Nevin Manimala

PLoS One. 2024 Mar 6;19(3):e0298597. doi: 10.1371/journal.pone.0298597. eCollection 2024.

ABSTRACT

Gum bleeding is a common dental problem, and numerous patients seek health-related information on this topic online. The YouTube website is a popular resource for people searching for medical information. To our knowledge, no recent study has evaluated content related to bleeding gums on YouTube™. Therefore, this study aimed to conduct a quantitative and qualitative analysis of YouTube videos related to bleeding gums. A search was performed on YouTube using the keyword “bleeding gums” from Google Trends. Of the first 200 results, 107 videos met the inclusion criteria. The descriptive statistics for the videos included the time since upload, the video length, and the number of likes, views, comments, subscribers, and viewing rates. The global quality score (GQS), usefulness score, and DISCERN were used to evaluate the video quality. Statistical analysis was performed using the Kruskal-Wallis test, Mann-Whitney test, and Spearman correlation analysis. The majority (n = 69, 64.48%) of the videos observed were uploaded by hospitals/clinics and dentists/specialists. The highest coverage was for symptoms (95.33%). Only 14.02% of the videos were classified as “good”. The average video length of the videos rated as “good” was significantly longer than the other groups (p <0.05), and the average viewing rate of the videos rated as “poor” (63,943.68%) was substantially higher than the other groups (p <0.05). YouTube videos on bleeding gums were of moderate quality, but their content was incomplete and unreliable. Incorrect and inadequate content can significantly influence patients’ attitudes and medical decisions. Effort needs to be expended by dental professionals, organizations, and the YouTube platform to ensure that YouTube can serve as a reliable source of information on bleeding gums.

PMID:38446816 | DOI:10.1371/journal.pone.0298597

PLoS One. 2024 Mar 6;19(3):e0295814. doi: 10.1371/journal.pone.0295814. eCollection 2024.

ABSTRACT

BACKGROUND: The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis.

METHODS: The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI).

RESULTS: The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations.

CONCLUSIONS: BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

PMID:38446811 | DOI:10.1371/journal.pone.0295814

PLoS One. 2024 Mar 6;19(3):e0299359. doi: 10.1371/journal.pone.0299359. eCollection 2024.

ABSTRACT

BACKGROUND: Public health expenditure is one of the fastest-growing spending items in EU member states. As the population ages and wealth increases, governments allocate more resources to their health systems. In view of this, the aim of this study is to identify the key determinants of public health expenditure in the EU member states.

METHODS: This study is based on macro-level EU panel data covering the period from 2000 to 2018. The association between explanatory variables and public health expenditure is analyzed by applying both static and dynamic econometric modeling.

RESULTS: Although GDP and out-of-pocket health expenditure are identified as the key drivers of public health expenditure, there are other variables, such as health system characteristics, with a statistically significant association with expenditure. Other variables, such as election year and the level of public debt, result to exert only a modest influence on the level of public health expenditure. Results also indicate that the aging of the population, political ideologies of governments and citizens’ expectations, appear to be statistically insignificant.

CONCLUSION: Since increases in public health expenditure in EU member states are mainly triggered by GDP increases, it is expected that differences in PHE per capita across member states will persist and, consequently, making it more difficult to attain the health equity sustainable development goal. Thus, measures to reduce EU economic inequalities, will ultimately result in reducing disparities in public health expenditures across member states.

PMID:38446804 | DOI:10.1371/journal.pone.0299359

PLoS One. 2024 Mar 6;19(3):e0299046. doi: 10.1371/journal.pone.0299046. eCollection 2024.

ABSTRACT

The agglomeration effect significantly influences firms’ site selection. Manufacturing firms often exhibit intricate spatial co-location patterns that are indicative of agglomerations due to their reliance on material input and product output across various subdivisions of manufacture. In this study, we present an analytical approach employing the Q statistic and additive color mixing visualization to assess co-location patterns of manufacturing firms. We identified frequent pairs and triplets of manufacturing divisions, mapping them to reveal distinct categories: labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters. These agglomeration categories concentrate in different regions of the city. Policy implications are proposed to promote the upgrade of labor-intensive divisions, enhance the operational efficiency of upstream/downstream industrial chains, and reinforce the spillover effects of technology-intensive divisions.

PMID:38446799 | DOI:10.1371/journal.pone.0299046

J Med Internet Res. 2024 Mar 6;26:e48026. doi: 10.2196/48026.

