Journal Description
International Journal of Environmental Research and Public Health
International Journal of Environmental Research and Public Health
is a transdisciplinary, peer-reviewed, open access journal published monthly online by MDPI. It covers Global Health, Healthcare Sciences, Behavioral and Mental Health, Infectious Diseases, Chronic Diseases and Disease Prevention, Exercise and Health Related Quality of Life, Environmental Health and Environmental Sciences. The International Society Doctors for the Environment (ISDE) and Italian Society of Environmental Medicine (SIMA) are affiliated with IJERPH and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, PubMed, MEDLINE, PMC, Embase, GEOBASE, CAPlus / SciFinder, and other databases.
- Journal Rank: CiteScore - Q1 (Public Health, Environmental and Occupational Health)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.3 days after submission; acceptance to publication is undertaken in 3.3 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about IJERPH.
- Sections: published in 7 topical sections.
- Companion journal: Air.
Latest Articles
Effects of Different Detraining Periods on the Physical Fitness of Older Adults with Cardiometabolic Risk Factors
Int. J. Environ. Res. Public Health 2024, 21(12), 1550; https://doi.org/10.3390/ijerph21121550 (registering DOI) - 23 Nov 2024
Abstract
Objective: To verify the effects of two different detraining periods on the physical fitness of older adults with cardiometabolic risk factors. Methods: This observational study encompassed older individuals with cardiometabolic risk factors, who were assessed after two different detraining periods: 1 month (1DT)
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Objective: To verify the effects of two different detraining periods on the physical fitness of older adults with cardiometabolic risk factors. Methods: This observational study encompassed older individuals with cardiometabolic risk factors, who were assessed after two different detraining periods: 1 month (1DT) and 3 months (3DT). Physical fitness was assessed using the 30-s sit to stand test (STS), 30-s arm curl, chair sit-and-reach, back scratch, timed up and go, and 6-min walk test (6MWT). The comparison between the different periods was carried out using absolute differences (Δ = posttest-pretest) and relative differences (Δ% = (Δ/pretest) × 100), with α = 0.05. Results: Eight older adults were assessed (70.3 ± 7.48 years, 4 female/4 male). Improvements in the STS (+1.88 repetitions; p = 0.007) and 6MWT (+17.38 m; p = 0.007) were found after 1DT. After 3DT, a worsening was observed in the 6MWT (−26.38 m; p = 0.018). The arm curl test worsened in both detraining periods (1DT: −1.38 repetitions; 3DT: −3.5 repetitions; p = 0.001). When comparing Δ% of 1DT and 3DT, STS and 6MWT showed differences, with p = 0.024 and p = 0.005, respectively. Conclusions: The 1-month detraining period had a positive effect on some physical fitness components, while 3 months induced a decline in cardiorespiratory fitness. Upper limb strength appears to be the component most susceptible to detraining.
Full article
(This article belongs to the Special Issue 2nd Edition: Physical Fitness in an Aged Population)
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Open AccessArticle
Encouraging Continuous Usage of Wearable Activity Trackers: The Interplay of Perceived Severity, Susceptibility and Social Media Influencers
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Anita Lennox, Re-an Müller and Isaac Sewornu Coffie
Int. J. Environ. Res. Public Health 2024, 21(12), 1549; https://doi.org/10.3390/ijerph21121549 - 22 Nov 2024
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While past studies have provided enough evidence to show consumer attitude as a key predictor of the adoption and continuous usage intention of wearable activity trackers (WATs), limited studies have examined the antecedents of consumers’ attitudes towards the adoption and continuous usage intention
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While past studies have provided enough evidence to show consumer attitude as a key predictor of the adoption and continuous usage intention of wearable activity trackers (WATs), limited studies have examined the antecedents of consumers’ attitudes towards the adoption and continuous usage intention of WATs. Drawing on the health belief model and cue utilization theory, the study seeks to examine the influence of perceived severity and vulnerability as antecedents of consumers’ attitudes towards the adoption and continuous usage intention of WATs as well as the role of social media influencers (SMIs) in influencing continuous usage of WATs. Online survey data from 966 participants (Mage = 40.79, STD = 13.49) was analyzed using SPSS 29and AMOS version 29. The result shows that though perceived severity and susceptibility are key significant predictors of consumers’ attitudes towards WATs, the relationship is stronger when SMIs’ personas are used as extrinsic cues. Additionally, while perceived barriers negatively affect consumers’ attitudes towards WATs, the negative effect is neutralized through SMIs’ message framing as an extrinsic cue. Theoretically, the study provides a new insight into the interplay of perceived severity, susceptibility, SMIs’ personas, and message framing on consumers’ attitudes towards the adoption and continuous usage intention of WATs.
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Open AccessReview
Contributions of Medical Greenhouse Gases to Climate Change and Their Possible Alternatives
by
Joyce Wang and Shiladitya DasSarma
Int. J. Environ. Res. Public Health 2024, 21(12), 1548; https://doi.org/10.3390/ijerph21121548 - 22 Nov 2024
Abstract
Considerable attention has recently been given to the contribution of the greenhouse gas (GHG) emissions of the healthcare sector to climate change. GHGs used in medical practice are regularly released into the atmosphere and contribute to elevations in global temperatures that produce detrimental
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Considerable attention has recently been given to the contribution of the greenhouse gas (GHG) emissions of the healthcare sector to climate change. GHGs used in medical practice are regularly released into the atmosphere and contribute to elevations in global temperatures that produce detrimental effects on the environment and human health. Consequently, a comprehensive assessment of their global warming potential over 100 years (GWP) characteristics, and clinical uses, many of which have evaded scrutiny from policy makers due to their medical necessity, is needed. Of major interest are volatile anesthetics, analgesics, and inhalers, as well as fluorinated gases used as tamponades in retinal detachment surgery. In this review, we conducted a literature search from July to September 2024 on medical greenhouse gases and calculated estimates of these gases’ GHG emissions in metric tons CO2 equivalent (MTCO2e) and their relative GWP. Notably, the anesthetics desflurane and nitrous oxide contribute the most emissions out of the major medical GHGs, equivalent to driving 12 million gasoline-powered cars annually in the US. Retinal tamponade gases have markedly high GWP up to 23,500 times compared to CO2 and long atmospheric lifetimes up to 10,000 years, thus bearing the potential to contribute to climate change in the long term. This review provides the basis for discussions on examining the environmental impacts of medical gases with high GWP, determining whether alternatives may be available, and reducing emissions while maintaining or even improving patient care.
Full article
(This article belongs to the Special Issue Climate Change and Medical Responses)
Open AccessReview
Impact of the Quadriceps Angle on Health and Injury Risk in Female Athletes
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Hannah Gant, Nabin Ghimire, Kisuk Min, Ibrahim Musa, Maryam Ashraf and Ahmed Lawan
Int. J. Environ. Res. Public Health 2024, 21(12), 1547; https://doi.org/10.3390/ijerph21121547 - 22 Nov 2024
Abstract
The quadriceps angle, knowns as the Q-angle, is an anatomical feature of the human body that is still largely unknown and unstudied despite its initial discovery in the 1950s. The strength disparities between male and female athletes are largely determined by the Q-angle.
