Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices.

OBJECTIVES: This study aimed to support the implementation of the 11th Revision of the International Classification of Diseases (ICD-11). We used common comorbidity indices as a case study for proactively assessing the impact of transitioning to ICD-11 for mortality and morbidity statistics (ICD-11-MMS) on real-world data analyses. MATERIALS AND METHODS: Using the MIMIC IV database and a table of mappings between the clinical modification of previous versions of ICD and ICD-11-MMS, we assembled a population whose diagnosis can be represented in ICD-11-MMS. We assessed the impact of ICD version on cross-sectional analyses by comparing the populations' distribution of Charlson and Elixhauser comorbidity indices (CCI, ECI) across different ICD versions, along with the adjustment in comorbidity weighting. RESULTS: We found that ICD versioning could lead to (1) alterations in the population distribution and (2) changes in the weight that can be assigned to a comorbidity category in a reweighting initiative. In addition, this study allowed the creation of the corresponding ICD-11-MMS codes list for each component of the CCI and the ECI. DISCUSSION: In common with the implementations of previous versions of ICD, implementation of ICD-11-MMS potentially hinders comparability of comorbidity burden on health outcomes in research and clinical settings. CONCLUSION: Further research is essential to enhance ICD-11-MMS usability, while mitigating, after identification, its adverse effects on comparability of analyses.

Automated Credibility Assessment of Web-Based Health Information Considering Health on the Net Foundation Code of Conduct (HONcode): Model Development and Validation Study.

BACKGROUND: An increasing number of users are turning to web-based sources as an important source of health care guidance information. Thus, trustworthy sources of information should be automatically identifiable using objective criteria. OBJECTIVE: The purpose of this study was to automate the assessment of the Health on the Net Foundation Code of Conduct (HONcode) criteria, enhancing our ability to pinpoint trustworthy health information sources. METHODS: A data set of 538 web pages displaying health content was collected from 43 health-related websites. HONcode criteria have been considered as web page and website levels. For the website-level criteria (confidentiality, transparency, financial disclosure, and advertising policy), a bag of keywords has been identified to assess the criteria using a rule-based model. For the web page-level criteria (authority, complementarity, justifiability, and attribution) several machine learning (ML) approaches were used. In total, 200 web pages were manually annotated until achieving a balanced representation in terms of frequency. In total, 3 ML models-random forest, support vector machines (SVM), and Bidirectional Encoder Representations from Transformers (BERT)-were trained on the initial annotated data. A second step of training was implemented for the complementarity criterion using the BERT model for multiclass classification of the complementarity sentences obtained by annotation and data augmentation (positive, negative, and noncommittal sentences). Finally, the remaining web pages were classified using the selected model and 100 sentences were randomly selected for manual review. RESULTS: For web page-level criteria, the random forest model showed a good performance for the attribution criterion while displaying subpar performance in the others. BERT and SVM had a stable performance across all the criteria. BERT had a better area under the curve (AUC) of 0.96, 0.98, and 1.00 for neutral sentences, justifiability, and attribution, respectively. SVM had the overall better performance for the classification of complementarity with the AUC equal to 0.98. Finally, SVM and BERT had an equal AUC of 0.98 for the authority criterion. For the website level criteria, the rule-based model was able to retrieve web pages with an accuracy of 0.97 for confidentiality, 0.82 for transparency, and 0.51 for both financial disclosure and advertising policy. The final evaluation of the sentences determined 0.88 of precision and the agreement level of reviewers was computed at 0.82. CONCLUSIONS: Our results showed the potential power of automating the HONcode criteria assessment using ML approaches. This approach could be used with different types of pretrained models to accelerate the text annotation, and classification and to improve the performance in low-resource cases. Further work needs to be conducted to determine how to assign different weights to the criteria, as well as to identify additional characteristics that should be considered for consolidating these criteria into a comprehensive reliability score.

Emergency Department Use, Hospitalization, and Their Sociodemographic Determinants among Patients with Substance-Related Disorders: A Worldwide Systematic Review and Meta-Analysis.

