Outcomes of Population Surveillance Data Collection Pilots and the Behavioral Risk Factor Surveillance System: What Happens in Texas.

Declining response rates and rising costs have prompted the search for alternatives to traditional random-digit dialing (RDD) interviews. In 2021, three Behavioral Risk Factor Surveillance System (BRFSS) pilots were conducted in Texas: data collection using an RDD short message service (RDD SMS) text-messaging push-to-web pilot, an address-based push-to-web pilot, and an internet panel pilot. We used data from the three pilots and from the concurrent Texas BRFSS Computer Assisted Telephone Interview (CATI). We compared unweighted data from these four sources to demographic information from the American Community Survey (ACS) for Texas, comparing respondents' health information across the protocols as well as cost and response rates. Non-Hispanic White adults and college graduates disproportionately responded in all survey protocols. Comparing costs across protocols was difficult due to the differences in methods and overhead, but some cost comparisons could be made. The cost per complete for BRFSS/CATI ranged from $75 to $100, compared with costs per complete for address-based sampling ($31 to $39), RDD SMS ($12 to $20), and internet panel (approximately $25). There were notable differences among survey protocols and the ACS in age, race/ethnicity, education, and marital status. We found minimal differences in respondents' answers to heart disease-related questions; however, responses to flu vaccination questions differed by protocol. Comparable responses were encouraging. Properly weighted web-based data collection may help use data collected by new protocols as a supplement to future BRFSS efforts.

Estimating vaccination coverage for routinely recommended vaccines among children aged 24 months and adolescents aged 13 through 17 years using data from immunization information systems in the United States.

OBJECTIVE: To use a model-based approach to estimate vaccination coverage of routinely recommended childhood and adolescent vaccines for the United States. METHODS: We used a hierarchical model with retrospective cohort data from eleven IIS jurisdictions, which contains vaccination records submitted by providers. Numerators included data from 2014 to 2019 at the county level for 2.4 million children at age 24 months and 14.4 million adolescents aged 13-17. Age-appropriate Census populations were used as denominators. Covariates associated with childhood and adolescent vaccinations were included in the model. Model-based estimates for each county were generated and aggregated to the national level to produce national vaccination coverage estimates and compared to National Immunization Survey (NIS) estimates of vaccination coverage. Trends of estimated vaccination coverage were compared between the model-based approach and NIS. RESULTS: From 2014 to 18, model-based national vaccination coverage estimates were within ten percentage points of NIS-Child vaccination coverage estimates for most vaccines among children at age 24 months. One notable difference was higher model-based vaccination coverage estimates for hepatitis B birth dose compared to NIS-Child coverage estimates. From 2014 to 19, model-based national vaccination coverage estimates were within ten percentage points of NIS-Teen vaccination coverage estimates for most vaccines among adolescents aged 13-17 years. Model-based vaccination coverage estimates were notably lower for varicella, MMR, and Hepatitis B compared to NIS-Teen coverage estimates among adolescents. Trends in estimates of national vaccination coverage were similar between model-based estimates for children and adolescents as compared to NIS-Child and NIS-Teen, respectively. CONCLUSIONS: A hierarchical model applied to data from IIS may be used to estimate coverage for routinely recommended vaccines among children and adolescents and allows for timely analyses of childhood and adolescent vaccines to quickly assess trends in vaccination coverage across the United States. Monitoring real-time vaccination coverage can help promote immunizations to protect children and adolescents against vaccine-preventable diseases.

Influenza Vaccinations During the COVID-19 Pandemic - 11 U.S. Jurisdictions, September-December 2020.

