Table 1.
Input data for comparative risk assessment on effect of nationwide salt substitute intervention in the adult Chinese population on cardiovascular disease burden
| Model and data | Group | Year | Value | Distribution | Note | Source |
|---|---|---|---|---|---|---|
| Blood pressure | ||||||
| Pre-intervention SBP | Sex, age | 2015 | Age and sex specific estimates (eTable 1) | γ | Standard deviation of SBP in each group (Chinese adults aged ≥25) was assumed equal to 15% of the mean for that specific group, based on age and sex specific SBP distributions estimated in >500 000 Chinese adults.16 For each iteration (n=1000), random draws from normal distributions of age-sex specific means from GBD were used to calculate assumed standard deviation and γparameters | GBD, 2017* |
| Intervention effect on SBP | Age | N/A | −2.82 (−4.75 to −0.89) mm Hg at age 65 years, with 0.13 (−0.02 to 0.27) mm Hg lesser or greater reduction for each year of age younger or older than 65, respectively | Normal | Estimates and their uncertainties of treatment effect and age-treatment interaction were estimated using interim measurements (1-3 years after baseline) in >4500 participants of the SSaSS. For each iteration (n=1000), random draws from normal distributions of main and interaction effects were used to calculate age specific effects on SBP | SSaSS (unpublished)† |
| SBP effect on CVD risk | Age | N/A | Disease and age specific estimates (eTable 4) | Log normal | For each iteration (n=1000), random draws of age specific log relative risks of CVD subtypes (n=8) were made | GBD, 2017* Singh23 |
| SBP effect on CKD risk | N/A | N/A | RR 1.28 (95% CI 1.18 to 1.39) per 10 mm Hg | Log normal | For each iteration (n=1000), random draws of log RR were made | GBD, 2014‡ |
| Current CVD and CKD burden | Sex, age | 2015 | Disease, sex, and age specific estimates | Normal | Estimates and corresponding 95% uncertainty intervals for CVD subtypes (n=11) and CKD were retrieved from the GBD Results Tool | GBD Results Tool§ |
| Prevalence of CKD (%) | Sex, age | 2007-10 | Age and sex specific estimates (eTable 1) | Normal | Age and sex specific prevalence of CKD (eGFR <60 mL/min/1.73m2) was estimated by interpolation and extrapolation based on piecewise linear regression of age specific CKD prevalence in four age groups (18-39, 40-59, 60-69, ≥7017) plotted against the midpoint age of each age group. Similarly, 95% CIs for prevalence of CKD were estimated by interpolation and extrapolation based on piecewise linear regression of upper and lower 95% CI boundaries. For each iteration (n=1000), random draws of age and sex specific CKD prevalence (assuming normal distribution) were made | Zhang17 |
| Age specific HR of CVD mortality in CKD compared with non-CKD | Age | N/A | Age specific HR estimates (eTable 1) | Log normal | Age specific (18-54, 55-64, 65-74, ≥75) HRs comparing eGFR 50 v 80 mL/min/1.73m2 were assessed by Hallan18 in individual level meta-analysis of >2 million participants. We conducted linear extrapolation and interpolation of natural logarithms of the multivariable adjusted HRs plotted against the midpoint ages of the age groups evaluated by Hallan,18 to estimate HRs in 5 year age intervals | Hallan18 |
| Potassium | ||||||
| Prevalence of CKD by stage (%) | CKD stage¶ | 2007-10 | Stage G3a: 1.40 (95% CI 1.20 to 1.50); stage G3b: 0.20 (0.10 to 0.30); stage G4: 0.10 (0.06 to 0.20); stage G5: 0.03 (0.01 to 0.05) | Normal | Prevalence of CKD per eGFR level in the adult Chinese population. Random draws were made for each iteration (n=1000) | Zhang17 |
