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Adineh HA, Hoseini K, Zareban I, Jalali A, Nazemipour M, Mansournia MA. Comparison of outcomes between off-pump and on-pump coronary artery bypass graft surgery using collaborative targeted maximum likelihood estimation. Sci Rep 2024; 14:11373. [PMID: 38762564 PMCID: PMC11102550 DOI: 10.1038/s41598-024-61846-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 05/10/2024] [Indexed: 05/20/2024] Open
Abstract
There are some discrepancies about the superiority of the off-pump coronary artery bypass grafting (CABG) surgery over the conventional cardiopulmonary bypass (on-pump). The aim of this study was estimating risk ratio of mortality in the off-pump coronary bypass compared with the on-pump using a causal model known as collaborative targeted maximum likelihood estimation (C-TMLE). The data of the Tehran Heart Cohort study from 2007 to 2020 was used. A collaborative targeted maximum likelihood estimation and targeted maximum likelihood estimation, and propensity score (PS) adjustment methods were used to estimate causal risk ratio adjusting for the minimum sufficient set of confounders, and the results were compared. Among 24,883 participants (73.6% male), 5566 patients died during an average of 8.2 years of follow-up. The risk ratio estimates (95% confidence intervals) by unadjusted log-binomial regression model, PS adjustment, TMLE, and C-TMLE methods were 0.86 (0.78-0.95), 0.88 (0.80-0.97), 0.88 (0.80-0.97), and 0.87(0.85-0.89), respectively. This study provides evidence for a protective effect of off-pump surgery on mortality risk for up to 8 years in diabetic and non-diabetic patients.
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Affiliation(s)
- Hossein Ali Adineh
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hoseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Iraj Zareban
- Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Arash Jalali
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
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Khodamoradi F, Nazemipour M, Mansournia N, Yazdani K, Khalili D, Arshadi M, Etminan M, Mansournia MA. The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population. BMC Public Health 2023; 23:2058. [PMID: 37864179 PMCID: PMC10588163 DOI: 10.1186/s12889-023-16863-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
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.
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Affiliation(s)
- Farzad Khodamoradi
- Department of Social Medicine, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Nasrin Mansournia
- Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran
| | - Kamran Yazdani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maedeh Arshadi
- Department of Epidemiology and Biostatistics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mahyar Etminan
- Departments of Ophthalmology and Visual Sciences, Medicine and Pharmacology, University of British Columbia, Vancouver, Canada
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran.
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Malekifar P, Nedjat S, Abdollahpour I, Nazemipour M, Malekifar S, Mansournia MA. Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis. ARCHIVES OF IRANIAN MEDICINE 2023; 26:567-574. [PMID: 38310413 PMCID: PMC10862089 DOI: 10.34172/aim.2023.83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/06/2023] [Indexed: 02/05/2024]
Abstract
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.
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Affiliation(s)
- Pooneh Malekifar
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saharnaz Nedjat
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Ibrahim Abdollahpour
- Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Science, Isfahan, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Malekifar
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Smith MJ, Phillips RV, Luque-Fernandez MA, Maringe C. Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review. Ann Epidemiol 2023; 86:34-48.e28. [PMID: 37343734 DOI: 10.1016/j.annepidem.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/24/2023] [Accepted: 06/06/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. METHODS We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. RESULTS Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. CONCLUSIONS There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.
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Affiliation(s)
- Matthew J Smith
- Inequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK.
| | - Rachael V Phillips
- Division of Biostatistics, School of Public Health, University of California at Berkeley, Berkeley, CA
| | - Miguel Angel Luque-Fernandez
- Inequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK; Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - Camille Maringe
- Inequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK
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Chen Y, Jiang P, Geng Y. The role of breastfeeding in breast cancer prevention: a literature review. Front Oncol 2023; 13:1257804. [PMID: 37746260 PMCID: PMC10512942 DOI: 10.3389/fonc.2023.1257804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Breast cancer stands as the most prevalent malignancy globally. Despite the array of treatment options, its mortality rate continues to rise annually. Thus, reevaluation of factors directly linked to breast cancer emergence is imperative, alongside the development of more effective preventive measures. Estrogen levels, profoundly tied to reproduction, play a pivotal role in breast cancer development. Speculation centers on the potential of breastfeeding to mitigate cancer risk in women. However, the precise mechanism remains elusive. Breastfeeding is a modifiable reproductive factor extensively studied. Studies highlight a direct connection between lack of breastfeeding and breast cancer emergence, potentially linked to DNA methyltransferase expression alteration, aberrant methylation levels, pregnancy-associated plasma protein-A, cellular microenvironment, and oncogenes. This study reviews recent mechanisms underlying breastfeeding's role in reducing breast cancer incidence.
