Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries
- School of Medicine Alberto Hurtado, Universidad Peruana Cayetano Heredia, Peru
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Peru
- Sociedad Científica de Estudiantes de Medicina Cayetano Heredia (SOCEMCH), Universidad Peruana Cayetano Heredia, Peru
- Universidad de Lima, Peru
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
Figures
Figure 1
Observed and predicted mean salt intake (g/day) by sex in each survey included in the machine learning (ML) model development.
Exact estimates (along with their 95% CI) are presented in Appendix 1—table 2. These results were computed with the test dataset only. Results are for the HuR algorithm, which was the model with the best performance.
Figure 2
Predicted mean salt intake (g/day) by sex in each of the 54 national surveys included in the application of the model herein developed.
Exact estimates (along with their 95% CI) are presented in Appendix 1—table 7. Countries are presented in ascending order based on their overall mean salt intake (i.e., countries with the highest mean salt intake are at the bottom).
Appendix 1—figure 1
Flowchart of data cleaning and inclusion criteria for model derivation.
Appendix 2—figure 1
Pipeline for data management and model development.
PCA, primary component analysis; LiR, linear regression; HuR, Hubber regressor; RiR, ridge regressor; MLP, multilayer perceptron; SVR, support vector regressor; KNN, k-nearest neighbors; RF, random forest; GBM, gradient boost machine; XGB, extreme gradient boosting; NN, neural network.
Appendix 2—figure 2
Neural network implementation.
Appendix 2—figure 3
Comparison between mean difference between observed and predicted salt intake across the best algorithms.
CNN, customized neural network; HuR: Hubber regressor; MLP, multilayer perceptron.
Tables
Appendix 1—table 1
Weighted distribution of predictors in each survey included in the machine learning model development.
| Country | Year | Sample size | Mean age (years) | Age range (years) | Proportion of men (%) | Mean, minimum and maximum values of SBP (mmHg) | Mean, minimum, and maximum values of DBP (mmHg) | Mean, minimum, and maximum values of weight (kg) | Mean, minimum, and maximum values of height (m) | Mean, minimum, and maximum values of urinary sodium (mmol/L) | Mean, minimum, and maximum values of urinary creatinine (mmol/L) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Armenia | 2016 | 1074 | 40 | 18–69 | 49.7 | 129 (86–238) | 85 (49–148) | 70.9 (35–139) | 1.66 (1.27–1.89) | 128.6 (10.6–237.6) | 10.1 (1.9–27.3) |
| Azerbaijan | 2017 | 2359 | 39 | 18–69 | 49.5 | 126 (82–230) | 81 (48–142) | 73.1 (36–174) | 1.67 (1.15–1.98) | 167.7 (2–389) | 11.9 (1.8–31.8) |
| Bangladesh | 2018 | 6200 | 39 | 18–69 | 46.9 | 121 (72–251) | 79 (32–147) | 55.9 (28–111) | 1.56 (1–2.11) | 119.6 (4–422) | 8.4 (2.2–32.3) |
| Belarus | 2017 | 4503 | 43 | 18–69 | 47.1 | 135 (88–257) | 85 (54–147) | 77.7 (41–144) | 1.7 (1.05–1.99) | 149.5 (10.5–371.4) | 12.2 (1.8–32.7) |
| Bhutan | 2014 | 6163 | 38 | 18–69 | 59.2 | 126 (75–228) | 85 (46–142) | 61.4 (23–115) | 1.6 (1.11–1.96) | 142.1 (6–388) | 8.1 (1.9–29.7) |
| Bhutan | 2019 | 6163 | 34 | 15–69 | 56.8 | 124 (85–224) | 82 (44–137) | 61.9 (28.5–140) | 1.58 (1.07–1.92) | 129.9 (4.7–444.9) | 10.4 (1.8–32.7) |
| Brunei Darussalam | 2016 | 1635 | 35 | 18–69 | 51.4 | 123 (76–218) | 78 (46–138) | 69 (31.2–138.3) | 1.59 (1.32–1.84) | 122.6 (19.9–329) | 12.6 (1.8–32.6) |
| Chile | 2017 | 2952 | 39 | 15–69 | 49.8 | 120 (81–226) | 74 (44–130) | 75.7 (38.3–146.9) | 1.63 (1.34–1.96) | 135.8 (10–324) | 12.1 (1.8–32.2) |
| Jordan | 2019 | 1040 | 37 | 18–69 | 50.2 | 118 (75–200) | 78 (50–120) | 76.3 (35.5–159.5) | 1.66 (1.36–1.95) | 165.4 (13–365) | 13.6 (1.8–32.5) |
| Lebanon | 2017 | 998 | 42 | 17–69 | 48.7 | 129 (80–214) | 77 (35–123) | 78.3 (40–141) | 1.68 (1.2–1.96) | 124.4 (4–385) | 11.5 (1.9–32) |
| Malawi | 2017 | 1601 | 35 | 18–69 | 56.4 | 122 (74–222) | 76 (40–142) | 58.5 (33.6–119) | 1.61 (1.36–1.96) | 186.5 (11–399.9) | 10.7 (1.9–32.4) |
| Mongolia | 2013 | 7505 | 42 | 15–64 | 50.3 | 129 (88–220) | 82 (50–134) | 71 (30.6–138) | 1.62 (1.27–1.92) | 134.1 (13.1–515) | 10.9 (1.8–31.9) |
| Mongolia | 2019 | 7505 | 36 | 15–69 | 50.9 | 120 (76–254) | 77 (48–143) | 68.4 (29–159) | 1.64 (1.34–1.98) | 117 (2.1–348.9) | 7.5 (1.8–28.3) |
| Morocco | 2017 | 3435 | 40 | 18–69 | 50.6 | 128 (83–228) | 78 (40–139) | 70.9 (35–168) | 1.66 (1.34–1.95) | 122.3 (26.3–575.2) | 10.3 (1.8–31.4) |
| Nepal | 2019 | 2560 | 36 | 15–69 | 41 | 124 (81–239) | 81 (55–146) | 54.6 (26–160) | 1.55 (1.21–2.03) | 140.9 (3–437) | 5.6 (1.8–25.5) |
| SolomonIslands | 2015 | 172 | 38 | 18–69 | 61.4 | 121 (88–188) | 77 (52–104) | 67.9 (38.5–122) | 1.61 (1.41–1.8) | 99.3 (7–250) | 9.7 (1.9–28.4) |
| Sudan | 2016 | 571 | 36 | 18–69 | 55.9 | 128 (89–231) | 85 (58–132) | 72.2 (35.6–174) | 1.67 (1.42–1.92) | 128.5 (5–459) | 14 (1.9–32.4) |
| Tokelau | 2014 | 181 | 35 | 18–63 | 56 | 125 (76–184) | 79 (53–128) | 94.8 (58–158.3) | 1.71 (1.16–1.88) | 62.4 (20–265) | 5 (2–7.7) |
| Tonga | 2017 | 755 | 40 | 18–69 | 35.7 | 131 (96–208) | 83 (53–148) | 98.6 (48.1–181) | 1.69 (1.4–1.94) | 101.9 (4–327) | 15.3 (1.8–32.7) |
| Turkmenistan | 2018 | 3584 | 37 | 18–69 | 52.7 | 127 (88–268) | 83 (54–149) | 72.4 (39–142) | 1.68 (1.16–1.98) | 109.2 (10–163) | 11.1 (4.5–18.3) |
| Zambia | 2017 | 2488 | 33 | 18–69 | 50.3 | 125 (73–248) | 77 (36–148) | 60.9 (33.8–150) | 1.62 (1.01–2.07) | 137.2 (10–375) | 12.2 (1.8–32.4) |
Appendix 1—table 2
Observed and predicted mean salt intake (g/day) by sex in each survey included in the machine learning model development.
