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Nevin Manimala Statistics

Accurate detection of atrial fibrillation events with R-R intervals from ECG signals

PLoS One. 2022 Aug 4;17(8):e0271596. doi: 10.1371/journal.pone.0271596. eCollection 2022.

ABSTRACT

Atrial fibrillation (AF) is a typical category of arrhythmia. Clinical diagnosis of AF is based on the detection of abnormal R-R intervals (RRIs) with an electrocardiogram (ECG). Previous studies considered this detection problem as a classification problem and focused on extracting a number of features. In this study we demonstrate that instead of using any specific numerical characteristic as the input feature, the probability density of RRIs from ECG conserves comprehensive statistical information; hence, is a natural and efficient input feature for AF detection. Incorporated with a support vector machine as the classifier, results on the MIT-BIH database indicates that the proposed method is a simple and accurate approach for AF detection in terms of accuracy, sensitivity, and specificity.

PMID:35925979 | DOI:10.1371/journal.pone.0271596

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Nevin Manimala Statistics

Assessing risk factors for latent and active tuberculosis among persons living with HIV in Florida: A comparison of self-reports and medical records

PLoS One. 2022 Aug 4;17(8):e0271917. doi: 10.1371/journal.pone.0271917. eCollection 2022.

ABSTRACT

PURPOSE: This study examined factors associated with TB among persons living with HIV (PLWH) in Florida and the agreement between self-reported and medically documented history of tuberculosis (TB) in assessing the risk factors.

METHODS: Self-reported and medically documented data of 655 PLWH in Florida were analyzed. Data on sociodemographic factors such as age, race/ethnicity, place of birth, current marital status, education, employment, homelessness in the past year and ‘ever been jailed’ and behavioural factors such as excessive alcohol use, marijuana, injection drug use (IDU), substance and current cigarette use were obtained. Health status information such as health insurance status, adherence to HIV antiretroviral therapy (ART), most recent CD4 count, HIV viral load and comorbid conditions were also obtained. The associations between these selected factors with self-reported TB and medically documented TB diagnosis were compared using Chi-square and logistic regression analyses. Additionally, the agreement between self-reports and medical records was assessed.

RESULTS: TB prevalence according to self-reports and medical records was 16.6% and 7.5% respectively. Being age ≥55 years, African American and homeless in the past 12 months were statistically significantly associated with self-reported TB, while being African American homeless in the past 12 months and not on antiretroviral therapy (ART) were statistically significantly associated with medically documented TB. African Americans compared to Whites had odds ratios of 3.04 and 4.89 for self-reported and medically documented TB, respectively. There was moderate agreement between self-reported and medically documented TB (Kappa = 0.41).

CONCLUSIONS: TB prevalence was higher based on self-reports than medical records. There was moderate agreement between the two data sources, showing the importance of self-reports. Establishing the true prevalence of TB and associated risk factors in PLWH for developing policies may therefore require the use of self-reports and confirmation by screening tests, clinical signs and/or microbiologic data.

PMID:35925972 | DOI:10.1371/journal.pone.0271917

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Nevin Manimala Statistics

Assessment of Tennessee’s county-level vulnerability to hepatitis C virus and HIV outbreaks using socioeconomic, healthcare, and substance use indicators

PLoS One. 2022 Aug 4;17(8):e0270891. doi: 10.1371/journal.pone.0270891. eCollection 2022.

ABSTRACT

BACKGROUND: Human immunodeficiency virus (HIV), hepatitis C virus (HCV), and injection drug use are syndemic in the central Appalachian states. In Tennessee (TN), declines in HIV among persons who inject drugs (PWID) stalled, and HCV infection rates increased significantly from 2013-2017. To better target strategies to address the syndemic, county-level socioeconomic, opioid use, access to healthcare, and health factors were modeled to identify indicators predictive of vulnerability to an HIV/HCV outbreak among PWID in TN.

METHODS: Newly reported chronic HCV cases among persons aged 13-39 years in 2016-2017 were used as a proxy for county-level HIV/HCV vulnerability among TN’s 95 counties. Seventy-five publicly available county-level measures from 2016-2017 were collected and reduced through multiple dimension reduction techniques. Negative binomial regression identified indicators associated with HCV which were used to calculate county-level vulnerability to a local HIV/HCV outbreak.

