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Purpose: The aim of this study was to analyze hospital-discharged acute myocardial infarction (AMI) patients in Korea (2006-2020) to understand how pre-existing conditions affect mortality rates. Participants and Methods: This study utilized the 2006-2020 Korean National Hospital Discharge In-depth Injury Survey data. A weighted frequency analysis estimated discharged AMI patients and calculated age-standardized discharge and mortality rates, Charlson Comorbidity Index (CCI) score distribution, and general patient characteristics. Weighted logistic regression analysis examined influencing mortality factors. Results: There were 486,464 AMI patients (143,751 female), with AMI-related mortality rates at 7.5% (36,312): 5.7% for males (19,190) and 11.8% for females (17,122). The highest mortality rate was among individuals aged 70-79 years (25%). Factors influencing mortality included sex, insurance type, admission route, hospital bed count, region, operation status, and CCI score. Mortality risk at discharge was 1.151 times higher in females than males (95% CI: 1.002-1.322), 0.787 times lower among those with national health insurance than Medicaid recipients (95% CI 0.64-0.967), 2.182 times higher among those admitted via the emergency department than the outpatient department (95% CI 1.747-2.725), and 3.402 times higher in patients with a CCI score of 3 points than those with 0 points (95% CI 1.263-9.162). Conclusion: The number of discharged AMI patients and related mortality rates increased, underscoring the need for proactive management of chronic diseases, particularly for those with higher CCI scores.

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Childhood overweight is not only an immediate health concern due to its implications but also significantly increases the risk of persistent obesity and consequently CVD in the future, posing a serious threat to public health. The objective of this study was to examine the trends and associated factors of childhood overweight in India, using nationally representative data from three rounds of the National Family Health Survey (NFHS). For the primary analysis, we used data from 199 375 children aged 0-59 months from fifth round of the NFHS (NFHS-5). Overweight was defined as BMI-for-age Z (BMI Z) score > +2 sd above the WHO growth standards median. We compared the prevalence estimates of childhood overweight with third round of the third round of NFHS and fourth round of the NFHS. Potential risk factors were identified through multiple logistic regression analyses. The prevalence of overweight increased from 1·9 % in third round of NFHS to 4·0 % in NFHS-5, a trend seen across most states and union territories, with the Northeast region showing the highest prevalence. The BMI Z-score distributions from the latest two surveys indicated that the increase in overweight was substantially larger than the decrease in underweight. The consistent upward trend in the prevalence across different demographic groups raises important public health concerns. While undernutrition rates have remained relatively stable, there has been a noticeable rise in the incidence of overweight during the same time frame. The increasing trend of overweight among children in India calls for immediate action.

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Phase IV clinical trials are designed to monitor long-term side effects of medical treatment. For instance, childhood cancer survivors treated with chest radiation and/or anthracycline are often at risk of developing cardiotoxicity during their adulthood. Often the primary focus of a study could be on estimating the cumulative incidence of a particular outcome of interest such as cardiotoxicity. However, it is challenging to evaluate patients continuously and usually, this information is collected through cross-sectional surveys by following patients longitudinally. This leads to interval-censored data since the exact time of the onset of the toxicity is unknown. Rai et al. computed the transition intensity rate using a parametric model and estimated parameters using maximum likelihood approach in an illness-death model. However, such approach may not be suitable if the underlying parametric assumptions do not hold. This manuscript proposes a semi-parametric model, with a logit relationship for the treatment intensities in two groups, to estimate the transition intensity rates within the context of an illness-death model. The estimation of the parameters is done using an EM algorithm with profile likelihood. Results from the simulation studies suggest that the proposed approach is easy to implement and yields comparable results to the parametric model.

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BACKGROUND: The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised. OBJECTIVE: The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter. METHODS: Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination. RESULTS: Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non-Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non-Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non-Twitter users. CONCLUSIONS: Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.

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Few studies have investigated parent's experiences with racism in the neonatal intensive care unit (NICU). Our objective was to explore how parents perceive their interactions with NICU staff and if/how racism in the NICU was experienced. Parents of infants receiving care in an urban NICU completed fixed choice surveys regarding their experiences and demographics, with 6 open-ended questions to elaborate on their fixed-choice responses. Using a constant comparative method informed by Constructivist Grounded Theory, we identified 3 main themes from the comments provided by 97 respondents: Care and harm coexisting, racism often manifesting as neglectful care, and the power differential is most impactful during times of parent advocacy. Parents spoke positively regarding their experiences and also reported disparate treatment attributed to their racial/ethnic identity. Racism was experienced by inappropriate comments and apathy toward parent requests, occurring during intimate interactions between staff and parents. Descriptions of parental advocacy efforts highlighted the lack of power they held in relation to the NICU staff. We recommend strengthening the focus on equity and mitigating power imbalances in the NICU.