Influenza causes considerable morbidity and mortality in the United States. Between 2010 and 2020, an estimated 9-41 million cases resulted in 140,000-710,000 hospitalizations and 12,000-52,000 deaths annually (1). As the United States enters the 2021-22 influenza season, the potential impact of influenza illnesses is of concern given that influenza season will again coincide with the ongoing COVID-19 pandemic, which could further strain overburdened health care systems. The Advisory Committee on Immunization Practices (ACIP) recommends routine annual influenza vaccination for the 2021-22 influenza season for all persons aged ≥6 months who have no contraindications (2). To assess the potential impact of the COVID-19 pandemic on influenza vaccination coverage, the percentage change between administration of at least 1 dose of influenza vaccine during September-December 2020 was compared with the average administered in the corresponding periods in 2018 and 2019. The data analyzed were reported from 11 U.S. jurisdictions with high-performing state immunization information systems.* Overall, influenza vaccine administration was 9.0% higher in 2020 compared with the average in 2018 and 2019, combined. However, in 2020, the number of influenza vaccine doses administered to children aged 6-23 months and children aged 2-4 years, was 13.9% and 11.9% lower, respectively than the average for each age group in 2018 and 2019. Strategic efforts are needed to ensure high influenza vaccination coverage among all age groups, especially children aged 6 months-4 years who are not yet eligible to receive a COVID-19 vaccine. Administration of influenza vaccine and a COVID-19 vaccine among eligible populations is especially important to reduce the potential strain that influenza and COVID-19 cases could place on health care systems already overburdened by COVID-19.

Impact of the COVID-19 Pandemic on Administration of Selected Routine Childhood and Adolescent Vaccinations - 10 U.S. Jurisdictions, March-September 2020.

After the March 2020 declaration of the COVID-19 pandemic in the United States, an analysis of provider ordering data from the federally funded Vaccines for Children program found a substantial decrease in routine pediatric vaccine ordering (1), and data from New York City and Michigan indicated sharp declines in routine childhood vaccine administration in these areas (2,3). In November 2020, CDC interim guidance stated that routine vaccination of children and adolescents should remain an essential preventive service during the COVID-19 pandemic (4,5). To further understand the impact of the pandemic on routine childhood and adolescent vaccination, vaccine administration data during March-September 2020 from 10 U.S. jurisdictions with high-performing* immunization information systems were assessed. Fewer administered doses of routine childhood and adolescent vaccines were recorded in all 10 jurisdictions during March-September 2020 compared with those recorded during the same period in 2018 and 2019. The number of vaccine doses administered substantially declined during March-May 2020, when many jurisdictions enacted stay-at-home orders. After many jurisdictions lifted these orders, the number of vaccine doses administered during June-September 2020 approached prepandemic baseline levels, but did not increase to the level that would have been necessary to catch up children who did not receive routine vaccinations on time. This lag in catch-up vaccination might pose a serious public health threat that would result in vaccine-preventable disease outbreaks, especially in schools that have reopened for in-person learning. During the past few decades, the United States has achieved a substantial reduction in the prevalence of vaccine-preventable diseases driven in large part to the ongoing administration of routinely recommended pediatric vaccines. These efforts need to continue even during the COVID-19 pandemic to reduce the morbidity and mortality from vaccine-preventable diseases. Health care providers should assess the vaccination status of all pediatric patients, including adolescents, and contact those who are behind schedule to ensure that all children are fully vaccinated.

Frequency and cost of live vaccines administered too soon after prior live vaccine in children aged 12 months through 6 years, 2014-2017.

OBJECTIVE: To identify number of children who received live vaccines outside recommended intervals between doses and calculate corrective revaccination costs. METHODS: We analyzed >1.6 million vaccination records for children aged 12 months through 6 years from six immunization information system (IIS) Sentinel Sites from 2014-15 when live attenuated influenza vaccine (LAIV, FluMist® Quadrivalent) was recommended for use, and from 2016-17, when not recommended for use. Depending on the vaccine, insufficient intervals between live vaccine doses are less than 24 or 28 days from a preceding live vaccine dose. Private and public purchase costs of vaccines were used to determine revaccination costs of live vaccine doses administered during the live vaccine conflict interval. Measles, mumps, rubella (MMR), varicella, combined MMRV, and LAIV were live vaccines evaluated in this study. RESULTS: Among 946,659 children who received at least one live vaccine dose from 2014-15, 4,873 (0.5%) received at least one dose too soon after a prior live vaccine (revaccination cost, $786,413) with a median conflict interval of 16 days. Among 704,591 children who received at least one live vaccine dose from 2016-17, 1,001 (0.1%) received at least one dose too soon after a prior live vaccine (revaccination cost, $181,565) with a median conflict interval of 14 days. The live vaccine most frequently administered outside of the recommended intervals was LAIV from 2014-15, and varicella from 2016-17. CONCLUSIONS: Live vaccine interval errors were rare (0.5%), indicating an adherence to recommendations. If all invalid doses were corrected by revaccination over the two time periods, the cost within the IIS Sentinel Sites would be nearly one million dollars. Provider awareness about live vaccine conflicts, especially with LAIV, could prevent errors, and utilization of clinical decision support functionality within IISs and Electronic Health Record Systems can facilitate better vaccination practices.

