Tag: statistics

BMC Pediatr. 2022 Jul 22;22(1):439. doi: 10.1186/s12887-022-03499-5.

ABSTRACT

BACKGROUND: Vitamin A is a nutrient that is required in a small amount for normal visual system function, growth and development, epithelia’s cellular integrity, immune function, and reproduction. Vitamin A has a significant and clinically important effect since it has been associated with a reduction in all-cause and diarrhea mortality. The aim of this study was to determine factors associated with national vitamin A supplementation among children aged 6-35 months.

METHOD: The data for this study was extracted from the 2019 Ethiopian Mini Demographic and Health Survey. A total weighted sample of 2242 women with children aged 6-35 months was included in the study. The analysis was performed using Stata version 14.2 software. Applying sampling weight for descriptive statistics and complex sample design for inferential statistics, a manual backward stepwise elimination approach was applied. Finally, statistical significance declared at the level of p value < 0.05.

RESULT: The overall coverage of vitamin A supplementation among children aged 6-35 months for the survey included was 44.4 95% CI (40.15, 48.74). In the multivariable analysis, mothers who had four or more antenatal visits [AOR = 2.02 (95% CI: 1.34, 3.04)] were two times more likely to receive vitamin A capsules for their children than mothers who had no antenatal visits. Children from middle-wealth quintiles had higher odds of receiving vitamin A capsules in comparison to children from the poorest wealth quintile [AOR = 1.77 (95% CI: 1.14, 2.73)]. Older children had higher odds of receiving vitamin A capsules than the youngest ones. Other factors that were associated with vitamin A supplementation were mode of delivery and region.

CONCLUSION: The coverage of vitamin A supplementation in Ethiopia remains low and it is strongly associated with antenatal visit, household wealth index and age of child. Expanding maternal health services like antenatal care visits should be prioritized.

PMID:35864488 | DOI:10.1186/s12887-022-03499-5

BMC Pediatr. 2022 Jul 22;22(1):441. doi: 10.1186/s12887-022-03503-y.

ABSTRACT

BACKGROUND: Mortality among under-five children in Tanzania remains high. While early presentation for treatment increases likelihood of survival, delays to care are common and factors causing delay to presentation among critically ill children are unknown. In this study delay was defined as presentation to the emergency department of tertially hospital i.e. Muhimbili National Hospital, more than 48 h from the onset of the index illness.

METHODOLOGY: This was a prospective cohort study of critically ill children aged 28 days to 14 years attending emergency department at Muhimbili National Hospital in Tanzania from September 2019 to January 2020. We documented demographics, time to ED presentation, ED interventions and 30-day outcome. The primary outcome was the association of delay with mortality and secondary outcomes were predictors of delay among critically ill paediatric patients. Logistic regression and relative risk were calculated to measure the strength of the predictor and the relationship between delay and mortality respectively.

RESULTS: We enrolled 440 (59.1%) critically ill children, their median age was 12 [IQR = 9-60] months and 63.9% were males. The median time to Emergency Department arrival was 3 days [IQR = 1-5] and more than half (56.6%) of critically ill children presented to Emergency Department in > 48 h whereby being an infant, self-referral and belonging to poor family were independent predictors of delay. Infants and those referred from other facilities had 2.4(95% CI 1.4-4.0) and 1.8(95% CI 1.1-2.8) times increased odds of presenting late to the Emergency Department respectively. The overall 30-day in-hospital mortality was 26.5% in which those who presented late were 1.3 more likely to die than those who presented early (RR = 1.3, CI: 0.9-1.9). Majority died > 24 h of Emergency Department arrival (P-value = 0.021).

CONCLUSION: The risk of in-hospital mortality among children who presented to the ED later than 48 h after onset of illness was 1.3 times higher than for children who presented earlier than 48 h. It could be anywhere from 10% lower to 90% higher than the point estimate. However, the effect size was statistically not significant since the confidence interval included the null value Qualitative and time-motion studies are needed to evaluate the care pathway of critically ill pediatric patients to identify preventable delays in care.