| Country | Year | Sex | Mean salt intake | Mean salt intake lower 95% confidence interval | Mean salt intake upper 95% confidence interval | Category |
|---|---|---|---|---|---|---|
| Armenia | 2016 | Men | 9.24 | 9.04 | 9.45 | ML predicted |
| Armenia | 2016 | Men | 9.46 | 9.11 | 9.81 | Observed |
| Armenia | 2016 | Women | 7.43 | 7.3 | 7.57 | ML predicted |
| Armenia | 2016 | Women | 7.44 | 7.26 | 7.62 | Observed |
| Azerbaijan | 2017 | Men | 9.43 | 9.33 | 9.53 | ML predicted |
| Azerbaijan | 2017 | Men | 10.39 | 10.06 | 10.72 | Observed |
| Azerbaijan | 2017 | Women | 7.43 | 7.31 | 7.55 | ML predicted |
| Azerbaijan | 2017 | Women | 7.94 | 7.75 | 8.14 | Observed |
| Bangladesh | 2018 | Men | 8.87 | 8.8 | 8.93 | ML predicted |
| Bangladesh | 2018 | Men | 8.59 | 8.42 | 8.75 | Observed |
| Bangladesh | 2018 | Women | 7.18 | 7.13 | 7.24 | ML predicted |
| Bangladesh | 2018 | Women | 7.27 | 7.17 | 7.37 | Observed |
| Belarus | 2017 | Men | 9.49 | 9.42 | 9.56 | ML predicted |
| Belarus | 2017 | Men | 10.14 | 9.94 | 10.35 | Observed |
| Belarus | 2017 | Women | 7.53 | 7.45 | 7.61 | ML predicted |
| Belarus | 2017 | Women | 7.56 | 7.41 | 7.72 | Observed |
| Bhutan | 2014 | Men | 9.14 | 9.04 | 9.25 | ML predicted |
| Bhutan | 2014 | Men | 9.58 | 9.27 | 9.88 | Observed |
| Bhutan | 2014 | Women | 7.38 | 7.3 | 7.46 | ML predicted |
| Bhutan | 2014 | Women | 8.1 | 7.94 | 8.27 | Observed |
| Bhutan | 2019 | Men | 9.33 | 9.25 | 9.41 | ML predicted |
| Bhutan | 2019 | Men | 9.1 | 8.85 | 9.35 | Observed |
| Bhutan | 2019 | Women | 7.43 | 7.36 | 7.49 | ML predicted |
| Bhutan | 2019 | Women | 7.53 | 7.33 | 7.73 | Observed |
| Brunei Darussalam | 2016 | Men | 9.78 | 9.57 | 9.99 | ML predicted |
| Brunei Darussalam | 2016 | Men | 8.95 | 8.66 | 9.25 | Observed |
| Brunei Darussalam | 2016 | Women | 7.64 | 7.5 | 7.77 | ML predicted |
| Brunei Darussalam | 2016 | Women | 7.3 | 7.05 | 7.54 | Observed |
| Chile | 2017 | Men | 9.65 | 9.56 | 9.75 | ML predicted |
| Chile | 2017 | Men | 9.75 | 9.18 | 10.31 | Observed |
| Chile | 2017 | Women | 7.86 | 7.8 | 7.93 | ML predicted |
| Chile | 2017 | Women | 7.64 | 7.45 | 7.83 | Observed |
| Jordan | 2019 | Men | 9.31 | 9.03 | 9.6 | ML predicted |
| Jordan | 2019 | Men | 10.2 | 9.52 | 10.88 | Observed |
| Jordan | 2019 | Women | 7.78 | 7.53 | 8.03 | ML predicted |
| Jordan | 2019 | Women | 8.1 | 7.75 | 8.45 | Observed |
| Lebanon | 2017 | Men | 9.88 | 9.62 | 10.14 | ML predicted |
| Lebanon | 2017 | Men | 9.53 | 9.06 | 9.99 | Observed |
| Lebanon | 2017 | Women | 7.63 | 7.39 | 7.86 | ML predicted |
| Lebanon | 2017 | Women | 7.51 | 7.07 | 7.95 | Observed |
| Malawi | 2017 | Men | 8.76 | 8.66 | 8.86 | ML predicted |
| Malawi | 2017 | Men | 9.54 | 9.16 | 9.91 | Observed |
| Malawi | 2017 | Women | 7.1 | 6.97 | 7.24 | ML predicted |
| Malawi | 2017 | Women | 8.34 | 8.03 | 8.64 | Observed |
| Mongolia | 2013 | Men | 9.5 | 9.32 | 9.68 | ML predicted |
| Mongolia | 2013 | Men | 9.83 | 9.34 | 10.32 | Observed |
| Mongolia | 2013 | Women | 7.63 | 7.51 | 7.74 | ML predicted |
| Mongolia | 2013 | Women | 7.79 | 7.6 | 7.97 | Observed |
| Mongolia | 2019 | Men | 9.32 | 9.23 | 9.42 | ML predicted |
| Mongolia | 2019 | Men | 9.68 | 9.5 | 9.85 | Observed |
| Mongolia | 2019 | Women | 7.39 | 7.32 | 7.46 | ML predicted |
| Mongolia | 2019 | Women | 7.46 | 7.34 | 7.59 | Observed |
| Morocco | 2017 | Men | 9.06 | 8.97 | 9.15 | ML predicted |
| Morocco | 2017 | Men | 9.03 | 8.82 | 9.24 | Observed |
| Morocco | 2017 | Women | 7.49 | 7.43 | 7.56 | ML predicted |
| Morocco | 2017 | Women | 7.47 | 7.35 | 7.59 | Observed |
| Nepal | 2019 | Men | 9 | 8.83 | 9.18 | ML predicted |
| Nepal | 2019 | Men | 9.55 | 9.22 | 9.87 | Observed |
| Nepal | 2019 | Women | 7.07 | 6.98 | 7.15 | ML predicted |
| Nepal | 2019 | Women | 7.84 | 7.65 | 8.04 | Observed |
| Solomon Islands | 2015 | Men | 9.42 | 9.25 | 9.59 | ML predicted |
| Solomon Islands | 2015 | Men | 8.74 | 8.08 | 9.4 | Observed |
| Solomon Islands | 2015 | Women | 7.54 | 7.29 | 7.79 | ML predicted |
| Solomon Islands | 2015 | Women | 7.03 | 6.42 | 7.64 | Observed |
| Sudan | 2016 | Men | 9.07 | 8.76 | 9.37 | ML predicted |
| Sudan | 2016 | Men | 8.53 | 7.73 | 9.33 | Observed |
| Sudan | 2016 | Women | 7.62 | 7.27 | 7.97 | ML predicted |
| Sudan | 2016 | Women | 7.49 | 7.09 | 7.88 | Observed |
| Tokelau | 2014 | Men | 10.64 | 10.34 | 10.93 | ML predicted |
| Tokelau | 2014 | Men | 10.29 | 10.18 | 10.4 | Observed |
| Tokelau | 2014 | Women | 8.96 | 8.71 | 9.21 | ML predicted |
| Tokelau | 2014 | Women | 8.12 | 7.61 | 8.63 | Observed |
| Tonga | 2017 | Men | 10.5 | 10.31 | 10.69 | ML predicted |
| Tonga | 2017 | Men | 9.19 | 8.89 | 9.48 | Observed |
| Tonga | 2017 | Women | 8.85 | 8.65 | 9.04 | ML predicted |
| Tonga | 2017 | Women | 7.63 | 7.45 | 7.81 | Observed |
| Turkmenistan | 2018 | Men | 9.38 | 9.28 | 9.48 | ML predicted |
| Turkmenistan | 2018 | Men | 8.94 | 8.79 | 9.09 | Observed |
| Turkmenistan | 2018 | Women | 7.2 | 7.13 | 7.27 | ML predicted |
| Turkmenistan | 2018 | Women | 6.76 | 6.68 | 6.83 | Observed |
| Zambia | 2017 | Men | 8.92 | 8.84 | 9 | ML predicted |
| Zambia | 2017 | Men | 8.45 | 8.15 | 8.75 | Observed |
| Zambia | 2017 | Women | 7.04 | 6.96 | 7.12 | ML predicted |
| Zambia | 2017 | Women | 7.01 | 6.81 | 7.22 | Observed |
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ML: machine learning; SBP: systolic blood pressure; DBP: diastolic blood pressure.
Appendix 1—table 3
Observed and predicted mean salt intake (g/day) by age, body mass index (BMI) category, and blood pressure status across all surveys included in the machine learning model development dataset.
| Attributed | Salt consumption (g/day) observed using surveys included in the derivation model | Salt consumption (g/day) estimated using the surveys included in the derivation model | ||
|---|---|---|---|---|
| Mean | p-Value for independent t-test or ANOVA test | Mean | p-Value for independent t-test or ANOVA test | |
| Age <30 years | 7.9 | <0.001 | 8.0 | < 0.001 |
| Age ≥ 30 years | 8.4 | 8.3 | ||
| BMI <18.5 kg/m2 | 7.0 | < 0.001 | 7.0 | < 0.001 |
| BMI 18.5–24.9 kg/m2 | 7.8 | 7.7 | ||
| BMI 25.0–29.9 kg/m2 | 8.6 | 8.4 | ||
| BMI ≥ 30 kg/m2 | 9.3 | 9.3 | ||
| Raised blood pressure ( ≥ 140/90 mmHg) | 8.7 | < 0.001 | 8.6 | < 0.001 |
| No raised blood pressure | 8.2 | 8.1 | ||
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These results do not consider the survey sampling design.
Appendix 1—table 4
Mean difference (g/day) between observed and predicted salt intake by sex in each survey included in the machine learning (ML) model development.
| Country | Year | Sex | Mean difference | Mean difference lower 95% confidence interval | Mean difference upper 95% confidence interval | p-Value |
|---|---|---|---|---|---|---|
| Armenia | 2016 | Men | 0.22 | –0.06 | 0.5 | 0.0007 |
| Armenia | 2016 | Women | 0.01 | –0.12 | 0.13 | 0.1953 |
| Azerbaijan | 2017 | Men | 0.96 | 0.67 | 1.26 | < 0.0001 |
| Azerbaijan | 2017 | Women | 0.52 | 0.37 | 0.66 | < 0.0001 |
| Bangladesh | 2018 | Men | –0.28 | –0.44 | –0.12 | < 0.0001 |
| Bangladesh | 2018 | Women | 0.09 | –0.01 | 0.19 | 0.0004 |
| Belarus | 2017 | Men | 0.66 | 0.47 | 0.84 | < 0.0001 |
| Belarus | 2017 | Women | 0.03 | –0.09 | 0.16 | 0.6258 |
| Bhutan | 2014 | Men | 0.43 | 0.17 | 0.7 | < 0.0001 |
| Bhutan | 2014 | Women | 0.72 | 0.57 | 0.88 | < 0.0001 |
| Bhutan | 2019 | Men | –0.23 | –0.48 | 0.02 | 0.0007 |
| Bhutan | 2019 | Women | 0.1 | –0.08 | 0.28 | 0.7508 |
| Brunei Darussalam | 2016 | Men | –0.82 | –1.06 | –0.58 | < 0.0001 |
| Brunei Darussalam | 2016 | Women | –0.34 | –0.55 | –0.13 | < 0.0001 |
| Chile | 2017 | Men | 0.1 | –0.39 | 0.58 | 0.0001 |
| Chile | 2017 | Women | –0.22 | –0.36 | –0.08 | < 0.0001 |
| Jordan | 2019 | Men | 0.89 | 0.31 | 1.46 | 0.0065 |
| Jordan | 2019 | Women | 0.32 | 0 | 0.64 | 0.4142 |
| Lebanon | 2017 | Men | –0.36 | –0.85 | 0.14 | 0.2074 |
| Lebanon | 2017 | Women | –0.12 | –0.45 | 0.22 | 0.1591 |
| Malawi | 2017 | Men | 0.77 | 0.39 | 1.16 | < 0.0001 |
| Malawi | 2017 | Women | 1.23 | 0.95 | 1.51 | < 0.0001 |
| Mongolia | 2013 | Men | 0.33 | –0.02 | 0.68 | 0.0184 |
| Mongolia | 2013 | Women | 0.16 | –0.03 | 0.35 | 0.2655 |
| Mongolia | 2019 | Men | 0.35 | 0.23 | 0.48 | < 0.0001 |
| Mongolia | 2019 | Women | 0.08 | –0.01 | 0.17 | 0.5155 |
| Morocco | 2017 | Men | –0.03 | –0.21 | 0.14 | 0.3083 |
| Morocco | 2017 | Women | –0.02 | –0.13 | 0.09 | 0.7259 |
| Nepal | 2019 | Men | 0.54 | 0.25 | 0.83 | < 0.0001 |
| Nepal | 2019 | Women | 0.78 | 0.61 | 0.94 | < 0.0001 |
| Solomon Islands | 2015 | Men | –0.68 | –1.26 | –0.1 | 0.0477 |
| Solomon Islands | 2015 | Women | –0.51 | –1.1 | 0.09 | 0.0539 |
| Sudan | 2016 | Men | –0.53 | –1.15 | 0.08 | 0.2111 |
| Sudan | 2016 | Women | –0.13 | –0.45 | 0.19 | 0.0674 |
| Tokelau | 2014 | Men | –0.35 | –0.53 | –0.16 | 0.2248 |
| Tokelau | 2014 | Women | –0.84 | –1.22 | –0.45 | 0.0026 |
| Tonga | 2017 | Men | –1.31 | –1.58 | –1.05 | < 0.0001 |
| Tonga | 2017 | Women | –1.22 | –1.39 | –1.05 | < 0.0001 |
| Turkmenistan | 2018 | Men | –0.44 | –0.52 | –0.36 | < 0.0001 |
| Turkmenistan | 2018 | Women | –0.45 | –0.51 | –0.39 | < 0.0001 |
| Zambia | 2017 | Men | –0.47 | –0.74 | –0.19 | < 0.0001 |
| Zambia | 2017 | Women | –0.02 | –0.21 | 0.17 | 0.3438 |
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p-Value for paired t Student test between observed and predicted.
