Balanced Repeated Replication Method Research Articles

Abstract 15711: Association Between State Tobacco Surcharge Policy and Health Insurance Enrollment, Health Care Utilization, and Smoking Cessation Behaviors

Introduction: The Patient Protection and Affordable Care Act allows insurers to charge up to 50% higher premiums to people who use tobacco. Several states have outlawed tobacco surcharges or capped the surcharges to lower levels. Evidence on the impact of state policies limiting tobacco surcharges is limited. This study aims to examine the association between state tobacco surcharge policies and individuals’ health insurance enrollment, health care utilization, and smoking cessation behaviors. Methods: This study used data from the Population Assessment of Tobacco and Health (PATH) Study Wave 6 (Mar-Nov 2021). The study sample was adults 18-64 years old who reported smoking cigarettes 20 or more days in the past 30 days (N=4,235). Exposure was living in states limiting tobacco surcharges (outlawed or capped). Outcome variables were currently covered by health insurance, seen a health professional in the past 12 months, intention to quit within 12 months, and any quit attempt in the past 12 months. Single-wave sample weights and balanced repeated replication method were applied to generate representative estimates and variances. Multivariate logistic regressions were used to estimate the associations between exposure and outcomes, controlling for socio-demographic variables, other tobacco use status, and state Medicaid expansion and tobacco control policies. Results: Among study sample, 19.8% (95% CI: 16.0%-24.3%) lived in states limiting tobacco surcharges. Bivariate associations showed that people living in states limiting surcharges were more likely to be covered by health insurance (84.5%, 95% CI: 81.2%-87.3% vs. 75.0%, 95% CI: 72.9%-77.1%, p < 0.001). After adjusting for covariates, the association between state policies and health insurance coverage became statistically insignificant (adjusted odds ratio, aOR=1.09, 95% CI: 0.80-1.47). Participants living in states limiting surcharges were less likely to have intention to quit within 12 months (aOR=0.78, 95% CI: 0.65-0.94). Other associations were not statistically significant. Conclusions: This study indicated that people living in states limiting tobacco surcharges had lower intention to quit smoking. More studies are needed to evaluate the impact of tobacco surcharge policies.

The Recalled Age of Initiation of Multiple Tobacco Products among 26-34 Year Olds: Findings from the Population Assessment of Tobacco and Health (PATH) Study Wave 1 (2013-2014).

This study examined the recalled age of initiation of seven different tobacco products (TPs) and explored potential influences of sex, race/ethnicity, and cigarette-smoking status on tobacco use initiation among adults 26–34 years old using the PATH study. Methods: Secondary analyses were conducted in the adult restricted PATH wave 1 (2013–2014) dataset. Weighted statistics are reported using the balanced repeated replication method and Fay’s correction to account for PATH’s complex study design. Distributions and histograms of the recalled age of initiation of seven different TPs (cigarettes, cigarillos, traditional cigars, filtered cigars, hookah, smokeless tobacco, and e-cigarettes) are reported, as well as the impact of sex and race/ethnicity using Cox proportional hazard models. The impact of cigarette-smoking status on the recalled age of initiation of each tobacco product other than cigarettes was explored. Results: The highest modes of the recalled age of initiation of cigarette use were at 14–15 and 15–16 years old. The distributions of the recalled age of initiation of cigarillos, traditional cigars, filtered cigars, hookah, and smokeless tobacco occurred later, with the highest modes at 15–16 and 17–18 years old. The distribution of the recalled age of initiation of e-cigarettes had a different shape than the other TPs, with the highest mode reported at 27–28 years old. Conclusion: Due to the ever-changing tobacco marketplace, understanding when contemporary adults aged 26–34 years recall initiating TP use is important and will inform prevention researchers.

ESTIMATION OF INCOME CHARACTERISTICS FOR REGIONS IN POLAND USING SPATIO-TEMPORAL SMALL AREA MODELS

Abstract The paper presents the comparison of estimation results for spatial and spatiotemporal small area models. The study was carried out for income-related variables drawn from the Polish Household Budget Survey and explanatory variables from the Polish Local Data Bank for the years 2003-2013. The properties of EBLUPs (Empirical Best Linear Unbiased Predictors) based on spatiotemporal models, which utilize spatial correlation between neighbouring areas as well as historical data, were compared and contrasted with EBLUPs based on spatial models obtained separately for each year and with EBLUPs based on the Rao-Yu model. The computations were performed using sae, sae2 and spdep packages for R-project environment. In the case of sae package, the eblupFH, eblupSFH and the eblupSTFH functions were used for point estimation along with the mseFH, mseSFH and the pbmseSTFH functions for the MSE estimation, whereas the eblupRY function was applied for the purposes of sae2 package. The precision of direct estimators was guaranteed by the adoption of the Balanced Repeated Replication method. The results of the analysis demonstrate that a visible reduction of the estimation error was achieved for the implemented spatiotemporal small-area models, especially when significant spatial and time autocorrelations were observed. These results are even more valuable than those achieved by the means of the Rao-Yu model. In the computations, three author-defined functions were adopted, which not only enabled the author to perform the extract of random effects for spatial, spatiotemporal and Rao-Yu models, but also made it possible to obtain their decomposition with respect to spatial and temporal parts, thus creating a novel solution. The comparison was carried out using choropleth maps for spatial effects and distributions of temporal random effects for the considered years.

