The Effect of Patient Neighbourhood Income Level on the Purchase of Continuous Positive Airway

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1 Online Data Supplement The Effect of Patient Neighbourhood Income Level on the Purchase of Continuous Positive Airway Pressure Treatment among Sleep Apnea Patients Tetyana Kendzerska, MD, PhD, Andrea S. Gershon, MD, MSc, George Tomlinson, PhD, Richard S. Leung, MD, PhD 1. Definition of Exposure: Neighbourhood Income Level 2. Definition of Outcome: Funding Respiratory Equipment & Supplies form 3. Missing values 4. Model diagnostics 5. Table S1. Association between patient neighbourhood income level and acceptance of CPAP among patients with severe OSA regardless of daytime sleepiness and with moderate to severe OSA and excessive daytime sleepiness tested in Cox-regression analyses controlled for age, sex, body mass index, smoking status, comorbidities at baseline (hypertension, chronic heart failure, diabetes, depression, chronic obstructive pulmonary disease), severity of disease (apnea hypopnea index, time spent with oxygen saturation <90%, the Epworth Sleepiness Score), total sleep time, and primary care exposure before diagnostic sleep study. 6. References

2 Definition of Exposure: Neighbourhood Income Level A patient's residential neighbourhood income was defined from the Ontario Census. Ontario neighbourhoods are classified into one of the five approximately equal-sized income quintiles, ranked from poorest (Q1) to wealthiest (Q5) 1. A neighborhood was defined as equivalent to a census dissemination area (DA), which is a smallest standard geographic area covered by a single census data collector and contains persons ( Neighbourhood income is also a household sizeadjusted measure of household income, based on 2006 census summary data at the DA level, and using person-equivalents implied by the 2006 low income cut-offs. Note that the 2001 single person equivalents were 1.00 for 1 person, 1.25 for 2 persons, 1.55 for 3 persons, 1.95 for 4 or 5 persons, and 2.44 for 6 or more persons sharing the same household (regardless of age). Next, DAs within each census metropolitan area were ranked from the lowest average income to the highest, and DAs were assigned to approximately equal-sized five groups. The quintiles were then pooled across the areas. Each patient was assigned to a DA and the income quintile based on the patient s postal code at the time of index diagnostic sleep study using Statistics Canada s Postal Code Conversion File 2, 3. E2

3 Definition of Outcome: Funding Respiratory Equipment & Supplies form from which data was extracted. Description of the devise, ADP portion paid and date of claim were extracted from this form. The procedure of CPAP prescription is stable over time considered in our study. E3

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7 Missing values, n (%)* Total Sample (n=695) Not Purchased CPAP (n=295) Purchased CPAP (n=400) BMI 30 (4.3) 13 (4.4) 17 (4.3) Smoking Status 32 (4.6) 15 (5.1) 17 (4.3) Income status 15 (2.2) 14 (4.7) 1 (0.25) SR Snoring 13 (1.9) 6 (2.0) 7 (1.8) SR Stop Breathing 34 (4.9) 15 (5.1) 19 (4.8) SR Restless Sleep 49 (7.1) 20 (6.8) 29 (7.3) SR Wake Refresh 29 (4.2) 15 (5.1) 14 (3.5) SR Morning headache 26 (3.7) 4 (1.4) 22 (5.5) SE 25 (3.6) 12 (4.1) 13 (3.3) Time with SaO2<90% 55 (7.9) 42 (14.2) 13 (3.3) TST 25 (3.6) 12 (4.1) 13 (3.3) Income status 15 (2.2) 14 (4.7) 1 (0.25) BMI body mass index; SaO2-oxygen saturation; SE - sleep efficiency; SR self-reported, TST total sleep time *had no missing values on AHI and the ESS. E7

