Validity and reliability of the persian version of the world health organization quality of life disabilities module

This study aimed to assess the validity and reliability of the world health organization quality of life questionnaire for people with intellectual disability (WHOQOL-DIS-ID). This was a cross-sectional study of 118 adults with intellectual disability. Internal consistency and temporal reliability were evaluated using Cronbach's alpha and intraclass correlation coefficient (ICC), respectively. Construct validity of the structure was assessed using confirmatory factor analysis. Multiple linear regression analysis was performed to determine the factors associated with the quality of life. Cronbach's alpha for all, but not for discrimination area, as well as ICC for all questions except for 23 and 6, were higher than 0.7. The variables such as periodic check-up, number of friends, entertainment outside the home, parental separation, physical activity, unmet needs score, caregiver's mental health status, and available facilities were the predictors of QOL. The Persian version of the WHOQOL-DIS-ID shows acceptable validity and reliability.

Adjustment for collider bias in the hospitalized Covid-19 setting

Background

Causal directed acyclic graphs (cDAGs) are frequently used to identify confounding and collider bias. We demonstrate how to use causal directed acyclic graphs to adjust for collider bias in the hospitalized Covid-19 setting.

Materials and methods

According to the cDAGs, three types of modeling have been performed. In model 1, only vaccination is entered as an independent variable. In model 2, in addition to vaccination, age is entered the model to adjust for collider bias due to the conditioning of hospitalization. In model 3, comorbidities are also included for adjustment of collider bias due to the conditioning of hospitalization in different biasing paths intercepting age and comorbidities.

Results

There was no evidence of the effect of vaccination on preventing death due to Covid-19 in model 1. In the second model, where age was included as a covariate, a protective role for vaccination became evident. In model 3, after including chronic diseases as other covariates, the protective effect was slightly strengthened.

Conclusion

Studying hospitalized patients is subject to collider-stratification bias. Like confounding, this type of selection bias can be adjusted for by inclusion of the risk factors of the outcome which also affect hospitalization in the regression model.

Additive interaction between dietary inflammatory index and some key risk factors of multiple sclerosis: a population-based incident case-control study

BACKGROUND

Recently, dietary inflammatory index (DII) has been introduced as a significant risk factor for MS. We examined the interaction between dietary inflammatory index and some formerly demonstrated key risk factors of multiple sclerosis (MS).

MATERIAL AND METHODS

We conducted a population-based incident case-control study of 547 MS cases and 1057 controls. Multiplicative and additive interaction were assessed using interaction term in the logistic regression model and synergy index (SI), respectively.

RESULTS

Additive interaction was detected between DII and drug abuse (SI = 2.58; 95% CI: 1.14-5.82), gender (SI = 2.00; 95% CI: 1.39-2.87) and history of depression (SI = 1.68; 95% CI: 1.04-2.72) on the risk scale. The risk of MS in drug abusers with DII ≥ 0 was 10.4-times higher than that in non-drug abusers with DII < 0 (OR = 10.4, 95% CI: 5.12-21.02, P < 0.001). We also found that women with DII ≥ 0 had a 9.2 times larger risk compared with the men with DII < 0(OR = 9.2, 95% CI: 6.3-13.5, P < 0.001). Similarly, the risk of MS was remarkably higher in those with a history of depression and DII >0 (OR = 7.6, 95% CI: 5.1-11.5, P < 0.001). There was no evidence of multiplicative interaction between DII and the other risk factors of MS on the risk scale.

CONCLUSIONS

We identified additive interaction between DII and drug abuse, gender and history of depression on MS. Further studies are needed to understand the underlying mechanisms of these detected interactions.

Prevention of Re-attempt Suicide Through Brief Contact Interventions: A Systematic Review, Meta-analysis, and Meta-regression of Randomized Controlled Trials

Brief contact intervention (BCI) is a low-cost intervention to prevent re-attempt suicide. This meta-analysis and meta-regression study aimed to evaluate the effect of BCI on re-attempt prevention following suicide attempts (SAs). We systematically searched using defined keywords in MEDLINE, Embase, and Scopus up to April, 2023. All randomized controlled trials (RCTs) were eligible for inclusion after quality assessment. Random-effects model and subgroup analysis were used to estimate pooled risk difference (RD) and risk ratio (RR) between BCI and re-attempt prevention with 95% confidence intervals (CIs). Meta-regression analysis was carried out to explore the potential sources of heterogeneity. The pooled estimates were (RD = 4%; 95% CI 2-6%); and (RR = 0.62; 95% CI 0.48-0.77). Subgroup analysis demonstrated that more than 12 months intervention (RR = 0.46; 95% CI 0.10-0.82) versus 12 months or less (RR = 0.67; 95% CI 0.54-0.80) increased the effectiveness of BCI on re-attempt suicide reduction. Meta-regression analysis explored that BCI time (more than 12 months), BCI type, age, and female sex were the potential sources of the heterogeneity. The meta-analysis indicated that BCI could be a valuable strategy to prevent suicide re-attempts. BCI could be utilized within suicide prevention strategies as a surveillance component of mental health since BCI requires low-cost and low-educated healthcare providers.