Background: Identifying the determinants of emergency department (ED) use and hospitalization among patients with substance-related disorders (SRD) can help inform healthcare services and case management regarding their unmet health needs and strategies to reduce their acute care. Objectives: The present study aimed to identify sociodemographic characteristics, type of used drug, and risky behaviors associated with ED use and hospitalization among patients with SRD. Methods: Studies in English published from January 1st, 1995 to April 30th, 2022 were searched from PubMed, Scopus, Cochrane Library, and Web of Science to identify primary studies on ED use and hospitalization among patients with SRD. Results: Of the 17,348 outputs found, a total of 39 studies met the eligibility criteria. Higher ED use and hospitalization among patients with SRD were associated with a history of homelessness (ED use: OR = 1.93, 95%CI = 1.32-2.83; hospitalization: OR = 1.53, 95%CI = 1.36-1.73) or of injection drug use (ED use: OR = 1.34, 95%CI = 1.13-1.59; hospitalization: OR = 1.42, 95%CI = 1.20-1.69). Being female (OR = 1.24, 95%CI = 1.14-1.35), using methamphetamine (OR = 1.99, 95%CI = 1.24-3.21) and tobacco (OR = 1.25, 95%CI = 1.11-1.42), having HIV (OR = 1.70, 95%CI = 1.47-1.96), a history of incarceration (OR = 1.90, 95%CI = 1.27-2.85) and injury (OR = 2.62, 95%CI = 1.08-6.35) increased ED use only, while having age over 30 years (OR = 1.40, 95%CI = 1.08-1.81) and using cocaine (OR = 1.60, 95%CI = 1.32-1.95) increased hospitalization only among patients with SRD. Conclusions: The finding outline the necessity of developing outreach program and primary care referral for patients with SRD. Establishing a harm reduction program, incorporating needle/syringe exchange programs, and safe injection training with the aim of declining ED use and hospitalization, is likely be another beneficial strategy for patients with SRD.

Developing the breast cancer risk prediction system using hybrid machine learning algorithms.

BACKGROUND: Breast cancer (BC) is the most common cause of cancer-related deaths in women globally. Currently, many machine learning (ML)-based predictive models have been established to assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation even with having healthy lifestyle behaviors or preventing disease at early stages can significantly lead to optimal population-wide BC health. Thus, we aimed to develop a prediction model by using a genetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC. MATERIAL AND METHODS: The data of 3168 healthy individuals and 1742 patient case records in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a modified hybrid GA was used to perform feature selection and optimization of selected features. Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward, the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support system based on the best model was developed. RESULTS: After performing feature selection, age, consumption of dairy products, BC family history, breast biopsy, chest X-ray, hormone therapy, alcohol consumption, being overweight, having children, and education statuses were selected as the most important features for prediction of BC. The experimental results showed that the decision tree yielded a superior performance than other ML models, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively. CONCLUSION: The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies.

Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis.

OBJECTIVES: All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model. METHODS: Four-hundred-and-ninety data from the Liver and Gastroenterology Research Center of Shahid Beheshti University of Medical Sciences in the period 2014-2021, were analyzed applying models including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression. RESULTS: RF and SVM had the best results in general for all grades of EV. RF showed remarkably better results and the highest area under the curve (AUC). After that, SVM and ANN had the AUC of 98%, for grade 3, the SVM algorithm had the highest AUC after RF (89%). CONCLUSIONS: The findings may help to better predict EV with high precision and accuracy and also can help reduce the burden of frequent visits to endoscopic centers. It can also help practitioners to manage cirrhosis by predicting EV with lower costs.

Quality of life and its correlated factors among patients with substance use disorders: a systematic review and meta-analysis.

