Nevin Manimala

Front Public Health. 2024 Jun 19;12:1414361. doi: 10.3389/fpubh.2024.1414361. eCollection 2024.

ABSTRACT

INTRODUCTION: Non-Hispanic Black (NHB) Americans have a higher incidence of colorectal cancer (CRC) and worse survival than non-Hispanic white (NHW) Americans, but the relative contributions of biological versus access to care remain poorly characterized. This study used two nationwide cohorts in different healthcare contexts to study health system effects on this disparity.

METHODS: We used data from the Surveillance, Epidemiology, and End Results (SEER) registry as well as the United States Veterans Health Administration (VA) to identify adults diagnosed with colorectal cancer between 2010 and 2020 who identified as non-Hispanic Black (NHB) or non-Hispanic white (NHW). Stratified survival analyses were performed using a primary endpoint of overall survival, and sensitivity analyses were performed using cancer-specific survival.

RESULTS: We identified 263,893 CRC patients in the SEER registry (36,662 (14%) NHB; 226,271 (86%) NHW) and 24,375 VA patients (4,860 (20%) NHB; 19,515 (80%) NHW). In the SEER registry, NHB patients had worse OS than NHW patients: median OS of 57 months (95% confidence interval (CI) 55-58) versus 72 months (95% CI 71-73) (hazard ratio (HR) 1.14, 95% CI 1.12-1.15, p = 0.001). In contrast, VA NHB median OS was 65 months (95% CI 62-69) versus NHW 69 months (95% CI 97-71) (HR 1.02, 95% CI 0.98-1.07, p = 0.375). There was significant interaction in the SEER registry between race and Medicare age eligibility (p < 0.001); NHB race had more effect in patients <65 years old (HR 1.44, 95% CI 1.39-1.49, p < 0.001) than in those ≥65 (HR 1.13, 95% CI 1.11-1.15, p < 0.001). In the VA, age stratification was not significant (p = 0.21).

DISCUSSION: Racial disparities in CRC survival in the general US population are significantly attenuated in Medicare-aged patients. This pattern is not present in the VA, suggesting that access to care may be an important component of racial disparities in this disease.

PMID:38962767 | PMC:PMC11220245 | DOI:10.3389/fpubh.2024.1414361

Front Public Health. 2024 Jun 19;12:1379326. doi: 10.3389/fpubh.2024.1379326. eCollection 2024.

ABSTRACT

INTRODUCTION: Premarital screening (PMS) is an essential global measure that seeks to reduce the occurrence of specific genetic disorders and sexually transmitted diseases common in consanguineous marriages. Due to the lack of a nationwide study, this research was designed to comprehend how unmarried individuals perceive the risks and benefits of PMS.

METHOD: A cross-sectional study was conducted using an online questionnaire distributed through different social media platforms, responses from the native adult population (18-49 years) Saudi Arabia was only included in the study. The questionnaire was based on the Health Belief Model (HBM) to assessing seven different constructs including susceptibility, seriousness, benefits-, barriers-, & cues- to action, self-efficacy, and social acceptance. Data frequency was represented by mean and standard deviation; chi-square and t-tests were conducted for the comparison of independent and dependent variables. A multinomial logistic regression was used to predict factors influencing decisions related to PMS.

RESULTS: 1,522 participants completed the survey, mostly 18-25 years old and most of them were women. The majority were single with 85 men and 1,370 women. Most participants (59.6%) believed their parents were related, while 40.5% did not. 122 respondents reported they had to marry within their tribe. Findings revealed significant correlations among all HBM themes, with varying strengths. Notably, a moderate positive relationship was found between the perception of benefits and cues to action, suggesting that enhancing the perceived benefits of PMS could facilitate safe marriage practices. Multinomial regression analysis revealed that demographic factors and health beliefs significantly influence individuals’ intentions and behaviors toward PMS and safe marriage.

CONCLUSION: The study concludes that by identifying and addressing barriers, and promoting positive social acceptance, PMS can significantly contribute to preventing genetic diseases and promoting safe marriage practices, although the cross-sectional design limits the establishment of causal relationships and further research is needed.

PMID:38962764 | PMC:PMC11219822 | DOI:10.3389/fpubh.2024.1379326

Front Public Health. 2024 Jun 19;12:1362392. doi: 10.3389/fpubh.2024.1362392. eCollection 2024.