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OBJECTIVE: Identifying the effects of comorbidity on healthcare utilization is critical for understanding the benefits of improved comorbidity management. Asthma is a common respiratory condition, associated with gastrointestinal, metabolic, psychiatric, and other respiratory conditions. Adults with asthma represent a key population in understanding comorbidity and its consequences. The objective was to explore the relationship between comorbidity and overnight hospitalizations in U.S. adults with asthma. STUDY DESIGN AND METHODS: A cross-sectional sample of 3,887 subjects aged 20-79 was aggregated from seven cycles (2005-2018) of the National Health and Nutrition Examination Survey (NHANES). The survey design was created using the full seven cycles, then a subpopulation was used for the analysis. Design-based modified Poisson regression with robust standard errors compared the prevalence of overnight hospitalizations in subjects with and without comorbidities. Comorbidity was defined as the presence of one or more additional chronic conditions. RESULTS: Over half (61.6%) of patients with asthma reported having comorbidities. The overnight hospitalization prevalence was higher in those with comorbidities (21.6%) than those without (7.4%). The adjusted prevalence ratio of overnight hospitalizations in those with comorbidities vs. those without was 2.02 (95% CI: 1.54-2.66). Conclusions from sensitivity analyses remained the same. CONCLUSIONS: Comorbidity in U.S. adult asthma patients is associated with increased overnight hospitalizations. Study results concur with examinations of other healthcare utilization outcomes, revealing how comorbidity influences healthcare utilization patterns in patients with asthma. The reduction of overnight hospitalizations should be a targeted goal when developing and evaluating interventions to manage comorbidities in patients with asthma.

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OBJECTIVE: Analyze changes in abortion practices during the early coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: Sites recruited by Society of Family Planning participated in a longitudinal descriptive analysis comprising three surveys between February and October 2020. Average monthly total and medication abortion volume was analyzed by χ2 and linear regression. RESULTS: Total average volume of abortion services did not change among participating sites (p = 0.79). Volume and proportion of medication abortion increased over the study period (p = 0.02, p < 0.01). CONCLUSIONS: The early COVID-19 pandemic marked a shift toward medication abortion. IMPLICATIONS: Our findings are interlaced in a national trend predating the Dobbs ruling, likely accelerated by the COVID-19 pandemic.

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BACKGROUND: Early diagnosis and prompt treatment of malaria in young children are crucial for preventing the serious stages of the disease. If delayed treatment-seeking habits are observed in certain areas, targeted campaigns and interventions can be implemented to improve the situation. METHODS: This study applied multivariate binary logistic regression model diagnostics and geospatial logistic model to identify traditional authorities in Malawi where caregivers have unusual health-seeking behaviour for childhood malaria. The data from the 2021 Malawi Malaria Indicator Survey were analysed using R software version 4.3.0 for regressions and STATA version 17 for data cleaning. RESULTS: Both models showed significant variability in treatment-seeking habits of caregivers between villages. The mixed-effects logit model residual identified Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole as outliers in the model. Despite characteristics that promote late reporting of malaria at clinics, most mothers in these traditional authorities sought treatment within twenty-four hours of the onset of malaria symptoms in their children. On the other hand, the geospatial logit model showed that late seeking of malaria treatment was prevalent in most areas of the country, except a few traditional authorities such as Mwakaboko, Mwenemisuku, Mwabulambya, Mmbelwa, Mwadzama, Zulu, Amidu, Kasisi, and Mabuka. CONCLUSIONS: These findings suggest that using a combination of multivariate regression model residuals and geospatial statistics can help in identifying communities with distinct treatment-seeking patterns for childhood malaria within a population. Health policymakers could benefit from consulting traditional authorities who demonstrated early reporting for care in this study. This could help in understanding the best practices followed by mothers in those areas which can be replicated in regions where seeking care is delayed.