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The quadriceps angle, knowns as the Q-angle, is an anatomical feature of the human body that is still largely unknown and unstudied despite its initial discovery in the 1950s. The strength disparities between male and female athletes are largely determined by the Q-angle. In spite of a growing number of women participating in sports such as track, tennis, soccer, gymnastics, basketball, volleyball, swimming, and softball, studies investigating injuries in this group are scanty. Even though the Q-angle has been the subject of many studies carried out all over the world, a review of the literature regarding its effects on health and injury risk in female athletes has not yet been completed. The aim of this review is to examine the crucial role of the Q-angle in the biomechanics of the knee joint and its effect on performance and injury risk, particularly in female athletes. Furthermore, we highlight the greater likelihood of knee-related injuries seen in female athletes being caused by the Q-angle. Athletes, coaches, healthcare professionals, and athletic trainers can better comprehend and prepare for the benefits and drawbacks resulting from the Q-angle by familiarizing themselves with the research presented in this review.
Full article
(This article belongs to the Section Exercise and Health-Related Quality of Life)
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Open AccessArticle
Phylum Firmicutes in the Faecal Microbiota Demonstrates a Direct Association with Arterial Hypertension in Individuals of the Kazakh Population Without Insulin Resistance
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Gulshara Abildinova, Tamara Vochshenkova, Alisher Aitkaliyev, Aizhan Abildinova, Valeriy Benberin, Anna Borovikova, Nazira Bekenova and Balzhan Kassiyeva
Int. J. Environ. Res. Public Health 2024, 21(12), 1546; https://doi.org/10.3390/ijerph21121546 - 22 Nov 2024
Abstract
The gut microbiota plays a fundamental role in the host’s energy metabolism and the development of metabolic diseases such as arterial hypertension, insulin resistance, and atherosclerosis. Our study aimed to investigate the potential role of the gut microbiota in arterial hypertension among individuals
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The gut microbiota plays a fundamental role in the host’s energy metabolism and the development of metabolic diseases such as arterial hypertension, insulin resistance, and atherosclerosis. Our study aimed to investigate the potential role of the gut microbiota in arterial hypertension among individuals of the Kazakh population without insulin resistance. 16S rRNA gene sequencing of faecal samples from 197 Kazakh subjects was performed. Preliminary binary comparisons of the faecal microbiota composition depending on the presence of arterial hypertension and insulin resistance revealed statistically significant differences in the abundance of the phylum Firmicutes. Logistic regression analysis showed that only the phylum Firmicutes influenced hypertension risk in individuals without insulin resistance after adjusting for age, sex, BMI, fasting glucose, triglycerides, and triglyceride–glucose index. The higher the abundance of the phylum Firmicutes in faeces, the greater the risk of arterial hypertension (OR = 1.064 [95% CI 1.005–1.125]). Correlation analysis revealed a negative association between the abundance of the phylum Firmicutes and the triglyceride–glucose index, primarily driven by triglyceride levels. These findings suggest the potential role of the gut microbiota, especially the phylum Firmicutes, in the development of hypertension in individuals without insulin resistance.
Full article
(This article belongs to the Special Issue New Insights into Understudied Phenomena in Healthcare)
Open AccessArticle
Contextualized Experiences and Predictors of Condom Use in a Flemish Population: A Mixed Methods Study
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Alexis Dewaele, Eva Koppen and Sandra Van den Eynde
Int. J. Environ. Res. Public Health 2024, 21(12), 1545; https://doi.org/10.3390/ijerph21121545 - 21 Nov 2024
Abstract
This study aims to address the gap in understanding condom use (CU) behavior in Flanders (the Dutch-speaking community in Belgium) by applying a mixed methods approach, integrating both quantitative and qualitative data. Utilizing a large-scale survey of over 14,000 participants and 11 in-depth
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This study aims to address the gap in understanding condom use (CU) behavior in Flanders (the Dutch-speaking community in Belgium) by applying a mixed methods approach, integrating both quantitative and qualitative data. Utilizing a large-scale survey of over 14,000 participants and 11 in-depth interviews, the study explores key factors influencing CU, including (amongst others) relationship status, attitudes toward condoms, and STI testing. Quantitative findings highlight significant predictors such as the type of partner (casual vs. steady), STI testing behaviors, and negative attitudes toward condoms. Qualitative insights further reveal personal experiences that complicate CU decisions, such as the disruption of sexual flow and emotional dynamics within relationships. These findings underscore the complexity of CU behavior, showing that practical barriers (e.g., discomfort, fit) and personal beliefs play pivotal roles. The study concludes that targeted public health interventions could focus on improving condom accessibility and addressing both practical and emotional factors. Recommendations for sexual health education include promoting communication around CU and enhancing condom experimentation and fit. These findings contribute valuable insights into enhancing sexual health outcomes through more nuanced, contextualized approaches to condom use.
Full article
Open AccessArticle
Changes in Availability and Affordability on the University Food Environment: The Potential Influence of the COVID-19 Pandemic
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Patrícia Maria Périco Perez, Maria Eduarda Ribeiro José, Isabella Fideles da Silva, Ana Cláudia Mazzonetto and Daniela Silva Canella
Int. J. Environ. Res. Public Health 2024, 21(12), 1544; https://doi.org/10.3390/ijerph21121544 - 21 Nov 2024
Abstract
Background: The COVID-19 pandemic has had an impact on the eating habits of the general population, among other reasons, because it has affected access to commercial establishments since some of them closed. This study aimed to describe potential changes that occurred between 2019
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Background: The COVID-19 pandemic has had an impact on the eating habits of the general population, among other reasons, because it has affected access to commercial establishments since some of them closed. This study aimed to describe potential changes that occurred between 2019 and 2022 in the availability and affordability of food and beverages in the food environment of a Brazilian public university. Methods: Cross-sectional and descriptive study conducted at a public university located in Rio de Janeiro, Brazil. Audits were carried out in all establishments selling food and beverages at the university’s main campus in 2019 and 2022. Descriptive analysis with frequencies and means was carried out and the 95% confidence intervals were compared. Results: Over the period, there was a decrease in the on-campus number of establishments, dropping from 20 to 14, and ultra-processed foods became more prevalent. In general, the decrease in the number of establishments led to a reduction in the supply of fresh or minimally processed foods and beverages, and higher average prices were noted. Conclusions: The pandemic seems to have deteriorated the availability and the prices of healthy food in the university food environment, making healthy choices harder for students and the university community.