Comparison of corticosteroids types, dexamethasone, and methylprednisolone in patients hospitalized with COVID-19: A systematic review and network meta-analysis

Background

COVID-19 is associated with severe pneumonia lung damage, acute respiratory distress syndrome (ARDS), and mortality. In this study, we aimed to compare corticosteroids' effect on the mortality risk in patients hospitalized with COVID-19.

Methods

PubMed, Web of Science, Scopus, Cochrane Library, and Embase, were searched using a predesigned search strategy. Randomized controlled trials (RCTs) that had compared the corticosteroid drugs were included. The hazard ratio (HR) with a 95% confidence interval (CI) was used to summarize the effect size from the network meta-analysis (NMA).

Results

Out of 329 retrieved references, 12 RCTs with 11,455 participants met the eligibility criteria in this review. The included RCTs formed one network with six treatments. In addition, five treatments in two RCTs were not connected to the network. Methylprednisolone + usual care (UC) versus UC decreased the risk of death by 0.65 (95% CI: 0.47, 0.90). Among treatments in the network the highest P-score (0.89) was related to Methylprednisolone + UC.

Conclusion

Based on the results of this NMA it seems Methylprednisolone + UC to be the best treatment option in patients with COVID-ARDS and COVID pneumonia.

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population

BACKGROUND

The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis.

METHODS

In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005-2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014-2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set.

RESULTS

Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome.

CONCLUSIONS

Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Plant-based diet and COVID-19 severity: results from a cross-sectional study

Although previous findings have shown the beneficial role of healthy eating pattern on the human immune system, the association between plant-based diet and COVID-19 severity has not yet been elucidated. This study aimed to determine the possible role of plant-based diet index (PDI) in COVID-19 severity. This cross-sectional, multicentral study was conducted on 141 patients with confirmed COVID-19. Dietary intakes of the patients were evaluated using a validated food frequency questionnaire. Then, PDI was compared between patients who needed to be hospitalised (considered severe cases), and those who got treatment at home (considered non-severe cases). After adjustment for confounders including age, sex, energy intake and body mass index, lower odds of hospitalisation were found for participants having a greater score of overall PDI (OR per 10 units increase: 0.42; 95% CI 0.22 to 0.80) and healthy PDI (OR per 10 unit increase: 0.45; 95% CI 0.26 to 0.78). In conclusion, our data presented that there is a relation between PDI and lower risk of hospitalisation in COVID-19 patients, possibly through boosting the immune function.

Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis

BACKGROUND

The etiology of multiple sclerosis (MS) is still not well-demonstrated, and assessment of some risk factors like alcohol consumption has problems like confounding and measurement bias. To determine the causal effect of alcohol consumption on MS after adjusting for alcohol consumption misclassification bias and confounders.

METHODS

In a population-based incident case-control study, 547 patients with MS and 1057 healthy people were recruited. A minimally sufficient adjustment set of confounders was derived using the causal directed acyclic graph. The probabilistic bias analysis method (PBAM) using beta, logit-logistic, and triangular probability distributions for sensitivity/specificity to adjust for misclassification bias in self-reporting alcohol consumption and model-based standardization (MBS) to estimate the causal effect of alcohol consumption were used. Population attributable fraction (PAF) estimates with 95% Monte Carlo sensitivity analysis (MCSA) intervals were calculated using PBAM and MBS analysis. Bootstrap was used to deal with random errors.

RESULTS

The adjusted risk ratio (95% MCSA interval) from the probabilistic bias analysis and MBS between alcohol consumption and MS using the three distribution was in the range of 1.93 (1.07 to 4.07) to 2.02 (1.15 to 4.69). The risk difference (RD) in all three scenarios was 0.0001 (0.0000 to 0.0005) and PAF was in the range of 0.15 (0.010 to 0.50) to 0.17 (0.001 to 0.47).

CONCLUSION

After adjusting for measurement bias, confounding, and random error alcohol consumption had a positive causal effect on the incidence of MS.

Effect of alcohol consumption on breast cancer: probabilistic bias analysis for adjustment of exposure misclassification bias and confounders

PURPOSE

This study was conducted to evaluate the effect of alcohol consumption on breast cancer, adjusting for alcohol consumption misclassification bias and confounders.

METHODS

This was a case-control study of 932 women with breast cancer and 1000 healthy control. Using probabilistic bias analysis method, the association between alcohol consumption and breast cancer was adjusted for the misclassification bias of alcohol consumption as well as a minimally sufficient set of adjustment of confounders derived from a causal directed acyclic graph. Population attributable fraction was estimated using the Miettinen's Formula.

RESULTS

Based on the conventional logistic regression model, the odds ratio estimate between alcohol consumption and breast cancer was 1.05 (95% CI: 0.57, 1.91). However, the adjusted estimates of odds ratio based on the probabilistic bias analysis ranged from 1.82 to 2.29 for non-differential and from 1.93 to 5.67 for differential misclassification. Population attributable fraction ranged from 1.51 to 2.57% using non-differential bias analysis and 1.54-3.56% based on differential bias analysis.

CONCLUSION

A marked measurement error was in self-reported alcohol consumption so after correcting misclassification bias, no evidence against independence between alcohol consumption and breast cancer changed to a substantial positive association.

Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial

Previous analysis of the action to control cardiovascular risk in diabetes showed an increased risk of mortality among patients receiving intensive glucose lowering therapy using conventional regression method with intention to treat approach. This method is biased when time-varying confounder is affected by the previous treatment. We used 15 follow-up visits of ACCORD trial to compare the effect of time-varying intensive vs. standard treatment of glucose lowering drugs on cardiovascular and mortality outcomes in diabetic patients. The treatment effect was estimated using G-estimation and compared with accelerated failure time model using two modeling strategies. The first model adjusted for baseline confounders and the second adjusted for both baseline and time-varying confounders. While the hazard ratio of all-cause mortality for intensive compared to standard therapy in AFT model adjusted for baseline confounders was 1.17 (95% CI 1.01-1.36), the result of time-dependent AFT model  was compatible with both protective and risk effects. However, the hazard ratio estimated by G-estimation was 0.64 (95% CI 0.39-0.92). The results of this study revealed a protective effect of intensive therapy on all-cause mortality compared with standard therapy in ACCORD trial.

A practical guide to handling competing events in etiologic time-to-event studies

Competing events are events that preclude the occurrence of the primary outcome. Much has been written on mainly the statistics behind competing events analyses. However, many of these publications and tutorials have a strong statistical tone and might fall short in providing a practical guide to clinician researchers as to when to use a competing event analysis and more importantly which method to use and why. Here we discuss the different target effects in the Fine-Gray and cause-specific methods using simple causal diagrams and provide strengths and limitations of both approaches for addressing etiologic questions. We argue why the Fine-Gray method might not be the best approach for handling competing events in etiological time-to-event studies.

P-value, compatibility, and S-value

Misinterpretations of P-values and 95% confidence intervals are ubiquitous in medical research. Specifically, the terms significance or confidence, extensively used in medical papers, ignore biases and violations of statistical assumptions and hence should be called overconfidence terms. In this paper, we present the compatibility view of P-values and confidence intervals; the P-value is interpreted as an index of compatibility between data and the model, including the test hypothesis and background assumptions, whereas a confidence interval is interpreted as the range of parameter values that are compatible with the data under background assumptions. We also suggest the use of a surprisal measure, often referred to as the S-value, a novel metric that transforms the P-value, for gauging compatibility in terms of an intuitive experiment of coin tossing.

Longitudinal effects of lipid indices on incident cardiovascular diseases adjusting for time-varying confounding using marginal structural models: 25 years follow-up of two US cohort studies

Background

This study assesses the effect of blood lipid indices and lipid ratios on cardiovascular diseases (CVDs) using inverse probability-of-exposure weighted estimation of marginal structural models (MSMs).

Methods

A pooled dataset of two US representative cohort studies, including 16736 participants aged 42-84 years with complete information at baseline, was used. The effect of each lipid index, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), ratios of TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C on coronary heart disease (CHD) and stroke were estimated using weighted Cox regression.

Results

There were 1638 cases of CHD and 1017 cases of stroke during a median follow-up of 17.1 years (interquartile range: 8.5 to 25.7). Compared to optimal levels, the risk of CVD outcomes increased substantially in high levels of TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C. If everyone had always had high levels of TC (≥240 mg/dL), risk of CHD would have been 2.15 times higher, and risk of stroke 1.35 times higher than if they had always had optimal levels (<200 mg/dL). Moreover, if all participants had been kept at very high (≥190 mg/dL) levels of LDL-C, risk of CHD would have been 2.62 times higher and risk of stroke would have been 1.92 times higher than if all participants had been kept at optimal levels, respectively. Our results suggest that high levels of HDL-C may be protective for CHD, but not for stroke. There was also no evidence of an adverse effect of high triglyceride levels on stroke.

Conclusions

Using MSM, this study highlights the effect of TC and LDL-C on CVD, with a stronger effect on CHD than on stroke. There was no evidence for a protective effect of high levels of HDL-C on stroke. Besides, triglyceride was not found to affect stroke.

Development and validation of an online tool for assessment of health care providers' management of suspected malaria in an area, where transmission has been interrupted

BACKGROUND

The alertness and practice of health care providers (HCPs) in the correct management of suspected malaria (CMSM) (vigilance) is a central component of malaria surveillance following elimination, and it must be established before malaria elimination certification can be granted. This study was designed to develop and validate a rapid tool, Simulated Malaria Online Tool (SMOT), to evaluate HCPs' practice in relation to the CMSM.

METHODS

The study was conducted in East Azerbaijan Province, Islamic Republic of Iran, where no malaria transmission has been reported since 2005. An online tool presenting a suspected malaria case for detection of HCPs' failures in recognition, diagnosis, treatment and reporting was developed based on literature review and expert opinion. A total of 360 HCPs were allocated to two groups. In one group their performance was tested by simulated patient (SP) methodology as gold standard, and one month later by the online tool to allow assessment of its sensitivity. In the other group, they were tested only by the online tool to allow assessment of any possible bias incurred by the exposure to SPs before the tool.