BACKGROUND: Patients with substance use disorder (SUD) usually report lower quality of life (QoL) than other patients and as much as patients with other mental disorders. The present study investigated variables associated with QoL domains among patients with SUD. METHODS: Studies in English published before December 1st 2021, were searched for on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on factors associated with QoL domains among patients with SUD. After reviewing for study duplicates, the full-texts of selected papers were assessed for eligibility using PECO (Participants, Exposures, Comparison and Outcome) criteria: (a) participants: patients with SUD; (b) exposures: sociodemographic factors, clinical, and service use variables; (c) comparison: patient groups without SUD; and (d) outcomes: four domains of QoL (physical, mental, social, and environmental domains). Three researchers recorded the data independently using predefined Excel spreadsheets. The Newcastle-Ottawa Scale (NOS) was used for assessing risk of bias and rated each study in terms of exposure, outcome, and comparability. Pooled odds ratios (ORs) and ß coefficient were utilized at a 95% confidence level, and because sampling methods differed between studies' pooled estimates, a random effects model was utilized. RESULTS: After the assessment of over 10,230 papers, a total of 17 studies met the eligibility criteria. Five studies (1260 participants) found that patients with SUD who were older were less likely to have a good physical Qol (OR = 0.86, 95% CI = 0.78, 0.95). Two studies (1171 participants) indicated that patients with SUD who were homeless were less likely to have a good environmental Qol (ß = -0.47, p = 0.003). However, a better mental QoL was observed in four studies (1126 participants) among those receiving support from their family or friends (social networks) (OR = 1.05, 95% CI = 1.04, 1.07). Two studies (588 participants) showed that those using cocaine were less likely to have a good mental QoL (OR = 0.83, 95% CI = 0.75, 0.93). Two studies (22,534 participants) showed that those using alcohol were less likely to have a good physical QoL (ß = -2.21, p = 0.001). Two studies (956 participants) showed that those having severe substance use disorders were less likely to have a good mental (ß = -5.44, p = 0.002) and environmental (ß = -0.59, p = 0.006) QoL respectively. Four studies (3515 participants) showed that those having mental disorders were less likely to have a good physical QoL (ß = -1.05, p = 0.001), and another three studies (1211 participants) that those having mental disorders were less likely to have a good mental QoL (ß = -0.33, p = 0.001). Finally, two studies (609 and 682 participants) showed that individuals who experienced trauma symptoms or mental disorders were less likely to have good social and environmental QoL, respectively (OR = 0.78, 95% CI = 0.61, 1.00) and (OR = 0.92, 95% CI = 0.9, 0.94). CONCLUSIONS: The findings suggest the need for mental health services to improve the QOL among patients with SUD but further study is needed. Cocaine may cause behavioral changes which can increase the possibility of reckless and suicidal behaviors. Therefore, identifying cocaine user access, adherence, and satisfaction with treatment is recommended as an important component of adaptive functioning. Interventions that help patients with SUD get support from people within their social networks who support their recovery are also essential to their QoL.

Prevalence and associates of non-fatal overdose among people who inject drugs in Saveh, Iran.

BACKGROUND: As a public health issue, non-fatal overdose (NFOD) is highly prevalent among people who inject drugs (PWID). This can lead to an elevated risk of future overdose, causing various harms including possible death. It is essential to improve knowledge concerning this problem and its associated risk factors to inform overdose prevention and assistance programs. The primary aim of the present study was to determine the prevalence of NFOD and associated risk factors among PWID in Saveh, Iran. METHODS: In the present cross-sectional study, 272 PWID living in Saveh, Iran were interviewed face-to-face using a structured survey. Data concerning socio-demographics, substance use, risky behaviors, and services utilization data were collected. The outcome variable (i.e., NFOD) was assessed by answering "Yes" to the question: "In the past three months, have you ever overdosed (at least once) by accident?" RESULTS: The prevalence of NFOD among PWID in the past three months was 54%. The characteristics and behaviors that were associated with an increased risk of experiencing NFOD in the past three months were being of older age (AOR = 5.2, p < 0.05), drug use initiation under the age of 22 years (AOR = 7.8, p < 0.05), being an alcohol user (AOR = 3.0, p < 0.05), and being a simultaneous multiple drug user (AOR = 5.8, p < 0.05). Also, more recent initiates to injecting (< 2 years) had an increased risk of experiencing a non-fatal overdose in the past three months. Findings also indicated that those who (i) attended a needle and syringe program (AOR: 0.3, p < 0.05), (ii) were visited by a general practitioner (AOR: 0.03, p < 0.05), and (iii) received a psychosocial intervention (AOR: 0.1, p < 0.05) were 0.3, 0.03 and 0.1 times less likely to report non-fatal overdosing than other participants, respectively. CONCLUSIONS: The results indicate that intervention and prevention initiatives seeking to reduce NFOD among PWID should not only be focused on the primary drug used but also the use of alcohol and polysubstance use. Specific and tailored psychological interventions combined with pharmacotherapy may be highly beneficial for PWID who experience more severe types of substance use, including alcohol use disorders and/or polysubstance abuse.

