bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inequalities in utilization of maternal health services in Ethiopia: evidence from the PMA Ethiopia longitudinal survey
Authors: Hagos Asebe , Tiruneh Misganaw Guadie , Jejaw Melak , Demissie Kaleb Assegid , Baffa Lemlem Daniel , Geberu Demiss Mulatu , Teshale Getachew , Tafere Tesfahun Zemene 
Published: January 2025

BackgroundPrevious studies documented the existence of substantial inequalities in the utilization of maternal health services across different population subgroups in Ethiopia. Regularly monitoring the state of inequality could enhance efforts to address health inequality in the utilization of maternal health services. Therefore, this study aimed to measure the level of inequalities in the utilization of maternal health services in Ethiopia.MethodThe study used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) dataset. Longitudinal data was collected from a weighted sample of 1966 postpartum women using multistage stratified cluster sampling techniques between November 2021 to October 2022. We assessed inequality in maternal health services using three indicators: antenatal care four (ANC), skilled birth attendants (SBA), and postnatal care (PNC). Age, economic status, education level, place of residence, and subnational regions were used as dimensions for measuring inequality. The analysis was conducted using Health Equity Assessment Toolkit Plus (HEAT Plus) software. We computed the summary measure of health inequality: Difference (D), Ratio (R), Population Attributable Risk (PAR), and Population Attributable Fraction (PAF).ResultThe simple summary measures of inequality difference (D) reported a high level of inequality in the utilization of maternal health services in ANC four, SBA, and PNC across economic, education, residence, and subnational regions. The difference (D) in maternal health service utilization between advantaged and disadvantaged population groups exceeded 20 percentage points in all four dimensions of inequality for the three maternal health indicators. Similarly, the complex summary measures of inequality (PAR and PAF) also showed high levels of inequality in the utilization of ANC four, SBA, and PNC across all four dimensions of inequality. However, there was no age-related inequality in the use of maternal health services.ConclusionA high level of socioeconomic and geographic area related inequality was observed in the utilization of ANC four, SBA, and PNC services in Ethiopia. Women from socioeconomically disadvantaged subgroups and women from disadvantaged geographic areas significantly lagged behind in the utilization of maternal health services. Therefore, implementing targeted interventions for the most disadvantaged groups can help to reduce inequality in accessing maternal health services.
Self-rated health status and associated factors in Ilam, west of Iran: results of a population-based cross-sectional study
Authors: Bazyar Mohammad , Kakaei Hojatollah , Azadi Hamed , Jalilian Mohsen , Mansournia Mohammad Ali , Malekan Kamran , Pakzad Reza 
Published: January 2025

BackgroundSelf-rated health (SRH) is a single-item subjective indicator that asks individuals to assess their overall health and acts as a good indicator to reveal general health status. This study aimed to determine the SRH status and determining factors.MethodsThis was a population-based cross sectional study conducted in Ilam city (West of Iran) in 2023. A total of 1,370 people were invited to participate in the study using multi-stage stratified cluster random sampling method. Demographic and SRH status data were collected by face-to-face interview. SRH was indicated by a single question in five scales of very good, good, fair, poor and very poor. Multiple ordinal logistic regression was used for data analysis.ResultsThe 59.38% (95% CI: 56.76 to 62) participants reported a good SRH status. By ordinal multiple logistic regression, odds ratio (OR) and 95% confidence interval (CI) was calculated and based on that, female gender [OR: 1.68 (1.29 to 2.20)], not having insurance coverage [OR: 1.35; (1.01 to 1.80)], history of job loss [OR: 1.72; (1.28 to 2.31)], hopelessness for the future [OR: 5.07; (3.96 to 6.49)], and having underlying diseases [OR: 2.95; (2.25 to 3.88)], were positively associated with poor SRH status. The Kurd race [OR: 0.45; (0.25 to 0.78)], higher economic status [OR: 0.72; (0.54 to 0.96)] and use of health care service [OR: 0.68; (0.53 to 0.88)] were negatively associated with poor SRH status. The most effective variables for poor SRH status were hopelessness about the future and suffering from underlying diseases.ConclusionIt is important to devise corrective measures and effective public health policies to address causes and factors associated with poor SRH. It is also necessary for local health officials to allocate financial resources and introduce other kinds of supportive initiatives to provide targeted support for those who are struggling with poverty and suffering chronic diseases.
Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment
Authors: Li Yue , Nie Shengxiao , Wang Lei , Li Dongsheng , Ma Shengmiao , Li Ting , Sun Hong 
Published: January 2025

