Bello‐Chavolla 2021.

Study characteristics
Notes	English title Unequal impact of structural health determinants and comorbidity on COVID‐19 severity and lethality in older Mexican adults: considerations beyond chronological aging Study setting Start of study recruitment (MM/YYYY): NR End of study recruitment (MM/YYYY): 06/2020 Study design: Registry data Study centre(s): Multiple centres/clinics/areas within a country Number of centres, clinics or areas: 475 Study setting: Outpatient and inpatient Number of participants recruited: 101,238 Sampling method: Consecutive participants Participants Female participants (absolute number): 44,239 Age measure, value: NR Inclusion criteria: All SARS‐CoV‐2 PCR‐positive cases up to June 3, 2020, in individuals aged 60 and older. Exclusion criteria: NR Smoking frequency: 8333 Diabetes frequency: 17,489 Hypertension frequency: 20,955 Cardiovascular disease frequency: 2594 Asthma frequency: 2930 Chronic obstructive pulmonary disease frequency: 1990 Other pulmonary disease frequency: 26,925 Immunosuppression frequency: 1555 Chronic kidney disease frequency: 2339 Cancer frequency: NR Steroid administration frequency: NR Supplemental oxygen administration frequency: NR Other treatments (frequency): NR Prognostic factor(s) Study’s definition for obesity: NR The time when obesity has been measured: Unspecified Main variable used for determination of obesity: NR Threshold used for definition: NR Obesity frequency (absolute number): 20,599 Prognostic factor(s): Obesity Outcome(s) Pneumonia Hospitalisation ICU admission Mechanical ventilation Mortality Outcome (prognostic factor) Pneumonia (obesity) Follow‐up Number of patients followed completely for the outcome: 101,238 Number of obese patients followed completely for the outcome: 20,599 Number of non‐obese patients followed completely for the outcome: 80,639 Univariable unadjusted analysis for obesity Effect measure for obesity: NR Effect measure value (95% CI), P value: NR Multivariable analysis for obesity Modelling method: Logistic regression The set of prognostic factors used for adjustment: Age, male sex, indigenous, CVD, CKD, COPD, immunosuppression, smoking, diabetes, obesity, hypertension, social lag index Effect measure for obesity: Odds ratio Effect measure value (95% CI), P value: 1.26 (1.11, 1.36), NR Outcome (prognostic factor) Hospitalisation (obesity) Follow‐up Number of patients followed completely for the outcome: 101,238 Number of obese patients followed completely for the outcome: 20,599 Number of non‐obese patients followed completely for the outcome: 80,639 Univariable unadjusted analysis for obesity Effect measure for obesity: NR Effect measure value (95% CI), P value: NR Multivariable analysis for obesity Modelling method: Logistic regression The set of prognostic factors used for adjustment: Age, male sex, indigenous, CVD, CKD, COPD, immunosuppression, smoking, diabetes, obesity, hypertension, social lag index Effect measure for obesity: Odds ratio Effect measure value (95% CI), P value: 1.09 (1.01, 1.18), NR Outcome (prognostic factor) ICU admission (obesity) Follow‐up Number of patients followed completely for the outcome: 101,238 Number of obese patients followed completely for the outcome: 20,599 Number of non‐obese patients followed completely for the outcome: 80,639 Univariable unadjusted analysis for obesity Effect measure for obesity: NR Effect measure value (95% CI), P value: NR Multivariable analysis for obesity Modelling method: Logistic regression The set of prognostic factors used for adjustment: Age, male sex, indigenous, CVD, CKD, COPD, immunosuppression, smoking, diabetes, obesity, hypertension, social lag index Effect measure for obesity: Odds ratio Effect measure value (95% CI), P value: 1.26 (1.09, 1.45), NR Outcome (prognostic factor) Mechanical ventilation (obesity) Follow‐up Number of patients followed completely for the outcome: 101,238 Number of obese patients followed completely for the outcome: 20,599 Number of non‐obese patients followed completely for the outcome: 80,639 Univariable unadjusted analysis for obesity Effect measure for obesity: NR Effect measure value (95% CI), P value: NR Multivariable analysis for obesity Modelling method: Logistic regression The set of prognostic factors used for adjustment: Age, male sex, indigenous, CVD, CKD, COPD, immunosuppression, smoking, diabetes, obesity, hypertension, social lag index Effect measure for obesity: Odds ratio Effect measure value (95% CI), P value: 1.31 (1.15, 1.50), NR Outcome (prognostic factor) Mortality (obesity) Follow‐up Number of patients followed completely for the outcome: 101,238 Number of obese patients followed completely for the outcome: 20,599 Number of non‐obese patients followed completely for the outcome: 80,639 Univariable unadjusted analysis for obesity Effect measure for obesity: NR Effect measure value (95% CI), P value: NR Multivariable analysis for obesity Modelling method: Cox regression The set of prognostic factors used for adjustment: Age, male sex, indigenous, CVD, CKD, COPD, immunosuppression, smoking, diabetes, obesity, hypertension, social lag index Effect measure for obesity: Hazard ratio Effect measure value (95% CI), P value: 1.19 (1.12, 1.27), NR
Item	Authors' judgement	Support for judgement
Study Participation	Yes	Appendix 3
Study Attrition Mortality	Unclear	Appendix 3
Study Attrition Mechanical ventilation	Unclear	Appendix 3
Study Attrition ICU admission	Unclear	Appendix 3
Study Attrition Hospitalisation	Unclear	Appendix 3
Study Attrition Pneumonia	Unclear	Appendix 3
Prognostic Factor Measurement	Unclear	Appendix 3
Outcome Measurement Mortality	Yes	Appendix 3
Outcome Measurement Mechanical ventilation	Yes	Appendix 3
Outcome Measurement ICU admission	Yes	Appendix 3
Outcome Measurement Hospitalisation	Yes	Appendix 3
Outcome Measurement Pneumonia	Yes	Appendix 3
Confounding Bias Mortality	Yes	Appendix 3
Confounding Bias Mechanical ventilation	Yes	Appendix 3
Confounding Bias ICU admission	Yes	Appendix 3
Confounding Bias Hospitalisation	Yes	Appendix 3
Confounding Bias Pneumonia	Yes	Appendix 3
Statistical Analysis Bias	Yes	Appendix 3