Identifying predictors of varices grading in patients with cirrhosis using ensemble learning.

OBJECTIVES: The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis. METHODS: In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed. RESULTS: The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR). CONCLUSIONS: Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.

Prevalence, socio-demographics and service use determinants associated with disclosure of HIV/AIDS status to infected children: a systematic review and meta-analysis by 1985-2021.

BACKGROUND: Human immunodeficiency virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS) is a public health issue of global importance. To our knowledge, no previous meta-analysis documenting the prevalence, socio-demographic, and service use determinants associated with HIV/AIDS disclosure to infected children has been conducted. The present study aimed to determine the prevalence, socio-demographics and service use determinants associated with the disclosure of HIV/AIDS status to infected children. METHODS: Studies in English published between 01 January 1985 and 01 November 2021, and available on PubMed, Scopus, Web of Science, and Cochrane electronic databases were searched. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes (PICO) criteria. We used fixed and random-effects meta-analysis models to estimate the pooled prevalence, pooled odds ratio (OR), and 95% confidence intervals. RESULTS: After article duplicates were excluded, assessments of abstracts were completed, and full-text papers evaluated, 37 studies were included in this meta-analysis. The prevalence of the disclosure of HIV status to children was measured to be 41% in this research. The odds that a child of 10 years and older is informed that they are HIV-positive is 3.01 time the odds that younger children are informed. Those children who had primary or lower schooling level were 2.41 times more likely to be informed of their HIV-positive status than children with higher levels of schooling. Children who had a non-biological parents were 3.17 times more likely to have been disclose being HIV-positive; social support (OR = 8.29, 95%CI = 2.34, 29.42), children who had higher levels of social supports were 8.29 times more likely to disclose HIV-positive; the primary educational level of caregivers (OR = 2.03, 95%CI = 1.43, 2.89), respondents who had caregivers with primary education level were 2.03 times more likely to disclose HIV-positive; antiretroviral treatment (ART) adherence (OR = 2.59, 95%CI = 1.96, 3.42), participants who adhered to ART were 2.59 times more likely to disclose HIV-positive and hospital follow-up (OR = 2.82, 95%CI = 1.85, 4.29), those who had hospital follow-up were 2.82 times more likely to disclose HIV-positive; were all significantly associated with the disclosure of HIV/AIDS status to infected children. CONCLUSION: Such data are of importance for healthcare pediatrics HIV care professionals. Facilitating HIV diagnosis and disclosure to the infected children and ensuring access to HIV treatment will likely prevent secondary HIV transmission. Healthcare professionals are expected to provide age-appropriate counseling services to this population.

Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study.

OBJECTIVES: The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. METHODS: In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3-5.2.0 64 bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms. RESULTS: According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%. CONCLUSIONS: We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.

Drug type and risk behaviors associated with non-fatal overdose among people who use drugs: a systematic review and meta-analysis.

The present study aimed to determine the association between drug type, risk behaviors and non-fatal overdose among people who use drugs (PWUD). We searched for studies in English published before February 1, 2021, on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on the factors associated with non-fatal overdose among PWUD. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes (PICO) criteria. After a detailed assessment of over 13,845 articles, a total of 49 studies met the eligibility criteria. We found that non-injection opioid use, heroin injection, cocaine use, concurrent use of buprenorphine and benzodiazepines, benzodiazepine use, incarceration, injecting drugs, and duration of injecting were associated with greater odds of non-fatal overdose among PWUD. The findings of the current meta-analysis support the requirement to improve suitable harm reduction strategies for drug users, such as peer-based overdose management, and further focusing on the need to balance the current emphasis on enforcement-based responses to illegal drug use with health-related interventions.