RESULTS

The sensitivity of the tool was (98.7%; CI 93.6-99.3). The overall agreement and kappa statistics were 96.6% and 85.6%, respectively. In the group tested by both methods, the failure proportion by SP was 86.1% (CI 80.1-90.8) and by tool 87.2% (CI 81.4-91.7). In the other group, the tool found 85.6% (CI 79.5-90.3) failures. There were no significant differences in detecting failures within or between the groups.

CONCLUSION

The SMOT tool not only showed high validity for detecting HCPs' failures in relation to CMSM, but it had high rates of agreement with the real-world situation, where malaria transmission has been interrupted. The tool can be used by program managers to evaluate HCPs' performance and identify sub-groups, whose malaria vigilance should be strengthened. It could also contribute to the evidence base for certification of malaria elimination, and to strengthening prevention of re-establishment of malaria transmission.

Interaction Contrasts and Collider Bias

Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider.

Does motor control training improve pain and function in adults with symptomatic lumbar disc herniation? A systematic review and meta-analysis of 861 subjects in 16 trials

OBJECTIVE

To evaluate the effectiveness of motor control training (MCT) compared with other physical therapist-led interventions, minimal/no intervention or surgery in patients with symptomatic lumbar disc herniation (LDH).

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Eight databases and the ClinicalTrials.gov were searched from inception to April 2021.

ELIGIBILITY CRITERIA

We included clinical trial studies with concurrent comparison groups which examined the effectiveness of MCT in patients with symptomatic LDH. Primary outcomes were pain intensity and functional status which were expressed as mean difference (MD) and standardised mean difference (SMD), respectively.

RESULTS

We screened 6695 articles, of which 16 clinical trials (861 participants) were eligible. Fourteen studies were judged to have high risk of bias and two studies had some risk of bias. In patients who did not undergo surgery, MCT resulted in clinically meaningful pain reduction compared with other physical therapist-led interventions (ie, transcutaneous electrical nerve stimulation (TENS)) at short-term (MD -28.85, -40.04 to -17.66, n=69, studies=2). However, the robustness of the finding was poor. For functional status, a large and statistically significant treatment effect was found in favour of MCT compared with traditional/classic general exercises at long-term (SMD -0.83 to -1.35 to -0.31, n=63, studies=1) and other physical therapist-led interventions (ie, TENS) at short-term (SMD -1.43 to -2.41 to -0.46, n=69, studies=2). No studies compared MCT with surgery. In patients who had undergone surgery, large SMDs were seen. In favour of MCT compared with traditional/classic general exercises (SMD -0.95 to -1.32 to -0.58, n=124, studies=3), other physical therapist-led interventions (ie, conventional treatments; SMD -2.30 to -2.96 to -1.64, n=60, studies=1), and minimal intervention (SMD -1.34 to -1.87 to -0.81, n=68, studies=2) for functional improvement at short-term. The overall certainty of evidence was very low to low.

CONCLUSION

At short-term, MCT improved pain and function compared with TENS in patients with symptomatic LDH who did not have surgery. MCT improved function compared with traditional/classic general exercises at long-term in patients who had undergone surgery. However, the results should be interpreted with caution because of the high risk of bias in the majority of studies.

PROSPERO REGISTRATION NUMBER

CRD42016038166.

Does weight mediate the effect of smoking on coronary heart disease? Parametric mediational g-formula analysis

Background

In settings in which there are time-varying confounders affected by previous exposure and a time-varying mediator, natural direct and indirect effects cannot generally be estimated unbiasedly. In the present study, we estimate interventional direct effect and interventional indirect effect of cigarette smoking as a time-varying exposure on coronary heart disease while considering body weight as a time-varying mediator.

Methods

To address this problem, the parametric mediational g-formula was proposed to estimate interventional direct effect and interventional indirect effect. We used data from the Multi-Ethnic Study of Atherosclerosis to estimate effect of cigarette smoking on coronary heart disease, considering body weight as time-varying mediator.

Results

Over a 11-years period, smoking 20 cigarettes per day compared to no smoking directly (not through weight) increased risk of coronary heart disease by an absolute difference of 1.91% (95% CI: 0.49%, 4.14%), and indirectly decreased coronary heart disease risk by -0.02% (95% CI: -0.05%, 0.04%) via change in weight. The total effect was estimated as an absolute 1.89% increase (95% CI: 0.49%, 4.13%).

Conclusion

The overall absolute impact of smoking to incident coronary heart disease is modest, and we did not discern any important contribution to this effect relayed through changes to bodyweight. In fact, changes in weight because of smoking have no meaningful mediating effect on CHD risk.

The Effects of Smoking on Metabolic Syndrome and Its Components Using Causal Methods in the Iranian Population

Background

The aim of this study was to estimate the effect of smoking on metabolic syndrome (MS) and its components applying inverse probability-of-treatment weighting (IPTW) and propensity score (PS) matching.

Methods

Using data from Tehran Lipid and Glucose Study, 4857 participants aged over 20 years with information on smoking and confounders in the third phase (2005-2008) were included, and the MS was assessed in the fifth phase (2011-2014). IPTW and PS matching were used to adjust for confounders.

