Human papillomavirus vaccine coverage among females aged 11 to 17 in Texas counties: an application of multilevel, small area estimation

Human Papillomavirus Vaccination and Human Papillomavirus-Related Cancer Rates

Importance

To inform the design and implementation of targeted interventions to reduce the future burden of human papillomavirus (HPV)-related cancers in Texas, it is necessary to examine the county and health service region (HSR) levels of (1) the proportion of children and teenagers aged 9 to 17 years who initiated and were up to date for HPV vaccination series and (2) HPV-related cancer incidence rates (IRs).

Objective

To evaluate temporal trends and geospatial patterns of HPV vaccination initiation and up-to-date status as well as HPV-related cancer rates at county and HSR levels in Texas.

Design, Setting, and Participants

This population-based cross-sectional study used data from the Texas Immunization Registry, the National Cancer Institute's Surveillance, Epidemiology, and End Results Program database, and Texas Department of State Health Services annual population counts from 2006 to 2022. The analysis of HPV vaccination rates was conducted among children and teenagers aged 9 to 17 years; the analysis of HPV-related cancer rates was conducted among adults aged 20 years and older. Data were extracted between June and July 2023 and statistical analysis was performed from February to April 2024.

Main Outcomes and Measures

HPV vaccination initiation and up-to-date status rates and HPV-related cancer IR at county and HSR levels.

Results

A total of 32 270 243 children and teenagers (65.8% female individuals and 34.2% male individuals) and 22 490 105 individuals aged 20 years and older (50.7% female individuals and 49.3% male individuals) were included. The mean 2021 to 2022 county-level HPV vaccination series initiation estimates ranged from 6.3% to 69.1% for female and from 7.0% to 77.6% for male children and teenagers aged 9 to 17 years. County-level vaccination up-to-date estimates were generally lower compared with those of initiation estimates and ranged from 1.6% to 30.4% for female and from 2.1% to 34.8% for male children and teenagers. The pattern of HPV vaccination rates stratified by sex were similar across counties and HSRs. The age-adjusted annual HPV-related cancer IR by county for years 2016 to 2020 ranged from 0 to 154.2 per 100 000 for female individuals and from 0 to 60.1 per 100 000 for male individuals. The counties located in North Texas, HSRs 2/3 and 4/5N, had lower HPV vaccination rates and higher IRs of HPV-related cancers for both female and male individuals compared with other regions.

Conclusions and Relevance

In this study, the incidence of HPV-related cancers varied widely across the counties and HSRs of Texas. More counties in North Texas, HSRs 2/3 and 4/5N, had higher IRs of HPV-related cancers and a lower proportion of HPV vaccination rates than counties in other regions. Designing and implementing targeted interventions to increase uptake and completion of HPV vaccination series across counties with low HPV vaccination rates may help to reduce future the burden of HPV-related cancers.

Associations of geographic-based socioeconomic factors and HPV vaccination among male and female children in five US states

BACKGROUND

We assessed whether five geographic-based socioeconomic factors (medically underserved area (MUA); healthcare provider shortage area (HPSA); persistent poverty; persistent child poverty; and social vulnerability index (SVI)) were associated with the odds of HPV vaccination initiation, series completion, and parental vaccine hesitancy, and whether the observed relationships varied by gender of the child.

METHODS

An online panel service, administered through Qualtrics®, was used to recruit parents of adolescents 9-17 years of age to complete a one-time survey in 2021. Coverage of the panel included five US states: Arkansas, Mississippi, Missouri, Tennessee, and Southern Illinois. Generalized estimating equation (GEE) models were used to assess population-level associations between five geographic-based socioeconomic factors (MUA; HPSA; persistent poverty; persistent child poverty; and SVI) and three HPV vaccination outcomes (initiation, series completion, and hesitancy). All GEE models were adjusted for age of child and clustering at the state level.

RESULTS

Analyses were conducted using responses from 926 parents about their oldest child in the target age range (9-17 years). The analytic sample consisted of 471 male children and 438 female children across the five states. In adjusted GEE models, persistent child poverty and HPSA were negatively associated with HPV vaccination initiation and series completion among female children, respectively. Among male children, high social vulnerability was negatively associated with HPV vaccine series completion. Additionally, persistent poverty and high social vulnerability were negatively associated with HPV vaccine hesitancy in male children.