Appendix 1—table 5
Observed mean salt intake (g/day) by equation and sex in each survey included in the machine learning (ML) model development.
| Country | Year | Sex | Mean salt intake | Mean salt intake lower 95% confidence interval | Mean salt intake upper 95% confidence interval | Category |
|---|---|---|---|---|---|---|
| Armenia | 2016 | Men | 9.46 | 9.11 | 9.81 | Observed_intersalt |
| Armenia | 2016 | Men | 14.58 | 13.71 | 15.44 | Observed_kawasaki |
| Armenia | 2016 | Men | 10.21 | 9.71 | 10.7 | Observed_tanaka |
| Armenia | 2016 | Men | 12.71 | 12.19 | 13.23 | Observed_toft |
| Armenia | 2016 | Women | 7.44 | 7.26 | 7.62 | Observed_intersalt |
| Armenia | 2016 | Women | 12.48 | 11.87 | 13.09 | Observed_kawasaki |
| Armenia | 2016 | Women | 9.98 | 9.59 | 10.36 | Observed_tanaka |
| Armenia | 2016 | Women | 8.41 | 8.26 | 8.57 | Observed_toft |
| Azerbaijan | 2017 | Men | 10.39 | 10.06 | 10.72 | Observed_intersalt |
| Azerbaijan | 2017 | Men | 14.82 | 14.21 | 15.42 | Observed_kawasaki |
| Azerbaijan | 2017 | Men | 10.31 | 9.98 | 10.64 | Observed_tanaka |
| Azerbaijan | 2017 | Men | 12.81 | 12.45 | 13.18 | Observed_toft |
| Azerbaijan | 2017 | Women | 7.94 | 7.75 | 8.14 | Observed_intersalt |
| Azerbaijan | 2017 | Women | 12.65 | 12.22 | 13.08 | Observed_kawasaki |
| Azerbaijan | 2017 | Women | 10.14 | 9.87 | 10.41 | Observed_tanaka |
| Azerbaijan | 2017 | Women | 8.45 | 8.33 | 8.56 | Observed_toft |
| Bangladesh | 2018 | Men | 8.59 | 8.42 | 8.75 | Observed_intersalt |
| Bangladesh | 2018 | Men | 12.59 | 12.25 | 12.93 | Observed_kawasaki |
| Bangladesh | 2018 | Men | 8.81 | 8.62 | 9.01 | Observed_tanaka |
| Bangladesh | 2018 | Men | 11.62 | 11.4 | 11.85 | Observed_toft |
| Bangladesh | 2018 | Women | 7.27 | 7.17 | 7.37 | Observed_intersalt |
| Bangladesh | 2018 | Women | 12.09 | 11.78 | 12.4 | Observed_kawasaki |
| Bangladesh | 2018 | Women | 9 | 8.82 | 9.19 | Observed_tanaka |
| Bangladesh | 2018 | Women | 8.33 | 8.25 | 8.42 | Observed_toft |
| Belarus | 2017 | Men | 10.14 | 9.94 | 10.35 | Observed_intersalt |
| Belarus | 2017 | Men | 14.22 | 13.85 | 14.6 | Observed_kawasaki |
| Belarus | 2017 | Men | 10.16 | 9.95 | 10.38 | Observed_tanaka |
| Belarus | 2017 | Men | 12.46 | 12.24 | 12.69 | Observed_toft |
| Belarus | 2017 | Women | 7.56 | 7.41 | 7.72 | Observed_intersalt |
| Belarus | 2017 | Women | 11.43 | 11.1 | 11.75 | Observed_kawasaki |
| Belarus | 2017 | Women | 9.59 | 9.37 | 9.8 | Observed_tanaka |
| Belarus | 2017 | Women | 8.09 | 8 | 8.18 | Observed_toft |
| Bhutan | 2014 | Men | 9.58 | 9.27 | 9.88 | Observed_intersalt |
| Bhutan | 2014 | Men | 15.05 | 14.23 | 15.87 | Observed_kawasaki |
| Bhutan | 2014 | Men | 10.06 | 9.64 | 10.48 | Observed_tanaka |
| Bhutan | 2014 | Men | 13 | 12.51 | 13.49 | Observed_toft |
| Bhutan | 2014 | Women | 8.1 | 7.94 | 8.27 | Observed_intersalt |
| Bhutan | 2014 | Women | 14.24 | 13.72 | 14.76 | Observed_kawasaki |
| Bhutan | 2014 | Women | 10.54 | 10.22 | 10.86 | Observed_tanaka |
| Bhutan | 2014 | Women | 8.85 | 8.72 | 8.99 | Observed_toft |
| Bhutan | 2019 | Men | 9.1 | 8.85 | 9.35 | Observed_intersalt |
| Bhutan | 2019 | Men | 12.81 | 12.23 | 13.39 | Observed_kawasaki |
| Bhutan | 2019 | Men | 8.81 | 8.51 | 9.11 | Observed_tanaka |
| Bhutan | 2019 | Men | 11.62 | 11.28 | 11.97 | Observed_toft |
| Bhutan | 2019 | Women | 7.53 | 7.33 | 7.73 | Observed_intersalt |
| Bhutan | 2019 | Women | 11.59 | 11.22 | 11.96 | Observed_kawasaki |
| Bhutan | 2019 | Women | 8.9 | 8.67 | 9.12 | Observed_tanaka |
| Bhutan | 2019 | Women | 8.18 | 8.07 | 8.28 | Observed_toft |
| Brunei Darussalam | 2016 | Men | 8.95 | 8.66 | 9.25 | Observed_intersalt |
| Brunei Darussalam | 2016 | Men | 11.51 | 10.95 | 12.08 | Observed_kawasaki |
| Brunei Darussalam | 2016 | Men | 8.17 | 7.89 | 8.45 | Observed_tanaka |
| Brunei Darussalam | 2016 | Men | 10.79 | 10.44 | 11.14 | Observed_toft |
| Brunei Darussalam | 2016 | Women | 7.3 | 7.05 | 7.54 | Observed_intersalt |
| Brunei Darussalam | 2016 | Women | 10.52 | 10.02 | 11.01 | Observed_kawasaki |
| Brunei Darussalam | 2016 | Women | 8.38 | 8.08 | 8.69 | Observed_tanaka |
| Brunei Darussalam | 2016 | Women | 7.88 | 7.73 | 8.03 | Observed_toft |
| Chile | 2017 | Men | 9.75 | 9.18 | 10.31 | Observed_intersalt |
| Chile | 2017 | Men | 12.86 | 12.07 | 13.66 | Observed_kawasaki |
| Chile | 2017 | Men | 9.25 | 8.84 | 9.66 | Observed_tanaka |
| Chile | 2017 | Men | 11.66 | 11.14 | 12.17 | Observed_toft |
| Chile | 2017 | Women | 7.64 | 7.45 | 7.83 | Observed_intersalt |
| Chile | 2017 | Women | 11.11 | 10.81 | 11.4 | Observed_kawasaki |
| Chile | 2017 | Women | 9.13 | 8.93 | 9.32 | Observed_tanaka |
| Chile | 2017 | Women | 8.06 | 7.97 | 8.15 | Observed_toft |
| Jordan | 2019 | Men | 10.2 | 9.52 | 10.88 | Observed_intersalt |
| Jordan | 2019 | Men | 13.98 | 12.73 | 15.23 | Observed_kawasaki |
| Jordan | 2019 | Men | 9.84 | 9.17 | 10.51 | Observed_tanaka |
| Jordan | 2019 | Men | 12.29 | 11.56 | 13.02 | Observed_toft |
| Jordan | 2019 | Women | 8.1 | 7.75 | 8.45 | Observed_intersalt |
| Jordan | 2019 | Women | 12.1 | 11.48 | 12.72 | Observed_kawasaki |
| Jordan | 2019 | Women | 9.74 | 9.34 | 10.13 | Observed_tanaka |
| Jordan | 2019 | Women | 8.34 | 8.17 | 8.5 | Observed_toft |
| Lebanon | 2017 | Men | 9.53 | 9.06 | 9.99 | Observed_intersalt |
| Lebanon | 2017 | Men | 12.72 | 11.65 | 13.79 | Observed_kawasaki |
| Lebanon | 2017 | Men | 9.22 | 8.61 | 9.84 | Observed_tanaka |
| Lebanon | 2017 | Men | 11.48 | 10.82 | 12.14 | Observed_toft |
| Lebanon | 2017 | Women | 7.51 | 7.07 | 7.95 | Observed_intersalt |
| Lebanon | 2017 | Women | 11.35 | 10.45 | 12.25 | Observed_kawasaki |
| Lebanon | 2017 | Women | 9.37 | 8.75 | 10 | Observed_tanaka |
| Lebanon | 2017 | Women | 8.03 | 7.76 | 8.3 | Observed_toft |
| Malawi | 2017 | Men | 9.54 | 9.16 | 9.91 | Observed_intersalt |
| Malawi | 2017 | Men | 14.02 | 13.4 | 14.64 | Observed_kawasaki |