BackgroundMachine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. It also seeks to identify key predictive factors for PICC-RVT using these models.MethodsWe conducted a retrospective multi-center cohort study involving 5,272 patients who underwent PICC placement. After preprocessing patient data, the models were trained. Demographic, clinical pathology, and treatment data were analyzed to identify predictive factors. A variable analysis was then conducted to determine the most significant predictors of PICC-RVT. Model performance was evaluated using the Concordance Index (c-index) and the composite Brier score, and the Intraclass Correlation Coefficient (ICC) from cross-validation folds assessed model stability.ResultsDeep learning models generally outperformed traditional machine learning models in terms of predictive accuracy (mean c-index: 0.949 vs. 0.732; mean integrated Brier score: 0.046 vs. 0.093). Specifically, the DeepSurv model demonstrated exceptional precision in risk assessment (c-index: 0.95). Stability varied with the number of predictive factors, with Cox-Time showing the highest ICC (0.974) with 16 predictive factors, and DeepSurv the most stable with 26 predictive factors (ICC: 0.983). Key predictors across models included albumin levels, prefill sealant type, and activated partial thromboplastin time.ConclusionMachine learning models that incorporate time-to-event data can effectively predict PICC-RVT risk. The DeepSurv model, in particular, shows excellent discriminative and calibration capabilities. Albumin levels, type of prefill sealant, and activated partial thromboplastin time are critical indicators for identifying and managing high-risk PICC-RVT patients.
Campylobacter colonization and undernutrition in infants in rural eastern Ethiopia — a longitudinal community-based birth cohort study
Authors: Chen Dehao , McKune Sarah Lindley , Yang Yang , Usmane Ibsa Aliyi , Ahmed Ibsa Abdusemed , Amin Jafer Kedir , Ibrahim Abdulmuen Mohammed , Seran Abadir Jemal , Shaik Nurmohammad , Ojeda Amanda , Hassen Bahar Mummed , Deblais Loic , Ahmedo Belisa Usmael , Hassen Kedir Abdi , Bhrane Mussie , Li Xiaolong , Singh Nitya , Roba Kedir Teji , French Nigel P. , Rajashekara Gireesh , Manary Mark J. , Hassen Jemal Yusuf , Havelaar Arie Hendrik ,   on behalf of the CAGED Research Team
Published: January 2025

BackgroundCampylobacter is associated with environmental enteric dysfunction (EED) and malnutrition in children. Campylobacter infection could be a linchpin between livestock fecal exposure and health outcomes in low-resource smallholder settings.MethodsWe followed a birth cohort of 106 infants in rural smallholder households in eastern Ethiopia up to 13 months of age. We measured anthropometry, surveyed sociodemographic determinants, and collected stool and urine samples. A short survey was conducted during monthly visits, infant stool samples were collected, and Campylobacter spp. was quantified using genus-specific qPCR. In month 13, we collected stool and urine samples to assay for EED biomarkers. We employed regression analyses to assess the associations of household determinants with Campylobacter colonization, EED, and growth faltering.ResultsThe Campylobacter load in infant stools increased with age. The mean length-for-age Z-score (LAZ) decreased from −0.45 at 3–4 months of age to −2.06 at 13 months, while the prevalence of stunting increased from 3 to 51%. The prevalence of EED at 13 months of age was 56%. A higher Campylobacter load was associated with more frequent diarrhea. Prelacteal feeding significantly increased Campylobacter load in the first month of life. Over the whole follow-up period, Campylobacter load was increased by keeping chickens unconfined at home and unsanitary disposal of infant stools while decreased by mothers’ handwashing with soap. Longitudinally, Campylobacter load was positively associated with food insecurity, introduction of complementary foods, and raw milk consumption. There were no significant associations between Campylobacter load, EED, and LAZ.ConclusionThis study found that most determinants associated with increased Campylobacter infection were related to suboptimal feeding practices and hygiene. The findings related to livestock-associated risks were inconclusive. Although stunting, EED, and Campylobacter prevalence rates all increased to high levels by the end of the first year of life, no significant association between them was identified. While additional research is needed to investigate whether findings from this study are replicable in other populations, community efforts to improve infant and young child feeding practices and food hygiene, and water, sanitation, and hygiene (WaSH) at the household level, could reduce (cross-)contamination at the point of exposure.
Exploring the association between socioeconomic inequalities in chronic respiratory disease and all-cause mortality in China: findings from the China Health and Retirement Longitudinal Study
Authors: Zhang Zhuo , Shi Guoshuai , Jin Faguang , Zhang Yan 
Published: January 2025