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The factors influencing caregivers' understanding of pediatric respiratory diseases, such as bronchiolitis, can guide patient care and the acceptability of treatment methods within the healthcare system. This study aims to identify illness perceptions and perform a needs assessment among caregivers of children diagnosed with respiratory diseases. This is a prospective, cross-sectional, questionnaire-driven study of a representative sample of caregivers whose children had an acute respiratory illness. The telephone-administered questionnaire was comprised of (1) demographic items; (2) illness perception questionnaire-revised (IPQ-R); and (3) items about personal barriers, the latter 2 of which employed a 5-point Likert response. Cronbach's alpha (α) was used to measure the internal consistency reliability for each item within the IPQ-R. The Pearson 2-tailed correlation coefficient was used to correlate questionnaire items. We included 75 caregivers whose children have been diagnosed with bronchiolitis (51%), reactive airway disease (RAD) (35%), asthma (33%), and wheezing (44%). We found no significance between the child's diagnosis and the site of recruitment. The most important components of the illness perception were illness coherence (α=0.849), psychological attributions (α=0.903), and barriers to diagnosis (α=0.633). Understanding caregivers' perceptions of respiratory diseases will lead to better treatment acceptance. We must clarify the terms used to define bronchiolitis from viral-induced wheezing, RAD, and the first asthma episode in older infants. Identifying caregivers' gaps in knowledge will help establish a cohesive approach to personalized treatment of respiratory diseases in children and their diagnosis.

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The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990-2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.

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Adolescent health research with biomarker data collection is limited due to difficulties in recruiting and engaging this age group. Thus, successful recruitment, engagement, and retention of adolescents in translational research are necessary to elucidate factors influencing mental and physical health conditions, uncover novel biomarkers, and expand prevention and treatment options. This paper describes strategies for effective recruitment and retention of adolescents in a research study, using a project examining depressive symptoms and the microbiome to illustrate these approaches. This cross-sectional study collected electronic self-reported survey data and self-collected biospecimens (stool and salivary samples) from adolescents 13-19 years old. All but two participants completed the questionnaires, with few missing responses. 94% provided at least one salivary sample and 89% supplied a stool sample. Participants were able to adhere to the study instructions. Using a participant-centered approach, our study successfully recruited and engaged the targeted 90 participants in self-collection of electronic survey data and biospecimens. Successful strategies of recruitment and retention included: 1) on-site clinic recruitment by research team, 2) active involvement of parents as appropriate, 3) use of electronic surveys and self-collection of biospecimens to foster control and ease of participation while addressing privacy concerns, 4) noninvasive collection of data on biospecimen, 5) frequent texting to communicate with participants, 6) flexibility in the pickup and transferring of biospecimens to accommodate adolescent schedules, 7) developmentally appropriate research, 8) participant reimbursement, and 9) sensitivity toward discussing stool sample materials. As a result of these strategies, adolescent participation in the research proved feasible.

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In this article, we present and empirically illustrate two concepts about vaccines and the way they are perceived by the Argentinean population and the easiness in accessing vaccination in developing countries. First, we focus on the perceptions of people about vaccines in general and develop a confidence index, and second, we analyze barriers to vaccination, measuring the burden citizens have when they intend to receive immunization (or as caretakers, trying to comply with the vaccination calendar of children and adolescents): for this second concept, we develop an access index. The data comes from representative annual surveys from Argentina from 2019 until 2022 (each one with approximately 7000 responders), which allows us to describe trends and check for changes in the confidence in vaccines and barriers towards vaccination. We find high confidence in vaccines in Argentina, although there is a "structural break" in the confidence for all years after 2020. Because we changed the questionnaire and methodology regarding the access to vaccines index in 2022, the discussion focuses on the cross-section of 2022, observing that barriers to vaccination tend to affect less educated caretakers.

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OBJECTIVE: Measure prevalence and overlap of secondhand harms from other people's use of alcohol, cannabis, opioid, or other drugs and examine sociodemographic and other correlates of these secondhand harms. METHODS: This cross-sectional analysis used data from 7,799 respondents (51.6% female; 12.9% Black, 15.6% Hispanic/Latiné; mean age: 47.6) in the 2020 U.S. National Alcohol Survey. Secondhand harms included family/marriage difficulties, traffic accidents, vandalism, physical harm, and financial difficulties. Weighted prevalence estimates provided nationally representative estimates of these harms. Logistic regression assessed associations between individual characteristics and secondhand harms. RESULTS: Lifetime prevalence of secondhand harms from alcohol, cannabis, opioid, or other drugs was 34.2%, 5.5%, 7.6%, and 8.3%, respectively. There was substantial overlap among lifetime harms: almost 30% of those reporting secondhand alcohol harms also reported secondhand drug harms. Significant correlates of secondhand substance harms included female sex (alcohol, other drug); white (alcohol, opioid), American Indian/Alaska Native (opioid), and Black (cannabis) race/ethnicity; and separated/divorced/widowed marital status (opioid). Those reporting family history of alcohol problems had significantly higher odds of reporting secondhand harms across substance types. Individuals who reported frequent cannabis use had higher odds of reporting secondhand alcohol and opioid harms compared to those with no cannabis use, (aOR=1.55; aOR=2.38), but lower odds of reporting secondhand cannabis harms (aOR=0.51). CONCLUSIONS: Although less prevalent than secondhand alcohol harms, 14% of participants reported secondhand harms from someone else's drug use and frequently experienced secondhand harms attributed to multiple substances. Population-focused interventions are needed to reduce the total burden of alcohol and drug use.

