Nevin Manimala

PLoS One. 2023 Apr 12;18(4):e0281043. doi: 10.1371/journal.pone.0281043. eCollection 2023.

ABSTRACT

Smart contracts (SC) are software programs that reside and run over a blockchain. The code can be written in different languages with the common purpose of implementing various kinds of transactions onto the hosting blockchain. They are ruled by the blockchain infrastructure with the intent to automatically implement the typical conditions of traditional contracts. Programs must satisfy context-dependent constraints which are quite different from traditional software code. In particular, since the bytecode is uploaded in the hosting blockchain, the size, computational resources, interaction between different parts of the program are all limited. This is true even if the specific programming languages implement more or less the same constructs as that of traditional languages: there is not the same freedom as in normal software development. The working hypothesis used in this article is that Smart Contract specific constraints should be captured by specific software metrics (that may differ from traditional software metrics). We tested this hypothesis on 85K Smart Contracts written in Solidity and uploaded on the Ethereum blockchain. We analyzed Smart Contracts from two repositories “Etherscan” and “Smart Corpus” and we computed the statistics of a set of software metrics related to Smart Contracts and compared them to the metrics extracted from more traditional software projects. Our results show that generally, Smart Contract metrics have more restricted ranges than the corresponding metrics in traditional software systems. Some of the stylized facts, like power law in the tail of the distribution of some metrics, are only approximate but the lines of code follow a log-normal distribution which reminds us of the same behaviour already found in traditional software systems.

PMID:37043512 | DOI:10.1371/journal.pone.0281043

PLoS One. 2023 Apr 12;18(4):e0282240. doi: 10.1371/journal.pone.0282240. eCollection 2023.

ABSTRACT

BACKGROUND: As adolescence is a transition period from childhood to adulthood malnutrition occurring at this age resonates through generations. Although there were many individual studies in Ethiopia about different form of malnutrition among adolescent, their results are inconclusive indicating the need for generating a pooled estimate of adolescent nutritional status and associated factors. This review and meta-analyses aimed at estimating the pooled prevalence of different forms of malnutrition and associated factors among adolescents in Ethiopia.

METHOD AND MATERIALS: We searched data bases from Pub Med, Cochrane Library, Health Inter Network Access to Research Initiative (HINARI), Science Direct and search engines; Google and Google Scholar and other sources; Reference of References and expert contact which were used to select the studies. Joanna Briggs Institute (JBI) quality appraisal tool was applied to identify eligible studies. STATA/SE V.14 was used to analyze the data. Effect size with 95% Confidence Interval (CI) and heterogeneity were estimated. Heterogeneity of studies was quantified with I2 statistic >50% used as an indicator of heterogeneity. Potential publication bias was assessed using Funnel plots and Egger’s regression test. Trim and fill analysis was also performed. The presences of a statistical association between independent and dependent variables were declared at P <0.05. The PROSPERO registration number for the review is CRD42020159734.

RESULTS: The pooled prevalence of overweight/obesity, stunting and thinness were 10.63% (95% CI: 8.86, 12.40), 20.06% (95% CI: 15.61, 24.51) and 21.68% (95% CI: 9.56, 33.81), respectively. Being female (OR: 2.02, CI: 1.22-3.34), low dietary diversity score (OR: 2.26 CI: 1.28-3.99) and high physical activity (OR: 0.36, 95%CI: 0.14-0.88) were significantly associated with adolescent overweight/obesity. Urban residence (OR: 0.82, 95%CI: 0.68-0.99), protected drinking water source (OR: 0.50, CI: 0.27-0.90) and having family size<5 people (OR: 0.54, CI: 0.44-0.66) were independent predictors of adolescent stunting. Early adolescent age (10-14 years) (OR: 2.38, CI: 1.70-3.34), protected water source for drinking (OR: 0.36, CI: 0.21-0.61), low wealth index (OR: 1.80, CI: 1.01-3.19) and family size <5 people (OR: 0.50, CI: 0.28-0.89) were significantly (P < 0.05) associated with adolescent thinness.

CONCLUSION: The prevalence of overweight/obesity, stunting and thinness are high in Ethiopian adolescents indicating the upcoming challenge of double burden of malnutrition. The results imply the presence of double burden of malnutrition among adolescents which heralds the need for programmatic and policy response in terms of addressing modifiable risk factors including: dietary practices, physical activity, water source and economic status of these adolescents.