ObjectiveResearch on the inequality of chronic respiratory disease (CRD) is limited, and the association between CRD and all-cause mortality is not well-established. Investigating the distribution of CRD and its associated mortality risks is essential for improving CRD conditions and developing targeted intervention measures. This study aimed to explore the relationship between inequalities in CRD and all-cause mortality in China.MethodsThis study utilized nationally representative baseline data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020, wave 1–wave 5), including a total of 14,743 subjects. The concentration index was employed to measure socioeconomic-related inequality in CRD, and the concentration index decomposition method was used to describe its influencing factors. Cox proportional hazards regression model was employed to examine the association between CRD and all-cause mortality.ResultsThe prevalence of CRD was 11.79% (95% CI: 10.98, 12.66) in China. The concentration index for CRD was −0.050 (95% CI: −0.075, −0.026), indicating a certain degree of inequality in its prevalence. Chronic lung disease (concentration index = −0.046, 95% CI: −0.073, −0.019), asthma (concentration index = −0.102, 95% CI: −0.148, −0.056), and asthma-chronic obstructive pulmonary disease overlap syndrome (concentration index = −0.114, 95% CI: −0.173, −0.055) also exhibited a pro-poor distribution. The decomposition analysis of the concentration index for CRD revealed that age, education level, and economic status played substantial roles in contributing to the observed inequality. Additionally, Cox regression analysis showed that participants with CRD had an increased risk of all-cause mortality (HR = 1.49, 95% CI: 1.34, 1.65).ConclusionInequalities exists in CRDs in China, with the prevalence of these diseases primarily concentrated among economically disadvantaged groups. Additionally, CRD increases the risk of all-cause mortality. Addressing the root causes of economic inequalities and enhancing the educational attainment of individuals with low socioeconomic status can help improve the situation.
Health literacy assessment and analysis of influencing factors in pregnant women with gestational diabetes mellitus in Southwest China
Authors: Tang Fangmei , Gu Li , Guo Xiujing , Fu Wenjing , He Benyi , Song Yuqing , Li Dehua 
Published: January 2025

ObjectiveThe main objectives of our study are evaluating the health literacy level among women with gestational diabetes mellitus (GDM) in Southwest China and explore the influencing factors, using a multidimensional health literacy assessment scale (Chinese version of the HLS-14). Given that the HLS-14 has not been used in GDM previously, its reliability and validity testing was included as a secondary objective.MethodIt was a cross-sectional survey with 565 GDM pregnancies. The Maternal and child health information access questionnaire, Chinese version of the HLS-14, Perceived Social Support Scale (PSSS) and General Self-efficacy Scale (GSES) was used to collect health information access behaviors, health literacy, social support and self-efficacy levels, respectively. SPSS 21.0 was used for descriptive statistical, multiple stepwise linear regression analysis and exploratory factor analysis (EFA). Amos 26.0 was used for confirmatory factor analysis (CFA).ResultsThe Chinese version of HLS-14 has good reliability and validity in GDM pregnancies. The Cronbach’s α are 0.849, 0.866, 0.859, and 0.883, respectively. The exploratory factor analysis extracted three common factors with a cumulative variance contribution rate of 68.405%. The confirmatory factor analysis model fit was good (χ2/df = 2.595, RMSEA = 0.055, IFI = 0.970, TLI = 0.963, CFI = 0.970). The HL level in pregnancies with GDM was moderate with a mean score of 3.26 ± 0.41, of which 24.10% had limited HL, 41.87% had moderate HL, and 34.03% had adequate HL. Regression analysis showed that the women with higher family support (β = 0.298, p &lt; 0.001), recording pregnancy management diary (β = 0.199, p &lt; 0.001), higher the family income (β = 0.140, p &lt; 0.001), lower pre-pregnancy BMI (β = −0.116, p = 0.004), longer time spent searching for health information (β = 0.111, p = 0.006), and searching for health information through a medical health information website (β = 0.093, p = 0.019) had higher HL levels. These variables explained 23.1% of the variance in HL.ConclusionThe Chinese version of the HLS-14 has good applicability in the GDM pregnancies. The HL level of them is moderate, needs to be improved. Healthcare professionals should focus on the GDM population with low family income and high pre-pregnancy BMI, fully mobilize their social support system, provide reliable access to information, encourage all GDM pregnancies to use pregnancy management diaries to record their self-management behaviors, and ensure the effectiveness of health education.
The burden of hepatitis E virus infection among Ghanaian pregnant women
Authors: Bagulo Husein , Majekodunmi Ayodele O. , Welburn Susan C. , Bimi Langbong 
Published: January 2025