Changes in diagnosed diabetes, obesity, and physical inactivity prevalence in US counties, 2004-2012.

Recent studies suggest that prevalence of diagnosed diabetes in the United States reached a plateau or slowed around 2008, and that this change coincided with obesity plateaus and increases in physical activity. However, national estimates can obscure important variations in geographic subgroups. We examine whether a slowing or leveling off in diagnosed diabetes, obesity, and leisure time physical inactivity prevalence is also evident across the 3143 counties of the United States. We used publicly available county estimates of the age-adjusted prevalence of diagnosed diabetes, obesity, and leisure-time physical inactivity, which were generated by the Centers for Disease Control and Prevention (CDC). Using a Bayesian multilevel regression that included random effects by county and year and applied cubic splines to smooth these estimates over time, we estimated the average annual percentage point change (APPC) from 2004 to 2008 and from 2008 to 2012 for diabetes, obesity, and physical inactivity prevalence in each county. Compared to 2004-2008, the median APPCs for diabetes, obesity, and physical inactivity were lower in 2008-2012 (diabetes APPC difference = 0.16, 95%CI 0.14, 0.18; obesity APPC difference = 0.65, 95%CI 0.59, 0.70; physical inactivity APPC difference = 0.43, 95%CI 0.37, 0.48). APPCs and APPC differences between time periods varied among counties and U.S. regions. Despite improvements, levels of these risk factors remained high with most counties merely slowing rather than reversing, which suggests that all counties would likely benefit from reductions in these risk factors. The diversity of trajectories in the prevalence of these risk factors across counties underscores the continued need to identify high risk areas and populations for preventive interventions. Awareness of how these factors are changing might assist local policy makers in targeting and tracking the impact of efforts to reduce diabetes, obesity and physical inactivity.

Changes in Disparity in County-Level Diagnosed Diabetes Prevalence and Incidence in the United States, between 2004 and 2012.

BACKGROUND: In recent decades, the United States experienced increasing prevalence and incidence of diabetes, accompanied by large disparities in county-level diabetes prevalence and incidence. However, whether these disparities are widening, narrowing, or staying the same has not been studied. We examined changes in disparity among U.S. counties in diagnosed diabetes prevalence and incidence between 2004 and 2012. METHODS: We used 2004 and 2012 county-level diabetes (type 1 and type 2) prevalence and incidence data, along with demographic, socio-economic, and risk factor data from various sources. To determine whether disparities widened or narrowed over the time period, we used a regression-based ß-convergence approach, accounting for spatial autocorrelation. We calculated diabetes prevalence/incidence percentage point (ppt) changes between 2004 and 2012 and modeled these changes as a function of baseline diabetes prevalence/incidence in 2004. Covariates included county-level demographic and, socio-economic data, and known type 2 diabetes risk factors (obesity and leisure-time physical inactivity). RESULTS: For each county-level ppt increase in diabetes prevalence in 2004 there was an annual average increase of 0.02 ppt (p<0.001) in diabetes prevalence between 2004 and 2012, indicating a widening of disparities. However, after accounting for covariates, diabetes prevalence decreased by an annual average of 0.04 ppt (p<0.001). In contrast, changes in diabetes incidence decreased by an average of 0.04 ppt (unadjusted) and 0.09 ppt (adjusted) for each ppt increase in diabetes incidence in 2004, indicating a narrowing of county-level disparities. CONCLUSIONS: County-level disparities in diagnosed diabetes prevalence in the United States widened between 2004 and 2012, while disparities in incidence narrowed. Accounting for demographic and, socio-economic characteristics and risk factors for type 2 diabetes narrowed the disparities, suggesting that these factors are strongly associated with changes in disparities. Public health interventions that target modifiable risk factors, such as obesity and physical inactivity, in high burden counties might further reduce disparities in incidence and, over time, in prevalence.

Diabetes Among Asians and Native Hawaiians or other Pacific Islanders--United States, 2011-2014.