PMID:37043492 | DOI:10.1371/journal.pone.0282240

PLoS One. 2023 Apr 12;18(4):e0283327. doi: 10.1371/journal.pone.0283327. eCollection 2023.

ABSTRACT

BACKGROUND: Parentally bereaved children are at increased risk of negative consequences, and the mediating factors most consistently identified are found to be related to family function after the loss, including cohesion. However, existing evidence is limited, especially with respect to children and youths’ own perception of family cohesion and its long-term effects on health and well-being. Therefore, the aim of this study was to investigate self-reported family cohesion the first year after the loss of a parent to cancer and its association to long-term psychological health and well-being among young adults that were bereaved during their teenage years.

METHOD AND PARTICIPANTS: In this nationwide population-based study, 622 of 851 (73%) young adults (aged 18-26) responded to a study-specific questionnaire six to nine years after losing a parent to cancer at the age of 13 to 16. Associations were assessed with modified Poisson regression.

RESULTS: Bereaved youth that reported poor family cohesion the first year after losing a parent to cancer had a higher risk of reporting symptoms of moderate to severe depression six to nine years after the loss compared to those reporting good family cohesion. They also had a higher risk of reporting low levels of well-being, symptoms of anxiety, problematic sleeping and emotional numbness once a week or more at the time of the survey. These results remained statistically significant after adjusting for a variety of possible confounding factors.

CONCLUSION: Self-reported poor family cohesion the first year after the loss of a parent to cancer was strongly associated with long-term negative psychological health-related outcomes among bereaved youth. To pay attention to family cohesion and, if needed, to provide support to strengthen family cohesion in families facing bereavement might prevent long-term suffering for their teenage children.

PMID:37043474 | DOI:10.1371/journal.pone.0283327

PLoS One. 2023 Apr 12;18(4):e0284171. doi: 10.1371/journal.pone.0284171. eCollection 2023.

ABSTRACT

This study calls into question the default computation of the disposition effect that uses the average purchase price as a reference point. We show, through a lab experiment, that the reference price of participants can change depending on the experimental behavioral design that is used. Our results show that for the control group, different reference prices do not show significant differences in the computation of the disposition effect, thus supporting the use of the average purchase price as a reference. However, this is not the case for participants in the treatment group. With the addition of experimental treatment concerning the disclosure of the final balance of the participants, the reference prices to compute the disposition effect showed statistically significant differences. The need to display their results caused these participants to use the first purchase price as a reference point when selling their assets.

PMID:37043473 | DOI:10.1371/journal.pone.0284171

PLOS Glob Public Health. 2023 Apr 12;3(4):e0001741. doi: 10.1371/journal.pgph.0001741. eCollection 2023.

ABSTRACT

Healthcare workers (HCWs) have a significant occupational risk of hepatitis B virus (HBV) infection. Vaccination remains the most effective measure recommended to avert the risk. However, there’s limited information on hepatitis B vaccine uptake rates and the seroprotection status of HCWs, especially in sub-Saharan Africa. This study aimed to assess hepatitis B vaccination status and also seroprotection status of HCWs in three selected public hospitals in Kenya. This was a cross-sectional study carried out among HCWs at Kenyatta National Hospital (KNH), Naivasha and Mbagathi County hospitals. Data on participants’ demographics and hepatitis B vaccination status was collected using an interviewer-guided questionnaire. Blood samples were collected and tested for hepatitis B surface antigen (HBsAg), hepatitis B surface antibodies (anti-HBs), and hepatitis B core antibodies (anti-HBc) using Enzyme Linked Immuno Sorbent Assay technique. Data were analyzed using Statistical Package for the Social Sciences (SPSS) and Graph pad prism. Of the 145 eligible HCWs, 120 (82.8%) were vaccinated, with 77 (53.1%) having received the recommended three doses. Three quarters (108/145) of the vaccinated HCWs were seroprotected (titres ≥10 mIU/ml) against HBV infection, while 16.6% were non-responders (titres <10 mIU/ml). Vaccination with more than two doses and HBV exposure were significantly associated with anti-HBs titre levels (P<0.05). HCWs who received less than 2 doses of the vaccine were 70% less likely to have high anti-HBs titre levels (aOR, 0.3; 95% CI, 0.1-0.8; P = 0.013). Nearly all HCWs were vaccinated against hepatitis B virus. The majority of all HCWs were seroprotected against hepatitis B virus but a number of them had an insufficient immunity to the virus despite vaccination or prior exposure. There’s need to sensitize HCWs and enforce mandatory full vaccination as per the recommended vaccination schedule.