PMID:35864482 | DOI:10.1186/s12887-022-03503-y

BMC Infect Dis. 2022 Jul 21;22(1):637. doi: 10.1186/s12879-022-07617-7.

ABSTRACT

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.

METHODS: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S.

INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.

RESULTS: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).

CONCLUSION: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

PMID:35864468 | DOI:10.1186/s12879-022-07617-7

BMC Pregnancy Childbirth. 2022 Jul 21;22(1):582. doi: 10.1186/s12884-022-04856-z.

ABSTRACT

BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services.

METHODS: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space-time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services.

RESULTS: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area.

CONCLUSIONS: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.

PMID:35864462 | DOI:10.1186/s12884-022-04856-z

BMC Med Res Methodol. 2022 Jul 21;22(1):198. doi: 10.1186/s12874-022-01653-2.

ABSTRACT

BACKGROUND: The decision to initiate invasive long-term ventilation for a child with complex medical needs can be extremely challenging. TechChild is a research programme that aims to explore the liminal space between initial consideration of such technology dependence and the final decision. This paper presents a best practice example of the development of a unique use of the factorial survey method to identify the main influencing factors in this critical juncture in a child’s care.

METHODS: We developed a within-subjects design factorial survey. In phase 1 (design) we defined the survey goal (dependent variable, mode and sample). We defined and constructed the factors and factor levels (independent variables) using previous qualitative research and existing scientific literature. We further refined these factors based on expert feedback from expert clinicians and a statistician. In phase two (pretesting), we subjected the survey tool to several iterations (cognitive interviewing, face validity testing, statistical review, usability testing). In phase three (piloting) testing focused on feasibility testing with members of the target population (n = 18). Ethical approval was obtained from the then host institution’s Health Sciences Ethics Committee.

RESULTS: Initial refinement of factors was guided by literature and interviews with clinicians and grouped into four broad categories: Clinical, Child and Family, Organisational, and Professional characteristics. Extensive iterative consultations with clinical and statistical experts, including analysis of cognitive interviews, identified best practice in terms of appropriate: inclusion and order of clinical content; cognitive load and number of factors; as well as language used to suit an international audience. The pilot study confirmed feasibility of the survey. The final survey comprised a 43-item online tool including two age-based sets of clinical vignettes, eight of which were randomly presented to each participant from a total vignette population of 480.

CONCLUSIONS: This paper clearly explains the processes involved in the development of a factorial survey for the online environment that is internationally appropriate, relevant, and useful to research an increasingly important subject in modern healthcare. This paper provides a framework for researchers to apply a factorial survey approach in wider health research, making this underutilised approach more accessible to a wider audience.

PMID:35864457 | DOI:10.1186/s12874-022-01653-2

BMC Pregnancy Childbirth. 2022 Jul 21;22(1):581. doi: 10.1186/s12884-022-04884-9.

ABSTRACT

BACKGROUND AND OBJECTIVE: More than five million individuals died because of problems connected to COVID-19. SARS-Cov-2 poses a particular challenge to expectant mothers, who comprise one of the most vulnerable segments of the population. Our aim is to demonstrate the maternal and neonatal safety of the COVID-19 vaccine during pregnancy.

METHODS: We searched PubMed, Cochrane Library, Scopus, Web of Science (WOS), Embase, Ovid, MedRxiv, and BioRxiv databases from inception till December 2021 and then updated it in April 2022. Additionally, we searched ClinicalTrials.gov, Research Square and grey literature. Cohort, case-control studies, and randomized controlled trials detecting the safety of the Covid-19 vaccine during pregnancy were included. We used the Cochrane tool and Newcastle-Ottawa Scale to assess the risk of bias of the included studies and the GRADE scale to assess the quality of evidence. A meta-analysis was conducted using review manager 5.4.