| Malawi | 2017 | Men | 9.43 | 9.08 | 9.77 | Observed_tanaka |
| Malawi | 2017 | Men | 12.4 | 12.04 | 12.77 | Observed_toft |
| Malawi | 2017 | Women | 8.34 | 8.03 | 8.64 | Observed_intersalt |
| Malawi | 2017 | Women | 13.43 | 12.76 | 14.11 | Observed_kawasaki |
| Malawi | 2017 | Women | 10.17 | 9.75 | 10.58 | Observed_tanaka |
| Malawi | 2017 | Women | 8.64 | 8.47 | 8.82 | Observed_toft |
| Mongolia | 2013 | Men | 9.83 | 9.34 | 10.32 | Observed_intersalt |
| Mongolia | 2013 | Men | 13.37 | 12.74 | 14.01 | Observed_kawasaki |
| Mongolia | 2013 | Men | 9.48 | 9.13 | 9.83 | Observed_tanaka |
| Mongolia | 2013 | Men | 12.04 | 11.64 | 12.45 | Observed_toft |
| Mongolia | 2013 | Women | 7.79 | 7.6 | 7.97 | Observed_intersalt |
| Mongolia | 2013 | Women | 11.92 | 11.34 | 12.5 | Observed_kawasaki |
| Mongolia | 2013 | Women | 9.54 | 9.16 | 9.92 | Observed_tanaka |
| Mongolia | 2013 | Women | 8.24 | 8.08 | 8.4 | Observed_toft |
| Mongolia | 2019 | Men | 9.68 | 9.5 | 9.85 | Observed_intersalt |
| Mongolia | 2019 | Men | 14.83 | 14.49 | 15.17 | Observed_kawasaki |
| Mongolia | 2019 | Men | 10.14 | 9.95 | 10.32 | Observed_tanaka |
| Mongolia | 2019 | Men | 12.84 | 12.64 | 13.05 | Observed_toft |
| Mongolia | 2019 | Women | 7.46 | 7.34 | 7.59 | Observed_intersalt |
| Mongolia | 2019 | Women | 12.13 | 11.81 | 12.44 | Observed_kawasaki |
| Mongolia | 2019 | Women | 9.63 | 9.43 | 9.84 | Observed_tanaka |
| Mongolia | 2019 | Women | 8.31 | 8.23 | 8.4 | Observed_toft |
| Morocco | 2017 | Men | 9.03 | 8.82 | 9.24 | Observed_intersalt |
| Morocco | 2017 | Men | 13.04 | 12.63 | 13.44 | Observed_kawasaki |
| Morocco | 2017 | Men | 9.33 | 9.1 | 9.56 | Observed_tanaka |
| Morocco | 2017 | Men | 11.75 | 11.5 | 12 | Observed_toft |
| Morocco | 2017 | Women | 7.47 | 7.35 | 7.59 | Observed_intersalt |
| Morocco | 2017 | Women | 11.72 | 11.41 | 12.04 | Observed_kawasaki |
| Morocco | 2017 | Women | 9.48 | 9.28 | 9.68 | Observed_tanaka |
| Morocco | 2017 | Women | 8.18 | 8.09 | 8.26 | Observed_toft |
| Nepal | 2019 | Men | 9.55 | 9.22 | 9.87 | Observed_intersalt |
| Nepal | 2019 | Men | 16.6 | 15.92 | 17.27 | Observed_kawasaki |
| Nepal | 2019 | Men | 10.69 | 10.33 | 11.04 | Observed_tanaka |
| Nepal | 2019 | Men | 14.04 | 13.64 | 14.44 | Observed_toft |
| Nepal | 2019 | Women | 7.84 | 7.65 | 8.04 | Observed_intersalt |
| Nepal | 2019 | Women | 15.35 | 14.82 | 15.88 | Observed_kawasaki |
| Nepal | 2019 | Women | 10.9 | 10.57 | 11.24 | Observed_tanaka |
| Nepal | 2019 | Women | 9.12 | 8.99 | 9.25 | Observed_toft |
| Solomon Islands | 2015 | Men | 8.74 | 8.08 | 9.4 | Observed_intersalt |
| Solomon Islands | 2015 | Men | 12.99 | 11.06 | 14.93 | Observed_kawasaki |
| Solomon Islands | 2015 | Men | 8.87 | 7.97 | 9.77 | Observed_tanaka |
| Solomon Islands | 2015 | Men | 11.62 | 10.43 | 12.8 | Observed_toft |
| Solomon Islands | 2015 | Women | 7.03 | 6.42 | 7.64 | Observed_intersalt |
| Solomon Islands | 2015 | Women | 11.38 | 8.78 | 13.98 | Observed_kawasaki |
| Solomon Islands | 2015 | Women | 8.98 | 7.34 | 10.61 | Observed_tanaka |
| Solomon Islands | 2015 | Women | 7.95 | 7.26 | 8.64 | Observed_toft |
| Sudan | 2016 | Men | 8.53 | 7.73 | 9.33 | Observed_intersalt |
| Sudan | 2016 | Men | 11.66 | 10.66 | 12.66 | Observed_kawasaki |
| Sudan | 2016 | Men | 8.49 | 7.91 | 9.08 | Observed_tanaka |
| Sudan | 2016 | Men | 10.83 | 10.17 | 11.5 | Observed_toft |
| Sudan | 2016 | Women | 7.49 | 7.09 | 7.88 | Observed_intersalt |
| Sudan | 2016 | Women | 11.3 | 10.6 | 12.01 | Observed_kawasaki |
| Sudan | 2016 | Women | 9.31 | 8.85 | 9.78 | Observed_tanaka |
| Sudan | 2016 | Women | 8.09 | 7.89 | 8.3 | Observed_toft |
| Tokelau | 2014 | Men | 10.29 | 10.18 | 10.4 | Observed_intersalt |
| Tokelau | 2014 | Men | 14.33 | 13.16 | 15.5 | Observed_kawasaki |
| Tokelau | 2014 | Men | 10.1 | 9.48 | 10.72 | Observed_tanaka |
| Tokelau | 2014 | Men | 12.42 | 11.71 | 13.14 | Observed_toft |
| Tokelau | 2014 | Women | 8.12 | 7.61 | 8.63 | Observed_intersalt |
| Tokelau | 2014 | Women | 11.4 | 9.85 | 12.95 | Observed_kawasaki |
| Tokelau | 2014 | Women | 9.71 | 8.69 | 10.72 | Observed_tanaka |
| Tokelau | 2014 | Women | 8.15 | 7.76 | 8.54 | Observed_toft |
| Tonga | 2017 | Men | 9.19 | 8.89 | 9.48 | Observed_intersalt |
| Tonga | 2017 | Men | 10.06 | 9.17 | 10.95 | Observed_kawasaki |
| Tonga | 2017 | Men | 7.72 | 7.22 | 8.22 | Observed_tanaka |
| Tonga | 2017 | Men | 9.77 | 9.19 | 10.35 | Observed_toft |
| Tonga | 2017 | Women | 7.63 | 7.45 | 7.81 | Observed_intersalt |
| Tonga | 2017 | Women | 9.37 | 8.88 | 9.87 | Observed_kawasaki |
| Tonga | 2017 | Women | 8.41 | 8.06 | 8.76 | Observed_tanaka |
| Tonga | 2017 | Women | 7.53 | 7.37 | 7.68 | Observed_toft |
| Turkmenistan | 2018 | Men | 8.94 | 8.79 | 9.09 | Observed_intersalt |
| Turkmenistan | 2018 | Men | 12.11 | 11.93 | 12.3 | Observed_kawasaki |
| Turkmenistan | 2018 | Men | 8.85 | 8.74 | 8.96 | Observed_tanaka |
| Turkmenistan | 2018 | Men | 11.2 | 11.09 | 11.32 | Observed_toft |
| Turkmenistan | 2018 | Women | 6.76 | 6.68 | 6.83 | Observed_intersalt |
| Turkmenistan | 2018 | Women | 10.1 | 9.93 | 10.26 | Observed_kawasaki |
| Turkmenistan | 2018 | Women | 8.53 | 8.41 | 8.65 | Observed_tanaka |
| Turkmenistan | 2018 | Women | 7.78 | 7.73 | 7.83 | Observed_toft |
| Zambia | 2017 | Men | 8.45 | 8.15 | 8.75 | Observed_intersalt |
| Zambia | 2017 | Men | 12.7 | 12.09 | 13.3 | Observed_kawasaki |
| Zambia | 2017 | Men | 8.8 | 8.46 | 9.13 | Observed_tanaka |
| Zambia | 2017 | Men | 11.48 | 11.11 | 11.86 | Observed_toft |
| Zambia | 2017 | Women | 7.01 | 6.81 | 7.22 | Observed_intersalt |
| Zambia | 2017 | Women | 11.11 | 10.66 | 11.56 | Observed_kawasaki |
| Zambia | 2017 | Women | 8.8 | 8.52 | 9.09 | Observed_tanaka |
| Zambia | 2017 | Women | 8 | 7.86 | 8.13 | Observed_toft |
Appendix 1—table 6
Weighted distribution of predictors in each of the 54 national surveys included in the application of the model herein developed.