Full article
(This article belongs to the Special Issue Bridging the Gap in Studies on the Food Environment: The State-of-the-Art in Low- and Middle-Income Countries (LMICs))
Open AccessArticle
Evaluating the Implementation of Adolescent- and Youth-Friendly Services in the Selected Primary Healthcare Facilities in Vhembe District, Limpopo Province
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Mukovhe Rammela and Lufuno Makhado
Int. J. Environ. Res. Public Health 2024, 21(12), 1543; https://doi.org/10.3390/ijerph21121543 - 21 Nov 2024
Abstract
Background: The adolescent- and youth-friendly services (AYFS) programme has the potential to address several diverse problems within adolescents’ healthcare systems by improving the quality, accessibility, efficiency, and effectiveness of healthcare services. The country continues to suffer from structural and systemic factors that hinder
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Background: The adolescent- and youth-friendly services (AYFS) programme has the potential to address several diverse problems within adolescents’ healthcare systems by improving the quality, accessibility, efficiency, and effectiveness of healthcare services. The country continues to suffer from structural and systemic factors that hinder the effective provision and implementation of AYFS despite its comprehensive legal and policy framework and commitment to enhancing young people’s health. Vhembe District has not been evaluated regarding the implementation of AYFS based on WHO global standards. Therefore, the objective of this study was to evaluate the implementation of AYFS against the World Health Organization (WHO) global standards for quality healthcare services for adolescents to strengthen these services in Vhembe District, Limpopo. Methods: A cross-sectional study was used to evaluate the implementation of AYFS against the WHO global standards for quality healthcare services for adolescents in Vhembe District, Limpopo. Evaluating the implementation of AYFS was conducted through questionnaires distributed to healthcare providers in the selected primary healthcare facilities in Vhembe District. For descriptive statistical analysis, research data were analysed using Statistical Package for the Social Sciences (SPSS). Results: The AYFS have been evaluated in depth across eight WHO global standards for quality health-care services for adolescents, with areas of success and areas for improvement identified. Provider competency reveals a disparity, with a majority (67.0%) of healthcare providers trained in effective communication with adolescents. In comparison, significantly fewer have received specific training in AYFS (16%) or on Pre-Exposure Prophylaxis (PrEP) (25.9%), underscoring the need for a more balanced approach to training focus. Conclusion: Research findings highlight the strengths and gaps of AYFS in Vhembe District, aligned with government and WHO priorities for adolescent health. Addressing the identified gaps is vital to ensuring that healthcare facilities are adolescent- and youth-friendly, easily accessible, and can be implemented effectively to address adolescent and youth health challenges in Vhembe District.
Full article
(This article belongs to the Special Issue Prevention and Management of Sexually Transmitted Disease)
Open AccessArticle
Medicine and Pharmacy Students’ Role in Decreasing Substance Use Disorder Stigma: A Qualitative Study
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Alina Cernasev, Rachel Barenie, Hayleigh Hallam, Kenneth C. Hohmeier and Shandra Forrest
Int. J. Environ. Res. Public Health 2024, 21(12), 1542; https://doi.org/10.3390/ijerph21121542 - 21 Nov 2024
Abstract
Background: A strong body of research has established stigma as a barrier to care for patients with substance use disorders (SUDs), which can lead to poorer patient outcomes. Prior qualitative research on healthcare practitioners’ perceptions is limited. This study aimed to describe healthcare
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Background: A strong body of research has established stigma as a barrier to care for patients with substance use disorders (SUDs), which can lead to poorer patient outcomes. Prior qualitative research on healthcare practitioners’ perceptions is limited. This study aimed to describe healthcare professional students’ perceived roles in decreasing SUD stigma. Methods: A qualitative design using focus groups (FGs) was employed. This study applied the stigma conceptualization approach by Link and Phelan to develop the FG guide, including labeling, stereotyping, separation, status loss, and discrimination. These components are linked to the construction of cognitive categories that lead to stereotyped beliefs. The FG participants were graduate-level healthcare students recruited via email from the University of Tennessee Health Science Center (UTHSC). The research team analyzed the transcripts using Braun and Clarke’s approach to identify emergent themes in the data. Dedoose® Version 9.0.107, a qualitative data analysis software platform, was utilized to facilitate data manipulation and retrieval during the analysis. Steps were taken to ensure the reliability of the qualitative data using Lincoln and Guba’s criteria. Results: Among thirty-one pharmacy and medical student participants, three themes emerged from the data: (1) student recognition of stigma, (2) the role of healthcare professionals in harm reduction, and (3) calls to enhance advocacy efforts to improve patient outcomes. These themes collectively encompass key members of the healthcare team’s perceptions and solutions to SUD stigma. Conclusions: This research reveals the importance of expanding training opportunities to go beyond the SUD disease state, to other evidence-based approaches such as effective advocacy, harm reduction, and stigma, which impact the delivery of that care.
Full article
(This article belongs to the Special Issue Substance Use Research Methods: Ethics, Culture, and Health Equity)
Open AccessArticle
Exposure to Environmental Chemicals and Infertility Among US Reproductive-Aged Women
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Valerie Martinez, Irene H. Yen, Camila Alvarez, Andrew D. Williams and Sandie Ha
Int. J. Environ. Res. Public Health 2024, 21(12), 1541; https://doi.org/10.3390/ijerph21121541 - 21 Nov 2024
Abstract
Environmental chemical exposure has been rising over the past few decades but its impact on fertility remains uncertain. We assessed exposures to 23 common chemicals across a range of sociodemographic characteristics and their relationship with self-reported infertility. The analytic sample was non-pregnant women
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Environmental chemical exposure has been rising over the past few decades but its impact on fertility remains uncertain. We assessed exposures to 23 common chemicals across a range of sociodemographic characteristics and their relationship with self-reported infertility. The analytic sample was non-pregnant women aged 18–49 years without a history of hysterectomy or oophorectomy (n = 2579) from the National Health and Nutrition Examination Survey (2013–2016). Environmental chemical exposure was assessed with biospecimens and dichotomized as high and low levels of exposure based on the median. Logistic regression models estimated the adjusted odds ratio (aOR) and 95% confidence intervals (CIs) for the association between high levels of exposure and infertility, adjusted for age, race, education level, family income, and smoking status. We observed associations between infertility and cadmium [aOR: 1.88; 95% CI: 1.02–3.47] and arsenic [aOR: 1.88 (1.05–3.36)]. Two pesticides hexachlorobenzene [OR: 2.04 (1.05–3.98)] and oxychlordane [OR: 2.04 (1.12–3.69)] were also associated with infertility in unadjusted analyses. There were negative associations with two Per- and polyfluoroalkyl substances with n-perfluorooctanoic acid [aOR: 0.51: (0.30–0.86)] and n-perfluorooctane sulfonic acid [aOR: 0.51: (0.26–0.97). Specific chemicals may contribute to infertility risk, highlighting the need for targeted public health strategies to mitigate exposure.
