We found that medicines for cancer treatment are unaffordable without government subsidies. Moreover, poor-quality chemotherapy medicines are hard to detect without chemical testing. Government policies and supply chain practice changes are needed to improve the availability, affordability, and quality of chemotherapy medications in sub-Saharan Africa.

This study aims to describe the cost of publication in orthopaedic journals and correlation to journal impact factor metrics. Orthopaedic surgery and sports medicine journals were included for analysis. Data were recorded from publicly available records. Statistical analysis was conducted using R studio version 4.2.3. One-way analysis of variance with post hoc analysis (Tukey, Honest Significant Difference(HSD) was performed to compare SCImago Journal Rank (SJR) scores, H-index, and average cost (including the average submission, publication, or article processing charges [APCs] of a published article) between journals based on SJR quartile. Independent sample t-test and chi square or Fisher exact test were used to compare variables, with a Bonferroni P-value adjustment. Pearson correlation coefficient was calculated for SJR score, H-index, total journal references and publications as of 2022, years in publication, average cost, and APC (ie, the cost of publication associated with Open-Access publication). Correlation strengths were defined as excellent (>0.7), excellent-good (0.61 to 0.7), good (0.31 to 0.6), or poor (0.2 to 0.3). P values ≤0.05 were considered significant. Of 306 orthopaedic journals, 4.9% had submission fees. Subscription publication was available in 55.56%. The average APC for open-access options was $1,975.69 ± 1,524.53 US dollars (USD). Comparison of publication cost for journals with and without subscription options demonstrated significance ($2,939.19 vs. $857.94 USD, P < 0.005). No strong correlation was found between journal impact factor metrics and publication costs. The top 5 main publishers are listed. The top publisher, Elsevier, responsible for 13.07% of orthopaedic journals, offered subscription publication options for only 32.5% of journals, with an average APC of $2,180.21 ± 1,521.27 USD. The United States published the most (n = 86 journals), followed by the United Kingdom (n = 61). Open-access publication aims to ensure equitable access but is limited by large costs associated with publication charged to the authors, which do not strongly correlate with journal impact factor metrics.

In developing nations, particularly countries that depend significantly on imports like Afghanistan, food price volatility continues to pose a serious threat to both economic stability and food security. By using the (WFP) Food Prices dataset for Afghanistan, this study presents a machine learning based system for forecasting changes in food prices under currency fluctuation scenarios. The models reflect the implicit effects of exchange rate fluctuations on domestic food markets by combining pricing data denominated in Afghani (AFN) and U.S. dollars (USD). Three algorithms from ML: Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks, were trained and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). With an MAE of 3.2, RMSE of 4.3, and R² of 0.89, the Random Forest model outperformed both LSTM (MAE 3.8, RMSE 4.9, R² 0.86) and Linear Regression (MAE 4.5, RMSE 5.8, R² 0.82). The findings show a strong relationship between market inflation and exchange rate volatility, indicating that currency depreciation significantly increases domestic food prices. The study emphasizes the value of machine learning in creating early warning systems and policy tools that enhance food price stability and support food security in poor countries like Afghanistan.

We performed a microsimulation analysis predicting the societal cost of antimicrobial resistance (AMR), which represents the potential cost savings if Ghana eliminates AMR. This study combined bacterial resistance epidemiology and cost data from Ghana to perform a microsimulation analysis focusing on sociodemographic groups, predicting the potential societal cost savings should Ghana eliminate AMR. The nationally representative data were collected from 12 reference laboratories across Ghana's three geographical belts between June 2021 and December 2023. Case definition was enterobacterial third-generation cephalosporin (3GC) resistant infections, methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant Mycobacterium tuberculosis. Using an adapted microsimulation framework, the simulation incorporated four integrated data modules: population demographics, infection epidemiology, healthcare resource use and expenditure and labour market characteristics. This approach allowed for the construction of synthetic individuals from national data sets and the projection of annual outcomes over a 7-year horizon. Costs were calculated from a societal perspective under a status quo scenario, assuming that admission rates, resistant infection probabilities and mortality rates remain the same. This analysis also considers a 2.1% annual population growth rate, a 5% discount rate for future costs and age-specific resistance risk profile. We stratified the outcome of interest by age groups, sex and wealth quintiles to account for distributional effects and reported the costs in purchasing power parity equivalent in international US dollars. Ghana in West Africa. A simulated population of AMR patients of all ages and sex. Societal cost attributable to AMR in Ghana. Assuming probabilities of all-cause hospital admissions of 0.102 for females and 0.093 for males, along with probabilities of AMR infections of 0.239 and 0.193, we predicted nearly 78 000 (95% CI 72 000 to 83 520) annual AMR infections and approximately 6300 (95% CI 3900 to 8638) attributable deaths. MRSA and 3GC-resistant infections made up 20.2% and 79.2% of the predicted annual infections, corresponding to an estimated mean societal cost of about US$435 million. In decreasing order of magnitude, the estimated mean annual cost of productivity loss due to AMR attributable mortality accounted for 40.6% of the mean annual societal cost, followed by the cost to healthcare providers (24.1%), direct medical cost to patients and caregivers (22.4%), productivity loss for surviving patients and caregivers (10.4%) and direct non-medical costs to patients and caregivers (2.6%). Resistant infections in children under 5 and adults over 60 years contributed 48.2% and 26.9% of the estimated annual societal cost, respectively. Except for the number of resistant infections, the estimated mean annual costs between wealth quintile groups were significantly different (p=0.03) due to differences in productivity costs between wealth quintile groups. The study shows that the societal cost implications attributable to AMR are enormous, requiring a concerted effort by society to mitigate the development and spread of AMR organisms.

Cost-Effectiveness Analysis of Bivalent Respiratory Syncytial Virus Prefusion F (RSVpreF) Maternal Vaccine for the Prevention of RSV Illness Among Infants in Hong Kong.

Investors in emerging ASEAN-6 equity markets face persistent challenges in identifying reliable safe-haven assets to protect portfolios during periods of heightened market volatility. Traditional safe havens, such as gold, may not consistently provide the expected protection in these markets, while the safe-haven role of the US dollar (USD) remains relatively underexplored. This study examines whether the USD and gold function as safe havens, hedges, or diversifiers for ASEAN-6 stock indices and evaluates their effectiveness within portfolio allocation strategies. Using daily data spanning 2013 to 2023, we estimate pairwise time-varying dependence between the USD, gold, and six ASEAN equity markets employing DCC-GARCH and dynamic copula models. The results indicate that the roles of the USD and gold vary across markets and over time. The USD consistently acts as a safe haven and a hedge, whereas gold’s role alternates between that of a hedge and a diversifier depending on market conditions. Portfolio allocation results further show that the USD is generally prioritized over gold, particularly in strategies focused on variance reduction and diversification maximization. Overall, the findings offer important insights for portfolio management and risk mitigation in emerging ASEAN equity markets.