Results

Based on average treatment effect (ATE) estimates, smoking decreased the risk of hypertension (RR: 0.62; 95% CI: 0.43, 0.88), but increased the risk of low HDL cholesterol (1.20; 0.98, 1.48). Similarly, the average treatment effect in the treated (ATT) estimates using IPTW and PS matching suggested that smoking decreased the risk of hypertension (0.63; 0.52, 0.76, and 0.68; 0.54, 0.85), and increased the risk of low HDL cholesterol (1.24; 1.07, 1.43, and 1.28; 1.06, 1.54), respectively.

Conclusions

Smoking seems to increase the risk of low HDL cholesterol but decreases the risk of hypertension.

Population attributable fraction in textbooks: Time to revise

Introduction

The population attributable fraction is an important measure for assessing the impact of intervention on the disease risk in populations, but it is frequently misused in the research literature.

Methods

In this study, we review the definition, calculation, interpretation and assumptions of PAF in 43 textbooks and highlight important shortcomings.

Results

While the Levin formula was proposed as a method of calculation in 29 (67%) textbooks, only in 4 (9%) was the Miettinen formula or its generalization for multilevel exposure recommended to calculate a confounding-adjusted population attributable fraction. Other concepts such as generalized impact fraction and prevented and preventable fractions were briefly discussed in few textbooks.

Discussion

We recommend the authors revise the textbooks in light of our proposed framework for teaching the population attributable fraction.

Risk factors of developing critical conditions in Iranian patients with COVID-19

COVID-19 due to novel Coronavirus was first reported in Wuhan, China. Nowadays, the Islamic Republic of Iran stands among countries with high COVID-19 prevalence and high burden of disease. Since the medical resources are limited, we aimed to identify the risk factors for patients developing critical conditions. This can help to improve resource management and treatment outcomes. In this retrospective study, we included 12,677 patients who were from 26 hospitals, supervised by Tehran University of Medical Sciences with signs and symptoms of COVID-19, until April 12. University integrated IT system was adopted to collect the data. We performed Logistic regression to evaluate the association between death in COVID-19 positive patients and other variables. Cough, respiratory distress and fever were the most common symptoms in our patients, respectively. Cancer, chronic lung diseases and chronic neurologic diseases were the strongest risk factors for death in COVID-19 patients.

Bland-Altman methods for comparing methods of measurement and response to criticisms

Introduced in 1983, Bland-Altman methods is now considered the standard approach for assessment of agreement between two methods of measurement. The method is widely used by researchers in various disciplines so that the Bland-Altman 1986 Lancet paper has been named as the 29th mostly highly cited paper ever, over all fields. However, two papers by Hopkins (2004) and Krouwer (2007) questioned the validity of the Bland-Altman analysis. We review the points of critical papers and provide responses to them. The discussions in the critical papers of the Bland-Altman method are scientifically delusive. Hopkins misused the Bland-Altman methodology for research question of model validation and also incorrectly used least-square regression when there is measurement error in the predictor. The problem with Krouwers' paper is making sweeping generalisation of a very narrow and somewhat unrealistic situation. The method proposed by Bland and Altman should be used when the research question is method comparison.

Serum lysyl oxidase concentration increases in long-standing systemic sclerosis: Can lysyl oxidase change over time?

Objectives

This study aims to investigate the association of serum lysyl oxidase (LOX) levels with systemic sclerosis (SSc), to examine the relationship between LOX and disease onset, and to evaluate the probable effects of hyperlipidemia on the circulating levels of LOX among patients with SSc.

Patients and methods

Between May 2017 and November 2018, a total of 39 patients with SSc (2 males, 37 females; mean age: 46.6±12.3 years; range, 18 to 65 years) and 35 healthy controls (4 males, 31 females; mean age: 43.1±14.1 years; range, 18 to 65 years) were included. Serum LOX concentration was measured using the enzyme-linked immunoassay in triplicate.

Results

We found higher levels of serum LOX in patients with SSc compared to healthy controls. There was a significant relationship between serum LOX levels and disease onset. Patients with long-standing disease demonstrated increased levels of LOX in the blood compared to the recent-onset group. Hyperlipidemia did not have a significant effect on circulating levels of LOX. There was a significant negative correlation between LOX levels and modified Rodnan Skin Score in the subgroup of patients with skin involvement only and in patients without gastrointestinal involvement.

Conclusion

Our study findings show an increased level of LOX protein level in the blood of patients diagnosed with SSc. Hyperlipidemia seems not to affect the concentrations of LOX in the peripheral blood of patients with SSc.

The Associations Between Serum Concentrations of Irisin and Glucose-dependent Insulinotropic Polypeptide with Body Mass Index Among Women with and Without Polycystic Ovary Syndrome

BACKGROUND

The actions of adipocytokines may be a connective factor between obesity and polycystic ovary syndrome (PCOS). It is suggested that irisin, as recently described cytokine secreted by skeletal muscles and glucose-dependent insulinotropic polypeptide (GIP) as an incretin hormone that induces cytokine expression, may play key roles in favoring obesity in these women.

OBJECTIVES

We aimed to evaluate the association between body mass index (BMI) and serum concentrations of irisin/GIP in women with and without PCOS in the linear and non-linear models.