Job Satisfaction: A Critical, Understudied Facet of Workforce Development in Public Health.

The field of public health faces multiple challenges in its efforts to recruit and retain a robust workforce. Public health departments offer salaries that are lower than the private sector, and government bureaucracy can be a deterrent for those seeking to make a difference. The objective of this research was to explore the relationship between general employee satisfaction and specific characteristics of the job and the health agency and to make recommendations regarding what health agencies can do to support recruitment and retention. This is a cross-sectional study using data collected from the 2014 Public Health Workforce Interests and Needs Survey (PH WINS). A nationally representative sample was constructed from 5 geographic (paired adjacent HHS [US Department of Health and Human Services]) regions and stratified by population and state governance type. Descriptive and inferential statistics were analyzed using the balanced repeated replication method to account for the complex sampling design. A multivariate linear regression was used to examine job satisfaction and factors related to supervisory and organizational support adjusting for relevant covariates. PH WINS data were collected from state health agency central office employees using an online survey. Level of job satisfaction using the Job in General Scale (abridged). State health agency central office staff (n = 10,246) participated in the survey (response rate 46%). Characteristics related to supervisory and organizational support were highly associated with increased job satisfaction. Supervisory status, race, organization size, and agency tenure were also associated with job satisfaction. Public health leaders aiming to improve levels of job satisfaction should focus on workforce development and training efforts as well as adequate supervisory support, especially for new hires and nonsupervisors.

Estimation of ROC curve with complex survey data.

The receiver operating characteristic (ROC) curve can be utilized to evaluate the performance of diagnostic tests. The area under the ROC curve (AUC) is a widely used summary index for comparing multiple ROC curves. Both parametric and nonparametric methods have been developed to estimate and compare the AUCs. However, these methods are usually only applicable to data collected from simple random samples and not surveys and epidemiologic studies that use complex sample designs such as stratified and/or multistage cluster sampling with sample weighting. Such complex samples can inflate variances from intra-cluster correlation and alter the expectations of test statistics because of the use of sample weights that account for differential sampling rates. In this paper, we modify the nonparametric method to incorporate sampling weights to estimate the AUC and employ leaving-one-out jackknife methods along with the balanced repeated replication method to account for the effects of the complex sampling in the variance estimation of our proposed estimators of the AUC. The finite sample properties of our methods are evaluated using simulations, and our methods are illustrated by comparing the estimated AUC for predicting overweight/obesity using different measures of body weight and adiposity among sampled children and adults in the US Hispanic Health and Nutrition Examination Survey.

The Determinants of Chronic Bronchitis in Aboriginal Children and Youth

There is limited knowledge concerning chronic bronchitis (CB) in Canadian Aboriginal peoples. To determine the prevalence (crude and adjusted) of CB and its associated risk factors in Canadian Aboriginal children and youth six to 14 years of age. Data from the cross-sectional Aboriginal Peoples Survey were analyzed in the present study. Logistic regression analysis was used to determine risk factors influencing the prevalence of CB among Aboriginal children and youth. The balanced repeated replication method was used to compute standard errors of regression coefficients to account for clustering inherent in the study design. The outcome of interest was based on the question: "Have you been told by a doctor, nurse or other health professional that you have chronic bronchitis?" Demographics, environment and population characteristics (predisposing and enabling resources) were tested for an association with CB. The prevalence of CB was 3.1% for boys and 2.8% for girls. Other significant risk factors of CB were age (OR 1.38 [95% CI 1.24 to 1.52] for 12 to 14 year olds versus six to eight year olds), income (OR 2.28 [95% CI 2.02 to 2.59] for income category <$25,000/year versus ≥$85,000/year), allergies (OR 1.96 [95% CI 1.78 to 2.16] for having allergies versus no allergies), asthma (OR 7.61 [ 95% CI 6.91 to 8.37] for having asthma versus no asthma) and location of residence (rural/urban and geographical location). A significant two-way interaction between sex and body mass index indicated that the relationship between the prevalence of CB and body mass index was modified by sex. The prevalence of CB was related to well-known risk factors among adults, including older age and lower annual income.