Socio-demographic, clinical and service use determinants associated with HIV related stigma among people living with HIV/AIDS: a systematic review and meta-analysis.

BACKGROUND: Defining HIV-related stigma (HRS) can be problematic due to structural inequalities, cultural differences, discrimination by health care providers and the limitations of tools measuring stigma for people living with HIV (PLWH). This meta-analysis aimed to determine self-reported HRS and its association with socio-demographic and clinical determinants. METHODS: PubMed, Scopus, Web of Science, PsycInfo, SciELO and Cochrane electronic databases were searched and after reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes criteria. We used fixed and random-effects meta-analysis models to estimate the pooled prevalence, pooled odds ratio (OR) and 95% confidence intervals. RESULTS: Thirty-one studies containing 10,475 participants met the eligibility criteria. Among the potential risk factors: age > 30 years (OR = 0.93, 95%CI = 0.86, 1), living with a spouse (OR = 0.07, 95%CI = 0.02, 0.17), CD4 count < 200 (OR = 0.5, 95% CI = 0.31, 0.68), medication adherence (OR = 0.96, 95%CI = 0.94, 0.99), poor access to care (OR = 0.79, 95%CI = 0.65, 0.93), time since diagnosis, and accessibility to care (OR = 0.37, 95%CI = 0.11, 0.86) were all significantly associated with self-reported HIV stigma among PWLH. CONCLUSION: Stigma is correlated with numerous negative consequences in marginalised populations including PLWH. Considering the negative association that stigma has on HIV prevention and treatment targeted evidence-based stigma reduction interventions are recommended. Interventions that are focused on a particular group, such as healthcare professionals are warranted. Rigorously designed studies with specific and validated outcome measures associated with targeted interventions may help to improve the reduction of HRS for PLWH.

Poor oral health-related quality of life among pregnant women: A systematic review and meta-analysis.

OBJECTIVES: The aim of this systematic review was to assess the impacts of oral health determinants (eg frequency of brushing < 2 day, poor life style and non-white ethnicity) and clinical oral caries indices (eg Decayed, Missing, and Filled Teeth (DMFT)) and periodontal disease) on Poor Quality of Life (PQoL) among PW. METHODS: The search strategy was restricted to publications in English before 1 December 2019 in the PsycINFO, PubMed, SciELO, Scopus and Web of Science databases. We only included oral diseases considered as public health issues with a global burden. As a result, investigations reporting the frequency of brushing, poor lifestyle behaviours, non-white ethnicity, DMFT scores and periodontal disease as an outcome were included. The two reviewers resolved any disagreements. Reviewers analysed the full texts, considering the inclusion and exclusion criteria. Also, a manual search of the reference lists was performed on all the selected studies. RESULTS: In total, 11 publications were included in the meta-analysis. Findings indicate a positive association between non-white ethnicity and PQoL among PW. Among PW, those who had non-white ethnicity were 1.43 times more likely to have PQoL (OR = 1.43, 95% CI = 1.17, 1.70). A positive association between DMFT and poor QoL among PW was also observed. Those who has suffered DMFT were 1.4 times more likely to have poor QoL (OR = 1.4, 95% CI = 1.24, 1.55). CONCLUSIONS: Results from this meta-analysis support the need for behavioural interventions for improving oral hygiene in expectant mothers. This could help to decrease periodontal conditions and improve their oral and general life quality.

Non -fatal overdose among people who inject drugs in Tehran, Iran.