IntroductionHepatitis E virus (HEV) infection poses a significant burden on pregnant women, with associated negative outcomes. Although well-described in many developed countries, the epidemiology of the disease and its impact on maternal and fetal health in Ghana is not fully understood.Materials and methodsA cross-sectional survey was conducted in the antenatal clinics of 10 district hospitals in five regions of Ghana. The study involved 1,000 pregnant women attending antenatal care. Serological and virological assays were employed to determine HEV seroprevalence and prevalence. Logistic regression analysis was carried out in univariate and multivariate models to assess risk factors associated with HEV infection.ResultsHEV-Immunoglobulin G (IgG) seroprevalence of 8.3% was recorded among the pregnant women with 1% HEV-antigen prevalence. However, none were positive for HEV-IgM and HEV RNA. 19.8% of the pregnant women reported poor pregnancy outcomes in previous pregnancies. Age, educational attainment, and region were significant predictors of HEV IgG seropositivity in the univariate regression model, while age and region were the only significant predictors in a multivariate model. Also, the drinking water source and the toilet type accurately predicted HEV IgG seroprevalence in both univariate and multivariate models.DiscussionPregnancy care must be significantly improved to reduce maternal and foetal morbidity and mortality.
Applying a sustainability perspective in the literature on physical therapy in relation to pharmaceuticals: a scoping review
Authors: Svensson Patric , Jacobsson Magdalena , Palstam Annie , Lange Elvira 
Published: January 2025

IntroductionPhysical therapy encompasses a broad range of treatment options, often utilized in clinical settings where pharmaceutical interventions are standard. The potential for physical therapy to contribute to sustainable healthcare by reducing environmental impact, while maintaining the quality of care, remains underexplored. This study aimed to map existing research comparing physical therapy to pharmaceuticals, with a specific focus on whether these studies address aspects of sustainable development.MethodsA scoping review was conducted, systematically searching the PubMed, Cinahl, and Pedro databases using keywords related to physical therapy, pharmaceuticals, and comparative studies. Two assessors independently reviewed and selected relevant studies, followed by data extraction and summarization of results.ResultsA total of 27 studies were included, varying in design, population, and healthcare context. The most commonly addressed conditions were osteoarthritis and musculoskeletal pain, with analgesics being the most frequently studied pharmaceutical interventions. While several studies touched upon economic and social dimensions of sustainable development, none examined environmental sustainability. This highlights a critical gap in current research.DiscussionFuture studies are needed to assess how physical therapy, when compared to pharmaceutical treatments, can contribute to sustainable healthcare by offering a low-carbon, resource-efficient alternative without compromising social sustainability through adverse effects. This knowledge could be instrumental in guiding healthcare systems toward more sustainable practices.Systematic review registrationA study protocol was registered in Open Science Framework 2023-03-31 (Available from: https://osf.io/we58g).
Instagram's impact on dental consumers: analyzing toothpaste hashtags
Authors: Al-Khalifa Khalifa S. , AlSheikh Rasha , Alakloby Basmah O. , Alharbi Hind M. , Alghamdi Razan F. , Ali Saqib , Al Dehailan Laila 
Published: January 2025

BackgroundToothbrushing is the basic step in maintaining oral hygiene and managing caries. The type of toothpaste used, combined with effective toothbrushing techniques, significantly influences oral health outcomes. Information shared on social media platforms can create awareness, generate interest, and influence perceptions regarding toothpaste brands and their benefits. This raised the necessity to comprehensively understand the influence of social media, particularly Instagram, on consumer decision-making processes and behavior related to toothpaste selection.Aimto explore toothpaste-related content on Instagram by addressing this gap and highlighting the characteristics of the top-performing toothpaste posts. Method: data were acquired for the “Top 12 posts” for each selected hashtag listed by the Instagram search algorithm. The contents of each post, including the number of likes, number of followers, content type, poster role, post content, post theme, post type, and account type were collected. Moreover, whether the poster was a dentist, patient, or dental interest group was identified. Data was then analyzed using SPSS with a statistical significance level set at p = .05.ResultsThe study analyzed a total of 1,054,985 posts revealing varying levels of engagement and content characteristics. Notably, the #Toothpaste hashtag garnered the highest number of posts, while #ToothpasteNatural had the lowest. Posts were predominantly promotional (61.1%) compared to educational (38.9%), with marketing being the primary theme. Educational content attracted significantly more engagement, with more likes/views and comments than promotional content. Additionally, the role of the poster influenced content type, with patients and dentists associated more with educational content, while dental interest groups and companies favored promotional material. However, there was no significant difference in the accuracy of claims between educational and promotional content.ConclusionInstagram shapes consumer behavior in toothpaste selection, with promotional content dominating despite higher engagement with educational posts. Limited fact-based content highlights the need for stricter regulations and increased contributions from dental professionals to improve oral health education.