Full article
(This article belongs to the Special Issue Sexual, Reproductive and Maternal Health)
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Identifying and Ranking Strategies to Address Housing Insecurity and Homelessness Within the LGBTQIA+ Community in Southern Nevada: Utilization of Community-Based Participatory Research and Concept Mapping
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Emylia Terry, Jennifer Pharr, Renato M. Liboro, Courtney Coughenour, Krystal Kittle, John Waldron and Jason D. Flatt
Int. J. Environ. Res. Public Health 2024, 21(12), 1540; https://doi.org/10.3390/ijerph21121540 - 21 Nov 2024
Abstract
Housing insecurity is a critical issue within Southern Nevada. However, little is known about the housing-insecurity-related needs of LGBTQIA+ Southern Nevadans. The aim of this study was to identify strategies to address housing insecurity among this vulnerable community. Utilizing community-based participatory research and
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Housing insecurity is a critical issue within Southern Nevada. However, little is known about the housing-insecurity-related needs of LGBTQIA+ Southern Nevadans. The aim of this study was to identify strategies to address housing insecurity among this vulnerable community. Utilizing community-based participatory research and concept mapping, the most salient solutions were identified and prioritized at a Community Housing Forum. This Forum brought together stakeholders with expertise in housing or who work with the LGBTQIA+ community. The most important identified solutions consistently emphasized the criticality of culturally competent mental health services; the need for affordable housing options; and various social and environmental factors. There is a continued need for research and collaboration among organizations and providers to better serve LGBTQIA+ individuals experiencing housing insecurity. Additional research is needed to determine the efficacy of the identified solutions and to inform the development of context-specific and broadly applicable strategies to address housing insecurity within this community.
Full article
(This article belongs to the Special Issue Built Environment and Public Health: Land Use, Neighborhoods, and Housing)
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Open AccessReview
Parenting Interventions to Prevent and Reduce Physical Punishment: A Scoping Review
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Isabel Garces-Davila, Ashley Stewart-Tufescu, Janice Linton, Julie-Anne McCarthy, Sonya Gill, Aleksandra Ciochon Newton, Samantha Salmon, Tamara Taillieu and Tracie O. Afifi
Int. J. Environ. Res. Public Health 2024, 21(11), 1539; https://doi.org/10.3390/ijerph21111539 - 20 Nov 2024
Abstract
Physical punishment is the most common form of violence against children worldwide and is associated with an increased risk of long-term adverse outcomes. Interventions targeting parents/caregivers are frequently implemented to prevent and reduce the use of physical punishment. This scoping review aimed to
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Physical punishment is the most common form of violence against children worldwide and is associated with an increased risk of long-term adverse outcomes. Interventions targeting parents/caregivers are frequently implemented to prevent and reduce the use of physical punishment. This scoping review aimed to map the existing literature on evidence-informed parenting interventions targeting physical punishment. A scoping review following the World Health Organization (WHO) Review Guide, the Joanna Briggs Institute (JBI) 2020 Guide for scoping reviews, was conducted to address the objective of this review. An academic health sciences librarian systematically searched electronic databases (EBSCO, MEDLINE, EMBASE, SCOPUS) for peer-reviewed journal articles. Two reviewers independently screened titles and abstracts, followed by a full-text review according to inclusion and exclusion criteria following the Participants, Concept, and Context framework. Eighty-one studies were included for full-text eligibility. The results suggest that most interventions examined were conducted in North America, targeted mothers and fathers, and were delivered in person. The results from this scoping review describe the state of evidence-informed parenting interventions to prevent and reduce physical punishment. This review found opportunities for future research to implement effective parenting interventions on a larger societal scale and use mixed methods approaches to evaluate parenting interventions.
Full article
(This article belongs to the Special Issue Childhood Violence: Risks, Consequences, and Prevention Strategies)
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Open AccessArticle
Spatial and Temporal Analysis of Hospitalizations Due to Primary Care–Sensitive Conditions Related to Diabetes Mellitus in a State in the Northeast of Brazil
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Afonso Abreu Mendes Júnior, Álvaro Francisco Lopes de Sousa, Guilherme Reis de Santana Santos, Shirley Verônica Melo Almeida Lima, Allan Dantas dos Santos, Valdemar Silva Almeida, Ernanes Menezes dos Santos, Maria Idelcacia Nunes Oliveira, José Cleyton Santana Góis, Regina Cláudia Silva Souza, Liliane Moretti Carneiro, Maria do Carmo de Oliveira, Emerson Lucas Silva Camargo and Caíque Jordan Nunes Ribeiro
Int. J. Environ. Res. Public Health 2024, 21(11), 1538; https://doi.org/10.3390/ijerph21111538 - 20 Nov 2024
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Hospitalizations due to primary care–sensitive conditions (PCSCs) can be considered a proxy for the effectiveness of primary healthcare (PHC), especially diabetes mellitus (DM). The aim of this study was to analyze the temporal, spatial, and space–time patterns of PCSCs associated with DM in
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Hospitalizations due to primary care–sensitive conditions (PCSCs) can be considered a proxy for the effectiveness of primary healthcare (PHC), especially diabetes mellitus (DM). The aim of this study was to analyze the temporal, spatial, and space–time patterns of PCSCs associated with DM in a state in Northeast Brazil from 2008 to 2022. An ecological and time–series study that included all records related to PCSCs–DM from the 75 municipalities of Sergipe was conducted. Segmented linear regression, global (I) and local (LISA) Moran indices, spatial scanning, Spearman correlation tests, bivariate I, and LISA were used in our analysis to examine the temporal trends and clusters of high spatial risk. Overall, 14,390 PCSCs–DM were recorded between 2008 and 2022. There was a higher prevalence of PCSCs–DM among women (53.75%) and individuals over 70 years old (57.60%). Temporal trends in PCSCs–DM were increasing with regard to the overall rate (AAPC: 2.39); males (AAPC: 3.15); age groups ≤ 19 years (AAPC: 6.13), 20–39 years (AAPC: 4.50), and 40–59 years (AAPC: 2.56); and 3 out of the 7 health regions. There was a positive spatial correlation between per capita income (I = −0.23; p = 0.004) and diabetic foot examination being performed by a nurse in a PHC (I = −0.18; p = 0.019) setting. The heterogeneous spatial distribution of DM hospitalizations demonstrated that this condition is a persistent public health problem in Sergipe.