| Country | Year | Sample size | Mean age (years) | Age range (years) | Proportion of men (%) | Mean, minimum, and maximum values of SBP (mmHg) | Mean, minimum, and maximum values of DBP (mmHg) | Mean, minimum, and maximum values of weight (kg) | Mean, minimum, and maximum values of height (m) |
|---|---|---|---|---|---|---|---|---|---|
| American Samoa | 2004 | 2043 | 40 | 25–64 | 50.3 | 131 (84–230) | 82 (46–134) | 100.4 (38.6–219.1) | 1.69 (1.36–2.19) |
| Benin | 2015 | 4841 | 34 | 18–69 | 49.6 | 126 (74–254) | 82 (45–142) | 62.3 (30–167) | 1.64 (1.21–1.98) |
| Bahamas | 2012 | 1400 | 42 | 24–64 | 49.9 | 127 (73–248) | 82 (32–140) | 84.8 (27.9–184.9) | 1.67 (1.15–2.03) |
| Barbados | 2007 | 282 | 43 | 25–69 | 51.9 | 122 (86–191) | 80 (55–115) | 77.5 (40.6–232.1) | 1.67 (1.17–1.93) |
| British Virgin Islands | 2009 | 1067 | 43 | 25–64 | 54.1 | 130 (81–226) | 80 (48–126) | 83.2 (39.6–176.9) | 1.7 (1.14–2.26) |
| Botswana | 2014 | 3894 | 33 | 15–69 | 52.1 | 128 (84–262) | 80 (47–148) | 63.9 (31.7–171.1) | 1.66 (1.02–2) |
| Cook Islands | 2015 | 879 | 39 | 18–64 | 46.5 | 128 (92–194) | 79 (45–118) | 98.6 (49.1–205.1) | 1.69 (1.07–1.96) |
| Comoros | 2011 | 5029 | 39 | 25–64 | 52.6 | 128 (82–236) | 79 (48–144) | 64.2 (23.5–166) | 1.61 (1–2.15) |
| Cabo Verde | 2007 | 1723 | 38 | 25–64 | 50.3 | 133 (86–234) | 80 (48–140) | 68.3 (35–150) | 1.68 (1.23–1.96) |
| Cayman Islands | 2012 | 1229 | 42 | 24–64 | 50.7 | 125 (84–208) | 76 (46–127) | 82.3 (31–196) | 1.69 (1–2.1) |
| Algeria | 2017 | 6536 | 38 | 18–69 | 51.7 | 127 (77–227) | 75 (32–137) | 73.3 (25–174) | 1.67 (1.02–2.05) |
| Ecuador | 2018 | 4466 | 40 | 18–69 | 49.4 | 120 (78–220) | 76 (42–130) | 69.2 (33.4–198.4) | 1.59 (1.24–1.93) |
| Eritrea | 2010 | 5651 | 42 | 25–69 | 17.2 | 117 (72–230) | 74 (46–130) | 51.8 (28.1–99.1) | 1.6 (1.16–1.89) |
| Ethiopia | 2015 | 9270 | 31 | 15–69 | 56.1 | 120 (71–250) | 78 (30–142) | 54.4 (20–99.5) | 1.63 (1.05–2) |
| Fiji | 2011 | 2492 | 42 | 25–64 | 51 | 130 (84–228) | 80 (39–143) | 78.6 (30.3–198.1) | 1.68 (1.03–1.94) |
| Gambia | 2010 | 3496 | 38 | 25–64 | 50.4 | 130 (85–252) | 80 (44–144) | 64.8 (26.5–168.9) | 1.64 (1-2) |
| Grenada | 2011 | 1055 | 41 | 25–64 | 50.7 | 131 (71–212) | 80 (50–128) | 77.6 (40.8–158.8) | 1.7 (1.32–2.49) |
| Guyana | 2016 | 2625 | 37 | 18–69 | 52 | 126 (74–245) | 78 (37–149) | 69.9 (26.4–198) | 1.63 (1.01–2.07) |
| Iraq | 2015 | 3655 | 35 | 18–69 | 53.6 | 128 (78–225) | 83 (45–150) | 76.5 (36.6–187.2) | 1.65 (1.01–1.97) |
| Kenya | 2015 | 4270 | 34 | 16–69 | 50.6 | 125 (76–262) | 81 (46–146) | 63.2 (30–171.3) | 1.65 (1.01–1.95) |
| Kyrgyzstan | 2013 | 2539 | 41 | 25–64 | 51.9 | 133 (82–244) | 87 (56–150) | 71.7 (36.6–162.4) | 1.64 (1.38–1.95) |
| Cambodia | 2010 | 5223 | 40 | 25–64 | 49.4 | 116 (70–226) | 72 (42–138) | 53.7 (21.1–111) | 1.57 (1.24–1.85) |
| Kiribati | 2016 | 1240 | 40 | 18–69 | 42.8 | 128 (85–220) | 85 (49–148) | 81.1 (30–219) | 1.64 (1.22–1.89) |
| Kuwait | 2014 | 2871 | 36 | 18–69 | 49.5 | 120 (70–240) | 77 (50–130) | 80.5 (37.3–195) | 1.65 (1.04–1.96) |
| Lao People’s Democratic Republic | 2013 | 2464 | 39 | 16–65 | 42.3 | 119 (72–240) | 76 (30–130) | 54.2 (27–103.1) | 1.54 (1.16–1.97) |
| Liberia | 2011 | 2242 | 40 | 25–64 | 50.7 | 129 (88–232) | 80 (32–138) | 65.4 (32–163) | 1.58 (1–2.5) |
| Libya | 2009 | 3223 | 37 | 25–64 | 51.5 | 133 (74–238) | 79 (44–148) | 77 (31.7–186.2) | 1.67 (1–1.97) |
| Sri Lanka | 2015 | 4566 | 39 | 18–69 | 51.5 | 125 (74–258) | 81 (36–150) | 58 (26.2–156.9) | 1.59 (1.02–1.9) |
| Lesotho | 2012 | 2162 | 38 | 25–64 | 49.8 | 126 (78–250) | 83 (46–146) | 66.2 (21.5–164.6) | 1.61 (1.02–1.97) |
| Republic of Moldova | 2013 | 4077 | 39 | 18–69 | 52.5 | 133 (83–257) | 85 (49–148) | 75 (32.5–166) | 1.68 (1.2–1.98) |
| Marshall Islands | 2018 | 2657 | 39 | 17–69 | 48.5 | 120 (70–220) | 75 (40–134) | 74.4 (27–226.5) | 1.58 (1.01–2.15) |
| Myanmar | 2014 | 7892 | 42 | 25–64 | 50.4 | 126 (70–252) | 82 (35–144) | 57.1 (26.3–173) | 1.59 (1–2.18) |
| Mozambique | 2005 | 723 | 41 | 24–64 | 46 | 139 (85–220) | 82 (46–143) | 56.7 (33.4–109.5) | 1.6 (1.02–1.89) |
| Namibia | 2005 | 752 | 41 | 25–64 | 41.3 | 137 (87–230) | 86 (50–132) | 63.7 (26.5–134.3) | 1.63 (1.12–2) |
| Niger | 2007 | 2638 | 37 | 15–64 | 54.1 | 134 (70–260) | 82 (40–145) | 59.5 (24.3–162.2) | 1.67 (1.01–2.1) |
| Niue | 2012 | 779 | 40 | 15–69 | 50.1 | 128 (89–223) | 76 (44–117) | 91.5 (44.7–165.9) | 1.69 (1.17–1.96) |
| Nauru | 2016 | 1037 | 36 | 18–69 | 50 | 123 (76–223) | 80 (46–125) | 92.4 (43.4–197.9) | 1.63 (1.41–1.86) |
| Palau | 2013 | 2148 | 43 | 25–64 | 53 | 138 (87–236) | 85 (40–135) | 79.4 (32–180.6) | 1.62 (1.02–2.03) |
| French Polynesia | 2010 | 2239 | 36 | 18–64 | 50.7 | 125 (86–230) | 79 (48–150) | 86.2 (41–193) | 1.7 (1.41–2) |
| Qatar | 2012 | 2287 | 35 | 18–64 | 50.9 | 119 (78–203) | 79 (46–130) | 79.1 (34.4–190.5) | 1.64 (1.35–2) |
| Rwanda | 2013 | 6882 | 32 | 15–64 | 48.8 | 121 (75–250) | 78 (45–140) | 57 (23.1–165.8) | 1.6 (1–1.91) |
| Sierra Leone | 2009 | 4473 | 40 | 25–64 | 50.3 | 131 (72–220) | 81 (42–148) | 60 (28–185) | 1.62 (1–2.34) |
| Sao Tome and Principe | 2008 | 2272 | 40 | 25–64 | 48.4 | 135 (78–240) | 82 (34–143) | 66.1 (30–186.2) | 1.64 (1.01–1.98) |
| Eswatini | 2014 | 3042 | 31 | 15–69 | 47.4 | 124 (72–252) | 80 (42–150) | 67.8 (22.2–227.6) | 1.63 (1.01–2.02) |
| Togo | 2011 | 3995 | 32 | 15–64 | 49.3 | 123 (70–251) | 77 (31–142) | 61.6 (26–165) | 1.64 (1.02–1.99) |
| Tajikistan | 2017 | 2643 | 32 | 18–69 | 53.8 | 129 (81–267) | 84 (54–150) | 66.7 (27.8–148) | 1.63 (1.09–2) |
| Timor-Leste | 2014 | 2480 | 36 | 18–69 | 63.8 | 130 (72–235) | 84 (42–136) | 52 (27–165) | 1.57 (1.24–1.83) |
| Tuvalu | 2015 | 1024 | 39 | 18–69 | 54.9 | 134 (92–246) | 84 (48–145) | 91.9 (35.8–181.8) | 1.68 (1.17–2.06) |
| United Republic of Tanzania | 2012 | 5381 | 39 | 25–64 | 50.6 | 129 (80–240) | 80 (40–146) | 60.6 (29–171.1) | 1.63 (1.13–1.97) |
| Uganda | 2014 | 3673 | 35 | 18–69 | 50.5 | 125 (83–249) | 81 (50–148) | 59.4 (30.2–165) | 1.62 (1.15–2.03) |
| Uruguay | 2014 | 2207 | 38 | 15–64 | 47.8 | 125 (82–232) | 79 (44–134) | 74.6 (34.3–158) | 1.67 (1.36–2.05) |
| Vietnam | 2015 | 3033 | 39 | 18–69 | 50.4 | 120 (71–224) | 77 (40–128) | 54.7 (27.8–106.4) | 1.58 (1.01–1.98) |
| Vanuatu | 2011 | 4420 | 40 | 25–64 | 47.7 | 130 (77–269) | 80 (38–139) | 69.4 (28.3–199.8) | 1.63 (1.02–2.1) |
| Samoa | 2013 | 1490 | 37 | 18–64 | 54.1 | 125 (80–222) | 75 (44–132) | 90.3 (32.1–160) | 1.68 (1.22–1.97) |
-
SBP: systolic blood pressure; DBP: diastolic blood pressure.
