Table 2.
Adjusted odds of extracorporeal membrane oxygenation use, based on patient demographics
| Demographic Category | Multivariable Hierarchical Logistic Regression aOR (95% CI) |
E-Value Estimates* | IPTW Analysis aOR (95% CI) |
|---|---|---|---|
| Sex | |||
| Female | 0.73 (0.70–0.75) | 2.08 | 0.77 (0.75–0.79)† |
| Male | Reference‡ | — | Reference |
| Primary insurance | |||
| Medicare | 0.50 (0.48–0.52) | 3.41 | — |
| Medicaid | 0.55 (0.52–0.57) | 3.04 | 0.57 (0.55–0.58)§ |
| Other | 0.64 (0.60–0.68) | 2.50 | — |
| Private insurance | Reference‖ | — | Reference |
| Median income of patient zip code¶ | |||
| Quartile 1 | 0.63 (0.60–0.67) | 2.55 | 0.64 (0.61–0.66)** |
| Quartile 2 | 0.78 (0.74–0.81) | 1.88 | — |
| Quartile 3 | 0.87 (0.83–0.91) | 1.56 | — |
| Quartile 4 | Reference†† | — | Reference |
| Intersectionality Identity¶ | |||
| Female, income quartile 1 | 0.50 (0.46–0.53) | 3.41 | —‡‡ |
| Male, income quartile 1 | 0.64 (0.61–0.68) | 2.50 | — |
| Female, income quartile 2 | 0.56 (0.53–0.60) | 2.97 | — |
| Male, income quartile 2 | 0.80 (0.76–0.85) | 1.81 | — |
| Female, income quartile 3 | 0.64 (0.60–0.69) | 2.50 | — |
| Male, income quartile 3 | 0.89 (0.84–0.94) | 1.50 | — |
| Female, income quartile 4 | 0.74 (0.69–0.79) | 2.04 | — |
| Male, income quartile 4 | Reference§§ | — | — |
Definition of abbreviations: aOR = adjusted odds ratio; CI = confidence interval; ECMO = extracorporeal membrane oxygenation; IPTW = inverse probability of treatment weighting.
The E-value estimates the strength that an unmeasured confounder would need to possess in order to shift the observed association to the null. For example, an unmeasured confounder would need to have an adjusted odds ratio of 2.08 in order to shift the observed adjusted association of ECMO use and patient sex from 0.73 to the null (1.00). E-values are calculated from the estimates obtained from multivariable hierarchical logistic regression models (the primary analysis) (32–34).
See Table E8 for standardized mean differences achieved by weighting observations on the inverse probability of the exposure (female vs. male).
See Table E5 for full model parameter estimates including fit statistics.
See Table E9 for standardized mean differences achieved by weighting observations on the inverse probability of the exposure (Medicaid vs. private insurance). Inverse probability of treatment weighting has better performance characteristics for binary exposures, so the analysis focused on patients with Medicaid compared with patients with private insurance.
See Table E6 for full model parameter estimates including fit statistics.
Cutoffs for median income quartiles vary by year. Quartile 1 indicates the lowest income level, and quartile 4 is the highest income level. Full documentation of income levels can be found at https://www.hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nrdnote.jsp.
See Table E10 for standardized mean differences achieved by weighting observations on the inverse probability of the exposure (lowest income quartile 1 vs. highest income quartile 4). Inverse probability of treatment weighting has better performance characteristic for binary exposures, so the analysis focused on patients with lowest income quartile 1 compared with patients with highest income quartile 4.
See Table E7 for full model parameter estimates including fit statistics.
Given smaller cohort sizes for each intersectional category, IPTW was not performed for the intersectional analysis.
See Table E11 for full model parameter estimates including fit statistics.