Full article
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<p>Temporal trends in PCSCs–DM by state, gender, and age group for Sergipe, Northeast, Brazil, 2008–2022. (<b>A</b>): Entire state. (<b>B</b>): Male. (<b>C</b>): Female. (<b>D</b>): Age group: <19 years. (<b>E</b>): Age group: 20 to 39 years. (<b>F</b>): Age group: 40 to 59 years. (<b>G</b>): Age group: ≥60 years.</p> Full article ">Figure 2
<p>Spatial distribution of PCSCs–DM. Sergipe, Northeast, Brazil, 2008–2022. (<b>A</b>): Smoothed PCSCs–DM (%). (<b>B</b>): Moran map (Univariate LISA). (<b>C</b>): Relative risk (spatial scan analysis). (<b>D</b>): Per capita income (R<span>$</span>). (<b>E</b>): Moran map per capita income (univariate LISA). (<b>F</b>): Moran map per capita income (bivariate LISA). (<b>G</b>): Nurse examination rate (%). (<b>H</b>): Moran map nurse examination rate (univariate LISA). (<b>I</b>): Moran map nurse examination rate (bivariate LISA).</p> Full article ">
<p>Temporal trends in PCSCs–DM by state, gender, and age group for Sergipe, Northeast, Brazil, 2008–2022. (<b>A</b>): Entire state. (<b>B</b>): Male. (<b>C</b>): Female. (<b>D</b>): Age group: <19 years. (<b>E</b>): Age group: 20 to 39 years. (<b>F</b>): Age group: 40 to 59 years. (<b>G</b>): Age group: ≥60 years.</p> Full article ">Figure 2
<p>Spatial distribution of PCSCs–DM. Sergipe, Northeast, Brazil, 2008–2022. (<b>A</b>): Smoothed PCSCs–DM (%). (<b>B</b>): Moran map (Univariate LISA). (<b>C</b>): Relative risk (spatial scan analysis). (<b>D</b>): Per capita income (R<span>$</span>). (<b>E</b>): Moran map per capita income (univariate LISA). (<b>F</b>): Moran map per capita income (bivariate LISA). (<b>G</b>): Nurse examination rate (%). (<b>H</b>): Moran map nurse examination rate (univariate LISA). (<b>I</b>): Moran map nurse examination rate (bivariate LISA).</p> Full article ">
Open AccessArticle
Prevalence and Determinants of Household Self-Reported Diabetes Mellitus in Gauteng, South Africa
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Shoeshoe Mokhele and Tholang Mokhele
Int. J. Environ. Res. Public Health 2024, 21(11), 1537; https://doi.org/10.3390/ijerph21111537 - 20 Nov 2024
Abstract
Diabetes mellitus is one of the leading causes of morbidity and mortality worldwide. Type 2 diabetes mellitus is the most prevalent type of diabetes mellitus, and it is associated with both hereditary and lifestyle risk factors. South Africa is not exempt from this
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Diabetes mellitus is one of the leading causes of morbidity and mortality worldwide. Type 2 diabetes mellitus is the most prevalent type of diabetes mellitus, and it is associated with both hereditary and lifestyle risk factors. South Africa is not exempt from this pandemic; hence, this paper aims to assess the prevalence and determinants of household self-reported diabetes mellitus in Gauteng, South Africa. Data were sourced from the Gauteng City-Region Observatory (GCRO) quality of life survey (2020/2021). Bivariate and multivariate logistic regressions were applied. The prevalence of household self-reported diabetes mellitus in Gauteng was 11.1%. The ‘other population’ group (which included Whites, Coloureds and Indians), as well as older respondents, higher household monthly food expenditure, poor self-perceived health status and household self-reported hypertension were factors that increased the odds of household self-reported diabetes mellitus. Only informal housing decreased the odds of household self-reported diabetes mellitus. Screening of diabetes mellitus among those with poor living conditions, no medical aid and lack of access to healthcare facilities such as Gauteng township and informal settlement residents should be intensified. This secondary disease prevention intervention is crucial, as it will enhance the appropriate referrals and timeous chronic treatment for those with diabetes mellitus.
Full article
(This article belongs to the Special Issue Diabetes Care: Prevention, Diagnosis and Treatment)
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Open AccessArticle
The Prevalence of Childhood Asthma, Respiratory Symptoms and Associated Air Pollution Sources Among Adolescent Learners in Selected Schools in Vhembe District, South Africa
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Funzani Rathogwa-Takalani, Thabelo Rodney Mudau, Sean Patrick, Joyce Shirinde and Kuku Voyi
Int. J. Environ. Res. Public Health 2024, 21(11), 1536; https://doi.org/10.3390/ijerph21111536 - 20 Nov 2024
Abstract
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This study investigated the prevalence of childhood asthma and respiratory symptoms with their associated air pollution sources among adolescents aged 13–14 years residing in a Malaria-endemic region. Methods: A cross-sectional survey was conducted with 2855 adolescents from fourteen (14) selected schools in communities
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This study investigated the prevalence of childhood asthma and respiratory symptoms with their associated air pollution sources among adolescents aged 13–14 years residing in a Malaria-endemic region. Methods: A cross-sectional survey was conducted with 2855 adolescents from fourteen (14) selected schools in communities exposed to high levels of air pollution from indoor residual spraying (IRS) that is used for malaria vector control in the Vhembe region. Data were collected using a self-administered standardized International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Statistical software STATA version 17 was used to analyze the data. Binary logistic regression was used to determine the relationship between air pollution sources and childhood asthma/symptoms. Results: The prevalences of asthma, ‘wheeze ever’ and ‘wheeze in the past’ were 18.91%, 37.69% and 24.69%, respectively. The results from the adjusted binary logistic regression model indicated that exposure to tobacco smoke (OR = 1.84; 95% CI: 1.08–3.16), smoking a water pipe (OR = 1.65; 95% CI: 1.16–2.36) and the use of paraffin as fuel for heating (OR = 1.70; 95% CI: 0.97–2.88) and cooking (OR = 0.48; 95% CI: 0.29–1.00) were significant risk factors for asthma. Trucks passing through the streets, having a cat at home and using open fires were significantly associated with ‘wheeze in the past’. Finally, using gas for cooking (OR = 0.72; 95% CI: 0.53–0.99), open fires for heating (OR = 0.53; 95% CI: 0.35–0.80) and smoking a water pipe (OR = 2.47; 95% CI: 1.78–3.44) were associated with ‘wheeze ever’. Conclusions: School children living in these communities had an increased risk of developing asthma and presenting with wheezing due to exposure to environmental air pollution sources.
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Open AccessArticle
Older Autistic People, Access and Experiences of Services, and the Factors That Affect This
by
Marion Hersh, Panda Mery and Michael Dawson
Int. J. Environ. Res. Public Health 2024, 21(11), 1535; https://doi.org/10.3390/ijerph21111535 - 19 Nov 2024
Abstract
This paper presents new empirical data obtained from interviews and focus groups on older (50 and over) autistic people’s experiences of accessing a variety of services. The involvement of older autistic people and giving voice to their experiences was central to all aspects
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This paper presents new empirical data obtained from interviews and focus groups on older (50 and over) autistic people’s experiences of accessing a variety of services. The involvement of older autistic people and giving voice to their experiences was central to all aspects of the research process. This work makes a significant contribution to the scarce literature on older autistic people. In particular, it discusses the factors that act as barriers and enablers to the access to and productive use of services, the strategies used by participants to manage and improve their experiences, and the success of these strategies. It shows older autistic people as autonomous adults and active protagonists in their own lives, taking action to overcome the barriers they experience to accessing services on the same terms as everyone else, but that lack of understanding and support from service providers and the general public can undermine their strategies. Finally, this work provides a series of recommendations for service providers to improve (older) autistic people’s service access and experiences.
Full article
(This article belongs to the Special Issue Rising to the Healthy Ageing Challenge: Co-production with Older People and Business)
Open AccessArticle
Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri
by
Butros M. Dahu, Carlos I. Martinez-Villar, Imad Eddine Toubal, Mariam Alshehri, Anes Ouadou, Solaiman Khan, Lincoln R. Sheets and Grant J. Scott
Int. J. Environ. Res. Public Health 2024, 21(11), 1534; https://doi.org/10.3390/ijerph21111534 - 19 Nov 2024
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This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on
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This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC’s 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts.