| Crude HR of CVD mortality compared with non-CKD | CKD stage¶ | N/A | Stage G3a: 4.03 (95% CI 2.76 to 5.88); stage G3b: 6.95 (4.35 to 11.10); stage G4: 9.93 (6.09 to 16.17); stage G5: 17.51 (9.72 to 31.55) | Log normal | Unadjusted HRs for each CKD stage using midpoint eGFR (ie, stage G3a 52.5, stage G3b 37.5, stage G4 22.5, stage G5 7.5 mL/min/1.73m2) v reference point (eGFR 95 mL/min/1.73m2) were extracted from eFig 7b in Matsushita.19 For each iteration (n=1000), random draws of stage specific HR (assuming log normal distribution) were made and used to calculate pre-intervention CVD deaths per CKD stage | Matsushita19 |
| Mean (SD) current serum potassium distribution (mmol/L) | CKD stage4 | N/A | Stage G3a: 4.34 (0.45); stage G3b: 4.42 (0.51); stage G4: 4.52 (0.59); stage G5: 4.63 (0.67) | Normal | Extrapolated and interpolated from linear regressions of means and standard deviations of stage G3 and ≥G4 reported by the CKD-PC. For each iteration (n=1000), random draws of stage specific serum potassium levels (assuming normal distribution) were made | Kovesdy8 |
| Increase in potassium intake by salt substitute intervention (g/day) | N/A | N/A | 0.86 (95% CI 1.14 to 0.68) | Normal | Calculated by multiplying urinary excretion in SSaSS (0.66 g/24 hours; 95% CI 0.52 to 0.80) by a factor of 1.3 based on mean differences between intake and excretion20 | Huang 2018** |
| Dietary potassium effect on serum potassium level (mmol/L per g/day) | CKD stage4 | N/A | Stage G3a: 0.23 (95% CI 0.08 to 0.38); stage G3b: 0.47 (0.33 to 0.61); stage G4: 0.93 (0.66 to 1.21); stage G5: 1.87 (1.33 to 2.41) | Normal | Estimated in clinical trial; β and SE assumed to be 2× effect estimated in stage G3a; β and SE assumed to be 4× effect estimated in stage G3a; β and SE assumed to 8× effect estimated in stage G3a | Turban (unpublished)†† |
| Serum potassium effect on CVD mortality | N/A | N/A | HR (95% CI) compared with serum potassium 4.2 mmol/L available for each 0.05 increment of serum potassium in the interval 4.2 to 6.5 mmol/L | Log normal | CKD-PC data suggest no evidence of different HR per eGFR level | Kovesdy8 |
β=regression coefficient of serum to dietary potassium; CKD=chronic kidney disease; CKD-PC=Chronic Kidney Disease Prognosis Consortium; CVD=cardiovascular disease; eGFR=estimated glomerular filtration rate; GBD=Global Burden of Disease Study; HR=hazard ratio; N/A=not applicable; SBP=systolic blood pressure; SD=standard deviation; SE=standard error; SSaSS=Salt Substitute and Stroke Study; RR=relative risk.
2015 blood pressure levels by age and sex were extracted from the Global Burden of Disease Study 2015. Hypertension and systolic blood pressure of at least 110 to 115 mm Hg 1990-2015. Seattle: Institute for Health Metrics and Evaluation (IHME), 2017.3
Effects on systolic blood pressure and potassium intake were assessed in the ongoing Salt Substitute and Stroke Study, a cluster randomised trial conducted in 600 villages across five Chinese provinces.
Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol 2014;2:634-47 (reference 10 from web appendix).
Data (estimates and uncertainties) on deaths from cardiovascular disease by age and sex were retrieved from the Institute for the GBD Results Tool (Health Metrics and Evaluation (IHME) GBD Results Tool). Seattle, WA: IHME, University of Washington, 2019. http://ghdx.healthdata.org/gbd-results-tool.
Chronic kidney disease stages: G3a (estimated glomerular filtration rate 45-59 mL/min/1.73 m2); G3b (30-44); G4 (15-29); and G5 (<15-29).
Increase in intake of potassium was assessed using 24 hour urinary excretion of potassium from the ongoing Salt Substitute and Stroke Study, a cluster randomised trial conducted in 600 villages across China.
The effects of dietary potassium on blood pressure in adults with stage 3 chronic kidney disease: Results from the CKD-K trial (unpublished data).