METHODS
Findings from this systematic review and meta-analysis were reported based on Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guideline [17].
Search strategy: A systematic literature review was conducted by using the databases PubMed/Medline, Scopus, and ISI Web of Science up to February 2020 with no language or time restriction. Details of the search terms are provided in Supplemental Table 1 . Furthermore, the reference list of the relevant articles was manually searched.
Inclusion criteria: Published studies that met the following criteria were included: 1) observational prospective studies conducted on adults; 2) reported effect sizes (ESs) including hazard ratios (HRs) or relative risks (RRs) or odds ratios (ORs) with corresponding 95% CIs for the association between intakes of total water from all foods and beverages as well as drinking water as the exposure of interest and mortality from all causes and CVD as the outcome of interest.
Exclusion criteria: We excluded letters, comments, reviews, meta-analyses, and ecologic studies. We also did not include studies that performed on children or adolescences, those conducted among chronic kidney disease or hemodialysis patients, critically ill patients, and those enrollment acute respiratory distress syndrome patients. All outcomes were classified based on the World Health Organization’s international classification of disease criteria.
Data extraction:  The selection and data extraction process were executed by 2 independent reviewers (MM and FH). We extracted the following data from each study: name of the first author, publication year, study design, location of the study conducted, gender, the sample size of the cohort, the age range at entry, duration of follow-up, exposure, method of assessment of exposure, incidence of death, comparison categories and relevant effect size along with 95% CIs and list of confounders adjusted in the statistical analysis.
Risk of bias assessment: We used the Risk Of Bias In Non-randomized Studies of Exposures (ROBINS-E) tool to assess the risk of bias. The ROBINS-E tool comprises 7 domains: (1) bias due to confounding, (2) bias in selection of participants into the study, (3) bias in the classification of exposures, (4) bias due to departure from intended exposures, (5) bias due to missing data, (6) bias in the measurement of outcomes, and (7) bias in the selection of reported results. Studies were categorized as low risk, moderate risk, serious risk, and critical risk of bias under each domain. The results of risk of bias assessment are presented in Supplemental Table 2 .
Statistical analysis: ORs, RRs, and HRs (and 95% CIs) for comparison of the highest versus lowest categories of water intake were used to calculate log ORs, RRs, and HRs ± SE. The analyses were performed with the use of a random-effects model, in which we calculated both Q-statistic and I 2 as indicators of heterogeneity. As random-effects model can account for variation between studies, it can provide more conservative results than a fixed-effects model. For studies that reported effect sizes separately for drinking water and other fluids, we first combined the estimates using the fixed-effects model to obtain an overall estimate, and then, the pooled effect size was included in the meta-analysis. In the study of Wu et al that reported effect size for CKD and non-CKD patients separately, we included only non-CKD patients in the meta-analysis. Studies that investigated only CVD mortality in relation to water intake were also considered in the meta-analysis of all-cause mortality because CVD mortality accounts for a very high proportion of the all-cause mortality. considered in the meta-analysis of all-cause mortality. We conducted a sensitivity analysis, using a fixed-effects model, in which each prospective cohort study was excluded to examine the influence of that study on the overall estimate. In case of finding a significant between-study heterogeneity, we performed subgroup analysis to examine possible sources of heterogeneity. Between-subgroup heterogeneity was examined through the fixed-effects model. Publication bias was examined by visual inspection of funnel plots. Formal statistical assessment of funnel plot asymmetry was also done with the use of Egger’s test. In case of significant publication bias, the trim-and-fill method was used to detect the effect of missing studies on the overall effect of meta-analysis.
A method suggested by Greenland and Orsini was used to compute the trend from the ORs/RRs/HRs estimates and their 95% CIs across categories of water intake[18]. In this method, the distribution of cases and the ORs/RRs/HRs with the variance estimates for ≥3 quantitative categories of exposure were required. We considered the midpoint of water intake in each category. For studies that reported the water intake as range, we estimated the midpoint in each category by calculating the mean of the lower and upper bound. When the highest and lowest categories were open-ended, the length of these open-ended intervals was assumed to be the same as that of the adjacent intervals. A two-stage random-effects dose-response meta-analysis was applied to examine a possible non-linear association between water intake and mortality. This was done through modeling of water intake and restricted cubic splines with three knots at fixed percentiles of 10, 50, and 90% of the distribution. Based on the Orsini method[18], we calculated restricted cubic spline models using generalized least-squares trend estimation method, which takes into account the correlation within each set of reported ORs/RRs/HRs. Then, all the study-specific estimates were combined with the use of the restricted maximum likelihood method in a multivariate random-effects meta-analysis. A probability value for non-linearity was estimated using null hypothesis testing in which the coefficient of the second spline was considered equal to 0. A linear dose-response association was investigated using the two-stage generalized least-squares trend estimation method. First, study-specific slope lines were estimated, and then, these lines were combined to obtain an overall average slope.30 Study-specific slope lines were combined using a random-effects model. Statistical analyses were conducted using STATA version 14.0. P <0.05 was considered as statistically significant for all tests, including Cochran’s Q test.