Asians and Native Hawaiians or other Pacific Islanders (NHPIs) are fast-growing U.S. minority populations at high risk for type 2 diabetes. Although national studies have described diabetes prevalence, incidence, and risk factors among Asians and NHPIs compared with non-Hispanic whites, little is known about state-level diabetes prevalence among these two racial groups, or about how they differ from one another with respect to diabetes risk factors. To examine state-level prevalence of self-reported, physician-diagnosed (diagnosed) diabetes and risk factors among Asians and NHPIs aged ≥18 years, CDC analyzed data from the 2011-2014 Behavioral Risk Factor Surveillance System (BRFSS). Among five states and Guam with sufficient data about NHPIs for analysis, the age-adjusted diabetes prevalence estimate for NHPIs ranged from 13.4% (New York) to 19.1% (California). Among 32 states, the District of Columbia (DC), and Guam that had sufficient data about Asians for analysis, diabetes prevalence estimates for Asians ranged from 4.9% (Arizona) to 15.3% (New York). In the five states and Guam with sufficient NHPI data, NHPIs had a higher age-adjusted prevalence of diabetes than did Asians, and a higher proportion of NHPIs were overweight or obese and had less than a high school education compared with Asians. Effective interventions and policies might reduce the prevalence of diabetes in these growing, high-risk minority populations.

Geographic disparity of severe vision loss - United States, 2009-2013.

Vision loss and blindness are among the top 10 disabilities in the United States, causing substantial social, economic, and psychological effects, including increased morbidity, increased mortality, and decreased quality of life.* There are disparities in vision loss based on age, sex, race/ethnicity, socioeconomic status, and geographic location. Current surveillance activities using national and state surveys have characterized vision loss at national and state levels. However, there are limited data and research at local levels, where interventions and policy decisions to reduce the burden of vision loss and eliminate disparities are often developed and implemented. CDC analyzed data from the American Community Survey (ACS) to estimate county-level prevalence of severe vision loss (SVL) (being blind or having serious difficulty seeing even when wearing glasses) in the United States and to describe its geographic pattern and its association with poverty level. Distinct geographic patterns of SVL prevalence were found in the United States; 77.3% of counties in the top SVL prevalence quartile (≥4.2%) were located in the South. SVL was significantly correlated with poverty (r = 0.5); 437 counties were in the top quartiles for both SVL and poverty, and 83.1% of those counties were located in southern states. A better understanding of the underlying barriers and facilitators of access and use of eye care services at the local level is needed to enable the development of more effective interventions and policies, and to help planners and practitioners serve the growing population with and at risk for vision loss more efficiently.

Diabetes Interactive Atlas.

The Diabetes Interactive Atlas is a recently released Web-based collection of maps that allows users to view geographic patterns and examine trends in diabetes and its risk factors over time across the United States and within states. The atlas provides maps, tables, graphs, and motion charts that depict national, state, and county data. Large amounts of data can be viewed in various ways simultaneously. In this article, we describe the design and technical issues for developing the atlas and provide an overview of the atlas' maps and graphs. The Diabetes Interactive Atlas improves visualization of geographic patterns, highlights observation of trends, and demonstrates the concomitant geographic and temporal growth of diabetes and obesity.

Small area variation in diabetes prevalence in Puerto Rico.

OBJECTIVE: To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults ≥ 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. METHODS: A Bayesian multilevel model was fitted to the combined 2008-2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. RESULTS: The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%-18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. CONCLUSIONS: These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%-18.2%).

Small area variation in diabetes prevalence in Puerto Rico / Variación en un área pequeña de la prevalencia de la diabetes en Puerto Rico

OBJECTIVE: To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults > 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. METHODS: A Bayesian multilevel model was fitted to the combined 2008-2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. RESULTS: The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%-18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. CONCLUSIONS: These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%-18.2%).