BACKGROUND: With increasing frequencies of non-fatal overdose in people who inject drugs (PWID), it is essential to improve our knowledge about associated risk factors for overdose to inform overdose prevention and assistance programs. The aim of present study was to determine the prevalence of non-fatal overdose and the associated risk factors among PWID in Tehran, Iran. METHODS: Snowball sampling was used to collect data from 465 participants in Tehran using a cross-sectional survey. Consenting participants who reported drug injecting in the past month and were able to speak and comprehend Farsi enough to respond to survey questions were interviewed. The endpoint of interest was non-fatal overdose in the previous 6 months, or answering "Yes" to the question: "In the last six months, have you ever overdosed by accident? (at least once)". We used STATA v. 14 for this analysis. Statistical significance was defined as p < 0.05 for all analyses. RESULTS: Of 465 PWIDs who participated in this study, all were male, and about half had less than a high school education. The prevalence of self-reported non-fatal overdose in the past 6 months was 38% (CI95%: 34, 43%). Our findings indicate that characteristics and behaviors that were associated with an increased risk of experiencing an overdose in the past 6 months were drug use initiation under 22 years (AOR =2.2, P < 0.05), using methamphetamine (AOR =2.8, P < 0.05), and using multiple drugs at the same time (AOR =2.1, P < 0.05). Also, more recent initiates to injecting (< 2 years) had an increased risk of experiencing an overdose in the past 6 months. The odds of experiencing a non-fatal overdose among PWIDs who regularly attended NSP were 0.6 times less than for those who did not attend regularly (OR = 0.6,95% CI: 0.2-0.9). CONCLUSION: Methamphetamine and alcohol use were the most significant association for non-fatal overdose among PWIDs. Our results indicate that intervention and prevention initiatives seeking to reduce overdoses among PWIDs should not only be focused on the primary drug used but also the use of alcohol and poly-drug use.

The Association of Oral Health Status, demographic characteristics and socioeconomic determinants with Oral health-related quality of life among children: a systematic review and Meta-analysis.

BACKGROUND: Health-related quality of life (HQoL) indicators are considered valid measures of patient assessment in physical, mental and oral healthcare. This study aimed to examine the evidence on the relationship of oral health status, demographic and socioeconomic characteristics with oral health-related quality of life (OHRQoL) in children. METHODS: Studies in English published up to December 2019 were searched on PsycINFO, PubMed, SciELO, Scopus, and Web of Science databases. Epidemiological studies simultaneously assessing sociodemographic factors related to oral health (age, income, gender, maternal education), oral health measures (orthodontic treatment needs, dental caries and periodontal disease) and OHRQoL in children aged 3-12 years were included. Methodological quality was assessed using a Critical Appraisal Checklist. Meta-analysis was used to estimate pooled measures between sociodemographic factors and oral health measures with OHRQoL. RESULTS: Eleven articles were included. Lower children's age (3-5 years vs > 5), gender (girls vs boys), lower income (< 70$ vs ≥ $70), low maternal education (≤ 6 vs > 6 years) were associated with poor OHRQoL among children. Orthodontic treatment needs, dental caries and periodontal diseases were also associated with poor children's OHRQoL. Meta-regression showed that Human Development Index, sample size, year of publication and participant's age were relevant aspects that influenced the above mentioned relationships. CONCLUSIONS: Our findings suggest that oral health promotion strategies to improve children's OHRQoL should consider the social and environmental where they live as well their oral health status. Further longitudinal studies are needed to explore the determinants of OHQoL in children.

Methamphetamine use and HIV risk behavior among men who inject drugs: causal inference using coarsened exact matching.

BACKGROUND: Understanding the association between methamphetamine (MA) use and HIV risk behavior among people who inject drugs (PWID) will assist policy-makers and program managers to sharpen the focus of HIV prevention interventions. This study examines the relationship between MA use and HIV risk behavior among men who inject drugs (MWID) in Tehran, Iran, using coarsened exact matching (CEM). METHODS: Data for these analyses were derived from a cross-sectional study conducted between June and July 2016. We assessed three outcomes of interest-all treated as binary variables, including distributive and receptive needle and syringe (NS) sharing and condomless sex during the month before interview. Our primary exposure of interest was whether study participants reported any MA use in the month prior to the interview. Firstly, we report the descriptive statistics for the pooled samples and matched sub-samples using CEM. The pooled and matched estimates of the associations and their 95% CI were estimated using a logistic regression model. RESULTS: Overall, 500 MWID aged between 18 and 63 years (mean = 28.44, SD = 7.22) were recruited. Imbalances in the measured demographic characteristics and risk behaviors between MA users and non-users were attenuated using matching. In the matched samples, the regression models showed participants who reported MA use were 1.82 times more likely to report condomless sex (OR = 1.82 95% CI 1.51, 4.10; P = 0.031), and 1.35 times more likely to report distributive NS sharing in the past 30 days, as compared to MA non-users (OR = 1.35 95% CI 1.15-1.81). Finally, there was a statistically significant relationship between MA use and receptive NS sharing in the past month. People who use MA in the last month had higher odds of receptive NS sharing when compared to MA non-users (OR = 4.2 95% CI 2.7, 7.5; P = 0.013). CONCLUSIONS: Our results show a significant relationship between MA use and HIV risk behavior among MWID in Tehran, Iran. MA use was related with increased NS sharing, which is associated with higher risk for HIV exposure and transmission.

