Tag: nevin manimala

BMJ Open. 2023 May 17;13(5):e064382. doi: 10.1136/bmjopen-2022-064382.

ABSTRACT

OBJECTIVE: The objective of this study was to finalise the development of the Young Disability Questionnaire (YDQ-spine) to measure the consequences of neck, midback and low back pain, relevant for schoolchildren aged 9-12 years.

DESIGN: A cross-sectional field test of the YDQ-spine was carried out.

SETTING: Danish primary schools.

PARTICIPANTS: Children aged 9-12 years from all Danish schools were invited to complete the questionnaire.

METHODS: Eight hundred and seventy-three schools were invited to participate. Consenting schools received information material, instructions and a link to an electronic version of the prefinal YDQ-spine. Local teachers distributed the electronic YDQ-spine to children aged 9-12 years. Descriptive statistics and item characteristics were carried out. Item reduction was performed using partial interitem correlations (scrutinising correlations>0.3) and factor analyses (items loading>0.3 were retained) to eliminate redundant items and to obtain insight into the structure of the questionnaire.

RESULTS: A total of 768 children from 20 schools answered of the questionnaire and 280 fulfilled the inclusion criteria of having back and/or neck pain (36%). Multisite pain was reported by 38%. Partial interitem correlations and factor analyses resulted in elimination of four items which were considered redundant leaving 24 items in the final YDQ-spine with an optional section on what matters most to the child. The factor analyses showed a two-factor structure with a physical component (13 items) and a psychosocial component (10 items) in addition to one standalone item (sleep).

CONCLUSION: The YDQ-spine is a novel questionnaire with satisfactory content validity measuring physical and psychosocial components (including sleep disturbances) of spinal pain in children aged 9-12 years. It also offers an optional section on what matters most to the child allowing targeted care in clinical practice.

PMID:37197823 | DOI:10.1136/bmjopen-2022-064382

BMJ Open. 2023 May 17;13(5):e070203. doi: 10.1136/bmjopen-2022-070203.

ABSTRACT

OBJECTIVE: This study aimed to assess the sociodemographic and institutional determinants of zinc bundled with oral rehydration salt (ORS) utilisation among under-five children with diarrhoeal diseases in East Wallaga zone, western Ethiopia, in 2022.

METHODS: A community-based cross-sectional study was conducted among 560 randomly selected participants from 1 to 30 April 2022. Data were entered into EpiData V.3.1, then exported to the Statistical Package for Social Science (SPSS) V.25 for analysis. An adjusted OR (AOR) along with a 95% confidence level was estimated to assess the strength of the association, and a p value <0.05 was considered to declare the statistical significance.

RESULTS: About 39.6% of the participants had used zinc bundled with ORS for their children with diarrhoea at least once in the last 12 months. Being aged 40-49 years for mothers or caregivers (AOR 3.48, 95% CI 1.41, 8.53); merchant (AOR 4.11, 95% CI 1.73, 8.12); mothers or caregivers able to read and write (AOR 5.77, 95% CI 1.22, 11.67); visited secondary level (AOR 2.82, 95% CI 1.30, 6.10) and tertiary level health facilities (AOR 0.016, 95% CI 0.03, 0.97); degree and above (AOR 0.06, 95% CI 0.03, 0.12) and doctorate (AOR 0.13, 95% CI 0.04, 0.44) holder healthcare professionals were statistically associated with utilisation of zinc bundled with ORS.

CONCLUSION: The study found that about two in five of the participants had used zinc bundled with ORS for their under-five children with diarrhoeal diseases. Age, occupation, educational status, level of health facilities visited and level of health professionals provided care were determinants of zinc bundled with ORS utilisation. So, health professionals at different levels of the health system have to enhance the maximisation of its bundled uptake.

PMID:37197822 | DOI:10.1136/bmjopen-2022-070203

BMJ Open. 2023 May 17;13(5):e066702. doi: 10.1136/bmjopen-2022-066702.

ABSTRACT

OBJECTIVES: Examine the association between multiple clinical staff levels and case-mix adjusted patient mortality in English hospitals. Most studies investigating the association between hospital staffing levels and mortality have focused on single professional groups, in particular nursing. However, single staff group studies might overestimate effects or neglect important contributions to patient safety from other staff groups.

DESIGN: Retrospective observational study of routinely available data.

SETTING AND PARTICIPANTS: 138 National Health Service hospital trusts that provided general acute adult services in England between 2015 and 2019.

OUTCOME MEASURE: Standardised mortality rates were derived from the Summary Hospital level Mortality Indicator data set, with observed deaths as outcome in our models and expected deaths as offset. Staffing levels were calculated as the ratio of occupied beds per staff group. We developed negative binomial random-effects models with trust as random effects.

RESULTS: Hospitals with lower levels of medical and allied healthcare professional (AHP) staff (e.g, occupational therapy, physiotherapy, radiography, speech and language therapy) had significantly higher mortality rates (rate ratio: 1.04, 95% CI 1.02 to 1.06, and 1.04, 95% CI 1.02 to 1.06, respectively), while those with lower support staff had lower mortality rates (0.85, 95% CI 0.79 to 0.91 for nurse support, and 1.00, 95% CI 0.99 to 1.00 for AHP support). Estimates of the association between staffing levels and mortality were stronger between-hospitals than within-hospitals, which were not statistically significant in a within-between random effects model.