8 Model Diagnostics The Cox proportional hazards regression assumptions for each variable were tested using a graphical approach (visually examining distribution of Schoenfeld residuals), and analytical method (a global test of the proportional hazards) 4, 5. The assumption is that the hazard ratio for each variable is constant over time: if the proportional hazard assumption for a parameter is true, then a plot of residuals (r) for this parameter versus time should lie around a horizontal line (in a linear regression model: E(r)= +β*time, and beta = 0 if the proportional hazard assumption is true) 4. The global test also uses the scaled Schoenfeld residuals 5. If beta is significantly different from zero, then the proportional hazards assumption was rejected. E8

9 Table S1. Association between patient neighbourhood income level and acceptance of CPAP among patients with severe OSA regardless of daytime sleepiness and with moderate to severe OSA and excessive daytime sleepiness tested in Cox-regression analyses* controlled for age, sex, body mass index, smoking status, comorbidities at baseline (hypertension, chronic heart failure, diabetes, depression, chronic obstructive pulmonary disease), severity of disease (apnea hypopnea index, time spent with oxygen saturation <90%, the Epworth Sleepiness Score), total sleep time, and primary care exposure before diagnostic sleep study. AHI>30 AHI 15 and the ESS 10 Neighbourhood income status HR (95%CI), p value Univariate analyses Quintile 2 vs. 1 (poorest) 1.21 ( ), ( ), 0.22 Quintile 3 vs.1 (poorest) 1.11 ( ), ( ), 0.31 Quintile 4 vs.1 (poorest) 1.21 ( ), ( ), 0.07 Quintile 5 (wealthiest) vs.1 (poorest) 1.19 ( ), ( ), 0.55 Model with categorized income status Income status : Q2-5 vs. Q1 (poorest) 1.19 ( ), ( ), 0.15 Multivariable analyses Quintile 2 vs. 1 (poorest) 1.14 ( ), ( ), 0.21 Quintile 3 vs.1 (poorest) 1.05 ( ), ( ), 0.22 Quintile 4 vs.1 (poorest) 1.25 ( ), ( ), 0.02 Quintile 5 (wealthiest) vs.1 (poorest) 1.13 ( ), ( ), 0.24 Model fit with income status, (20) (20) LR chi 2 (df) Model fit without income status, (16) (16) LR chi 2 (df) LRT test, p value Model with categorized income status Income status : Q2-5 vs. Q1 (poorest) 1.14 ( ), ( ), 0.07 Model without smoking status Quintile 2 vs. 1 (poorest) 1.16 ( ), ( ), 0.15 Quintile 3 vs.1 (poorest) 1.05 ( ), ( ), 0.17 Quintile 4 vs.1 (poorest) 1.25 ( ), ( ), 0.01 Quintile 5 (wealthiest) vs.1 (poorest) 1.17 ( ), ( ), 0.17 Model with categorized income status Income status : Q2-5 vs. Q1 (poorest) 1.16 ( ), ( ), 0.04 *Complete case analysis AHI apnea-hypopnea index; CI confidence interval; ESS Epworth Sleepiness Scale; HR hazard ratio; LR - likelihood ratio; LRT - likelihood-ratio test; Q quintile E9

10 References 1. Ontario PH. Summary Measures of Socioeconomic Inequalities in Health Technical Report. In. [S.l.]: Public Health Ontario, Wilkins R. Use of postal codes and addresses in the analysis of health data. Health reports / Statistics Canada, Canadian Centre for Health Information = Rapports sur la sante / Statistique Canada, Centre canadien d'information sur la sante 1993;5(2): Southern DA, Faris PD, Knudtson ML, Ghali WA. Prognostic relevance of census-derived individual respondent incomes versus household incomes. Canadian journal of public health = Revue canadienne de sante publique 2006;97(2): Harrell FE. Regression modeling strategies : with applications to linear models, logistic regression, and survival analysis. New York: Springer, Grambsch P, Therneau T. Proportional hazards tests and diagnostics based on weighted residuals Biometrika 1994;81: E10

Appendix 2 (as supplied by the authors): Supplementary information on methods

Appendix 2 (as supplied by the authors): Supplementary information on methods Table of Contents Methods... 2 etable 2A: List of variables used collected in the sleep laboratory... 3 etable 2B: Description