Full article
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<p>Flowchart illustrating the estimation of obesity rates from satellite imagery using a combination of deep learning with ResNet-50 architecture and machine learning regression analysis.</p> Full article ">Figure 2
<p>Data processing workflow for Sentinel-2 satellite imagery within Missouri in 2022. (<b>A</b>) Displays the geographic coverage of 33 Sentinel-2 images across Missouri, with county boundaries. The central diagram outlines the normalization process and the cropping of images into 224 × 224 pixel chips. (<b>B</b>) Illustrates the distribution of 82,500 resultant image chips. The red box represents Mid-Missouri area and Boone County.</p> Full article ">Figure 3
<p>Choropleth map displaying the distribution of obesity rates percentage for individuals across Missouri census tracts in 2022. The variations in the color intensity reflect the range of obesity prevalence, with darker red indicating higher obesity rates. The color scale to the right quantifies the obesity rates corresponding to each color shade.</p> Full article ">Figure 4
<p>Multiscale analysis of satellite image chips and census tracts in Missouri. (<b>A</b>) exhibits a statewide view with image chips overlaying 1052 census tracts, indicating extensive data coverage. (<b>B</b>) zooms into the Boone County area, detailing the alignment of image chips to local geography. (<b>C</b>) details individual image chips boundaries, illustrating their overlap with seven distinct census tracts (numbered for reference). (<b>D</b>) further narrows down to Census Tract 0608, demonstrating the intersection with 150 specific image chips for granular analysis. The figure highlights the granularity and density of data distribution within the geographic study area.</p> Full article ">Figure 5
<p>Scatter plots show the relationship between actual and predicted obesity rates using a GLM machine learning model across 10 distinct cross-validation folds. The red dashed line represents perfect prediction accuracy.</p> Full article ">Figure 6
<p>Scatter plot displaying the relationship between actual and predicted obesity rates using Generalized Linear Regression (GLM), illustrating a moderate degree of correlation with an <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> value of 0.44 and an adjusted <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> of 0.43. The close fit is further evidenced by a moderate Mean Squared Error (MSE) of 18.64. These metrics are provided to assess the accuracy of the model predictions.</p> Full article ">Figure 7
<p>Scatter plot of the relationship between actual and predicted obesity rates using random forest, illustrating a moderate correlation with an <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> value of 0.48 and an adjusted <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> of 0.47. The close fit is further evidenced by a moderate Mean Squared Error (MSE) of 17.35.</p> Full article ">Figure 8
<p>The left map shows spatial distribution of Feature 1112 across Missouri, with red circles highlighting areas of high values (urban areas). The right map depicts actual obesity rates (%) across the state, with blue circles indicating regions with lower obesity prevalence. Notable discrepancies between feature values and obesity rates can be observed in several regions.</p> Full article ">Figure 9
<p>The spatial distribution of the 2nd and 3rd most important features across Missouri, (<b>left</b>) Feature 0095th and (<b>right</b>) Feature 1314. Urban areas, particularly around Kansas City for Feature 0095 and St. Louis for Feature 1314, show significant concentrations of these features.</p> Full article ">Figure 10
<p>Spatial distribution of the 4th and 5th most important features across Missouri, (<b>left</b>) Feature 0767 and (<b>right</b>) Feature 0239.</p> Full article ">Figure 11
<p>Geospatial analysis of obesity rates and prediction accuracy in Missouri. (<b>A</b>) displays the actual obesity rates, while (<b>B</b>) shows the predicted rates, both using a color gradient to represent percentages. (<b>C</b>) highlights areas with significant predictive errors by filtering out RMSE values below 4, focusing on regions where the model’s accuracy is lower. (<b>D</b>) refines this analysis by presenting a broader error distribution, including RMSE values of 2.5 and above, using the same color gradient for consistency. (<b>E</b>) Curve illustrating the signed error distribution of predicted obesity rates across census tracts. Negative signed errors indicate underpredictions (shown in red) and positive errors indicate overpredictions (shown in green). The census tracts are ranked by the magnitude of error, highlighting the asymmetry in predictive accuracy and potential systematic bias in the model.</p> Full article ">Figure A1
<p>Bar chart displays the top 10 visual features ranked by their importance as used in our obesity rates prediction model. The x-axis represents the feature numbers, which are specific identifiers for each visual feature. The y-axis indicates the importance of each feature in percentage terms.</p> Full article ">
<p>Flowchart illustrating the estimation of obesity rates from satellite imagery using a combination of deep learning with ResNet-50 architecture and machine learning regression analysis.</p> Full article ">Figure 2
<p>Data processing workflow for Sentinel-2 satellite imagery within Missouri in 2022. (<b>A</b>) Displays the geographic coverage of 33 Sentinel-2 images across Missouri, with county boundaries. The central diagram outlines the normalization process and the cropping of images into 224 × 224 pixel chips. (<b>B</b>) Illustrates the distribution of 82,500 resultant image chips. The red box represents Mid-Missouri area and Boone County.</p> Full article ">Figure 3
<p>Choropleth map displaying the distribution of obesity rates percentage for individuals across Missouri census tracts in 2022. The variations in the color intensity reflect the range of obesity prevalence, with darker red indicating higher obesity rates. The color scale to the right quantifies the obesity rates corresponding to each color shade.</p> Full article ">Figure 4
<p>Multiscale analysis of satellite image chips and census tracts in Missouri. (<b>A</b>) exhibits a statewide view with image chips overlaying 1052 census tracts, indicating extensive data coverage. (<b>B</b>) zooms into the Boone County area, detailing the alignment of image chips to local geography. (<b>C</b>) details individual image chips boundaries, illustrating their overlap with seven distinct census tracts (numbered for reference). (<b>D</b>) further narrows down to Census Tract 0608, demonstrating the intersection with 150 specific image chips for granular analysis. The figure highlights the granularity and density of data distribution within the geographic study area.</p> Full article ">Figure 5
<p>Scatter plots show the relationship between actual and predicted obesity rates using a GLM machine learning model across 10 distinct cross-validation folds. The red dashed line represents perfect prediction accuracy.</p> Full article ">Figure 6
<p>Scatter plot displaying the relationship between actual and predicted obesity rates using Generalized Linear Regression (GLM), illustrating a moderate degree of correlation with an <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> value of 0.44 and an adjusted <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> of 0.43. The close fit is further evidenced by a moderate Mean Squared Error (MSE) of 18.64. These metrics are provided to assess the accuracy of the model predictions.</p> Full article ">Figure 7
<p>Scatter plot of the relationship between actual and predicted obesity rates using random forest, illustrating a moderate correlation with an <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> value of 0.48 and an adjusted <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> of 0.47. The close fit is further evidenced by a moderate Mean Squared Error (MSE) of 17.35.</p> Full article ">Figure 8
<p>The left map shows spatial distribution of Feature 1112 across Missouri, with red circles highlighting areas of high values (urban areas). The right map depicts actual obesity rates (%) across the state, with blue circles indicating regions with lower obesity prevalence. Notable discrepancies between feature values and obesity rates can be observed in several regions.</p> Full article ">Figure 9