Type2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches.

BACKGROUND: About 90% of patients who have diabetes suffer from Type 2 DM (T2DM). Many studies suggest using the significant role of lncRNAs to improve the diagnosis of T2DM. Machine learning and Data Mining techniques are tools that can improve the analysis and interpretation or extraction of knowledge from the data. These techniques may enhance the prognosis and diagnosis associated with reducing diseases such as T2DM. We applied four classification models, including K-nearest neighbor (KNN), support vector machine (SVM), logistic regression, and artificial neural networks (ANN) for diagnosing T2DM, and we compared the diagnostic power of these algorithms with each other. We performed the algorithms on six LncRNA variables (LINC00523, LINC00995, HCG27_201, TPT1-AS1, LY86-AS1, DKFZP) and demographic data. RESULTS: To select the best performance, we considered the AUC, sensitivity, specificity, plotted the ROC curve, and showed the average curve and range. The mean AUC for the KNN algorithm was 91% with 0.09 standard deviation (SD); the mean sensitivity and specificity were 96 and 85%, respectively. After applying the SVM algorithm, the mean AUC obtained 95% after stratified 10-fold cross-validation, and the SD obtained 0.05. The mean sensitivity and specificity were 95 and 86%, respectively. The mean AUC for ANN and the SD were 93% and 0.03, also the mean sensitivity and specificity were 78 and 85%. At last, for the logistic regression algorithm, our results showed 95% of mean AUC, and the SD of 0.05, the mean sensitivity and specificity were 92 and 85%, respectively. According to the ROCs, the Logistic Regression and SVM had a better area under the curve compared to the others. CONCLUSION: We aimed to find the best data mining approach for the prediction of T2DM using six lncRNA expression. According to the finding, the maximum AUC dedicated to SVM and logistic regression, among others, KNN and ANN also had the high mean AUC and small standard deviations of AUC scores among the approaches, KNN had the highest mean sensitivity and the highest specificity belonged to SVM. This study's result could improve our knowledge about the early detection and diagnosis of T2DM using the lncRNAs as biomarkers.

Prevalence and high risk behaviours associated with HCV testing among people who inject drugs: a systematic review and Meta-analysis.

BACKGROUND: Hepatitis C diagnosis could be a gateway to behavioral change and subsequent decline in transmission among people who inject drugs (PWIDs). We assessed the association between the social determinants of PWID, their risk behaviors and hepatitis C testing. METHODS: We searched for studies in English published before May 1, 2020, on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on the factors associated with hepatitis C virus (HCV) testing among PWID. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes (PICO) criteria. i) population: individuals who report injecting drugs; ii) intervention: HCV testing in the past year; iii) comparator: PWIDs who did not have an HCV test; iv) outcome: HCV testing among PWIDs and v) study type: cross-sectional, cohort, and case-control studies. Two independent reviewers (author BA and AB) chose the references in a two-phased monitoring process. The authors gathered data from selected papers, including the surname of the first author, publication date, participant demographic data (age, sex, and level of education) and other characteristics like previous HCV testing, past treatment attempts, duration of injecting drug use and condomless sex. We used fixed and random-effects meta-analysis models to estimate the pooled prevalence, pooled odds ratio (OR), and 95% confidence intervals. The data were analyzed using Stata 12.0 software. RESULTS: After a detailed assessment of over 12,000 articles, a total of 16 studies containing 38,952 participants met the eligibility criteria. Our findings showed a pooled prevalence rate of 61.01% (95% CI, 34.65-84.32%) for recent HCV testing among PWIDs. Being female (OR = 1.69, 95%CI = 1.13, 2.26), aged > 30 years, (OR = 2.61, 95%CI = 1.66-3.56) having past treatment attempt (OR = 2.24, 95%CI = 1.80-2.68), and reporting a previous test (OR = 2.03, 95%CI = 1.23-2.82). were significantly associated with having a recent HCV test.,,. Finding of present study was that unprotected sex had a negative association with HCV testing. Those PWIDs who had unprotected sex were 0.56 times less likely to have completed HCV testing during last year (OR = 0.56, 95%CI = 0.33-0.78). CONCLUSION: Prevention programs that address age > 30 years, being female, past treatment attempt, previous testing of safe sexual practices, are strongly recommended to prioritize HCV risk reduction strategies.