Appendix 1—table 7
Predicted mean salt intake (g/day) by sex in each of the 54 national surveys included in the application of the model herein developed.
| Country | Year | Sex | Mean salt intake | Mean salt intake lower 95% confidence interval | Mean salt intake upper 95% confidence interval |
|---|---|---|---|---|---|
| Algeria | 2017 | Men | 9.26 | 9.22 | 9.3 |
| Algeria | 2017 | Women | 7.54 | 7.5 | 7.58 |
| Algeria | 2017 | Total | 8.43 | 8.39 | 8.47 |
| American Samoa | 2004 | Men | 10.9 | 10.8 | 10.99 |
| American Samoa | 2004 | Women | 9.03 | 8.96 | 9.11 |
| American Samoa | 2004 | Total | 9.97 | 9.93 | 10.01 |
| Bahamas | 2012 | Men | 10.09 | 9.83 | 10.35 |
| Bahamas | 2012 | Women | 8.11 | 7.81 | 8.4 |
| Bahamas | 2012 | Total | 9.1 | 8.9 | 9.29 |
| Barbados | 2007 | Men | 9.42 | 9.25 | 9.6 |
| Barbados | 2007 | Women | 7.85 | 7.54 | 8.17 |
| Barbados | 2007 | Total | 8.67 | 8.45 | 8.89 |
| Benin | 2015 | Men | 8.96 | 8.9 | 9.03 |
| Benin | 2015 | Women | 7.01 | 6.89 | 7.12 |
| Benin | 2015 | Total | 7.98 | 7.81 | 8.15 |
| Botswana | 2014 | Men | 8.74 | 8.68 | 8.79 |
| Botswana | 2014 | Women | 7.2 | 7.14 | 7.26 |
| Botswana | 2014 | Total | 8 | 7.94 | 8.06 |
| British Virgin Islands | 2009 | Men | 9.73 | 9.66 | 9.81 |
| British Virgin Islands | 2009 | Women | 7.85 | 7.82 | 7.88 |
| British Virgin Islands | 2009 | Total | 8.87 | 8.82 | 8.92 |
| Cabo Verde | 2007 | Men | 8.98 | 8.93 | 9.03 |
| Cabo Verde | 2007 | Women | 7.13 | 7.03 | 7.23 |
| Cabo Verde | 2007 | Total | 8.06 | 7.97 | 8.16 |
| Cambodia | 2010 | Men | 8.83 | 8.8 | 8.86 |
| Cambodia | 2010 | Women | 6.83 | 6.81 | 6.86 |
| Cambodia | 2010 | Total | 7.82 | 7.78 | 7.86 |
| Cayman Islands | 2012 | Men | 9.73 | 9.69 | 9.77 |
| Cayman Islands | 2012 | Women | 7.92 | 7.61 | 8.23 |
| Cayman Islands | 2012 | Total | 8.84 | 8.75 | 8.92 |
| Comoros | 2011 | Men | 9.06 | 9.02 | 9.1 |
| Comoros | 2011 | Women | 7.43 | 7.38 | 7.47 |
| Comoros | 2011 | Total | 8.29 | 8.24 | 8.33 |
| Cook Islands | 2015 | Men | 10.87 | 10.73 | 11.01 |
| Cook Islands | 2015 | Women | 8.74 | 8.63 | 8.86 |
| Cook Islands | 2015 | Total | 9.73 | 9.59 | 9.88 |
| Ecuador | 2018 | Men | 9.6 | 9.55 | 9.65 |
| Ecuador | 2018 | Women | 7.65 | 7.6 | 7.69 |
| Ecuador | 2018 | Total | 8.61 | 8.55 | 8.68 |
| Eritrea | 2010 | Men | 8.32 | 8.27 | 8.37 |
| Eritrea | 2010 | Women | 6.48 | 6.43 | 6.52 |
| Eritrea | 2010 | Total | 6.79 | 6.75 | 6.84 |
| Eswatini | 2014 | Men | 9.11 | 9.02 | 9.2 |
| Eswatini | 2014 | Women | 7.62 | 7.56 | 7.68 |
| Eswatini | 2014 | Total | 8.33 | 8.27 | 8.39 |
| Ethiopia | 2015 | Men | 8.52 | 8.49 | 8.54 |
| Ethiopia | 2015 | Women | 6.62 | 6.59 | 6.65 |
| Ethiopia | 2015 | Total | 7.68 | 7.65 | 7.72 |
| Fiji | 2011 | Men | 9.53 | 9.44 | 9.62 |
| Fiji | 2011 | Women | 7.84 | 7.76 | 7.91 |
| Fiji | 2011 | Total | 8.7 | 8.6 | 8.8 |
| French Polynesia | 2010 | Men | 10.1 | 10 | 10.2 |
| French Polynesia | 2010 | Women | 8 | 7.9 | 8.1 |
| French Polynesia | 2010 | Total | 9.06 | 8.98 | 9.15 |
| Gambia | 2010 | Men | 9.05 | 8.94 | 9.17 |
| Gambia | 2010 | Women | 7.17 | 7.1 | 7.25 |
| Gambia | 2010 | Total | 8.12 | 8.03 | 8.22 |
| Grenada | 2011 | Men | 9.21 | 9.12 | 9.31 |
| Grenada | 2011 | Women | 7.74 | 7.64 | 7.84 |
| Grenada | 2011 | Total | 8.49 | 8.4 | 8.58 |
| Guyana | 2016 | Men | 9.26 | 9.16 | 9.35 |
| Guyana | 2016 | Women | 7.67 | 7.6 | 7.74 |
| Guyana | 2016 | Total | 8.5 | 8.43 | 8.56 |
| Iraq | 2015 | Men | 9.66 | 9.58 | 9.75 |
| Iraq | 2015 | Women | 7.94 | 7.88 | 8.01 |
| Iraq | 2015 | Total | 8.87 | 8.8 | 8.93 |
| Kenya | 2015 | Men | 8.82 | 8.73 | 8.9 |
| Kenya | 2015 | Women | 7.13 | 7.04 | 7.21 |
| Kenya | 2015 | Total | 7.98 | 7.89 | 8.07 |
| Kiribati | 2016 | Men | 9.92 | 9.74 | 10.09 |
| Kiribati | 2016 | Women | 8.27 | 8.14 | 8.39 |
| Kiribati | 2016 | Total | 8.97 | 8.86 | 9.09 |
| Kuwait | 2014 | Men | 10.06 | 9.99 | 10.12 |
| Kuwait | 2014 | Women | 7.95 | 7.91 | 8 |
| Kuwait | 2014 | Total | 8.99 | 8.94 | 9.05 |
| Kyrgyzstan | 2013 | Men | 9.45 | 9.34 | 9.55 |
| Kyrgyzstan | 2013 | Women | 7.62 | 7.56 | 7.67 |
| Kyrgyzstan | 2013 | Total | 8.57 | 8.5 | 8.63 |
| Lao People’s Democratic Republic | 2013 | Men | 9.03 | 8.98 | 9.08 |
| Lao People’s Democratic Republic | 2013 | Women | 7.07 | 7.02 | 7.12 |
| Lao People’s Democratic Republic | 2013 | Total | 7.9 | 7.83 | 7.97 |
| Lesotho | 2012 | Men | 9.08 | 8.99 | 9.17 |
| Lesotho | 2012 | Women | 7.7 | 7.6 | 7.79 |
| Lesotho | 2012 | Total | 8.38 | 8.31 | 8.46 |
| Liberia | 2011 | Men | 9.43 | 9.32 | 9.55 |
| Liberia | 2011 | Women | 7.58 | 7.48 | 7.69 |
| Liberia | 2011 | Total | 8.52 | 8.41 | 8.63 |
| Libya | 2009 | Men | 9.51 | 9.44 | 9.59 |
| Libya | 2009 | Women | 7.81 | 7.73 | 7.89 |
| Libya | 2009 | Total | 8.69 | 8.63 | 8.75 |
| Marshall Islands | 2018 | Men | 9.92 | 9.86 | 9.99 |
| Marshall Islands | 2018 | Women | 8.16 | 8.1 | 8.21 |
| Marshall Islands | 2018 | Total | 9.01 | 8.96 | 9.07 |
| Mozambique | 2005 | Men | 8.72 | 8.62 | 8.83 |
| Mozambique | 2005 | Women | 6.92 | 6.84 | 7 |
| Mozambique | 2005 | Total | 7.75 | 7.63 | 7.87 |
| Myanmar | 2014 | Men | 8.81 | 8.74 | 8.88 |
| Myanmar | 2014 | Women | 7.07 | 6.97 | 7.17 |
| Myanmar | 2014 | Total | 7.95 | 7.88 | 8.02 |
| Namibia | 2005 | Men | 8.74 | 8.59 | 8.89 |
| Namibia | 2005 | Women | 7.24 | 6.93 | 7.56 |
| Namibia | 2005 | Total | 7.86 | 7.63 | 8.09 |
| Nauru | 2016 | Men | 10.98 | 10.87 | 11.1 |
| Nauru | 2016 | Women | 8.79 | 8.63 | 8.94 |
| Nauru | 2016 | Total | 9.89 | 9.74 | 10.03 |
| Niger | 2007 | Men | 8.56 | 8.52 | 8.6 |
| Niger | 2007 | Women | 6.67 | 6.63 | 6.71 |
| Niger | 2007 | Total | 7.69 | 7.65 | 7.74 |
| Niue | 2012 | Men | 10.39 | 10.28 | 10.51 |
| Niue | 2012 | Women | 8.39 | 8.27 | 8.51 |
| Niue | 2012 | Total | 9.4 | 9.29 | 9.5 |
| Palau | 2013 | Men | 10.18 | 10.07 | 10.28 |
| Palau | 2013 | Women | 7.99 | 7.9 | 8.08 |
| Palau | 2013 | Total | 9.15 | 9.05 | 9.25 |
| Qatar | 2012 | Men | 10.02 | 9.93 | 10.11 |
| Qatar | 2012 | Women | 7.94 | 7.85 | 8.04 |
| Qatar | 2012 | Total | 9 | 8.9 | 9.09 |
| Republic of Moldova | 2013 | Men | 9.51 | 9.45 | 9.57 |
| Republic of Moldova | 2013 | Women | 7.46 | 7.41 | 7.52 |
| Republic of Moldova | 2013 | Total | 8.54 | 8.48 | 8.6 |
| Rwanda | 2013 | Men | 8.87 | 8.85 | 8.9 |
| Rwanda | 2013 | Women | 7.02 | 6.99 | 7.05 |
| Rwanda | 2013 | Total | 7.92 | 7.89 | 7.96 |
| Samoa | 2013 | Men | 10.23 | 10.09 | 10.37 |
| Samoa | 2013 | Women | 8.61 | 8.51 | 8.71 |
| Samoa | 2013 | Total | 9.49 | 9.41 | 9.57 |
| Sao Tome and Principe | 2008 | Men | 9.05 | 8.97 | 9.12 |
| Sao Tome and Principe | 2008 | Women | 7.21 | 7.1 | 7.32 |
| Sao Tome and Principe | 2008 | Total | 8.1 | 7.99 | 8.2 |
| Sierra Leone | 2009 | Men | 8.85 | 8.76 | 8.94 |
| Sierra Leone | 2009 | Women | 7 | 6.9 | 7.11 |
| Sierra Leone | 2009 | Total | 7.93 | 7.82 | 8.04 |
| Sri Lanka | 2015 | Men | 8.91 | 8.86 | 8.95 |
| Sri Lanka | 2015 | Women | 7.07 | 7.03 | 7.1 |
| Sri Lanka | 2015 | Total | 8.01 | 7.97 | 8.06 |
| Tajikistan | 2017 | Men | 9.41 | 9.34 | 9.49 |
| Tajikistan | 2017 | Women | 7.35 | 7.3 | 7.41 |
| Tajikistan | 2017 | Total | 8.46 | 8.38 | 8.55 |
| Timor-Leste | 2014 | Men | 8.91 | 8.79 | 9.02 |
| Timor-Leste | 2014 | Women | 6.8 | 6.75 | 6.86 |
| Timor-Leste | 2014 | Total | 8.15 | 7.86 | 8.43 |
| Togo | 2011 | Men | 8.82 | 8.79 | 8.86 |
| Togo | 2011 | Women | 7.01 | 6.96 | 7.06 |
| Togo | 2011 | Total | 7.9 | 7.85 | 7.96 |
| Tuvalu | 2015 | Men | 10.37 | 10.24 | 10.5 |
| Tuvalu | 2015 | Women | 8.72 | 8.62 | 8.83 |
| Tuvalu | 2015 | Total | 9.63 | 9.53 | 9.73 |
| Uganda | 2014 | Men | 8.8 | 8.76 | 8.84 |
| Uganda | 2014 | Women | 7.02 | 6.96 | 7.07 |
| Uganda | 2014 | Total | 7.92 | 7.86 | 7.98 |
| United Republic of Tanzania | 2012 | Men | 8.71 | 8.63 | 8.79 |
| United Republic of Tanzania | 2012 | Women | 7.13 | 7.05 | 7.21 |
| United Republic of Tanzania | 2012 | Total | 7.93 | 7.88 | 7.98 |
| Uruguay | 2014 | Men | 9.55 | 9.48 | 9.63 |
| Uruguay | 2014 | Women | 7.47 | 7.41 | 7.52 |
| Uruguay | 2014 | Total | 8.46 | 8.39 | 8.53 |
| Vanuatu | 2011 | Men | 9.38 | 9.33 | 9.43 |
| Vanuatu | 2011 | Women | 7.45 | 7.4 | 7.5 |
| Vanuatu | 2011 | Total | 8.37 | 8.31 | 8.43 |
| Vietnam | 2015 | Men | 8.91 | 8.86 | 8.95 |
| Vietnam | 2015 | Women | 6.84 | 6.81 | 6.88 |
| Vietnam | 2015 | Total | 7.88 | 7.83 | 7.94 |
Appendix 1—table 8
Comparison between mean salt intake (g/day) predictions and global estimates across national surveys included in the application of our machine learning model.
