Extension of the Peters-Belson method to estimate health disparities among multiple groups using logistic regression with survey data

Sex disparities in the incidence of 21 cancer types: Quantification of the contribution of risk factors

BACKGROUND

Cancer incidence is higher in men than in women at most shared anatomic sites for currently unknown reasons. The authors quantified the extent to which behaviors (smoking and alcohol use), anthropometrics (body mass index and height), lifestyles (physical activity, diet, medications), and medical history collectively explain the male predominance of risk at 21 shared cancer sites.

METHODS

Prospective cohort analyses (n = 171,274 male and n = 122,826 female participants; age range, 50-71 years) in the National Institutes of Health-AARP Diet and Health Study (1995-2011). Cancer-specific Cox regression models were used to estimate male-to-female hazard ratios (HRs). The degree to which risk factors explained the observed male-female risk disparity was quantified using the Peters-Belson method.

RESULTS

There were 26,693 incident cancers (17,951 in men and 8742 in women). Incidence was significantly lower in men than in women only for thyroid and gallbladder cancers. At most other anatomic sites, the risks were higher in men than in women (adjusted HR range, 1.3-10.8), with the strongest increases for bladder cancer (HR, 3.33; 95% confidence interval [CI], 2.93-3.79), gastric cardia cancer (HR, 3.49; 95% CI, 2.26-5.37), larynx cancer (HR, 3.53; 95% CI, 2.46-5.06), and esophageal adenocarcinoma (HR, 10.80; 95% CI, 7.33-15.90). Risk factors explained a statistically significant (nonzero) proportion of the observed male excess for esophageal adenocarcinoma and cancers of liver, other biliary tract, bladder, skin, colon, rectum, and lung. However, only a modest proportion of the male excess was explained by risk factors (ranging from 50% for lung cancer to 11% for esophageal adenocarcinoma).

CONCLUSIONS

Men have a higher risk of cancer than women at most shared anatomic sites. Such male predominance is largely unexplained by risk factors, underscoring a role for sex-related biologic factors.

Navigating by Stars: Using CMS Star Ratings to Choose Hospitals for Complex Cancer Surgery

Background

The Centers for Medicare and Medicaid Services (CMS) developed risk-adjusted “Star Ratings,” which serve as a guide for patients to compare hospital quality (1 star = lowest, 5 stars = highest). Although star ratings are not based on surgical care, for many procedures, surgical outcomes are concordant with star ratings. In an effort to address variability in hospital mortality after complex cancer surgery, the use of CMS Star Ratings to identify the safest hospitals was evaluated.

Methods

Patients older than 65 years of age who underwent complex cancer surgery (lobectomy, colectomy, gastrectomy, esophagectomy, pancreaticoduodenectomy) were evaluated in CMS Medicare Provider Analysis and Review files (2013-2016). The impact of reassignment was modeled by applying adjusted mortality rates of patients treated at 5-star hospitals to those at 1-star hospitals (Peters-Belson method).

Results

There were 105 823 patients who underwent surgery at 3146 hospitals. The 90-day mortality decreased with increasing star rating (1 star = 10.4%, 95% confidence interval [CI] = 9.8% to 11.1%; and 5 stars = 6.4%, 95% CI = 6.0% to 6.8%). Reassignment of patients from 1-star to 5-star hospitals (7.8% of patients) was predicted to save 84 Medicare beneficiaries each year. This impact varied by procedure (colectomy = 47 lives per year; gastrectomy = 5 lives per year). Overall, 2189 patients would have to change hospitals each year to improve outcomes (26 patients moved to save 1 life).

Conclusions

Mortality after complex cancer surgery is associated with CMS Star Rating. However, the use of CMS Star Ratings by patients to identify the safest hospitals for cancer surgery would be relatively inefficient and of only modest impact.

Statistical inference of the relative concentration index for complex surveys

The relative concentration index is a widely used measure for assessing relative differences in health across all socioeconomic population groups. We extend its usage to individual-level data collected through complex surveys by deriving its variance using the Taylor linearization (TL) method. Two existing plug-in variance estimators that only require grouped data are also compared. We discuss sources of uncertainty that each variance estimator considers and present simulation studies to compare the performance of the three estimators under various sampling designs. The proposed TL variance estimator consistently produces valid results; however, it requires the access to individual-level data. Both plug-in variance estimators are biased because of failure to account for certain error sources. However, when only grouped data is available, one of the plug-in estimators can be valid as long as the socioeconomic groups are treated equally sized, a commonly used analytic strategy to emphasize group's instead of individual's burden of disease in health disparity assessment. We illustrate the three variance estimators by applying them to assessing socioeconomic disparities in child and adolescent obesity using complex survey sampled drawn from the National Health and Nutrition Examination Survey.

Statistical Inference on Health Disparity Indices for Complex Surveys

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.