CONCLUSIONS

The results of this cross-sectional study suggest that geographic-based socioeconomic factors, particularly, HPSA, persistent poverty, and SVI, should be considered when implementing efforts to increase HPV vaccine coverage for adolescents. The approaches to targeting these geographic factors should also be evaluated in future studies to determine if they need to be tailored for male and female children.

Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child

The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

Understanding Prognostic Factors for Human Papillomavirus Vaccination: A Rural Community Case-Control Study

HPV vaccination coverage rates can vary depending on several factors. The main objective of this study is to identify possible independent prognostic factors that have an impact on HPV vaccination in a rural community, specifically related to sexual and reproductive health. A case-control, retrospective, community-based study was carried out on women aged 15 to 40 in the primary health centers of Southern Catalonia's Terres de l'Ebre region, Spain, from 1 January 2020 to 31 December 2022. A random sample of 520 women with an average age of 29.3 (SD 7.8) years old was included in the study. Independent prognostic factors: age OR 0.680 (95% CI: 0.635-0.729, p < 0.001), immigrant origin OR 0.215 (95% CI: 0.109-0.422, p < 0.001), and HPV PCR OR 7.402 (95% CI: 2.504-21.880, p < 0.001). The variables that showed a barrier effect for HPV vaccination were age (OR 0.680, 95% CI 0.635-0.729, p < 0.001), and immigrant origin (OR 0.215, 95% CI 0.109-0.422, p < 0.001). The variable that showed a facilitating effect for HPV vaccination was HPV PCR (OR 7.402, 95% CI 2.504-21.880, p < 0.001).

Human Papillomavirus Vaccination and Human Papillomavirus-Associated Cancer Rates Within Florida Counties

INTRODUCTION

To direct interventions, the Florida counties with the greatest risk of current and future human papillomavirus‒associated cancers were identified by estimating county-level (1) percentages of adolescents aged 13-17 years who initiated (≥1 dose) and were up to date (2-3 doses) for the human papillomavirus vaccine and (2) human papillomavirus‒associated cancer incidence rates.

METHODS

Records were obtained for human papillomavirus vaccinations from the Florida immunization registry (2006-2019), incident cancer cases from the Florida registry (2013-2017), and annual population counts from the Florida Department of Health (2006-2019). In 2020, annual county-level human papillomavirus vaccine initiation, human papillomavirus vaccine up-to-date, and age-adjusted human papillomavirus‒associated cancer incidence rates were estimated.

RESULTS

Among adolescents aged 13-17 years, average 2018-2019 county-specific human papillomavirus vaccine initiation ranged from 38% to 100% for females and from 34% to 96% for males. Up-to-date estimates ranged from 20% to 72% for females and from 24% to 77% for males. The majority (78%) of counties with initiation and up-to-date estimates within the lowest tercile were located in Northern Florida. County-specific 2013-2017 annualized, adjusted human papillomavirus‒associated cancer incidence rates ranged from 0 to 29.8 per 100,000 among females and from 5.4 to 24.1 per 100,000 among males. Counties within the highest tercile for human papillomavirus‒associated cancers were primarily (90% for females and 77% for males) located in Northern Florida.

CONCLUSIONS

Human papillomavirus‒associated cancer risk varies widely across Florida counties, with particularly high risk within Northern Florida. Targeting interventions toward counties with low vaccination and high cancer rates may reduce human papillomavirus‒associated cancers.

What is the place for space in epidemiology?

At the heart of spatial epidemiology is the need to describe and understand variation in population health. In this review and introduction to the themed issue on "Spatial Analysis and GIS in Epidemiology," we present theoretical foundations and methodological developments in spatial epidemiology, discuss spatial analytical techniques and their public health applications, and identify novel data sources and applications with the potential to make epidemiology more consequential. Challenges with using georeferenced data are also explored, including dealing with small sample sizes, missingness, generalizability, and geographic scale. Given the increasing availability of spatial data and visualization tools, we have an opportunity to overcome traditionally siloed fields and practice settings to advance knowledge and more appropriately respond to emerging public health crises.