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The Patient Protection and Affordable Care Act (ACA) significantly reduced uninsured individuals and improved financial protection; however, escalating costs of cancer treatment has led to substantial out-of-pocket expenses, causing severe financial and mental health distress for individuals with cancer. Mixed evidence on the ACA's ongoing impact highlights the necessity of assessing health-spending changes across income groups for informed policy interventions. In our nationally representative survey evaluating the early- and long-term effects of the ACA on nonelderly adult patients with cancer, we categorized individuals-based income subgroups defined by the ACA for eligibility. We found that ACA implementation increased insurance coverage, which was particularly evident after 2 years of implementation. Early post-ACA (within two years of implementation), there were declines in out-of-pocket spending for the lowest and low-income groups by 26.52% and 38.31%, respectively, persisting long-term only for the lowest-income group. High-income groups experienced continuously increased out-of-pocket and premium spending by 25.39% and 34.28%, respectively, with a notable 122% increase in the risk of high-burden spending. This study provides robust evidence of income-based disparities in financial burden for cancer care, emphasizing the need for health care policies promoting equitable care and addressing spending disparities across income brackets.

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Understanding differences in how demographic groups experience telehealth may be relevant in addressing potential disparities in telehealth usage. We seek to identify and examine themes most pertinent to patients' negative telehealth experiences by age and race in order to inform interventions to improve patients' future telehealth experiences. We performed a content analysis of Press Ganey patient experience surveys from adult patients at 17 primary care sites of a large, public healthcare system with visits from April 30, 2020 to August 27, 2021. We used sentiment analysis to identify negative comments. We coded for content themes and analyzed their frequency, stratifying by age and race. We analyzed 745 negative comments. Most frequent themes differed by demographic categories, but overall, the most commonly applied codes were "Contacting the Clinic" (n = 97), "Connectivity" (n = 84), and "Webside Manner" (n = 79). The top three codes accounted for >40% of the negative codes in each race category and >35% of the negative codes in each age category. While there were common negative experiences among groups, patients of different demographics highlighted different aspects of their telehealth experiences for potential improvement.

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The aim of this study was to investigate whether seafarers' self-reported work experiences were associated with wellbeing and happiness while onboard. The study also examined which indicators of the work experiences had an effect in what direction. We analyzed the survey responses from 13 008 seafarers onboard, from 154 different nationalities, serving in 44 different international shipping companies. The outcome measures were wellbeing and happiness, and the exposure variables were work environment factors: satisfaction, expectations, ideal, skills and training, challenges, and workload. General psychosocial work environment onboard and socioeconomic independent variables were also included. We conducted different logistic regression analyses, and presented the results as odds ratios (OR) and 95% confidence intervals (CIs). The study found that most seafarers reported high levels of wellbeing and happiness and that these were significantly associated to the work environment factors, except for workload. A stratified analysis, showed that workload modified the effect of the other work environment factors. The study found that there were independently significant associations between work related factors and wellbeing and happiness among seafarers at sea. The findings suggest that a greater emphasis on these outcomes could have a positive impact both on crew retention and safety at sea.

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Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

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Real-time feedback is a growing trend in patient- and family experience (PFE) work as it allows for immediate service recovery, though it typically requires a significant investment of time and financial resources. We describe a partnership with our "edutainment" system to administer an automated daily experience question (the "Daily Pulse Measure [DPM]") that allowed targeted just-in-time responses to low scores with minimal administrative cost. Through a series of Plan-Do-Study-Act cycles guided by family feedback, the question was created and modified, and the use of the question spread to all hospital units. The response rate was 23%, similar to our Hospital Consumer Assessment of Healthcare Providers and Systems survey response rate of 24% during the study period. Though the DPM did not have a consistent impact on the results of the 2 PFE survey questions we evaluated, units with improved PFE scores after the DPM roll-out tended to have more robust service recovery than those with low scores.

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Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms-a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes-in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.