<p>The spatial distribution of the 2nd and 3rd most important features across Missouri, (<b>left</b>) Feature 0095th and (<b>right</b>) Feature 1314. Urban areas, particularly around Kansas City for Feature 0095 and St. Louis for Feature 1314, show significant concentrations of these features.</p> Full article ">Figure 10
<p>Spatial distribution of the 4th and 5th most important features across Missouri, (<b>left</b>) Feature 0767 and (<b>right</b>) Feature 0239.</p> Full article ">Figure 11
<p>Geospatial analysis of obesity rates and prediction accuracy in Missouri. (<b>A</b>) displays the actual obesity rates, while (<b>B</b>) shows the predicted rates, both using a color gradient to represent percentages. (<b>C</b>) highlights areas with significant predictive errors by filtering out RMSE values below 4, focusing on regions where the model’s accuracy is lower. (<b>D</b>) refines this analysis by presenting a broader error distribution, including RMSE values of 2.5 and above, using the same color gradient for consistency. (<b>E</b>) Curve illustrating the signed error distribution of predicted obesity rates across census tracts. Negative signed errors indicate underpredictions (shown in red) and positive errors indicate overpredictions (shown in green). The census tracts are ranked by the magnitude of error, highlighting the asymmetry in predictive accuracy and potential systematic bias in the model.</p> Full article ">Figure A1
<p>Bar chart displays the top 10 visual features ranked by their importance as used in our obesity rates prediction model. The x-axis represents the feature numbers, which are specific identifiers for each visual feature. The y-axis indicates the importance of each feature in percentage terms.</p> Full article ">
Open AccessArticle
The Disaster of the Century: Effects of the 6 February 2023 Kahramanmaraş Earthquakes on the Sleep and Mental Health of Healthcare Workers
by
Sema Çifçi and Zehra Kilinç
Int. J. Environ. Res. Public Health 2024, 21(11), 1533; https://doi.org/10.3390/ijerph21111533 - 19 Nov 2024
Abstract
It is known that disasters can have long-term effects on the mental health of individuals. In particular, healthcare workers may be under greater stress in a time of disaster, as they are not only affected by the disaster, but they also take part
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It is known that disasters can have long-term effects on the mental health of individuals. In particular, healthcare workers may be under greater stress in a time of disaster, as they are not only affected by the disaster, but they also take part in rescue efforts. This study was conducted to investigate the psychological effects of the Kahramanmaraş earthquakes on healthcare workers employed at the Adıyaman Training and Research Hospital. The sample in the cross-sectional study consisted of 299 healthcare personnel working in the Adıyaman Training and Research Hospital. The study data were collected through a questionnaire consisting of five sections. The data were analysed using SPSS 22 software. It was found that among healthcare workers, those who were women, married, individuals whose homes were damaged, injured, or lost a relative in the earthquake had experienced mental health problems such as post-traumatic stress disorder (PTSD), anxiety, depression, and poor sleep quality the most. The earthquakes that occurred on 6 February negatively affected the mental health of healthcare workers. In order to reduce these negative effects experienced by healthcare workers, various types of mental health screening should be performed, and supportive psychological services should be provided urgently.
Full article
(This article belongs to the Special Issue Promotion of Care and Psychological Well-Being for Healthcare Workers)
Open AccessSystematic Review
The Association Between Cadmium Exposure and Prostate Cancer: An Updated Systematic Review and Meta-Analysis
by
Giorgio Firmani, Manuela Chiavarini, Jacopo Dolcini, Stefano Quarta, Marcello Mario D’Errico and Pamela Barbadoro
Int. J. Environ. Res. Public Health 2024, 21(11), 1532; https://doi.org/10.3390/ijerph21111532 - 19 Nov 2024
Abstract
Prostate cancer (PCa) is a common cancer among men, and it has a multifactorial etiology. Cadmium (Cd), a toxic heavy metal classified as a carcinogen by the IARC, can cause various acute and chronic effects. This systematic review and meta-analysis aims to update
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Prostate cancer (PCa) is a common cancer among men, and it has a multifactorial etiology. Cadmium (Cd), a toxic heavy metal classified as a carcinogen by the IARC, can cause various acute and chronic effects. This systematic review and meta-analysis aims to update previous findings on the association between Cd exposure and PCa. We carried out a literature search in PubMed, Web of Science, and Scopus up to May 2024, identifying eight new articles. The effect size from the highest and lowest exposure categories were extracted and analyzed using a random-effects model. Heterogeneity was assessed with the I2 test, and publication bias was evaluated using funnel plot asymmetry. Overall, the effect size for PCa associated with Cd exposure, combining previous and new articles, was 1.11 (95% CI 0.85–1.45). Heterogeneity was high, but no significant publication bias was detected. For studies from Europe, the effect size was 1.47 (95% CI 1.00–2.17). Notably, 11 articles from the previous systematic review and meta-analysis highlighted that higher Cd exposure is significantly associated with more aggressive histopathological types of PCa (OR 1.50, 95% CI 1.08–2.07). These findings suggest a potential public health concern, indicating the need for further research to clarify the risk associated with Cd exposure.
Full article
(This article belongs to the Special Issue Relationship between Environmental Risk Factors and Cancer)
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<p>Flow diagram of the systematic literature search on cadmium (Cd) exposure and prostate cancer (PCa). <sup>a</sup> [<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>]; <sup>b</sup> [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>,<a href="#B41-ijerph-21-01532" class="html-bibr">41</a>,<a href="#B42-ijerph-21-01532" class="html-bibr">42</a>,<a href="#B43-ijerph-21-01532" class="html-bibr">43</a>,<a href="#B44-ijerph-21-01532" class="html-bibr">44</a>]; <sup>c</sup> [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>,<a href="#B44-ijerph-21-01532" class="html-bibr">44</a>]; <sup>d</sup> [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>]. From Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71. <a href="http://doi:10.113.6/bmj.n7" target="_blank">doi:10.113.6/bmj.n7</a>.</p> Full article ">Figure 2
<p>Forest plot of the association between Cd exposure and PCa considering (<b>1</b>) all articles [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>], (<b>2</b>) only articles [<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>] from the previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>], and (<b>3</b>) only new ones [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>].</p> Full article ">Figure 3
<p>Forest plot of the association between Cd exposure and PCa considering type of exposure: (<b>A</b>) dietary, (<b>B</b>) environmental, and (<b>C</b>) occupational. For each type of exposure, the forest plots are reported by (<b>1</b>) all articles [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>], (<b>2</b>) only articles [<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>] from the previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>], and (<b>3</b>) only new ones [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>].</p> Full article ">Figure 4
<p>Funnel plot of publication bias of the association between Cd exposure and PCa. (<b>a</b>) The 19 articles from a previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>] and the new review, (<b>b</b>) 11 articles from a previous systematic review and meta-analysis, (<b>c</b>) 8 articles from the new one.</p> Full article ">Figure 4 Cont.