ABSTRACT

BACKGROUND: Social media addiction (SMA) caused by excessive dependence on social media is becoming a global problem. At present, most of the SMA studies recruit college students as research participants, with very few studies involving workers and other age groups, especially in China.

OBJECTIVE: This study aims to investigate the current status of SMA among Chinese workers and analyze its influencing factors.

METHODS: From November 1, 2022, to January 30, 2023, we conducted an anonymous web-based questionnaire survey in mainland China, and a total of 5176 participants completed the questionnaire. The questionnaire included the Social Networking Service Addiction Scale, Maslach Burnout Inventory-General Survey scale, Mindful Attention Awareness Scale, as well as questionnaires regarding participants’ social media use habits and demographic information.

RESULTS: Through strict screening, 3468 valid questionnaires were included in this study. The main findings of this study revealed the following: the average SMA score of workers was higher (mean 53.19, SD 12.04), and some of them (393/3468, 11.33%) relied heavily on social media; there were statistically significant differences in SMA scores among workers in different industries (F_14,3453=3.98; P<.001); single workers (t₃₁₀₆=8.6; P<.001) and workers in a relationship (t₂₇₄₉=5.67; P<.001) had higher SMA scores than married workers, but some married workers (214/3468, 6.17%) were highly dependent on social media; the level of SMA among female workers was higher than that of male workers (t₃₄₆₆=3.65; P<.001), and the SMA score of workers negatively correlated with age (r=-0.22; P<.001) and positively correlated with education level (r=0.12; P<.001); the frequency of using social media for entertainment during work (r=0.33; P<.001) and the frequency of staying up late using social media (r=0.14; P<.001) were positively correlated with the level of SMA in workers; and the level of SMA in workers was significantly positively correlated with their level of burnout (r=0.35; P<.001), whereas it was significantly negatively correlated with their level of mindfulness (r=-0.55; P<.001).

CONCLUSIONS: The results of this study suggest that SMA among Chinese workers is relatively serious and that the SMA problem among workers requires more attention from society and academia. In particular, female workers, young workers, unmarried workers, highly educated workers, workers with bad social media habits, workers with high levels of job burnout, and workers with low levels of mindfulness were highly dependent on social media. In addition, occupation is an important influencing factor in SMA. Thus, the government should strengthen the supervision of social media companies. Medical institutions should provide health education on SMA and offer intervention programs for those addicted to social media. Workers should cultivate healthy habits while using social media.

PMID:38446542 | DOI:10.2196/48026

JMIR Med Inform. 2024 Mar 6;12:e51560. doi: 10.2196/51560.

ABSTRACT

BACKGROUND: Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

OBJECTIVE: This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

METHODS: A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

RESULTS: A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

CONCLUSIONS: The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

PMID:38446534 | DOI:10.2196/51560

JMIR Med Inform. 2024 Mar 6;12:e47744. doi: 10.2196/47744.

ABSTRACT

BACKGROUND: The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD).

OBJECTIVE: This study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment.

METHODS: To define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record-derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015.

RESULTS: A total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD.

CONCLUSIONS: The fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

PMID:38446504 | DOI:10.2196/47744

Infect Dis (Lond). 2024 Mar 6:1-16. doi: 10.1080/23744235.2024.2324355. Online ahead of print.

ABSTRACT

BACKGROUND: Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements.

METHODS: Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases.

RESULTS: Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001.

CONCLUSIONS: Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

PMID:38446488 | DOI:10.1080/23744235.2024.2324355

JAMA Netw Open. 2024 Mar 4;7(3):e241349. doi: 10.1001/jamanetworkopen.2024.1349.

ABSTRACT

IMPORTANCE: There are concerns about the safety of medications for treatment of attention-deficit/hyperactivity disorder (ADHD), with mixed evidence on possible cardiovascular risk.

OBJECTIVE: To assess whether short-term methylphenidate use is associated with risk of cardiovascular events.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective, population-based cohort study was based on national Swedish registry data. Participants were individuals with ADHD aged 12 to 60 years with dispensed prescriptions of methylphenidate between January 1, 2007, and June 30, 2012. Each person receiving methylphenidate (n = 26 710) was matched on birth date, sex, and county to up to 10 nonusers without ADHD (n = 225 672). Statistical analyses were performed from September 13, 2022, to May 16, 2023.

MAIN OUTCOMES AND MEASURES: Rates of cardiovascular events, including ischemic heart disease, venous thromboembolism, heart failure, or tachyarrhythmias, 1 year before methylphenidate treatment and 6 months after treatment initiation were compared between individuals receiving methylphenidate and matched controls using a bayesian within-individual design. Analyses were stratified by history of cardiovascular events.

