Preparedness components of health systems in the Eastern Mediterranean Region for effective responses to dust and sand storms: a systematic review

Identifying mitigation strategies of comprehensive health centers against dust hazard: a qualitative study in Iran

BACKGROUND

Exposure to dust can disrupt healthcare services and severely affect all activity domains of the health system. The aim of this study was to explore mitigation strategies for comprehensive health centers against dust hazard.

METHOD

The present study was conducted using a qualitative design with a conventional content analysis approach in 2023. The participants in this study were managers and staff of comprehensive health centers and experts in health in disasters and emergencies in Kerman, Bam, Regan, and Ahvaz. Data were collected through interviews. Data collection continued until data saturation. The collected data were analyzed based on the steps proposed by Graneheim and Lundman. Participants' statements, after recording and transcribing, were categorized into semantic units. Data were analyzed by using MAXQDA software version 2020.

RESULTS

The analysis of the data with 23 participants revealed 106 Codes, 13 sub- categories and 5 main categories including: (A) reducing the impact of dust hazards, (B) management functions, (C) empowerment and performance improvement, (D) maintaining and promoting safety, and (E) Inter-sectoral coordination to implement mitigation strategies.

CONCLUSION

The findings showed that the mitigation strategies and solutions can be used by health policymakers and planners to reduce the impact of dust hazard, empower and motivate healthcare staff, develop training protocols to enhance risk perception of the staff and members of the community, create the necessary infrastructure for adoption of effective mitigation strategies in healthcare centers to create resilience and continue service delivery.

Exploring the impact of particulate matter on mortality in coastal Mediterranean environments

Air pollution is one of the most important problems the world is facing nowadays, adversely affecting public health and causing millions of deaths every year. Particulate matter is a criteria pollutant that has been linked to increased morbidity, as well as all-cause and cause-specific mortality. However, this association remains under-investigated in smaller-size cities in the Eastern Mediterranean, which are also frequently affected by heat waves and dust storms. This study explores the impact of particulate matter with an aerodynamic diameter ≤ 10 μm (PM₁₀) and ≤ 2.5 μm (PM_2.5) on mortality (all-cause, cardiovascular, respiratory) in two coastal cities in the Eastern Mediterranean; Thessaloniki, Greece and Limassol, Cyprus. Generalized additive Poisson models were used to explore overall and gender-specific associations, controlling for long- and short-term patterns, day of week and the effect of weather variables. Moreover, the effect of different lags, season, co-pollutants and dust storms on primary associations was investigated. A 10 μg/m³ increase in PM_2.5 resulted in 1.10 % (95 % CI: -0.13, 2.34) increase in cardiovascular mortality in Thessaloniki, and in 3.07 % (95 % CI: -0.90, 7.20) increase in all-cause mortality in Limassol on the same day. Additionally, significant positive associations were observed between PM_2.5 as well as PM₁₀ and mortality at different lags up to seven days. Interestingly, an association with dust storms was observed only in Thessaloniki, having a protective effect, while the gender-specific analysis revealed significant associations only for the males in both cities. The outcome of this study highlights the need of city- or county-specific public health interventions to address the impact of climate, population lifestyle behaviour and other socioeconomic factors that affect the exposure to air pollution and other synergistic effects that alter the effect of PM on population health.

Improved indoor air quality during desert dust storms: The impact of the MEDEA exposure-reduction strategies

Desert dust storms (DDS) are natural events that impact not only populations close to the emission sources but also populations many kilometers away. Countries located across the main dust sources, including countries in the Eastern Mediterranean, are highly affected by DDS. In addition, climate change is expanding arid areas exacerbating DDS events. Currently, there are no intervention measures with proven, quantified exposure reduction to desert dust particles. As part of the wider "MEDEA" project, co-funded by LIFE 2016 Programme, we examined the effectiveness of an indoor exposure-reduction intervention (i.e., decrease home ventilation during DDS events and continuous use of air purifier during DDS and non-DDS days) across homes and/or classrooms of schoolchildren with asthma and adults with atrial fibrillation in Cyprus and Crete-Greece. Participants were randomized to a control or intervention groups, including an indoor intervention group with exposure reduction measures and the use of air purifiers. Particle sampling, PM₁₀ and PM_2.5, was conducted in participants' homes and/or classrooms, between 2019 and 2022, during DDS-free weeks and during DDS days for as long as the event lasted. In indoor and outdoor PM₁₀ and PM_2.5 samples, mass and content in main and trace elements was determined. Indoor PM_2.5 and PM₁₀ mass concentrations, adjusting for premise type and dust conditions, were significantly lower in the indoor intervention group compared to the control group (PM_{2.5-intervention}/PM_2.5-control = 0.57, 95% CI: 0.47, 0.70; PM_{10-intervention}/PM_10-control = 0.59, 95% CI: 0.49, 0.71). In addition, the PM_2.5 and PM₁₀ particles of outdoor origin were significantly lower in the intervention vs. the control group (PM_2.5 infiltration intervention-to-control ratio: 0.49, 95% CI: 0.42, 0.58; PM₁₀ infiltration intervention-to-control ratio: 0.68, 95% CI: 0.52, 0.89). Our findings suggest that the use of air purifiers alongside decreased ventilation measures is an effective protective measure that reduces significantly indoor exposure to particles during DDS and non-DDS in high-risk population groups.

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty

PURPOSE

Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA.

METHODS

A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis.

RESULTS

The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m²; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01).

CONCLUSION

The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning.

LEVEL OF EVIDENCE

IV.

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection

PURPOSE

This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection.

METHODS

A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis.

RESULTS

The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84).

CONCLUSION

This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes.

LEVEL OF EVIDENCE

IV.

The burden of stroke and its attributable risk factors in the Middle East and North Africa region, 1990-2019

Stroke is one of the leading causes of mortality and morbidity across the globe. Providing comprehensive data on the burden of stroke in the Middle East and North Africa (MENA) could be useful for health policy makers in the region. Therefore, this article reported the burden of stroke and its attributable risk factors between 1990 and 2019 by age, sex, type of stroke, and socio-demographic index. Data on the point prevalence, death, and disability-adjusted life-years (DALYs), due to stroke, were retrieved from the Global Burden of Disease study 2019 for the 21 countries located in the MENA region from 1990 to 2019. The counts and age-standardised rates (per 100,000) were presented, along with their corresponding 95% uncertainty intervals (UIs). In 2019, the regional age-standardised point prevalence and death rates of stroke were 1537.5 (95% UI: 1421.9–1659.9) and 87.7 (78.2–97.6) per 100,000, which represent a 0.5% (− 2.3 to 1.1) and 27.8% (− 35.4 to − 16) decrease since 1990, respectively. Moreover, the regional age-standardised DALY rate in 2019 was 1826.2 (1635.3–2026.2) per 100,000, a 32.0% (− 39.1 to − 23.3) decrease since 1990. In 2019, Afghanistan [3498.2 (2508.8–4500.4)] and Lebanon [752.9 (593.3–935.9)] had the highest and lowest age-standardised DALY rates, respectively. Regionally, the total number of stroke cases were highest in the 60–64 age group and was more prevalent in women in all age groups. In addition, there was a general negative association between SDI and the burden of stoke from 1990 to 2019. Also, in 2019, high systolic blood pressure [53.5%], high body mass index [39.4%] and ambient particulate air pollution [27.1%] made the three largest contributions to the burden of stroke in the MENA region. The stroke burden has decreased in the MENA region over the last three decades, although there are large inter-country differences. Preventive programs should be implemented which focus on metabolic risk factors, especially among older females in low SDI countries.