PMID:37043440 | DOI:10.1371/journal.pgph.0001741

PLOS Glob Public Health. 2023 Apr 12;3(4):e0001136. doi: 10.1371/journal.pgph.0001136. eCollection 2023.

ABSTRACT

During 2010-2012, extreme food insecurity and famine in Somalia were estimated to account for 256,000 deaths. Since 2014 Somalia has experienced recurrent below-average rainfall, with consecutive failed rains in late 2016 and 2017 leading to large-scale drought, displacement and epidemics. We wished to estimate mortality across Somalia from 2014 to 2018, and measure the excess death toll attributable to the 2017-2018 drought-triggered crisis. We used a statistical approach akin to small-area estimation, and relying solely on existing data. We identified and re-analysed 91 household surveys conducted at the district level and estimating the crude (CDR) and under 5 years death rate (U5DR) over retrospective periods of 3-4 months. We captured datasets of candidate predictors of mortality with availability by district and month. We also reconstructed population denominators by district-month combining alternative census estimates and displacement data. We combined these data inputs into predictive models to estimate CDR and U5DR and combined the predictions with population estimates to project death tolls. Excess mortality was estimated by constructing counterfactual no-crisis scenarios. Between 2013 and 2018, Somalia’s population increased from 12.0 to 13.5 million, and internally displaced people or returnees reached 20% of the population. We estimated an excess death toll of 44,700 in the most likely counterfactual scenario, and as high as 163,800 in a pessimistic scenario. By contrast to 2010-2012, excess deaths were widespread across Somalia, including central and northern regions. This analysis suggests that the 2017-2018 crisis had a lower, albeit still very substantial, mortality impact than its 2010-2012 predecessor. Despite modest elevations in death rate, crisis conditions were widespread and affected a population of millions. Humanitarian response to drought-related crises in Somalia needs to be strengthened, target the most vulnerable and emphasise very early interventions.

PMID:37043439 | DOI:10.1371/journal.pgph.0001136

PLoS One. 2023 Apr 12;18(4):e0282622. doi: 10.1371/journal.pone.0282622. eCollection 2023.

ABSTRACT

IMPORTANCE: Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders.

OBJECTIVE: The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology.

DESIGN, SETTING, AND PARTICIPANTS: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data.

METHODS: A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia.

RESULTS: Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%).

CONCLUSION: Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

PMID:37043435 | DOI:10.1371/journal.pone.0282622

Ann Med. 2023 Dec;55(1):2198255. doi: 10.1080/07853890.2023.2198255.

ABSTRACT

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D.Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case’s onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood.Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation.KEY MESSAGESThe ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.

PMID:37043275 | DOI:10.1080/07853890.2023.2198255

J Med Internet Res. 2023 Apr 12;25:e42455. doi: 10.2196/42455.

ABSTRACT

BACKGROUND: Cardiovascular diseases (CVDs) cause most deaths globally and can reduce quality of life (QoL) of rehabilitees with cardiac disease. The risk factors of CVDs are physical inactivity and increased BMI. With physical activity, it is possible to prevent CVDs, improve QoL, and help maintain a healthy body mass. Current literature shows the possibilities of digitalization and advanced technology in supporting independent self-rehabilitation. However, the interpretation of the results is complicated owing to the studies’ high heterogeneity. In addition, the added value of this technology has not been studied well, especially in cardiac rehabilitation.

OBJECTIVE: We aimed to examine the effectiveness of added remote technology in cardiac rehabilitation on physical function, anthropometrics, and QoL in rehabilitees with CVD compared with conventional rehabilitation.

METHODS: Rehabilitees were cluster randomized into 3 remote technology intervention groups (n=29) and 3 reference groups (n=30). The reference group received conventional cardiac rehabilitation, and the remote technology intervention group received conventional cardiac rehabilitation with added remote technology, namely, the Movendos mCoach app and Fitbit charge accelerometer. The 12 months of rehabilitation consisted of three 5-day in-rehabilitation periods in the rehabilitation center. Between these periods were two 6-month self-rehabilitation periods. Outcome measurements included the 6-minute walk test, body mass, BMI, waist circumference, and World Health Organization QoL-BREF questionnaire at baseline and at 6 and 12 months. Between-group differences were assessed using 2-tailed t tests and Mann-Whitney U test. Within-group differences were analyzed using a paired samples t test or Wilcoxon signed-rank test.