METHODS

This cross-sectional study was conducted among 159 PCOS and 82 healthy eumenorrheic non-hirsute women aged 20 - 50 years. The fractional-polynomial model was used to develop a model of continuous risk factors, which evaluates non-linear associations between irisin/GIP and BMI among women with and without PCOS.

RESULTS

Women with PCOS were significantly younger (28.2 ± 5.8 vs. 33.0 ± 7.8 years, P < 0.001) and had a greater BMI (26.6 ± 5.2 vs. 25.2 ± 4.8 kg/m², P = 0.04) than the healthy counterparts. There were no significant linear and non-linear associations between serum concentration of irisin/GIP and BMI in both groups. The analysis of pair-wise age and BMI matching of women with PCOS and controls confirmed these findings.

CONCLUSIONS

This study showed that irisin and GIP have no association with BMI in women with or without PCOS. This finding could help to better understand the underlying pathophysiological status of PCOS, insulin resistance, and obesity-related disorders. Further large cohort studies are needed to confirm these findings.

A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration

Misuse of statistics in medical and sports science research is common and may lead to detrimental consequences to healthcare. Many authors, editors and peer reviewers of medical papers will not have expert knowledge of statistics or may be unconvinced about the importance of applying correct statistics in medical research. Although there are guidelines on reporting statistics in medical papers, a checklist on the more general and commonly seen aspects of statistics to assess when peer-reviewing an article is needed. In this article, we propose a CHecklist for statistical Assessment of Medical Papers (CHAMP) comprising 30 items related to the design and conduct, data analysis, reporting and presentation, and interpretation of a research paper. While CHAMP is primarily aimed at editors and peer reviewers during the statistical assessment of a medical paper, we believe it will serve as a useful reference to improve authors' and readers' practice in their use of statistics in medical research. We strongly encourage editors and peer reviewers to consult CHAMP when assessing manuscripts for potential publication. Authors also may apply CHAMP to ensure the validity of their statistical approach and reporting of medical research, and readers may consider using CHAMP to enhance their statistical assessment of a paper.

To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies

Covariate adjustment is integral to the validity of observational studies assessing causal effects. It is common practice to adjust for as many variables as possible in observational studies in the hopes of reducing confounding by other variables. However, indiscriminate adjustment for variables using standard regression models may actually lead to biased estimates. In this paper, we differentiate between confounders, mediators, colliders, and effect modifiers. We will discuss that while confounders should be adjusted for in the analysis, one should be wary of adjusting for colliders. Mediators should not be adjusted for when examining the total effect of an exposure on an outcome. Automated statistical programs should not be used to decide which variables to include in causal models. Using a case scenario in cardiology, we will demonstrate how to identify confounders, colliders, mediators and effect modifiers and the implications of adjustment or non-adjustment for each of them.

Estimating the Marginal Causal Effect and Potential Impact of Waterpipe Smoking on Risk of Multiple Sclerosis Using the Targeted Maximum Likelihood Estimation Method: A Large, Population-Based Incident Case-Control Study

There are few if any reports regarding the role of lifetime waterpipe smoking in the etiology of multiple sclerosis (MS). In a population-based incident case-control study conducted in Tehran, Iran, we investigated the association between waterpipe smoking and MS, adjusted for confounders. Cases (n = 547) were patients aged 15-50 years identified from the Iranian Multiple Sclerosis Society between 2013 and 2015. Population-based controls (n = 1,057) were persons aged 15-50 years recruited through random digit telephone dialing. A doubly robust estimation method, the targeted maximum likelihood estimator (TMLE), was used to estimate the marginal risk ratio and odds ratio for the association between waterpipe smoking and MS. The estimated risk ratio and odds ratio were both 1.70 (95% confidence interval: 1.34, 2.17). The population attributable fraction was 21.4% (95% confidence interval: 4.0, 38.8). Subject to the limitations of case-control studies in interpreting associations causally, these results suggest that waterpipe use, or strongly related but undetermined factors, increases the risk of MS. Further epidemiologic studies, including nested case-control studies, are needed to confirm these findings.

The causal effect and impact of reproductive factors on breast cancer using super learner and targeted maximum likelihood estimation: a case-control study in Fars Province, Iran

OBJECTIVES

The relationship between reproductive factors and breast cancer (BC) risk has been investigated in previous studies. Considering the discrepancies in the results, the aim of this study was to estimate the causal effect of reproductive factors on BC risk in a case-control study using the double robust approach of targeted maximum likelihood estimation.

METHODS

This is a causal reanalysis of a case-control study done between 2005 and 2008 in Shiraz, Iran, in which 787 confirmed BC cases and 928 controls were enrolled. Targeted maximum likelihood estimation along with super Learner were used to analyze the data, and risk ratio (RR), risk difference (RD), andpopulation attributable fraction (PAF) were reported.