RESULTS: Thirteen county-level indicators were identified as strongly predictive of HIV/HCV vulnerability with the statistically significant indicators being percentage of the population aged 20-44 years, per capita income, teen birth rate, percentage of clients in TDMHSAS-funded opioid treatment and recovery, syphilis case rate, and percentage of homes with at least one vehicle. Based on the 13 indicators, we identified the distribution of vulnerability to an HIV/HCV outbreak among TN’s counties. Eleven high vulnerability counties were identified, with the preponderance located in east and middle TN.

CONCLUSION: This analysis identified the county-level factors most associated with vulnerability to an HIV/HCV outbreak among PWID in TN. These results, alongside routine surveillance, will guide targeted prevention and linkage to care efforts for the most vulnerable communities.

PMID:35925969 | DOI:10.1371/journal.pone.0270891

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Nevin Manimala Statistics

Causal relationships between sex hormone traits, lifestyle factors, and osteoporosis in men: A Mendelian randomization study

PLoS One. 2022 Aug 4;17(8):e0271898. doi: 10.1371/journal.pone.0271898. eCollection 2022.

ABSTRACT

Although observational studies have explored factors that may be associated with osteoporosis, it is not clear whether they are causal. Osteoporosis in men is often underestimated. This study aimed to identify the causal risk factors associated with bone mineral density(BMD) in men. Single nucleotide polymorphisms (SNPs) associated with the exposures at the genome-wide significance (p < 5×10-8) level were obtained from corresponding genome-wide association studies (GWASs) and were utilized as instrumental variables. Summary-level statistical data for BMD were obtained from two large-scale UK Biobank GWASs. A Mendelian randomization (MR) analysis was performed to identify causal risk factors for BMD. Regarding the BMD of the heel bone, the odds of BMD increased per 1-SD increase of free testosterone (FT) (OR = 1.13, P = 9.4 × 10-17), together with estradiol (E2) (OR = 2.51, P = 2.3 × 10-4). The odds of BMD also increased with the lowering of sex-hormone binding globulin (SHBG) (OR = 0.87, P = 7.4 × 10-8) and total testosterone (TT) (OR = 0.96, P = 3.2 × 10-2) levels. Regarding the BMD of the lumbar spine, the odds of BMD increased per 1-SD increase in FT (OR = 1.18, P = 4.0 × 10-3). Regarding the BMD of the forearm bone, the odds of BMD increased with lowering SHBG (OR = 0.75, P = 3.0 × 10-3) and TT (OR = 0.85, P = 3.0 × 10-3) levels. Our MR study corroborated certain causal relationships and provided genetic evidence among sex hormone traits, lifestyle factors and BMD. Furthermore, it is a novel insight that TT was defined as a disadvantage for osteoporosis in male European populations.

PMID:35925966 | DOI:10.1371/journal.pone.0271898

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Nevin Manimala Statistics

Causes of infant deaths and patterns of associated factors in Eastern Ethiopia: Results of verbal autopsy (InterVA-4) study

PLoS One. 2022 Aug 4;17(8):e0270245. doi: 10.1371/journal.pone.0270245. eCollection 2022.

ABSTRACT

BACKGROUND: In a range of setting, detecting and generate empirical information on the cause of infant death and contributing risk factors at population level is basically utmost essential to take evidence-based measures in reducing infant morbidity and mortality. An electronic verbal autopsy is suitable tool and best alternative solution to determine individuals’ cause of death in a setting where the majority of deaths occur at home and civil registration systems do not exist. The present study was undertaken to find out cause of infant death, applying computer-based probabilistic model (InterVA-4) and analyze the patterns of association factors of mother’s and the deceased infant’s characteristics to the leading cause-specific infant mortality in Eastern Ethiopia.

METHODS: The study employed a community-based prospective longitudinal survey, which was conducted with routinely enumeration of reported infant deaths for a period of two years (from September 2016 to August 2018) in Eastern part of Ethiopia. Using the two-stage cluster sampling technique, the study was undertaken in four randomly selected districts of West Hararghe zone and two districts of zone 3 in Oromia and Afar regional state, respectively. The study included a total of 362 infants who were deceased during the study period. Data was collected by trained enumerators by interviewing the mothers or guardians of the deceased infant using a 2014 standardize World Health Organization (WHO) Verbal Autopsy questionnaire. InterVA-4 model were used for processing and interpreting verbal autopsy data in order to arrive at the most likely causes of infant death. SPSS version 23 was also used for statistical analysis of frequency distribution and logistic regression for the association between covariates and outcomes.