Multilevel analysis in rural cancer control: A conceptual framework and methodological implications

Rural populations experience a myriad of cancer disparities ranging from lower screening rates to higher cancer mortality rates. These disparities are due in part to individual-level characteristics like age and insurance status, but the physical and social context of rural residence also plays a role. Our objective was two-fold: 1) to develop a multilevel conceptual framework describing how rural residence and relevant micro, macro, and supra-macro factors can be considered in evaluating disparities across the cancer control continuum and 2) to outline the unique considerations of multilevel statistical modeling in rural cancer research. We drew upon several formative frameworks that address the cancer control continuum, population-level disparities, access to health care services, and social inequities. Micro-level factors comprised individual-level characteristics that either predispose or enable individuals to utilize health care services or that may affect their cancer risk. Macro-level factors included social context (e.g. domains of social inequity) and physical context (e.g. access to care). Rural-urban status was considered a macro-level construct spanning both social and physical context, as "rural" is often characterized by sociodemographic characteristics and distance to health care services. Supra-macro-level factors included policies and systems (e.g. public health policies) that may affect cancer disparities. Our conceptual framework can guide researchers in conceptualizing multilevel statistical models to evaluate the independent contributions of rural-urban status on cancer while accounting for important micro, macro, and supra-macro factors. Statistically, potential collinearity of multilevel model predictive variables, model structure, and spatial dependence should also be considered.

Small area estimation of human papillomavirus vaccination coverage among school-age children in Alabama counties

OBJECTIVES

This study estimated county-level human papillomavirus (HPV) vaccination initiation rates in Alabama and determined whether disparities existed between counties in the Mississippi Delta region (MDR) and Appalachian region (AR).

STUDY DESIGN

This study used an observational cross-sectional design.

METHODS

We used small area estimation methodology to estimate rates of medical provider-verified HPV vaccine initiation among school-age children in Alabama. Data for the study were retrieved from the 2015 National Immunization Survey (n = 22,205) and the US Census Bureau.

RESULTS

The predictive model results showed that older age (odds ratio [OR] = 1.22, 95% confidence interval [CI] = 1.16, 1.29) was positively associated with vaccination initiation and black (OR = 0.79, 95% CI = 0.71, 0.87), white (OR = 0.56, 95% CI = 0.52, 0.60), and 'other' race/ethnicities (OR = 0.78, 95% CI = 0.70, 0.86), compared with Hispanics, and was negatively associated with vaccination initiation. The median (x̃)-modeled HPV vaccination initiation rate for all Alabama counties was 50.83% (interquartile range = 5.00%). Modeled HPV vaccination initiation rates were lowest in AR counties (x̃ = 49.81%), followed by counties not in the AR or MDR (x̃ = 53.26%) and MDR counties (x̃ = 54.90%).

CONCLUSIONS

Culturally sensitive school-based HPV vaccine delivery programs are needed for children living in AR counties in Alabama.

Are we speaking the same language? an argument for the consistent use of terminology and definitions for childhood vaccination indicators

Vaccination indicators are used to measure the health status of individuals or populations and to evaluate the effectiveness of vaccination programs or policies. Ensuring that vaccination indicators are clearly and consistently defined is important for effective communication of outcomes, accurate program evaluation, and comparison between different populations, times, and contexts. The purpose of this commentary is to describe commonly used vaccination indicators and to highlight inconsistencies in how childhood vaccine researchers use and define these terms. The indicators we describe are vaccine coverage, uptake, and rate; vaccination status, initiation, and completion; and up-to-date, timely, partial, and incomplete vaccination. We conclude that many vaccination indicators are not explicitly defined within published research studies and/or are used quite differently across studies. We also note that the choice of indicator in a given study is often driven by program or vaccine specific factors, may be constrained by data availability, and should be chosen to best reflect the outcome of interest. We conclude that the use of consistent language and definitions would promote more effective communication of research findings. We also propose some standardized definitions for common indicators, with the goal of provoking discussion and debate on the issue.