RESULTS

Our findings did not support parity and age at the first pregnancy as risk factors for BC. The risk of BC was higher among postmenopausal women (RR = 3.3, 95% confidence interval (CI) = (2.3, 4.6)), women with the age at first marriage ≥20 years (RR = 1.6, 95% CI = (1.3, 2.1)), and the history of oral contraceptive (OC) use (RR = 1.6, 95% CI = (1.3, 2.1)) or breastfeeding duration ≤60 months (RR = 1.8, 95% CI = (1.3, 2.5)). The PAF for menopause status, breastfeeding duration, and OC use were 40.3% (95% CI = 39.5, 40.6), 27.3% (95% CI = 23.1, 30.8) and 24.4% (95% CI = 10.5, 35.5), respectively.

CONCLUSIONS

Postmenopausal women, and women with a higher age at first marriage, shorter duration of breastfeeding, and history of OC use are at the higher risk of BC.

Longitudinal Causal Effects of Normalized Protein Catabolic Rate on All-Cause Mortality in Patients With End-Stage Renal Disease: Adjusting for Time-Varying Confounders Using the G-Estimation Method

In this study, we aimed to estimate the causal effect of normalized protein catabolic rate (nPCR) on mortality among end-stage renal disease (ESRD) patients in the presence of time-varying confounding affected by prior exposure using g-estimation. Information about 553 ESRD patients was retrospectively collected over an 8-year period (2011-2019) from hemodialysis facilities in Kerman, Iran. nPCR was dichotomized as <1.2 g/kg/day versus ≥1.2 g/kg/day. Then a standard time-varying accelerated failure time (AFT) Weibull model was built, and results were compared with those generated by g-estimation. After appropriate adjustment for time-varying confounders, weighted g-estimation yielded 78% shorter survival time (95% confidence interval (95% CI): -81, -73) among patients with a continuous nPCR <1.2 g/kg/day than among those who had nPCR ≥1.2 g/kg/day during follow-up, though it was 18% (95% CI: -57, 54) in the Weibull model. Moreover, hazard ratio estimates of 4.56 (95% CI: 3.69, 5.37) and 1.20 (95% CI: 0.66, 2.17) were obtained via weighted g-estimation and the Weibull model, respectively. G-estimation indicated that inadequate dietary protein intake characterized by nPCR increases all-cause mortality among ESRD patients, but the Weibull model provided an effect estimate that was substantially biased toward the null.

Reflection on modern methods: demystifying robust standard errors for epidemiologists

All statistical estimates from data have uncertainty due to sampling variability. A standard error is one measure of uncertainty of a sample estimate (such as the mean of a set of observations or a regression coefficient). Standard errors are usually calculated based on assumptions underpinning the statistical model used in the estimation. However, there are situations in which some assumptions of the statistical model including the variance or covariance of the outcome across observations are violated, which leads to biased standard errors. One simple remedy is to use robust standard errors, which are robust to violations of certain assumptions of the statistical model. Robust standard errors are frequently used in clinical papers (e.g. to account for clustering of observations), although the underlying concepts behind robust standard errors and when to use them are often not well understood. In this paper, we demystify robust standard errors using several worked examples in simple situations in which model assumptions involving the variance or covariance of the outcome are misspecified. These are: (i) when the observed variances are different, (ii) when the variance specified in the model is wrong and (iii) when the assumption of independence is wrong.

Seroprevalence of SARS-CoV-2 in Guilan Province, Iran, April 2020

We determined the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in an affected area in northern Iran in April 2020. Antibodies to SARS-CoV-2 were detected in 528 persons by using rapid tests. Adjusted prevalence of SARS-CoV-2 seropositivity was 22.2% (95% CI 16.4%–28.5%).

Unsafe Injection Is Associated with Higher HIV Testing after Bayesian Adjustment for Unmeasured Confounding

BACKGROUND

To apply a novel method to adjust for HIV knowledge as an unmeasured confounder for the effect of unsafe injection on future HIV testing.

METHODS

The data were collected from 601 HIV-negative persons who inject drugs (PWID) from a cohort in San Francisco. The panel-data generalized estimating equations (GEE) technique was used to estimate the adjusted risk ratio (RR) for the effect of unsafe injection on not being tested (NBT) for HIV. Expert opinion quantified the bias parameters to adjust for insufficient knowledge about HIV transmission as an unmeasured confounder using Bayesian bias analysis.

RESULTS

Expert opinion estimated that 2.5%-40.0% of PWID with unsafe injection had insufficient HIV knowledge; whereas 1.0%-20.0% who practiced safe injection had insufficient knowledge. Experts also estimated the RR for the association between insufficient knowledge and NBT for HIV as 1.1-5.0. The RR estimate for the association between unsafe injection and NBT for HIV, adjusted for measured confounders, was 0.96 (95% confidence interval: 0.89,1.03). However, the RR estimate decreased to 0.82 (95% credible interval: 0.64, 0.99) after adjusting for insufficient knowledge as an unmeasured confounder.

CONCLUSION

Our Bayesian approach that uses expert opinion to adjust for unmeasured confounders revealed that PWID who practice unsafe injection are more likely to be tested for HIV - an association that was not seen by conventional analysis.

Survival Percentile and Predictors of Difference in Survival among Hemodialysis Patients and Their Additive Interaction Using Laplace Regression

Background: Identifying survival modifiable factors and additive interaction between them could help in prioritizing the clinical care of Hemodialysis (HD) patients. We aimed to examine the survival rate and its predictors in HD patients; and explore the additive interaction between survival modifiable factors.