RESULTS: Overall, 59 rehabilitees aged 41 to 66 years (mean age 60, SD 6 years; n=48, 81% men) were included in the study. Decrement in waist circumference (6 months: 1.6 cm; P=.04; 12 months: 3 cm; P<.001) and increment in self-assessed QoL were greater (environmental factors: 0.5; P=.02) in the remote technology intervention group than the reference group. Both groups achieved statistically significant improvements in the 6-minute walk test in both time frames (P=.01-.03). Additionally, the remote technology intervention group achieved statistically significant changes in the environmental domain at 0-6 months (P=.03) and waist circumference at both time frames (P=.01), and reference group achieve statistically significant changes in waist circumference at 0-6 months (P=.02).

CONCLUSIONS: Remote cardiac rehabilitation added value to conventional cardiac rehabilitation in terms of waist circumference and QoL. The results were clinically small, but the findings suggest that adding remote technology to cardiac rehabilitation may increase beneficial health outcomes. There was some level of systematic error during rehabilitation intervention, and the sample size was relatively small. Therefore, care must be taken when generalizing the study results beyond the target population. To confirm assumptions of the added value of remote technology in rehabilitation interventions, more studies involving different rehabilitees with cardiac disease are required.

TRIAL REGISTRATION: ISRCTN Registry ISRCTN61225589; https://www.isrctn.com/ISRCTN61225589.

PMID:37043264 | DOI:10.2196/42455

JMIR Form Res. 2023 Apr 12;7:e45301. doi: 10.2196/45301.

ABSTRACT

BACKGROUND: Although there is an increasing use of digital adherence technologies (DATs), such as real-time monitors and SMS reminders in tuberculosis medication adherence, suboptimal patient engagement with various DATs has been reported. Additionally, financial constraints can limit DAT’s utility. The perceived usefulness and the design mechanisms of DATs linked to mobile money financial incentives for tuberculosis medication management remain unclear.

OBJECTIVE: The aim of this study is to describe the perceived usefulness and design mechanisms for a DAT intervention called My Mobile Wallet, which is composed of real-time adherence monitors, SMS reminders, and mobile money incentives to support tuberculosis medication adherence in a low-income setting.

METHODS: This study used mixed methods approaches among persons with tuberculosis recruited from the Tuberculosis Clinic in the Mbarara Regional Referral Hospital. We purposively sampled 21 persons with tuberculosis aged 18 years and older, who owned cell phones and were able to use SMS text messaging interventions. We also enrolled 9 participants who used DATs in our previous study. We used focus group discussions with the 30 participants to solicit perceptions about the initial version of the My Mobile Wallet intervention, and then iteratively refined subsequent versions of the intervention following a user-centered design approach until the beta version of the intervention that suited their needs was developed. Surveys eliciting information about participants’ cell phone use and perceptions of the intervention were also administered. Content analysis was used to inductively analyze qualitative data to derive categories describing the perceived usefulness of the intervention, concerns, and design mechanisms. Stata (version 13; StataCorp) was used to analyze survey data.

RESULTS: Participants expressed the perceived usefulness of the My Mobile Wallet intervention in terms of being reminded to take medication, supported with transport to the clinic, and money to meet other tuberculosis medication-related costs, all of which were perceived to imply care, which could create a sense of connectedness to health care workers. This could consequently cause participants to develop a self-perceived need to prove their commitment to adherence to health care workers who care for them, thereby motivating medication adherence. For fear of unintended tuberculosis status disclosure, 20 (67%) participants suggested using SMS language that is confidential-not easily related to tuberculosis. To reduce the possibilities of using the money for other competing demands, 25 (83%) participants preferred sending the money 1-2 days before the appointment to limit the time lag between receiving the money and visiting the clinic.

CONCLUSIONS: DATs complemented with mobile money financial incentives could potentially provide acceptable approaches to remind, support, and motivate patients to adhere to taking their tuberculosis medication.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05656287; https://clinicaltrials.gov/ct2/show/NCT05656287.

PMID:37043263 | DOI:10.2196/45301