RESULTS: We included 13 studies with a total number of 56,428 patients. Our analysis showed no statistically significant difference in the following outcomes: miscarriage (1.56% vs 0.3%. RR 1.23; 95%CI 0.54 to 2.78); length of maternal hospitalization (MD 0.00; 95%CI -0.08 to 0.08); puerperal fever (1.71% vs 1.1%. RR 1.04; 95%CI 0.67 to 1.61); postpartum hemorrhage (4.27% vs 3.52%. RR 0.84; 95%CI 0.65 to 1.09); instrumental or vacuum-assisted delivery (4.16% vs 4.54%. RR 0.94; 95%CI 0.57 to 1.56); incidence of Apgar score ≤ 7 at 5 min (1.47% vs 1.48%. RR 0.86; 95%CI 0.54 to 1.37); and birthweight (MD -7.14; 95%CI -34.26 to 19.99).

CONCLUSION: In pregnancy, the current meta-analysis shows no effect of SAR-CoV-2 vaccination on the risk of miscarriage, length of stay in the hospital, puerperal fever, postpartum hemorrhage, birth weight, or the incidence of an Apgar score of ≤ 7 at 5 min.

PMID:35864455 | DOI:10.1186/s12884-022-04884-9

BMC Med Res Methodol. 2022 Jul 21;22(1):199. doi: 10.1186/s12874-022-01670-1.

ABSTRACT

INTRODUCTION: Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate the sensitivity of estimates based on different ways of measuring unemployment and the choice of statistical model.

METHODS: The Northern Swedish cohort was used, and two follow-up surveys thereof from 1995 and 2007, as well as register data about unemployment. Self-reported current unemployment, self-reported accumulated unemployment and register-based accumulated unemployment were used to measure unemployment and its effect on self-reported health was evaluated. Analyses were conducted with G-computation, logistic regression and three estimators for the inverse probability weighting propensity scores, and 11 potentially confounding variables were part of the analyses. Results were presented with absolute differences in the proportion with poor self-reported health between unemployed and employed individuals, except when logistic regression was used alone.

RESULTS: Of the initial 1083 pupils in the cohort, our analyses vary between 488-693 individuals defined as employed and 61-214 individuals defined as unemployed. In the analyses, the deviation was large between the unemployment measures, with a difference of at least 2.5% in effect size when unemployed was compared with employed for the self-reported and register-based unemployment modes. The choice of statistical method only had a small influence on effect estimates and the deviation was in most cases lower than 1%. When models were compared based on the choice of potential confounders in the analytical model, the deviations were rarely above 0.6% when comparing models with 4 and 11 potential confounders. Our variable for health selection was the only one that strongly affected estimates when it was not part of the statistical model.

CONCLUSIONS: How unemployment is measured is highly important when the relationship between unemployment and health is estimated. However, misspecifications of the statistical model or choice of analytical method might not matter much for estimates except for the inclusion of a variable measuring health status before becoming unemployed. Our results can guide researchers when analysing similar research questions. Model diagnostics is commonly lacking in publications, but they remain very important for validation of analyses.

PMID:35864450 | DOI:10.1186/s12874-022-01670-1

Eur Arch Paediatr Dent. 2022 Jul 21. doi: 10.1007/s40368-022-00734-6. Online ahead of print.

ABSTRACT

OBJECTIVE: Physical and mechanical properties of three different (Ketac Molar Easymix, Dyract XP, Cention N (CN)) restoratives with different ingredients were evaluated.

MATERIALS AND METHODS: Four groups were obtained; Group 1: CN LightCure, Group 2: CN SelfCure, Group 3: Ketac Molar Easymix and Group 4: Dyract XP. Disk-shaped samples (n = 10) were prepared and evaluated for the surface roughness test using a profilometer. For the flexure strength test, 2 × 2 × 25 mm bar-shaped samples (n = 10) were prepared, and a three-point bending test was applied to the samples. After preparing cavities for microleakage tests, teeth were restored with restoratives, immersed in dye, and microleakage was assessed. For the microtensile bond strength (µTBS) test, ten sticks were obtained for each group and were stressed under tension.