Study design: A retrospective cohort study.

Methods: The present study was performed on 1142 HD patients in Hamadan Province, western Iran from 2007 to 2017. Data were collected through a researcher-made checklist on hospital records. Laplace regression was used to evaluate differences in 40^th survival percentiles in different levels of predictors as well as exploring the pairwise additive interactions between variables.

Results: We observed significantly higher survival in nonsmoker patients (40^th percentile difference = 5.34 months, 95% CI: 2.06, 8.61). Survival was shorter by more than 3 years in CRP positive patients (40^th percentile difference=36.9 months, 95% CI: 32.37, 41.42). Patients with normal albumin (40^th percentile difference =24.92, 95% CI: 18.04, 31.80) and hemoglobin (40^th percentile difference = 18.65, 95% CI: 12.43, 24.86) had significantly higher survival (P<0.001). There was super-additive interaction between being CRP negative and nonsmoker (β₃ = 9.42 months, 95% CI: 3.35, 15.49 (P=0.002)).

Conclusion: High CRP and low serum albumin and hemoglobin were associated with the increased risk of death in HD patients. The results of this study support the presence of super-additive interaction between CRP status with serum hemoglobin and also CRP status with smoking, resulting in excess survival in HD patients.

Prevalence of asymptomatic COVID-19 infection using a seroepidemiological survey

The prevalence of asymptomatic infection by coronavirus disease 2019 (COVID-19) as a critical measure for effectiveness of mitigation strategy has been reported to be widely varied. In this study, we aimed to determine the prevalence of asymptomatic infection using serosurvey on general population. In a cross-sectional seroprevalence survey in Guilan province, Iran, the specific antibody against COVID-19 in a representative sample was detected using rapid test kits. Among 117 seropositive subjects, prevalence of asymptomatic infection was determined based on the history of symptoms during the preceding 3 months. The design-adjusted prevalence of asymptomatic infection was 57.2% (95% confidence interval (CI) 44-69). The prevalence was significantly lower in subjects with previous contacts to COVID-19 patients (12%, 95% CI 2-49) than others without (69%, 95% CI, 46-86). The lowest prevalence was for painful body symptom (74.4%). This study revealed that more than half of the infected COVID-19 patients had no symptoms. The implications of our findings include the importance of adopting public health measures such as social distancing and inefficiency of contact tracing to interrupt epidemic transmission.

Effect of Smoking on Breast Cancer by Adjusting for Smoking Misclassification Bias and Confounders Using a Probabilistic Bias Analysis Method

Purpose

The aim of this study was to determine the association between smoking and breast cancer after adjusting for smoking misclassification bias and confounders.

Methods

In this case–control study, 1000 women with breast cancer and 1000 healthy controls were selected. Using a probabilistic bias analysis method, the association between smoking and breast cancer was adjusted for the bias resulting from misclassification of smoking secondary to self-reporting as well as a minimally sufficient adjustment set of confounders derived from a causal directed acyclic graph (cDAG). Population attributable fraction (PAF) for smoking was calculated using Miettinen’s formula.

Results

While the odds ratio (OR) from the conventional logistic regression model between smoking and breast cancer was 0.64 (95% CI: 0.36–1.13), the adjusted ORs from the probabilistic bias analysis were in the ranges of 2.63–2.69 and 1.73–2.83 for non-differential and differential misclassification, respectively. PAF ranges obtained were 1.36–1.72% and 0.62–2.01% using the non-differential bias analysis and differential bias analysis, respectively.

Conclusion

After misclassification correction for smoking, the non-significant negative-adjusted association between smoking and breast cancer changed to a significant positive-adjusted association.

Predicting Time to Reflux of Children With Antenatal Hydronephrosis: A Competing Risks Approach

The aim of this study was describing methodological aspects and applying a trivariate Weibull survival model using the competing risks concept to predict time to occurrence different types of reflux (unilateral (left, right) or bilateral) in children with antenatal hydronephrosis. Data from 333 children in Pediatric Urology Research Center of Children's Hospital Medical Center, affiliated with Tehran University of Medical Sciences was used. The effect of some demographic and clinical factors on child's reflux was studied. The assumption of independent between times of different types of reflux was evaluated. Of infants 80.5% were boy. The percentage of children experienced right, left and bilateral reflux or have been censored are 15.3%, 14.1%, 60.4% and 10.2% respectively. For the time of left reflux, variables, Week of diagnosis ANH, UC, UA, HUN, HN, APD_Right, Direction of ANH, CA19-9 baby, Urethra were significant. For the time of right reflux, variables, constipation, UC, UA, HUN, APD_Right, Direction and Severity of ANH, Bladder, and finally for the time of bilateral reflux, variables, Week of diagnosis ANH, Gender, UA, HUN, HN, APD_Left, Urethra, and Bladder were significant P<0.05. In the presence of competing risks, it is inappropriate to use the Kaplan-Meier method and standard Cox model which do not take competing risks into account. Trivariate Weibull survival model using competing risks not only is able to calculate the hazard rate of variables with different type of events but also it will be able to compare the hazard rate within the same type of event with different covariates.