Factors associated with injecting-related risk behaviors among people who inject drugs: a systematic review and meta-analysis study.

We conducted this systematic review and meta-analysis study to better understand the factors (being drug use, drug type (methamphetamine vs others), sex partner, frequency of injection, homelessness and being HIV positive) associated with injecting-related risk behaviors (lending and/or borrowing already used syringes) among people who inject drugs (PWID). We searched the English language citations in PubMed, Science Direct, Web of Science, and Cochrane electronic databases for previous studies on risk behaviors among PWID. Two independent researchers reviewed all search results, screened for eligibility and extracted data independently. A meta-analysis was conducted with pooled odds ratio and the 95% confidence intervals for factors associated with injecting risk behaviors among PWID. After detailed assessment of the citations, we included 14 studies in our meta-analysis. Binge drug use (OR, 1.39; 95% CI, 1.06-1.71) and drug type (methamphetamine vs. others) (OR, 1.50; 95% CI, 1.22-1.77) were positively associated with the borrowing of already used syringes among PWID. We also found that drug type (OR, 1.39; 95% CI, 1.16-1.64) and homelessness (OR, 1.89; 95% CI, 1.27-2.51) had a positive association with the lending of already used syringes among PWID. Additionally, being HIV positive was negatively associated with the lending of used syringes among PWID (OR, 0.58; 95% CI, 0.37-0.63). This systematic review and meta-analysis supports harm-reduction initiatives especially the establishment and maintenance of sterile needle syringe programs.

Factors associated with HIV testing among people who inject drugs: a meta-analysis.

HIV testing is the first step to early identification, treatment and management of HIV infection among people involved in high-risk behaviors specifically drug injection. Achieving declines in morbidity and mortality associated with HIV and reducing onward HIV transmission are key to testing and treatment. Evaluating the frequency of HIV testing among people who inject drugs (PWID) provides information that helps to identify missed opportunities to improve the efficacy of testing. This meta-analysis aimed to determine the association between HIV testing and risk taking behaviors among PWID. Two independent researchers reviewed the databases of PubMed, Scopus, Web of Science, and Cochrane electronic databases, for manuscripts published between 2000 and 2017, to identify primary studies on the factors associated with HIV testing among PWID. After reviewing for duplication, the full texts of 16 articles were assessed for eligibility. We used fixed and random-effects meta-analysis models to estimate the pooled prevalence, pooled odds ratio (OR) and 95% confidence intervals (CIs) from the data collected. From the four databases 15,965 studies were identified. After evaluations of the citations, article title and abstracts 16 studies were included in the analysis. Findings indicate a significant association between HIV testing and having ≥6 years education (OR = 1.19, 95% CI = 1.01-1.38), a history of imprisonment (OR = 1.92, 95% CI = 1.30-2.53), access to needle syringe programs (OR = 1.6, 95% CI = 1.00-2.21), and PWID attending a health professional or physician (OR = 1.46, 95% CI = 1.17-1.74). Appropriate strategies to increase awareness of primary care physicians on the importance of regular HIV testing will help with global targets for HIV elimination among PWID.