RESULTS: According to surface roughness tests, CN SelfCure showed the lowest value (0.13 μm), while Ketac Molar Easymix showed the highest value (0.28 μm), and significant differences were found between the groups. In flexural strength tests, the highest values were seen in CN SelfCure (82.94 MPa), with statistically significant differences between the groups. When CN SelfCure was applied with an adhesive, the teeth showed statistically decreased leakage than other groups on the gingival side. Higher leakage values were seen on the gingival side than the occlusal side in most groups, and the SelfCure groups showed decreased leakage than the LightCure groups. According to µTBS tests, the highest value was obtained in CN SelfCure-Adhesive group, while the lowest was in CN LightCure-Non-adhesive group. When µTBS was evaluated regardless of adhesive use, the SelfCure groups showed higher µTBS values than the LightCure groups. As a result of the µTBS and microleakage test, the difference between the use of CN with and without adhesive, regardless of the polymerization type, was found to be significant (p < 0.05).

CONCLUSION: Cention N showed better properties in SelfCure mode, compared to the rest materials tested, but further in vitro and in vivo studies are needed to investigate the effect of different polymerization modes and the oral environmental conditions.

PMID:35864436 | DOI:10.1007/s40368-022-00734-6

BMC Bioinformatics. 2022 Jul 21;23(1):288. doi: 10.1186/s12859-022-04826-4.

ABSTRACT

BACKGROUND: Methods for the multiview clustering and integration of multi-omics data have been developed recently to solve problems caused by data noise or limited sample size and to integrate multi-omics data with consistent (common) and differential cluster patterns. However, the integration of such data still suffers from limited performance and low accuracy.

RESULTS: In this study, a computational framework for the multiview clustering method based on the penalty model is presented to overcome the challenges of low accuracy and limited performance in the case of integrating multi-omics data with consistent (common) and differential cluster patterns. The performance of the proposed method was evaluated on synthetic data and four real multi-omics data and then compared with approaches presented in the literature under different scenarios. Result implies that our method exhibits competitive performance compared with recently developed techniques when the underlying clusters are consistent with synthetic data. In the case of the differential clusters, the proposed method also presents an enhanced performance. In addition, with regards to real omics data, the developed method exhibits better performance, demonstrating its ability to provide more detailed information within each data type and working better to integrate multi-omics data with consistent (common) and differential cluster patterns. This study shows that the proposed method offers more significant differences in survival times across all types of cancer.

CONCLUSIONS: A new multiview clustering method is proposed in this study based on synthetic and real data. This method performs better than other techniques previously presented in the literature in terms of integrating multi-omics data with consistent and differential cluster patterns and determining the significance of difference in survival times.

PMID:35864439 | DOI:10.1186/s12859-022-04826-4

Acta Neurol Belg. 2022 Jul 21. doi: 10.1007/s13760-022-02030-y. Online ahead of print.

ABSTRACT

OBJECTIVES: In this study, we aimed to evaluate the clinical significance of osmophobia and its effect on quality of life in people with migraine.

METHODS: A total of 145 people with migraine were included in this cross-sectional study. Patients were evaluated with the migraine data form, the Migraine 24-Hour Quality of Life Questionnaire (24-HrMQoLQ), the Migraine Disability Assessment Scale (MIDAS), the Patient Health Questionnaire-9 (PHQ-9), the Insomnia Severity Index (ISI), the Generalized Anxiety Disorder-7 (GAD-7), the Allodynia Symptom Checklist (ASC), and the Fatigue Severity Scale (FSS). To evaluate the presence of osmophobia retrospectively, a semi-structured interview was conducted with the patients by the neurologist.

RESULTS: The mean 24-Hr-MQoLQ of patients with osmophobia was significantly lower than those without osmophobia. The decrease in the 24-Hr-MQoLQ was statistically significant in the areas of feeling and concerns and social functionality. The mean of the MIDAS scale was higher significantly in patients with osmophobia than those without osmophobia. In addition, the mean ISI, PHQ-9, FSS and ASC scores of patients with osmophobia were statistically significantly higher than those without osmophobia.

CONCLUSIONS: Both 24-h and 3-month quality of life of people with migraine with osmophobia were more affected than those without osmophobia. At the same manner, insomnia, depression, fatigue and allodynia were observed at higher rates in people with migraine with osmophobia than in migraine without osmophobia. Osmophobia, which is one of the specific symptoms that distinguishes migraine from other headache disorders, deserves further and multifaceted investigation.

PMID:35864435 | DOI:10.1007/s13760-022-02030-y