BACKGROUND: In 2012, the U.S. Preventive Services Task Force (USPSTF) recommended against prostate cancer screening using the prostate-specific antigen (PSA) test for all age groups. In 2018 the USPSTF's recommendation shifted from a "D" (not recommended) to a "C" (selectively offering PSA-based screening based on professional judgment and patient preferences) in men ages 55-69. Limited reliable county-level prostate cancer screening data is available for cancer surveillance purposes. METHODS: Utilizing data from the National Health Interview Survey (NHIS) and Behavioral Risk Factor Surveillance System (BRFSS) collected in 2012-2019, state- and county-level small area models were developed for estimating PSA testing. Model diagnosis, internal validation, and external validation examining associations of PSA testing and prostate cancer incidence were conducted. RESULTS: Model-based estimates of PSA testing rate were produced for all U.S. states and 3,142 counties for two data periods: 2012-2016 and 2018-2019. Geographic variations across counties were demonstrated through maps. Moderate positive correlations between PSA-based screening and prostate cancer incidence were observed, for example, the state-level weighted Pearson's correlation coefficients were 0.5025 (p-value=0.0002) and 0.3691 (p-value=0.0077) for 2012-2016 and 2018-2019, respectively. CONCLUSIONS: These modeled estimates showed improved precision and adjusted for the differences between BRFSS and NHIS. The approach of combining NHIS and BRFSS utilized strengths of the larger sample size of BRFSS and generally higher response rates and better household coverage from the NHIS. IMPACT: The resulting small area estimates offer a valuable resource for the cancer surveillance community, aiding in targeted interventions, decision-making, and further research endeavors.

While web surveys have become increasingly popular as a method of data collection, there is concern that estimates obtained from web surveys may not reflect the target population of interest. Web survey estimates can be calibrated to existing national surveys using a propensity score adjustment, although requirements for the size and collection timeline of the reference data set have not been investigated. We evaluate health outcomes estimates from the National Center for Health Statistics' Research and Development web survey. In our study, the 2016 National Health Interview Survey as well as its quarterly subsets are considered as reference datasets for the web data. It is demonstrated that the calibrated health estimates overall vary little when using the quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This finding has many practical implications for constructing reference data, including the reduced cost and burden of a smaller sample size and a more flexible timeline.

BACKGROUND: The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3(rd) and 97(th) and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95(th) percentile. Extrapolating beyond the 97(th) percentile is not recommended and leads to compressed z-score values. AIM: This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95(th) percentile using an extended reference population. SUBJECTS AND METHODS: Data from youth at or above the 95(th) percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. RESULTS: The modelled distributions above the 95(th) percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. CONCLUSION: This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

Life expectancy is an important measure for health research and policymaking. Linking individual survey records to mortality data can overcome limitations in vital statistics data used to examine differential mortality by permitting the construction of death rates based on information collected from respondents at the time of interview and facilitating estimation of life expectancies for subgroups of interest. However, use of complex survey data linked to mortality data can complicate the estimation of standard errors. This paper presents a case study of approaches to variance estimation for life expectancies based on life tables, using the National Health Interview Survey Linked Mortality Files. The approaches considered include application of Chiang's traditional method, which is straightforward but does not account for the complex design features of the data; balanced repeated replication (BRR), which is more complicated but accounts more fully for the design features; and compromise, 'hybrid' approaches, which can be less difficult to implement than BRR but still account partially for the design features. Two tentative conclusions are drawn. First, it is important to account for the effects of the complex sample design, at least within life-table age intervals. Second, accounting for the effects within age intervals but not across age intervals, as is done by the hybrid methods, can yield reasonably accurate estimates of standard errors, especially for subgroups of interest with more homogeneous characteristics among their members. Published in 2011 by John Wiley & Sons, Ltd.

OBJECTIVES: We compared national and state-based estimates for the prevalence of mammography screening from the National Health Interview Survey (NHIS), the Behavioral Risk Factor Surveillance System (BRFSS), and a model-based approach that combines information from the two surveys. METHODS: At the state and national levels, we compared the three estimates of prevalence for two time periods (1997-1999 and 2000-2003) and the estimated difference between the periods. We included state-level covariates in the model-based approach through principal components. RESULTS: The national mammography screening prevalence estimate based on the BRFSS was substantially larger than the NHIS estimate for both time periods. This difference may have been due to nonresponse and noncoverage biases, response mode (telephone vs. in-person) differences, or other factors. However, the estimated change between the two periods was similar for the two surveys. Consistent with the model assumptions, the model-based estimates were more similar to the NHIS estimates than to the BRFSS prevalence estimates. The state-level covariates (through the principal components) were shown to be related to the mammography prevalence with the expected positive relationship for socioeconomic status and urbanicity. In addition, several principal components were significantly related to the difference between NHIS and BRFSS telephone prevalence estimates. CONCLUSIONS: Model-based estimates, based on information from the two surveys, are useful tools in representing combined information about mammography prevalence estimates from the two surveys. The model-based approach adjusts for the possible nonresponse and noncoverage biases of the telephone survey while using the large BRFSS state sample size to increase precision.