CONCLUSIONS: In additional to medicine and nursing, AHP staffing levels may influence hospital mortality rates. Considering multiple staff groups simultaneously when examining the association between hospital mortality and clinical staffing levels is crucial.

TRIAL REGISTRATION NUMBER: NCT04374812.

PMID:37197808 | DOI:10.1136/bmjopen-2022-066702

BMJ Ment Health. 2023 Feb;26(1):e300677. doi: 10.1136/bmjment-2023-300677.

ABSTRACT

In recent years, network meta-analyses have been increasingly carried out to inform clinical guidelines and policy. This approach is under constant development, and a broad consensus on how to carry out several of its methodological and statistical steps is still lacking. Therefore, different working groups might often make different methodological choices based on their clinical and research experience, with possible advantages and shortcomings. In this contribution, we will critically assess two network meta-analyses on the topic of pharmacological prevention of relapse in schizophrenia, carried out by two different research groups. We will highlight the implications of different methodological choices on the analysis results and their clinical-epidemiological interpretation. Moreover, we will discuss some of the most relevant technical issues of network meta-analyses for which there is not a broad methodological agreement, including the assessment of transitivity.

PMID:37197798 | DOI:10.1136/bmjment-2023-300677

J Med Genet. 2023 May 16:jmg-2022-109132. doi: 10.1136/jmg-2022-109132. Online ahead of print.

ABSTRACT

BACKGROUND: Limb-girdle muscular dystrophies (LGMD) are a heterogeneous group of genetically determined muscle disorders. TRAPPC11-related LGMD is an autosomal-recessive condition characterised by muscle weakness and intellectual disability.

METHODS: A clinical and histopathological characterisation of 25 Roma individuals with LGMD R18 caused by the homozygous TRAPPC11 c.1287+5G>A variant is reported. Functional effects of the variant on mitochondrial function were investigated.

RESULTS: The c.1287+5G>A variant leads to a phenotype characterised by early onset muscle weakness, movement disorder, intellectual disability and elevated serum creatine kinase, which is similar to other series. As novel clinical findings, we found that microcephaly is almost universal and that infections in the first years of life seem to act as triggers for a psychomotor regression and onset of seizures in several individuals with TRAPPC11 variants, who showed pseudometabolic crises triggered by infections. Our functional studies expanded the role of TRAPPC11 deficiency in mitochondrial function, as a decreased mitochondrial ATP production capacity and alterations in the mitochondrial network architecture were detected.

CONCLUSION: We provide a comprehensive phenotypic characterisation of the pathogenic variant TRAPPC11 c.1287+5G>A, which is founder in the Roma population. Our observations indicate that some typical features of golgipathies, such as microcephaly and clinical decompensation associated with infections, are prevalent in individuals with LGMD R18.

PMID:37197784 | DOI:10.1136/jmg-2022-109132

J Appl Stat. 2022 Apr 24;50(7):1650-1663. doi: 10.1080/02664763.2022.2064439. eCollection 2023.

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance.

PMID:37197760 | PMC:PMC10184601 | DOI:10.1080/02664763.2022.2064439

Biometrika. 2022 Aug 19;110(2):519-536. doi: 10.1093/biomet/asac050. eCollection 2023 Jun.

ABSTRACT

Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated parametrically or nonparametrically. To improve the accuracy, we leverage the characteristic of zero-inflated outcomes by estimating the conditional means in two parts, that is, separately modelling the probability of having positive outcomes given confounders, and the mean outcome conditional on its being positive and given the confounders. We show that the proposed estimator is consistent and asymptotically normal as either the sample size or the follow-up time goes to infinity. Moreover, the typical sandwich formula can be used to estimate the variance of treatment effect estimators consistently, without accounting for the variation due to estimating nuisance functions. Simulation studies and an application to a freemium mobile game dataset are presented to demonstrate the empirical performance of the proposed method and support our theoretical findings.

PMID:37197742 | PMC:PMC10183836 | DOI:10.1093/biomet/asac050

Biometrika. 2022 Jul 25;110(2):485-498. doi: 10.1093/biomet/asac042. eCollection 2023 Jun.

ABSTRACT

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure, and conclude with a substantive motivating example on the causal effect of education on income in the highly selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the [Formula: see text] package [Formula: see text].

PMID:37197741 | PMC:PMC10183833 | DOI:10.1093/biomet/asac042

Biometrika. 2022 Jul 12;110(2):339-360. doi: 10.1093/biomet/asac041. eCollection 2023 Jun.

ABSTRACT

Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficiently implemented with the rich toolbox developed for gradient methods from the deep learning literature. Most notably, these gradient-based algorithms can be combined with stochastic gradient descent methods to produce efficient online sparse principal component analysis algorithms with provable numerical and statistical performance guarantees. The practical performance and usefulness of the new algorithms are demonstrated in various simulation studies. As an application, we show how the scalability and statistical accuracy of our method enable us to find interesting functional gene groups in high-dimensional RNA sequencing data.

PMID:37197740 | PMC:PMC10183835 | DOI:10.1093/biomet/asac041

Biometrika. 2022 Aug 13;110(2):395-410. doi: 10.1093/biomet/asac047. eCollection 2023 Jun.

ABSTRACT

We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomial rates of convergence. Simulations and analysis of the Atherosclerosis Risk in Communities study data suggest that the new estimator brings higher expected outcomes than existing methods in various settings.

PMID:37197739 | PMC:PMC10183834 | DOI:10.1093/biomet/asac047