| Country | Year (machine learning predictions) | Machine learning predicted mean salt intake and 95% confidence interval | Year (global estimates) | Estimated mean salt intake and 95% confidence interval | Ratio between machine learning predicted and global estimates |
|---|---|---|---|---|---|
| Algeria | 2017 | 8.4 (8.4–8.5) | 2010 | 10.7 (9–12.5) | 0.8 |
| Bahamas | 2012 | 9.1 (8.9–9.3) | 2010 | 7.5 (6.2–8.8) | 1.2 |
| Barbados | 2007 | 8.7 (8.4–8.9) | 2010 | 8.6 (7.8–9.4) | 1 |
| Benin | 2015 | 8 (7.8–8.2) | 2010 | 7.1 (6.2–8.1) | 1.1 |
| Botswana | 2014 | 8 (7.9–8.1) | 2010 | 6.3 (5.4–7.4) | 1.3 |
| Cabo Verde | 2007 | 8.1 (8–8.2) | 2010 | 8.1 (6.8–9.7) | 1 |
| Cambodia | 2010 | 7.8 (7.8–7.9) | 2010 | 11 (9.3–12.9) | 0.7 |
| Comoros | 2011 | 8.3 (8.2–8.3) | 2010 | 4.2 (3.5–5) | 2 |
| Ecuador | 2018 | 8.6 (8.6–8.7) | 2010 | 7.6 (6.4–8.9) | 1.1 |
| Eritrea | 2010 | 6.8 (6.8–6.8) | 2010 | 5.9 (5–7) | 1.2 |
| Ethiopia | 2015 | 7.7 (7.7–7.7) | 2010 | 5.7 (4.9–6.7) | 1.4 |
| Fiji | 2011 | 8.7 (8.6–8.8) | 2010 | 7.2 (6–8.5) | 1.2 |
| Gambia | 2010 | 8.1 (8–8.2) | 2010 | 7.7 (6.5–8.9) | 1.1 |
| Grenada | 2011 | 8.5 (8.4–8.6) | 2010 | 6.5 (5.5–7.7) | 1.3 |
| Guyana | 2016 | 8.5 (8.4–8.6) | 2010 | 6.1 (5.1–7.3) | 1.4 |
| Iraq | 2015 | 8.9 (8.8–8.9) | 2010 | 9.4 (8–11.2) | 0.9 |
| Kenya | 2015 | 8 (7.9–8.1) | 2010 | 3.7 (3.4–4) | 2.2 |
| Kiribati | 2016 | 9 (8.9–9.1) | 2010 | 5.6 (4.6–6.7) | 1.6 |
| Kuwait | 2014 | 9 (8.9–9.1) | 2010 | 9.7 (8.7–10.8) | 0.9 |
| Kyrgyzstan | 2013 | 8.6 (8.5–8.6) | 2010 | 13.4 (11.4–15.8) | 0.6 |
| Lao People’s Democratic Republic | 2013 | 7.9 (7.8–8) | 2010 | 11.1 (9.4–13.2) | 0.7 |
| Lesotho | 2012 | 8.4 (8.3–8.5) | 2010 | 6.6 (5.5–7.8) | 1.3 |
| Liberia | 2011 | 8.5 (8.4–8.6) | 2010 | 6.7 (5.6–7.9) | 1.3 |
| Libya | 2009 | 8.7 (8.6–8.8) | 2010 | 10.6 (8.9–12.5) | 0.8 |
| Marshall Islands | 2018 | 9 (9–9.1) | 2010 | 6.4 (5.4–7.5) | 1.4 |
| Mozambique | 2005 | 7.8 (7.6–7.9) | 2010 | 5.6 (4.7–6.6) | 1.4 |
| Myanmar | 2014 | 8 (7.9–8) | 2010 | 11.2 (9.4–13.2) | 0.7 |
| Namibia | 2005 | 7.9 (7.6–8.1) | 2010 | 6.6 (5.6–7.7) | 1.2 |
| Niger | 2007 | 7.7 (7.7–7.7) | 2010 | 7.3 (6.2–8.6) | 1.1 |
| Qatar | 2012 | 9 (8.9–9.1) | 2010 | 10.5 (8.3–12.9) | 0.9 |
| Republic of Moldova | 2013 | 8.5 (8.5–8.6) | 2010 | 9.9 (8.3–11.6) | 0.9 |
| Rwanda | 2013 | 7.9 (7.9–8) | 2010 | 4 (3.3–4.9) | 2 |
| Samoa | 2013 | 9.5 (9.4–9.6) | 2010 | 5.2 (4.6–5.8) | 1.8 |
| Sao Tome and Principe | 2008 | 8.1 (8–8.2) | 2010 | 5.9 (4.9–6.9) | 1.4 |
| Sierra Leone | 2009 | 7.9 (7.8–8) | 2010 | 6.3 (5.3–7.3) | 1.3 |
| Sri Lanka | 2015 | 8 (8–8.1) | 2010 | 9.7 (8.2–11.3) | 0.8 |
| Tajikistan | 2017 | 8.5 (8.4–8.6) | 2010 | 13.5 (11.6–15.7) | 0.6 |
| Timor-Leste | 2014 | 8.2 (7.9–8.4) | 2010 | 11.2 (9.3–13.3) | 0.7 |
| Uganda | 2014 | 7.9 (7.9–8) | 2010 | 5.3 (4.4–6.3) | 1.5 |
| United Republic of Tanzania | 2012 | 7.9 (7.9–8) | 2010 | 6.9 (6.1–7.7) | 1.1 |
| Uruguay | 2014 | 8.5 (8.4–8.5) | 2010 | 6.8 (5.8–8) | 1.2 |
| Vanuatu | 2011 | 8.4 (8.3–8.4) | 2010 | 5.6 (4.8–6.6) | 1.5 |
| Vietnam | 2015 | 7.9 (7.8–7.9) | 2010 | 11.5 (9.5–13.7) | 0.7 |
-
There are 43 countries in this table; that is, countries included in our analysis that were not available in the previous global work were not included in this table (Powles et al., 2013).