ABSTRACT

BACKGROUND: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children under the age 5 in sub-Saharan Africa (SSA) using machine learning models.

METHODS: The sub-Saharan African countries’ demographic health survey was the source of the dataset. We used a weighted sample of 16,832 under-five children in this study. The data were processed using Python (version 3.9), and machine learning models such as extreme gradient boosting (XGB), random forest, decision tree, logistic regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure, to assess the performance of the predictive models.

RESULT: In this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the random forest (RF) was the best-predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers’ healthcare-seeking behavior for ARIs. The accuracy for Naïve Bayes was the lowest (66.41%) when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers’ age group of 35-49 years, and distance to health facilities were significant predictors for the absence of mothers’ healthcare-seeking behaviors for ARIs. On the other hand, undernourished children with stunting, underweight, and wasting status, diarrhea, birth size, married women, being a male or female sex child, and having a maternal occupation were significantly associated with good maternal healthcare-seeking behaviors for ARIs among under-five children.

CONCLUSION: The RF model provides greater predictive power for estimating mothers’ healthcare-seeking behaviors based on ARI risk factors. Machine learning could help achieve early prediction and intervention in children with high-risk ARIs. This leads to a recommendation for policy direction to reduce child mortality due to ARIs in sub-Saharan countries.

PMID:38962762 | PMC:PMC11220189 | DOI:10.3389/fpubh.2024.1362392

Front Public Health. 2024 Jun 19;12:1398236. doi: 10.3389/fpubh.2024.1398236. eCollection 2024.

ABSTRACT

BACKGROUND: Nutrition knowledge stands as a cornerstone in facilitating informed dietary choices, thereby profoundly impacting overall health and lifestyle outcomes. Malnutrition often correlates with deficient nutritional knowledge, highlighting the critical need for comprehensive understanding in this domain. While Ethiopia has seen considerable research on nutritional status and associated factors, there remains a paucity of studies specifically addressing nutrition knowledge among secondary school students, particularly within the Haramaya District. Therefore, this study aimed to meticulously assess nutrition knowledge and its determinants among secondary school students in Eastern Ethiopia.

METHODS: Employing an institutional-based cross-sectional design, we carefully selected 417 students from secondary schools in Haramaya District, Eastern Ethiopia, through simple random sampling. Data Research Topic entailed structured interviews, with subsequent entry into Epi Data version 3.1 for meticulous analysis utilizing SPSS version 21 software. Descriptive statistics summarized participant characteristics, while both bivariable and multivariable logistic regression analyses were conducted to elucidate factors associated with nutritional knowledge, setting statistical significance at p-value <0.05.

RESULTS: All 417 selected students participated in the study, yielding a commendable response rate of 100%. The median nutritional knowledge score among students stood at 58, with an interquartile range spanning from 44 to 66. Approximately 46.76% (95% CI: 42-51.59) of students exhibited good nutritional knowledge. Significant determinants of nutrition knowledge included sex [adjusted odds ratio (AOR) = 1.77, 95% CI: 1.03-3.04], being senior secondary students (AOR = 3.3, 95% CI: 1.95-5.73), and access to nutrition information (AOR = 3.3, 95% CI: 1.60-6.87).

CONCLUSION: Our findings illuminate a notable level of nutritional knowledge among secondary school students in Haramaya District. However, discernible disparities in nutrition knowledge emerged based on gender, educational level, and access to nutrition information. These insights underscore the exigency of targeted interventions aimed at enhancing nutrition literacy among students, thereby fostering holistic health promotion endeavors.

PMID:38962761 | PMC:PMC11221356 | DOI:10.3389/fpubh.2024.1398236

Front Public Health. 2024 Jun 19;12:1390424. doi: 10.3389/fpubh.2024.1390424. eCollection 2024.