FINDINGS: Of the overall (362) deceased infants’ during the study period, 53.0% of deaths occurred during neonatal time while 47.0% died in the post-neonatal period. Acute respiratory infection including neonatal and post-neonatal pneumonia (38.4%), birth asphyxia (16.4%), diarrheal diseases (16.3%), prematurity (7.4%) and malaria (4.3%) were found to be the leading causes of infant mortality in the study area. The independent factors strongly associated with probable ARI, including pneumonia related mortality as compared to all-causes of death were infants with maternal age lower than 20 years old (p = 0.001, AOR: 4.82, 95% CI: 1.88, 12.3) and infant being died outside of heath facilities (P = 0.007, AOR: 2.85, 95% CI: 1.33, 6.12). The post-neonatal period (P = 0.000, AOR: 15.5, 95% CI: 6.35, 37.8) and infant died in the wet season (P = 0.006, AOR: 2.38, 95% CI: 1.28, 4.44) had strong relationship with dying from diarrhea-related death than those infants died from all non-diarrhea. The death due to malaria robustly associated with infants whose mothers age between 20-35 years old (P = 0.024, AOR: 4.44, 95% CI: 1.22, 16.2) and infant who was dwelled in the districts of Afar region (P = 0.013, AOR: 4.08, 95% CI: 1.35, 12.4).

CONCLUSION: The highest cause of infant mortality was associated with disease of respiratory system, particularly acute respiratory infection, including both neonates and post-neonatal pneumonia. Most of the infant deaths existed are as a result of diseases and conditions that are readily preventable or treatable cause, similar to those reported in worldwide, which have needs of further attention. The patterns of significant associated factors across cause-specific mortality against all-cause of death were dissimilar. Therefore, strengthen maternal and child health program with effective preventive interventions emphasizing on the most common cause of infant deaths and those factors contributing in raising mortality risk are required.

PMID:35925957 | DOI:10.1371/journal.pone.0270245

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Nevin Manimala Statistics

Sustainable human resource management the mediating role between work engagement and teamwork performance

PLoS One. 2022 Aug 4;17(8):e0271134. doi: 10.1371/journal.pone.0271134. eCollection 2022.

ABSTRACT

The present work aims to analyze the properties of the working conditions recorded in the Sixth European Working Conditions Survey (EWCS); with it, it has being built seven independent indexes about different aspects of work’ quality in the health sector, and these constructs are used to evaluate their effects on work engagement (WE). In this sense, the originality of incorporating teamwork as a modulating variable is included. To analyze the effects of the job quality index (JQI) on the WE, a logistic regression model is proposed for a total of 3044 workers within the health sector, differentiating between those who work or not in a team; in a first stage and these estimates are compared with those obtained using an artificial neural network model, and both are used for the consideration of the research hypotheses about several causal factor. An important contributions of the study, it is related to how work commitment is mainly influenced by prospects, social environment, intensity and earnings, all of them related to job performance. Therefore, knowledge of the determinants of work commitment and the ability to modulate its effects in teamwork environments is necessary for the development of truly sustainable Human Resources policies.

PMID:35925955 | DOI:10.1371/journal.pone.0271134

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Nevin Manimala Statistics

Environmental drivers of the occurrence and abundance of the Irukandji jellyfish (Carukia barnesi)

PLoS One. 2022 Aug 4;17(8):e0272359. doi: 10.1371/journal.pone.0272359. eCollection 2022.

ABSTRACT

Understanding the links between species and their environment is critical for species management. This is particularly true for organisms of medical and/or economic significance. The ‘Irukandji’ jellyfish (Carukia barnesi) is well known for its small size, cryptic nature, and highly venomous sting. Being the namesake of the Irukandji syndrome, contact with this marine stinger often leads to hospitalization and can be fatal. Consequently, the annual occurrence of this organism is believed to cost the Australian government an estimated $AUD3 billion annually in medical costs and losses for tourism. Despite its economic importance the logistical difficulties related to surveying C.barnesi in situ has led to a paucity of knowledge regarding its ecology and significantly impeded management strategies to date. In this study, we use six years of direct C. barnesi capture data to explore patterns pertaining to the annual occurrence and abundance of this species in the nearshore waters of the Cairns coast. We provide novel insights into trends in medusae aggregations and size distribution and primarily focus on the potential role of environmental drivers for annual C. barnesi occurrence patterns. Using a two-part hurdle model, eight environmental parameters were investigated over four time periods for associations with records of medusa presence and abundance. Final models showed a small amount of variation in medusa presence and abundance patterns could be accounted for by long-term trends pertaining to rainfall and wind direction. However, the assessed environmental parameters could not explain high annual variation or site location effects. Ultimately best-fit models had very low statistical inference power explaining between 16 and 20% of the variance in the data, leaving approximately 80% of all variation in medusa presence and abundance unexplained.