Modified balanced repeated replication for complex survey data

SUMMARY Balanced repeated replication, commonly used to estimate the variances of nonlinear estimators from stratified sampling designs, can run into problems in estimating a ratio of small domain totals or in the case of poststratification or unit nonresponse adjustment involving ratio weighting within cells if some cell sizes are small. This problem is mainly caused by a sharp perturbation of the weights to construct replicate estimates. To avoid extreme replicate estimates, Robert Fay proposed a modified balanced repeated replication method using gentler perturbation of weights. Theoretical properties of the modified method are studied here. The method is extended to the case of imputation for item nonresponse. Both smooth and nonsmooth estimators are studied.

Balanced Repeated Replication for Stratified Multistage Survey Data under Imputation

Abstract Balanced repeated replication (BRR) is a popular method for variance estimation in surveys. The standard BRR method works by first creating a set of “balanced” pseudoreplicated datasets from the original dataset. For a survey estimator θ, the BRR variance estimator is the average of squared deviations θ(r) – θ, where θ(r) is the same as θ but based on the data in the rth pseudoreplicated dataset only. But when there are a large number of imputed missing values (nonrespondents), treating the imputed values as observed data and applying the standard BRR variance estimation formula does not produce valid variance estimators. Intuitively, the variation due to imputation can be captured by the BRR method if every pseudoreplicated dataset is imputed in exactly the same way as the original dataset is imputed (assuming that the dataset contains flags for nonrespondents). But when a random imputation method (such as random hot deck imputation, random ratio imputation, or random regression imputation) is used, imputing every pseudoreplicated dataset requires the generation of many random variates and is computationally expensive. We propose an adjusted BRR variance estimator that is exactly the same as the BRR variance estimator obtained by imputing every pseudoreplicated dataset when a deterministic imputation method (e.g., ratio or regression imputation) is used. For random imputation methods, the proposed adjustment does not require the generation of additional random variates, but it still captures the variation due to imputation. Under a general stratified multistage sampling design, consistency of the adjusted BRR variance estimators for functions of estimated totals (smooth statistics) or for sample quantiles (nonsmooth statistics) is established. A simulation study shows that the adjusted BRR method works well and is much better than the unadjusted BRR method. An example with real data is also presented.

Balanced Repeated Replication for Stratified Multistage Survey Data under Imputation

Balanced repeated replication (BRR) is a popular method for variance estimation in surveys. The standard BRR method works by first creating a set of “balanced” pseudoreplicated datasets from the original dataset. For a survey estimator θ, the BRR variance estimator is the average of squared deviations θ(r) – θ, where θ(r) is the same as θ but based on the data in the rth pseudoreplicated dataset only. But when there are a large number of imputed missing values (nonrespondents), treating the imputed values as observed data and applying the standard BRR variance estimation formula does not produce valid variance estimators. Intuitively, the variation due to imputation can be captured by the BRR method if every pseudoreplicated dataset is imputed in exactly the same way as the original dataset is imputed (assuming that the dataset contains flags for nonrespondents). But when a random imputation method (such as random hot deck imputation, random ratio imputation, or random regression imputation) is used, imputing every pseudoreplicated dataset requires the generation of many random variates and is computationally expensive. We propose an adjusted BRR variance estimator that is exactly the same as the BRR variance estimator obtained by imputing every pseudoreplicated dataset when a deterministic imputation method (e.g., ratio or regression imputation) is used. For random imputation methods, the proposed adjustment does not require the generation of additional random variates, but it still captures the variation due to imputation. Under a general stratified multistage sampling design, consistency of the adjusted BRR variance estimators for functions of estimated totals (smooth statistics) or for sample quantiles (nonsmooth statistics) is established. A simulation study shows that the adjusted BRR method works well and is much better than the unadjusted BRR method. An example with real data is also presented.

Asymptotic Properties of the Balanced Repeated Replication Method for Sample Quantiles

Inference, including variance estimation, can be made from stratified samples by selecting a balanced set of subsamples. This balanced subsampling method is generically called the balanced repeated replication method in survey data analysis, which includes McCarthy's balanced half-samples method and its extensions for more general stratified designs. We establish the asymptotic consistency of the balanced repeated replication variance estimators when the parameter of interest is the population quantile. The consistency results also hold when balanced subsampling is replaced by random subsampling. As a key technical prerequisite, we prove a Bahadur-type representation for sample quantiles in stratified random sampling.

On the stability of some replication variance estimators in the linear case

Application of the balanced repeated replication method of variance estimation can become cumbersome as well as expensive when the number of replicates involved is large. While a number of replication methods of variance estimation requiring a reduced number of replicates have been proposed, the corresponding reduction in computational effort is accompanied by a loss in precision. In this article, this loss in precision is evaluated in the linear case. The results obtained may be useful in practice in balancing precision against computational cost.