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Affiliation(s)
- Yulong Chen
- Department of Thyroid and Breast Surgery, The Fourth People’s Hospital of Jinan, Jinan, China
| | - Pengli Jiang
- Department of Breast Surgery, The Second Hospital of Jilin University, Changchun, China
| | - Yongqin Geng
- Department of Thyroid and Breast Surgery, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
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Eslahi M, Roshandel G, Khanjani N. Temporal Pattern and Age-Period-Cohort Analysis of Breast Cancer Incidence in Iranian Women (2009-2017). ARCHIVES OF IRANIAN MEDICINE 2023; 26:285-289. [PMID: 38310428 PMCID: PMC10685833 DOI: 10.34172/aim.2023.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/05/2022] [Indexed: 02/05/2024]
Abstract
BACKGROUND Breast cancer accounted for 28.1% of all female cancers in 2020 in Iran. This study was conducted to evaluate the time trend of breast cancer incidence and to identify the changes of breast cancer incidence in age, period, and birth cohorts in Iran, in the 2009-2017 timeframe. METHODS Annual cancer statistics for female breast cancer were obtained from the Iranian National Population-based Cancer Registry (INPCR) database from 2009 to 2017. The age-period-cohort (APC) analysis was used to evaluate the time trend of breast cancer incidence in age, period and birth cohorts between 2009 and 2017. R package (Epi) was used to analyze data. Results were considered statistically significant at P<0.05. RESULTS The age effect showed an increased incidence of breast cancer until the age of 45, and after this age the speed of increase was slower until 65 years. There was an increased diagnosis in 2015-2017 (period effect) for many age groups, especially in the 70- and over 80-year-old group. CONCLUSION Our findings indicated that breast cancer incidence peaks in the age of 45 in Iranian women, which is a decade earlier compared to the Western world. The period effect in 2015-2017 can be explained by the fact that in 2014, the former Iranian pathology-based cancer registry was upgraded to a population-based cancer registry, which resulted in improved coverage of cancer registry and case finding.
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Affiliation(s)
- Marzieh Eslahi
- Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Gholamreza Roshandel
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Narges Khanjani
- Neurology Research Center, Kerman University of Medical Sciences, Kerman, Iran
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Shakiba M, Nazemipour M, Mansournia N, Mansournia MA. Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial. Sci Rep 2023; 13:5833. [PMID: 37037931 PMCID: PMC10086045 DOI: 10.1038/s41598-023-32855-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/03/2023] [Indexed: 04/12/2023] Open
Abstract
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.
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Affiliation(s)
- Maryam Shakiba
- Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
- Department of Biostatistics and Epidemiology, School of Health, Guilan University of Medical Sciences, Rasht, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Nasrin Mansournia
- Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran.
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Koohi F, Khalili D, Soori H, Nazemipour M, Mansournia MA. 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. GLOBAL EPIDEMIOLOGY 2022; 4:100075. [PMID: 37637024 PMCID: PMC10445971 DOI: 10.1016/j.gloepi.2022.100075] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022] Open
Abstract
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.
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Affiliation(s)
- Fatemeh Koohi
- Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Soori
- Safety Promotion and Injury Prevention Research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Nazemipour
- Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Longitudinal causal effect of modified creatinine index on all-cause mortality in patients with end-stage renal disease: Accounting for time-varying confounders using G-estimation. PLoS One 2022; 17:e0272212. [PMID: 35984783 PMCID: PMC9390931 DOI: 10.1371/journal.pone.0272212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 07/14/2022] [Indexed: 11/19/2022] Open
Abstract
Background Standard regression modeling may cause biased effect estimates in the presence of time-varying confounders affected by prior exposure. This study aimed to quantify the relationship between declining in modified creatinine index (MCI), as a surrogate marker of lean body mass, and mortality among end stage renal disease (ESRD) patients using G-estimation accounting appropriately for time-varying confounders. Methods A retrospective cohort of all registered ESRD patients (n = 553) was constructed over 8 years from 2011 to 2019, from 3 hemodialysis centers at Kerman, southeast of Iran. According to changes in MCI, patients were dichotomized to either the decline group or no-decline group. Subsequently the effect of interest was estimated using G-estimation and compared with accelerated failure time (AFT) Weibull models using two modelling strategies. Results Standard models demonstrated survival time ratios of 0.91 (95% confidence interval [95% CI]: 0.64 to 1.28) and 0.84 (95% CI: 0.58 to 1.23) in patients in the decline MCI group compared to those in no-decline MCI group. This effect was demonstrated to be 0.57 (-95% CI: 0.21 to 0.81) using G-estimation. Conclusion Declining in MCI increases mortality in patients with ESRD using G-estimation, while the AFT standard models yield biased effect estimate toward the null.
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Khodamoradi F, Nazemipour M, Mansournia N, Yazdani K, Khalili D, Mansournia MA. The Effects of Smoking on Metabolic Syndrome and Its Components Using Causal Methods in the Iranian Population. Int J Prev Med 2021; 12:118. [PMID: 34760129 PMCID: PMC8551769 DOI: 10.4103/ijpvm.ijpvm_45_21] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 02/27/2021] [Indexed: 11/04/2022] Open
Abstract
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.
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Affiliation(s)
- Farzad Khodamoradi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Nazemipour
- Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Nasrin Mansournia
- Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran
| | - Kamran Yazdani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Tehran, Iran.,Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Khosravi A, Nazemipour M, Shinozaki T, Mansournia MA. Population attributable fraction in textbooks: Time to revise. GLOBAL EPIDEMIOLOGY 2021; 3:100062. [PMID: 37635714 PMCID: PMC10445975 DOI: 10.1016/j.gloepi.2021.100062] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/25/2021] [Accepted: 08/25/2021] [Indexed: 01/05/2023] Open
Abstract
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.
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Affiliation(s)
- Ahmad Khosravi
- Department of Epidemiology, Shahroud University of Medical Sciences, Shahroud, Iran
- Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Maryam Nazemipour
- Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Tomohiro Shinozaki
- Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Sciences, Tokyo, Japan
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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