ABSTRACT

During the course of their work, teachers may be subjected to conditions that cause different health problems. This study examines occupational health disorders in a representative sample of 858 teachers (528 female; age 44.0 ± 9.67 years) divided into three groups of teachers with specific occupational requirements: specialist physical education teachers (specialist PETs), classroom teachers, and specialist teachers. The number of health disorders in the last 12 months was recorded using the Chronic Health Disorders Questionnaire. The differences between the different types of teachers, controlled for sex and age, were analyzed using binary logistic regression. The results showed that 89% of teachers experienced colds as the most frequently reported health problem, followed by 58% for lower back problems, 57% for headaches, 51% for hoarseness, and 43% for neck problems. A binary logistic regression showed that specialist PETs were the group with the highest health risk. They were about twice as likely to have musculoskeletal or hearing disorders than the other two groups of teachers. They were also significantly more likely to suffer from hoarseness. Understanding these different health challenges is critical to developing targeted interventions and robust support systems. These interventions should include initiatives aimed at raising awareness of health risk factors, implementing injury interventions and vocal cord hygiene programs, making ergonomic adjustments, and promoting awareness of self-care (both mental and physical). Given that the teaching profession is currently struggling with an aging workforce and a shortage of teachers, addressing these challenges is critical to the continued well-being of the teaching professionals.

PMID:38962760 | PMC:PMC11219568 | DOI:10.3389/fpubh.2024.1390424

Front Med (Lausanne). 2024 Jun 19;11:1396858. doi: 10.3389/fmed.2024.1396858. eCollection 2024.

ABSTRACT

INTRODUCTION: A considerable percentage of daily emergency calls are for nursing home residents. With the ageing of the overall European population, an increase in emergency calls and interventions in nursing homes (NH) is to be expected. A proportion of these interventions and hospital transfers may be preventable and could be considered as inappropriate by prehospital emergency medical personnel. The study aimed to understand Belgian emergency physicians’ and emergency nurses’ perspectives on emergency calls and interventions in NHs and investigate factors contributing to their perception of inappropriateness.

METHODS: An exploratory non-interventional prospective study was conducted in Belgium among emergency physicians and emergency nurses, currently working in prehospital emergency medicine. Electronic questionnaires were sent out in September, October and November 2023. Descriptive statistics were used to analyze the overall results, as well as to compare the answers between emergency physicians and emergency nurses about certain topics.

RESULTS: A total of 114 emergency physicians and 78 nurses responded to the survey. The mean age was 38 years with a mean working experience of 10 years in prehospital healthcare. Nursing home staff were perceived as understaffed and lacking in competence, with an impact on patient care especially during nights and weekends. General practitioners were perceived as insufficiently involved in the patient’s care, as well as often unavailable in times of need, leading to activation of Emergency Medical Services (EMS) and transfers of nursing home residents to the Emergency Department (ED). Advance directives were almost never available at EMS interventions and transfers were often not in accordance with the patient’s wishes. Palliative care and pain treatment were perceived as insufficient. Emergency physicians and nurses felt mostly disappointed and frustrated. Additionally, differences in perception were noted between emergency physicians and nurses regarding certain topics. Emergency nurses were more convinced that the nursing home physician should be available 24/7 and that transfers could be avoided if nursing home staff had more authority regarding medical interventions. Emergency nurses were also more under the impression that pain management was inadequate, and emergency physicians were more afraid of the medical implications of doing too little during interventions than emergency nurses. Suggestions to reduce the number of EMS interventions were more general practitioner involvement (82%), better nursing home staff education/competences (77%), more nursing home staff (67%), mobile palliative care support teams (65%) and mobile geriatric nursing intervention teams (52%).

DISCUSSION AND CONCLUSION: EMS interventions in nursing homes were almost never seen as necessary or indicated by emergency physicians and nurses, with the appropriate EMS level almost never being activated. The following key issues were found: shortages in numbers and competence of nursing home staff, insufficient primary care due to the unavailability of the general practitioner as well as a lack of involvement in patient care, and an absence of readily available advance directives. General practitioners should be more involved in the decision to call the Emergency Medical Services (EMS) and to transfer nursing home residents to the Emergency Department. Healthcare workers should strive for vigilance regarding the patients’ wishes. The emotional burden of deciding on an avoidable hospital admission of nursing home residents, perhaps out of fear for medico-legal consequences if doing too little, leaves the emergency physicians and nurses frustrated and disappointed. Improvements in nursing home staffing, more acute and chronic general practitioner consultations, and mobile geriatric and palliative care support teams are potential solutions. Further research should focus on the structural improvement of the above-mentioned shortcomings.