PMID:35925949 | DOI:10.1371/journal.pone.0272359

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Nevin Manimala Statistics

Rapid identification of polypeptide from carbapenem-resistant and susceptible Escherichia coli via Orbitrap-MS and pattern recognition analyses

Chem Biodivers. 2022 Aug 4. doi: 10.1002/cbdv.202200118. Online ahead of print.

ABSTRACT

A rapid and accurate analytical method was established to identify CREC and CSEC. Orbitrap-MS was used to detect the polypeptide of CREC and CSEC strains, and MS data were analyzed by pattern recognition analyses such as hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). HCA based on the farthest distance method could well distinguish the two types of E. coli, and the cophenetic correlation coefficient of the farthest distance method was 0.901. Comparing the results of PCA, PLS-DA, and OPLS-DA, OPLS-DA exhibited the highest accuracy in predicting the CREC and CSEC strains. A total of 26 compounds were identified, and six of the compounds were the highly significant difference between the two types of strains. MS combined with pattern recognition can achieve a more comprehensive and efficient statistical analysis of complex biological samples.

PMID:35925667 | DOI:10.1002/cbdv.202200118

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Nevin Manimala Statistics

Supporting Self-management and Quality of Life in Bipolar Disorder With the PolarUs App (Alpha): Protocol for a Mixed Methods Study

JMIR Res Protoc. 2022 Aug 4;11(8):e36213. doi: 10.2196/36213.

ABSTRACT

BACKGROUND: Quality of life (QoL) is increasingly being recognized as a key outcome of interventions for bipolar disorder (BD). Mobile phone apps can increase access to evidence-based self-management strategies and provide real-time support. However, although individuals with lived experiences desire support with monitoring and improving broader health domains, existing BD apps largely target mood symptoms only. Further, evidence from the broader mobile health (mHealth) literature has shown that the desires and goals of end users are not adequately considered during app development, and as a result, engagement with mental health apps is suboptimal. To capitalize on the potential of apps to optimize wellness in BD, there is a need for interventions developed in consultation with real-world users designed to support QoL self-monitoring and self-management.

OBJECTIVE: This mixed methods pilot study was designed to evaluate the alpha version of the newly developed PolarUs app, developed to support QoL self-monitoring and self-management in people with BD. Co-designed using a community-based participatory research framework, the PolarUs app builds on the web-based adaptation of a BD-specific QoL self-assessment measure and integrates material from a web-based portal providing information on evidence-informed self-management strategies in BD. The primary objectives of this project were to evaluate PolarUs app feasibility (via behavioral use metrics), the impact of PolarUs (via the Brief Quality of Life in Bipolar Disorder scale, our primary outcome measure), and explore engagement with the PolarUs app (via quantitative and qualitative methods).

METHODS: Participants will be residents of North America (N=150), aged >18 years, with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnosis of BD type 1, BD type 2, or BD not otherwise specified as assessed by structured diagnostic interview. An embedded mixed methods research design will be adopted; qualitative interviews with a purposefully selected subsample (approximately, n=30) of participants will be conducted to explore in more depth feasibility, impact, and engagement with the PolarUs app over the 12-week study period.

RESULTS: At the time of publication of this protocol, the development of the alpha version of the PolarUs app was complete. Participant enrollment has begun in June 2022. Data collection is expected to be completed by December 2022.

CONCLUSIONS: Beyond contributing knowledge on the feasibility and impact of a novel app to support QoL and self-management in BD, this study will also provide new insights related to engagement with mHealth apps. Furthermore, it will function as a case study of successful co-design between people with BD, health care providers, and BD researchers, providing a template for the future use of community-based participatory research frameworks in mHealth intervention development. The results will be used to further refine the PolarUs app and inform the design of a larger clinical trial.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/36213.

PMID:35925666 | DOI:10.2196/36213

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Nevin Manimala Statistics

Regression Models for Ordinal Outcomes

JAMA. 2022 Aug 4. doi: 10.1001/jama.2022.12104. Online ahead of print.

NO ABSTRACT

PMID:35925592 | DOI:10.1001/jama.2022.12104