RESULTS: The cohort included 252 382 individuals (15 442 [57.8% men]; median age, 20 (IQR, 15-31) years). The overall incidence of cardiovascular events was 1.51 per 10 000 person-weeks (95% highest density interval [HDI], 1.35-1.69) for individuals receiving methylphenidate and 0.77 (95% HDI, 0.73-0.82) for the matched controls. Individuals treated with methylphenidate had an 87% posterior probability of having a higher rate of cardiovascular events after treatment initiation (incidence rate ratio [IRR], 1.41; 95% HDI, 1.09-1.88) compared with matched controls (IRR, 1.18; 95% HDI, 1.02-1.37). The posterior probabilities were 70% for at least a 10% increased risk of cardiovascular events in individuals receiving methylphenidate vs 49% in matched controls. No difference was found in this risk between individuals with and without a history of cardiovascular disease (IRR, 1.11; 95% HDI, 0.58-2.13).

CONCLUSIONS AND RELEVANCE: In this cohort study, individuals receiving methylphenidate had a small increased cardiovascular risk vs matched controls in the 6 months after treatment initiation. However, there was little evidence for an increased risk of 20% or higher and for differences in risk increase between people with and without a history of cardiovascular disease. Therefore, before treatment initiation, careful consideration of the risk-benefit trade-off of methylphenidate would be useful, regardless of cardiovascular history.

PMID:38446477 | DOI:10.1001/jamanetworkopen.2024.1349

JAMA Psychiatry. 2024 Mar 6. doi: 10.1001/jamapsychiatry.2024.0052. Online ahead of print.

ABSTRACT

IMPORTANCE: All-cause mortality and the risk for age-related medical disease is increased in individuals with psychiatric illness, but the underlying biological mechanisms are not known. Oxidative stress on nucleic acids (DNA and RNA; NA-OXS) is a molecular driver of aging and a potential pathophysiological mechanism in a range of age-related disorders.

OBJECTIVE: To study the levels of markers of NA-OXS in a large cohort of community-dwelling individuals with and without psychiatric illness and to evaluate their association with prospective all-cause mortality.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used a combined cohort of participants from 2 population-based health studies: the Danish General Suburban Population Study (January 2010 to October 2013) and nondiabetic control participants from the Vejle Diabetes Biobank study (March 2007 to May 2010). Individual history of psychiatric illness was characterized using register data on psychiatric diagnoses and use of psychotropic drugs before baseline examination. Urinary markers of systemic RNA (8-oxo-7,8-dihydroguanosine [8-oxoGuo]) and DNA (8-oxo-7,8-dihydro-2′-deoxyguanosine [8-oxodG]) damage from oxidation were measured by ultraperformance liquid chromatography-tandem mass spectrometry. Cox proportional hazard regression models were applied for survival analyses, using register-based all-cause mortality updated to May 2023. The follow-up time was up to 16.0 years.

EXPOSURES: History of psychiatric illness.

MAIN OUTCOMES AND MEASURES: Mortality risk according to psychiatric illness status and 8-oxoGuo or 8-oxodG excretion level.

RESULTS: A total of 7728 individuals were included (3983 [51.5%] female; mean [SD] age, 58.6 [11.9] years), 3095 of whom (40.0%) had a history of psychiatric illness. Mean (SD) baseline 8-oxoGuo was statistically significantly higher in individuals with psychiatric illness than in those without (2.4 [1.2] nmol/mmol vs 2.2 [0.9] nmol/mmol; P < .001), whereas 8-oxodG was not. All-cause mortality was higher in the psychiatric illness group vs the no psychiatric illness group (hazard ratio [HR], 1.44; 95% CI, 1.27-1.64; P < .001) and increased sequentially with each increasing tertile of 8-oxoGuo excretion in both groups to an almost doubled risk in the psychiatric illness/high 8-oxoGuo group compared to the no psychiatric illness/low 8-oxoGuo reference group (HR, 1.99; 95% CI, 1.58-2.52; P < .001). These results persisted after adjustment for a range of potential confounders and in a sensitivity analysis stratified for sex.

CONCLUSIONS AND RELEVANCE: This study establishes systemic oxidative stress-induced damage to RNA as a potential mechanism in the accelerated aging observed in psychiatric disorders and urinary 8-oxoGuo as a potentially useful marker of mortality risk in individuals with psychiatric illness.

PMID:38446448 | DOI:10.1001/jamapsychiatry.2024.0052