Appendix 1—table 9
Countries included in the analysis by income group according to the World Bank classification.
| Analysis | World region | Country | Year | Income group |
|---|---|---|---|---|
| Model application | Africa | Algeria | 2017 | Upper-middle |
| Model application | Western Pacific | American Samoa | 2004 | Upper-middle |
| Model application | Americas | Bahamas | 2012 | High |
| Model application | Americas | Barbados | 2007 | High |
| Model application | Africa | Benin | 2015 | Lower |
| Model application | Africa | Botswana | 2014 | Upper-middle |
| Model application | Americas | British Virgin Islands | 2009 | No data |
| Model application | Africa | Cabo Verde | 2007 | Lower-middle |
| Model application | Western Pacific | Cambodia | 2010 | Lower |
| Model application | Americas | Cayman Islands | 2012 | High |
| Model application | Africa | Comoros | 2011 | Lower |
| Model application | Western Pacific | Cook Islands | 2015 | No data |
| Model application | Americas | Ecuador | 2018 | Upper-middle |
| Model application | Africa | Eritrea | 2010 | Lower |
| Model application | Africa | Eswatini | 2014 | Lower-middle |
| Model application | Africa | Ethiopia | 2015 | Lower |
| Model application | Western Pacific | Fiji | 2011 | Lower-middle |
| Model application | Western Pacific | French Polynesia | 2010 | High |
| Model application | Africa | Gambia | 2010 | Lower |
| Model application | Americas | Grenada | 2011 | Upper-middle |
| Model application | Americas | Guyana | 2016 | Upper-middle |
| Model application | Eastern Mediterranean | Iraq | 2015 | Upper-middle |
| Model application | Africa | Kenya | 2015 | Lower-middle |
| Model application | Western Pacific | Kiribati | 2016 | Lower-middle |
| Model application | Eastern Mediterranean | Kuwait | 2014 | High |
| Model application | Eastern Mediterranean | Kyrgyzstan | 2013 | Lower-middle |
| Model application | Western Pacific | Lao People’s Democratic Republic | 2013 | Lower-middle |
| Model application | Africa | Lesotho | 2012 | Lower-middle |
| Model application | Africa | Liberia | 2011 | Lower |
| Model application | Eastern Mediterranean | Libya | 2009 | Upper-middle |
| Model application | Western Pacific | Marshall Islands | 2018 | Upper-middle |
| Model application | Africa | Mozambique | 2005 | Lower |
| Model application | Southeast Asia | Myanmar | 2014 | Lower-middle |
| Model application | Africa | Namibia | 2005 | Lower-middle |
| Model application | Western Pacific | Nauru | 2016 | Upper-middle |
| Model application | Africa | Niger | 2007 | Lower |
| Model application | Western Pacific | Niue | 2012 | No data |
| Model application | Western Pacific | Palau | 2013 | Upper-middle |
| Model application | Eastern Mediterranean | Qatar | 2012 | High |
| Model application | Europe | Republic of Moldova | 2013 | Lower-middle |
| Model application | Africa | Rwanda | 2013 | Lower |
| Model application | Western Pacific | Samoa | 2013 | Lower-middle |
| Model application | Africa | Sao Tome and Principe | 2008 | Lower-middle |
| Model application | Africa | Sierra Leone | 2009 | Lower |
| Model application | Southeast Asia | Sri Lanka | 2015 | Lower-middle |
| Model application | Europe | Tajikistan | 2017 | Lower |
| Model application | Southeast Asia | Timor-Leste | 2014 | Lower-middle |
| Model application | Africa | Togo | 2011 | Lower |
| Model application | Western Pacific | Tuvalu | 2015 | Upper-middle |
| Model application | Africa | Uganda | 2014 | Lower |
| Model application | Africa | United Republic of Tanzania | 2012 | Lower |
| Model application | Americas | Uruguay | 2014 | High |
| Model application | Western Pacific | Vanuatu | 2011 | Lower-middle |
| Model application | Western Pacific | Vietnam | 2015 | Lower-middle |
| Model derivation | Europe | Armenia | 2016 | Lower-middle |
| Model derivation | Europe | Azerbaijan | 2017 | Upper-middle |
| Model derivation | Southeast Asia | Bangladesh | 2018 | Lower-middle |
| Model derivation | Europe | Belarus | 2017 | Upper-middle |
| Model derivation | Southeast Asia | Bhutan | 2014 | Lower-middle |
| Model derivation | Southeast Asia | Bhutan | 2019 | Lower-middle |
| Model derivation | Western Pacific | Brunei Darussalam | 2016 | High |
| Model derivation | Americas | Chile | 2017 | High |
| Model derivation | Eastern Mediterranean | Jordan | 2019 | Upper-middle |
| Model derivation | Eastern Mediterranean | Lebanon | 2017 | Upper-middle |
| Model derivation | Africa | Malawi | 2017 | Lower |
| Model derivation | Western Pacific | Mongolia | 2013 | Lower-middle |
| Model derivation | Western Pacific | Mongolia | 2019 | Lower-middle |
| Model derivation | Eastern Mediterranean | Morocco | 2017 | Lower-middle |
| Model derivation | Southeast Asia | Nepal | 2019 | Lower-middle |
| Model derivation | Western Pacific | Solomon Islands | 2015 | Lower-middle |
| Model derivation | Eastern Mediterranean | Sudan | 2016 | Lower-middle |
| Model derivation | Western Pacific | Tokelau | 2014 | No data |
| Model derivation | Western Pacific | Tonga | 2017 | Upper-middle |
| Model derivation | Europe | Turkmenistan | 2018 | Upper-middle |
| Model derivation | Africa | Zambia | 2017 | Lower-middle |
Appendix 2—table 1
Performance of each algorithm and processing method.
| Algorithm | Processing | R2 | MAE | RMSE |
|---|---|---|---|---|
| LiR | Polynomial (g = 2) | 0.447 | 1.1138 | 1.4451 |
| HuR | Standardized | 0.447 | 1.1132 | 1.4442 |
| RiR | Polynomial (g = 2) | 0.446 | 1.1147 | 1.4459 |
| MLP | Min-max | 0.451 | 1.1101 | 1.4395 |
| SVR | Min-max | 0.446 | 1.0988 | 1.4459 |
| KNN | Standardized | 0.421 | 1.1426 | 1.4779 |
| RF | Polynomial (g = 2) | 0.417 | 1.1474 | 1.4835 |
| GBM | Min-max | 0.447 | 1.1147 | 1.4447 |
| XGB | Min-max | 0.431 | 1.1293 | 1.4646 |
| Customized NN | Box-Cox | 0.461 | 1.0953 | 1.4156 |
-
MAE: mean absolute error; RMSE: root mean square error; LiR: linear regression; HuR: Hubber regressor; RiR: ridge regressor; MLP: multilayer perceptron; SVR: support vector regressor; KNN: k-nearest neighbors; GBM: gradient boost machine; XGB: extreme gradient boosting; NN: neural network; RF: random forest.
Appendix 2—table 2
Mean difference between observed and predicted salt intake by sex across all machine learning algorithms.
| Machine learning algorithm | Mean difference between observed and predicted mean salt intake | Sex |
|---|---|---|
| CNN_boxcox | –0.0109 | Both sexes |
| CNN_standardize | –0.0075 | Both sexes |
| GBR_boxcox | 0.1373 | Both sexes |
| GBR_minmax | 0.1198 | Both sexes |
| GBR_orig | –0.0252 | Both sexes |
| GBR_standardized | 0.1231 | Both sexes |
| HuR_boxcox | 0.0389 | Both sexes |
| HuR_standardized | –0.0019 | Both sexes |
| KNN_boxcox | 0.0144 | Both sexes |
| KNN_standardized | –0.0172 | Both sexes |
| LiR_poly | –0.0292 | Both sexes |
| MLP_boxcox | –0.0069 | Both sexes |
| MLP_minmax | –0.019 | Both sexes |
| MLP_standardized | –0.0174 | Both sexes |
| RF_poly | –0.0479 | Both sexes |
| RiR_poly | –0.0304 | Both sexes |
| SVR_minmax | 0.1137 | Both sexes |
| XGB_boxcox | 0.0389 | Both sexes |
| XGB_orig | –0.0312 | Both sexes |
| XGB_standardized | –0.0329 | Both sexes |
| CNN_boxcox | 0.088 | Men |
| CNN_standardize | 0.0699 | Men |
| GBR_boxcox | 0.1591 | Men |
| GBR_minmax | 0.1381 | Men |
| GBR_orig | 0.0197 | Men |
| GBR_standardized | 0.1444 | Men |
| HuR_boxcox | 0.0265 | Men |
| HuR_standardized | –0.0119 | Men |
| KNN_boxcox | 0.0612 | Men |
| KNN_standardized | 0.0179 | Men |
| LiR_poly | 0.0069 | Men |
| MLP_boxcox | 0.0512 | Men |
| MLP_minmax | –0.0104 | Men |
| MLP_standardized | –0.0249 | Men |
| RF_poly | –0.0129 | Men |
| RiR_poly | 0.0063 | Men |
| SVR_minmax | 0.1265 | Men |
| XGB_boxcox | 0.0265 | Men |
| XGB_orig | 0.0147 | Men |
| XGB_standardized | 0.0069 | Men |
| CNN_boxcox | –0.1097 | Women |
| CNN_standardized | –0.085 | Women |
| GBR_boxcox | 0.1155 | Women |
| GBR_minmax | 0.1015 | Women |
| GBR_orig | –0.07 | Women |
| GBR_standardized | 0.1018 | Women |
| HuR_boxcox | 0.0514 | Women |
| HuR_standardized | 0.0082 | Women |
| KNN_boxcox | –0.0324 | Women |
| KNN_standardized | –0.0524 | Women |
| LiR_poly | –0.0653 | Women |
| MLP_boxcox | –0.0649 | Women |
| MLP_minmax | –0.0276 | Women |
| MLP_standardized | –0.0098 | Women |
| RF_poly | –0.0828 | Women |
| RiR_poly | –0.0671 | Women |
| SVR_minmax | 0.101 | Women |
| XGB_boxcox | 0.0514 | Women |
| XGB_orig | –0.0771 | Women |
| XGB_standardized | –0.0727 | Women |
Author response table 1
| Income | |||||||
|---|---|---|---|---|---|---|---|
| Total | Low | Lower-middle | Upper-middle | High | No group | ||
| Model development | Original | 17 | 1 | 9 | 5 | 1 | 1 |
| Revision | 19 | 1 | 9 | 6 | 2 | 1 | |
| Difference | +2 | +1 | +1 | ||||
| Model application | Original | 49 | 13 | 16 | 11 | 6 | 3 |
| Revision | 54 | 15 | 17 | 12 | 7 | 3 | |
| Difference | +5 | +2 | +1 | +1 | +1 |
Author response table 2
| Teufel F, et al. Body-mass index and diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults. Lancet. 2021;398(10296):238–48. |
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| Flood D, et al., The state of diabetes treatment coverage in 55 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 680 102 adults. The L,ancet Healthy Longevity. 2021;2(6):e340–51. |
| Peiris D, et al., Cardiovascular disease risk profile and management practices in 45 lowincome and middle-income countries: A cross-sectional study of nationally representative individual-level survey data. PLoS Med. 2021;18(3):e1003485. |
| Davies JI, et al., Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data. PLoS Med. 2020;17(11):e1003268. |
| Seiglie JA, et al., Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries. Diabetes Care. 2020;43(4):767–75. |
| Geldsetzer P, et al., The state of hypertension care in 44 low-income and middle-income countries: a cross-sectional study of nationally representative individual-level data from 1·1 million adults. Lancet. 2019;394(10199):652–62. |
| Manne-Goehler J, et al., Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys. PLoS Med. 2019;16(3):e1002751. |
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Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries
eLife 11:e72930.
https://doi.org/10.7554/eLife.72930