PMID:38962739 | PMC:PMC11220277 | DOI:10.3389/fmed.2024.1396858

Magn Reson Med Sci. 2024 Jul 2. doi: 10.2463/mrms.mp.2023-0124. Online ahead of print.

ABSTRACT

PURPOSE: We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatability and reproducibility of volume estimation using DLHBS and compare them with those of representative brain segmentation tools such as statistical parametric mapping (SPM) and FreeSurfer (FS).

METHODS: Hierarchical segmentation using multiple deep learning models was employed to segment brain subregions within a clinically feasible processing time. The T1WI and brain mask pairs in 486 subjects were used as training data for training of the deep learning segmentation models. Training data were generated using a multi-atlas registration-based method. The high quality of training data was confirmed through visual evaluation and manual correction by neuroradiologists. The brain 3D-T1WI scan-rescan data of the 11 healthy subjects were obtained using three MRI scanners for evaluating the repeatability and reproducibility. The volumes of the eight ROIs-including gray matter, white matter, cerebrospinal fluid, hippocampus, orbital gyrus, cerebellum posterior lobe, putamen, and thalamus-obtained using DLHBS, SPM 12 with default settings, and FS with the “recon-all” pipeline. These volumes were then used for evaluation of repeatability and reproducibility.

RESULTS: In the volume measurements, the bilateral thalamus showed higher repeatability with DLHBS compared with SPM. Furthermore, DLHBS demonstrated higher repeatability than FS in across all eight ROIs. Additionally, higher reproducibility was observed with DLHBS in both hemispheres of six ROIs when compared with SPM and in five ROIs compared with FS. The lower repeatability and reproducibility in DLHBS were not observed in any comparisons.

CONCLUSION: Our results showed that the best performance in both repeatability and reproducibility was found in DLHBS compared with SPM and FS.

PMID:38960679 | DOI:10.2463/mrms.mp.2023-0124

Am J Epidemiol. 2024 Jul 3:kwae171. doi: 10.1093/aje/kwae171. Online ahead of print.

ABSTRACT

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies and opening or closing an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case-study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect. We outline the assumptions required to identify causal effects, showing that many common methods rely on strong assumptions that are relaxed by machine learning models. For empirical validation, we also propose a simple diagnostic criterion, guarding against false effects in baseline years when there was no intervention. The framework is applied to study the impact of COVID-19 lockdowns on NO2 in the eastern US. The machine learning approaches guard against false effects better than common methods and suggest decreases in NO2 in Boston, New York City, Baltimore, and Washington D.C. The study showcases the importance of our validation framework in selecting a suitable method and the utility of a machine learning based CITS model for studying causal changes in air pollution time series.

PMID:38960671 | DOI:10.1093/aje/kwae171

Am J Epidemiol. 2024 Jul 3:kwae176. doi: 10.1093/aje/kwae176. Online ahead of print.

ABSTRACT

The objective of this study was to determine whether exposure to structural racism-related state laws is associated with cardiovascular health among a racially and ethnically diverse sample of US adults. Data were from the Database of Structural Racism-Related State Laws and the Behavioral Risk Factor Surveillance System (BRFSS). The sample included 958,019 BRFSS 2011 and 2013 respondents aged 18+ from all 50 US states. The exposure was a summary index of 22 state laws related to the criminal legal system, economics and labor, education, healthcare, housing, immigration, and political participation. The outcome was the American Heart Association’s Life’s Simple 7 (LS7), a summary index of seven cardiovascular health indicators. Linear regression models included fixed effects for year and state to control for time trends and unmeasured time-invariant state-level contextual factors. In the full sample, a one standard deviation increase in the structural racism state legal index was associated with a 0.06-unit decrease in the LS7 (b=-0.06; 95% CI:-0.09, 0.02; p=0.001), controlling for individual- and state-level covariates. Contrary to expectations, stratified models revealed no statistically significant differences by race and ethnicity in the association between the structural racism state legal index and the LS7.

PMID:38960630 | DOI:10.1093/aje/kwae176

BMJ. 2024 Jul 3;386:q1462. doi: 10.1136/bmj.q1462.

NO ABSTRACT

PMID:38960617 | DOI:10.1136/bmj.q1462