<p>Funnel plot of publication bias of the association between Cd exposure and PCa. (<b>a</b>) The 19 articles from a previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>] and the new review, (<b>b</b>) 11 articles from a previous systematic review and meta-analysis, (<b>c</b>) 8 articles from the new one.</p> Full article ">
<p>Flow diagram of the systematic literature search on cadmium (Cd) exposure and prostate cancer (PCa). <sup>a</sup> [<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>]; <sup>b</sup> [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>,<a href="#B41-ijerph-21-01532" class="html-bibr">41</a>,<a href="#B42-ijerph-21-01532" class="html-bibr">42</a>,<a href="#B43-ijerph-21-01532" class="html-bibr">43</a>,<a href="#B44-ijerph-21-01532" class="html-bibr">44</a>]; <sup>c</sup> [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>,<a href="#B44-ijerph-21-01532" class="html-bibr">44</a>]; <sup>d</sup> [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>]. From Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71. <a href="http://doi:10.113.6/bmj.n7" target="_blank">doi:10.113.6/bmj.n7</a>.</p> Full article ">Figure 2
<p>Forest plot of the association between Cd exposure and PCa considering (<b>1</b>) all articles [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>], (<b>2</b>) only articles [<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>] from the previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>], and (<b>3</b>) only new ones [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>].</p> Full article ">Figure 3
<p>Forest plot of the association between Cd exposure and PCa considering type of exposure: (<b>A</b>) dietary, (<b>B</b>) environmental, and (<b>C</b>) occupational. For each type of exposure, the forest plots are reported by (<b>1</b>) all articles [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>], (<b>2</b>) only articles [<a href="#B16-ijerph-21-01532" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01532" class="html-bibr">17</a>,<a href="#B25-ijerph-21-01532" class="html-bibr">25</a>,<a href="#B26-ijerph-21-01532" class="html-bibr">26</a>,<a href="#B27-ijerph-21-01532" class="html-bibr">27</a>,<a href="#B28-ijerph-21-01532" class="html-bibr">28</a>,<a href="#B29-ijerph-21-01532" class="html-bibr">29</a>,<a href="#B30-ijerph-21-01532" class="html-bibr">30</a>,<a href="#B31-ijerph-21-01532" class="html-bibr">31</a>,<a href="#B32-ijerph-21-01532" class="html-bibr">32</a>,<a href="#B33-ijerph-21-01532" class="html-bibr">33</a>] from the previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>], and (<b>3</b>) only new ones [<a href="#B15-ijerph-21-01532" class="html-bibr">15</a>,<a href="#B34-ijerph-21-01532" class="html-bibr">34</a>,<a href="#B35-ijerph-21-01532" class="html-bibr">35</a>,<a href="#B36-ijerph-21-01532" class="html-bibr">36</a>,<a href="#B37-ijerph-21-01532" class="html-bibr">37</a>,<a href="#B38-ijerph-21-01532" class="html-bibr">38</a>,<a href="#B39-ijerph-21-01532" class="html-bibr">39</a>,<a href="#B40-ijerph-21-01532" class="html-bibr">40</a>].</p> Full article ">Figure 4
<p>Funnel plot of publication bias of the association between Cd exposure and PCa. (<b>a</b>) The 19 articles from a previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>] and the new review, (<b>b</b>) 11 articles from a previous systematic review and meta-analysis, (<b>c</b>) 8 articles from the new one.</p> Full article ">Figure 4 Cont.
<p>Funnel plot of publication bias of the association between Cd exposure and PCa. (<b>a</b>) The 19 articles from a previous systematic review and meta-analysis [<a href="#B24-ijerph-21-01532" class="html-bibr">24</a>] and the new review, (<b>b</b>) 11 articles from a previous systematic review and meta-analysis, (<b>c</b>) 8 articles from the new one.</p> Full article ">
Open AccessSystematic Review
What Is the Impact of Leaders with Emotional Intelligence on Proxy Performance Metrics in 21st Century Healthcare?—A Systematic Literature Review
by
Aisha Chaudry, Parisah Maham Hussain, Simran Halari, Sohini Thakor, Aran Sivapalan, Abdul Ikar, Terrell Okhiria and Edgar Meyer
Int. J. Environ. Res. Public Health 2024, 21(11), 1531; https://doi.org/10.3390/ijerph21111531 - 18 Nov 2024
Abstract
►▼
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Emotional intelligence (EQ) in healthcare leadership has been a subject of debate regarding its significance in enhancing job performance and patient-centred care. This systematic review investigates the impact of EQ on organisational performance metrics in healthcare leaders. Eleven studies meeting the inclusion criteria
[...] Read more.
Emotional intelligence (EQ) in healthcare leadership has been a subject of debate regarding its significance in enhancing job performance and patient-centred care. This systematic review investigates the impact of EQ on organisational performance metrics in healthcare leaders. Eleven studies meeting the inclusion criteria were identified through a comprehensive database search. The findings suggest that EQ positively influences job satisfaction, with emotionally intelligent leaders fostering a positive work environment and commitment among employees. Moreover, EQ correlates negatively with emotional exhaustion, indicating its potential in mitigating burnout rates among healthcare professionals. EQ fosters teamwork, organisational culture and enhances job performance, with higher EQ levels in leaders associated with increased team empowerment and proactivity. Despite the compelling evidence, limitations in the study methodologies and heterogeneity in the reported outcomes challenge the establishment of definitive conclusions. Nevertheless, the findings underscore the importance of EQ in healthcare leadership and its potential to improve organisational dynamics and employee wellbeing. This review highlights the need for further research on EQ’s impact on patient satisfaction and calls for the development of EQ training programmes tailored for healthcare leaders.
Full article
Figure 1
Figure 1
<p>PRISMA flow diagram for systematic review.</p> Full article ">Figure 2
<p>Thematic analysis flow diagram of proxy performance metrics [<a href="#B8-ijerph-21-01531" class="html-bibr">8</a>,<a href="#B9-ijerph-21-01531" class="html-bibr">9</a>,<a href="#B10-ijerph-21-01531" class="html-bibr">10</a>,<a href="#B11-ijerph-21-01531" class="html-bibr">11</a>,<a href="#B12-ijerph-21-01531" class="html-bibr">12</a>,<a href="#B13-ijerph-21-01531" class="html-bibr">13</a>,<a href="#B14-ijerph-21-01531" class="html-bibr">14</a>,<a href="#B15-ijerph-21-01531" class="html-bibr">15</a>,<a href="#B16-ijerph-21-01531" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01531" class="html-bibr">17</a>,<a href="#B18-ijerph-21-01531" class="html-bibr">18</a>].</p> Full article ">
<p>PRISMA flow diagram for systematic review.</p> Full article ">Figure 2
<p>Thematic analysis flow diagram of proxy performance metrics [<a href="#B8-ijerph-21-01531" class="html-bibr">8</a>,<a href="#B9-ijerph-21-01531" class="html-bibr">9</a>,<a href="#B10-ijerph-21-01531" class="html-bibr">10</a>,<a href="#B11-ijerph-21-01531" class="html-bibr">11</a>,<a href="#B12-ijerph-21-01531" class="html-bibr">12</a>,<a href="#B13-ijerph-21-01531" class="html-bibr">13</a>,<a href="#B14-ijerph-21-01531" class="html-bibr">14</a>,<a href="#B15-ijerph-21-01531" class="html-bibr">15</a>,<a href="#B16-ijerph-21-01531" class="html-bibr">16</a>,<a href="#B17-ijerph-21-01531" class="html-bibr">17</a>,<a href="#B18-ijerph-21-01531" class="html-bibr">18</a>].</p> Full article ">
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