Impact of Pharmacists on Access to Vaccine Providers: A Geospatial Analysis

Policy Points: Policymakers in the United States should consider expanding pharmacy practice laws to allow pharmacists to vaccinate adolescents as a way to improve geographic access to adolescent vaccines, particularly for human papillomavirus (HPV) vaccine, which has low uptake. Our state-level analysis showed that pharmacists are more geographically dispersed than primary care physicians in the US state of Texas. Including pharmacists among available adolescent vaccine providers would improve the geographic distribution of vaccine providers, especially in areas with an inadequate number of primary care physicians.

CONTEXT

The largest disparities in human papillomavirus (HPV) vaccination in the United States are due to geography. One potential way of addressing these disparities is by improving geographic access to HPV vaccination. Two federal panels have recommended including community pharmacists as HPV vaccine providers as a strategy to improve opportunities for HPV vaccination for adolescents. We sought to evaluate whether community pharmacists can improve the number of vaccine providers in areas with primary care physician shortages in the US state of Texas.

METHODS

We gathered publicly available physician and pharmacist 2016 workforce data from the Texas Medical Board and Board of Pharmacy. We conducted geospatial analysis of census tracts to analyze the distribution of physicians and pharmacists and how pharmacists change vaccine provider coverage across the state.

FINDINGS

Census tracts with high numbers of physicians per capita tended to be located near one another, in 5 of 5 analyses of Moran's I (median = .04). In contrast, pharmacist rates were not spatially dependent on census tract in any of our analyses. If pharmacists were added to primary care physicians as vaccine providers, 35% of urban census tracts that previously had inadequate coverage would be adequately covered, while 18% of inadequately covered rural census tracts would become adequately covered. Overall, when pharmacists were included with primary care physicians as vaccine providers, vaccine providers per capita increased in 2,413 of the 4,508 urban census tracts (54%), while the rate increased in 223 of 746 rural census tracts (30%).

CONCLUSIONS

Pharmacists are more geographically dispersed across census tracts than primary care physicians. As a result, adding pharmacists to the workforce would increase the availability of vaccine providers in areas with inadequate primary care provider coverage.

Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013

Introduction

Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract–level prevalence estimates of 27 measures for the 500 largest US cities.

Methods

To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code–level estimates for the city of Boston, Massachusetts.

Results

By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators.

Conclusion

Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

Advances in spatial epidemiology and geographic information systems

The field of spatial epidemiology has evolved rapidly in the past 2 decades. This study serves as a brief introduction to spatial epidemiology and the use of geographic information systems in applied research in epidemiology. We highlight technical developments and highlight opportunities to apply spatial analytic methods in epidemiologic research, focusing on methodologies involving geocoding, distance estimation, residential mobility, record linkage and data integration, spatial and spatio-temporal clustering, small area estimation, and Bayesian applications to disease mapping. The articles included in this issue incorporate many of these methods into their study designs and analytical frameworks. It is our hope that these studies will spur further development and utilization of spatial analysis and geographic information systems in epidemiologic research.

Generating Health Estimates by Zip Code: A Semiparametric Small Area Estimation Approach Using the California Health Interview Survey

OBJECTIVES

We propose a method to meet challenges in generating health estimates for granular geographic areas in which the survey sample size is extremely small.

METHODS

Our generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model's feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, we demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for Zip Code Tabulation Areas (ZCTAs).

RESULTS

Our method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California. For some areas with great socioeconomic diversity across adjacent neighborhoods, such as Los Angeles County, the modeled uninsured estimates revealed much heterogeneity among geographically adjacent ZCTAs.

CONCLUSIONS

The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.

Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.

'In general, how do you feel today?'--self-rated health in the context of aging in India

This thesis is centered on self-rated health (SRH) as an outcome measure, as a predictor, and as a marker. The thesis uses primary data from the WHO Study on global AGEing and adult health (SAGE) implemented in India in 2007. The structural equation modeling approach is employed to understand the pathways through which the social environment, disability, disease, and sociodemographic characteristics influence SRH among older adults aged 50 years and above. Cox proportional hazard model is used to explore the role of SRH as a predictor for mortality and the role of disability in modifying this effect. The hierarchical ordered probit modeling approach, which combines information from anchoring vignettes with SRH, was used to address the long overlooked methodological concern of interpersonal incomparability. Finally, multilevel model-based small area estimation techniques were used to demonstrate the use of large national surveys and census information to derive precise SRH prevalence estimates at the district and sub-district level. The thesis advocates the use of such a simple measure to identify vulnerable communities for targeted health interventions, to plan and prioritize resource allocation, and to evaluate health interventions in resource-scarce settings. The thesis provides the basis and impetus to generate and integrate similar and harmonized adult health and aging data platforms within demographic surveillance systems in different regions of India and elsewhere.

Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system

A variety of small-area statistical models have been developed for health surveys, but none are sufficiently flexible to generate small-area estimates (SAEs) to meet data needs at different geographic levels. We developed a multilevel logistic model with both state- and nested county-level random effects for chronic obstructive pulmonary disease (COPD) using 2011 data from the Behavioral Risk Factor Surveillance System. We applied poststratification with the (decennial) US Census 2010 counts of census-block population to generate census-block-level SAEs of COPD prevalence which could be conveniently aggregated to all other census geographic units, such as census tracts, counties, and congressional districts. The model-based SAEs and direct survey estimates of COPD prevalence were quite consistent at both the county and state levels. The Pearson correlation coefficient was 0.99 at the state level and ranged from 0.88 to 0.95 at the county level. Our extended multilevel regression modeling and poststratification approach could be adapted for other geocoded national health surveys to generate reliable SAEs for population health outcomes at all administrative and legislative geographic levels of interest in a scalable framework.

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.

Application of the Carolina Framework for Cervical Cancer Prevention

OBJECTIVE

The Carolina Framework for Cervical Cancer Prevention describes 4 main causes of cervical cancer incidence: human papillomavirus (HPV) infection, lack of screening, screening errors, and not receiving follow-up care. We present 2 applications of the Carolina Framework in which we identify high-need counties in North Carolina and generate recommendations for improving prevention efforts.

METHODS

We created a cervical cancer prevention need index (CCPNI) that ranked counties on cervical cancer mortality, HPV vaccine initiation and completion, Pap smear screening, and provision of Pap tests to rarely- or never-screened women. In addition, we conducted in-depth interviews with 19 key informants from programs and agencies involved in cervical cancer prevention in North Carolina.

RESULTS

North Carolina's 100 counties varied widely on individual CCPNI components, including annual cervical cancer mortality (median 2.7/100,000 women; range 0.0-8.0), adolescent girls' HPV vaccine initiation (median 42%; range 15%-62%), and Pap testing in the previous 3 years among Medicaid-insured adult women (median 59%; range 40%-83%). Counties with the greatest prevention needs formed 2 distinct clusters in the northeast and south-central regions of the state. Interviews generated 9 recommendations to improve cervical cancer prevention in North Carolina, identifying applications to specific programs and policies in the state.

CONCLUSIONS

This study found striking geographic disparities in cervical cancer prevention need in North Carolina. Future prevention efforts in the state should prioritize high-need regions as well as recommended strategies and applications in existing programs. Other states can use the Carolina Framework to increase the impact of their cervical cancer prevention efforts.

County-level estimates of human papillomavirus vaccine coverage among young adult women in Texas, 2008

PURPOSE

The purpose of this study is to describe the county-level geographic distribution of human papillomavirus (HPV) vaccine coverage among young women aged 18-26 in Texas using multilevel, small area estimation.

METHODS

Multilevel (individual, county, public health region) random-intercept logit models were fit to HPV vaccination data (receipt of ≥ 1 dose Gardasil®) from the 2008 Behavioral Risk Factor Surveillance System and a number of secondary sources. Using the parameters from the final model, we simulated HPV vaccine coverage in each county.

RESULTS

Indirect county-level estimates ranged from 1.9-23.8%, with a weighted state average of 11.4%. The counties with the highest and lowest coverage estimates were Orange County, TX and Webb County, TX respectively. Significant correlations were observed between HPV vaccination and age, Hispanic ethnicity, and the percentage of uninsured at the county and public health region levels.

CONCLUSIONS

Small area analyses have been used in a variety of settings to assess a variety of health outcomes, and as shown in this study, can be used to highlight geographic disparities and opportunities for intervention in HPV vaccine coverage.