OBJETIVO: Calcular la prevalencia en el año 2009 de casos con diagnóstico de diabetes en Puerto Rico en adultos de 20 años de edad o mayores, para conocer mejor su distribución geográfica con objeto de que los responsables políticos puedan encauzar más eficientemente los programas de prevención y control. MÉTODOS: Se ajustó un modelo multinivel bayesiano a la combinación de datos del Sistema de Vigilancia de Factores de Riesgo del Comportamiento 2008-2010 y del Censo de los Estados Unidos del 2009 para calcular la prevalencia de la diabetes en cada uno de los 78 municipios de Puerto Rico. RESULTADOS: El cálculo del valor medio no ajustado para todos los municipios fue de 14,3% (intervalo por municipio de 9,9 a 18,0%). La amplitud promedio de los intervalos de confianza fue de 6,2%. Hubo poca diferencia entre los cálculos ajustados y los no ajustados. CONCLUSIONES: Los valores obtenidos mediante estos cálculos correspondientes a 78 municipios fueron por término medio más elevados y mostraron menor variabilidad (es decir, el intervalo era más pequeño) que los cálculos anteriormente publicados sobre la prevalencia de la diabetes en todos los municipios de los Estados Unidos en el 2008 (media, 9,9%; intervalo de 3,0 a 18,2%).

Bayesian Small Area Estimates of Diabetes Incidence by United States County, 2009.

In the United States, diabetes is common and costly. Programs to prevent new cases of diabetes are often carried out at the level of the county, a unit of local government. Thus, efficient targeting of such programs requires county-level estimates of diabetes incidence-the fraction of the non-diabetic population who received their diagnosis of diabetes during the past 12 months. Previously, only estimates of prevalence-the overall fraction of population who have the disease-have been available at the county level. Counties with high prevalence might or might not be the same as counties with high incidence, due to spatial variation in mortality and relocation of persons with incident diabetes to another county. Existing methods cannot be used to estimate county-level diabetes incidence, because the fraction of the population who receive a diabetes diagnosis in any year is too small. Here, we extend previously developed methods of Bayesian small-area estimation of prevalence, using diffuse priors, to estimate diabetes incidence for all U.S. counties based on data from a survey designed to yield state-level estimates. We found high incidence in the southeastern United States, the Appalachian region, and in scattered counties throughout the western U.S. Our methods might be applicable in other circumstances in which all cases of a rare condition also must be cases of a more common condition (in this analysis, "newly diagnosed cases of diabetes" and "cases of diabetes"). If appropriate data are available, our methods can be used to estimate proportion of the population with the rare condition at greater geographic specificity than the data source was designed to provide.

State-specific synthetic estimates of health status groups among inactive older adults with self-reported diabetes, 2000-2009.

INTRODUCTION: Physical activity helps diabetic older adults who have physical impairments or comorbid conditions to control their disease. To enable state planners to select physical activity programs for these adults, we calculated synthetic state-specific estimates of inactive older adults with diabetes, categorized by defined health status groups. METHODS: Using data from the 2000 through 2009 National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS), we calculated synthetic state-specific estimates of inactive adults with diabetes who were aged 50 years or older for 5 mutually exclusive health status groups: 1) homebound, 2) frail (functional difficulty in walking one-fourth mile, climbing 10 steps, standing for 2 hours, and stooping, bending, and kneeling), 3) functionally impaired (difficulty in 1 to 3 of these functions), 4) having 1 or more comorbid conditions (with no functional impairments), and 5) healthy (no impairments or comorbid conditions). We combined NHIS regional proportions for the health status groups of inactive, older diabetic adults with BRFSS data of older diabetic adults to estimate state-specific proportions and totals. RESULTS: State-specific estimates of health status groups among all older adults ranged from 2.2% to 3.0% for homebound, 5.8% to 8.8% for frail, 20.1% to 26.1% for impaired, 34.9% to 43.7% for having comorbid conditions, and 4.0% to 6.9% for healthy; the remainder were older active diabetic adults. Except for the homebound, the percentages in these health status groups varied significantly by region and state. CONCLUSION: These state-specific estimates correspond to existing physical activity programs to match certain health status characteristics of groups and may be useful to program planners to meet the needs of inactive, older diabetic adults.

Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt.

BACKGROUND: The American "stroke belt" has contributed to the study of stroke. However, U.S. geographic patterns of diabetes have not been as specifically characterized. PURPOSE: This study identifies a geographically coherent region of the U.S. where the prevalence of diagnosed diabetes is especially high, called the "diabetes belt." METHODS: In 2010, data from the 2007 and 2008 Behavioral Risk Factor Surveillance System were combined with county-level diagnosed diabetes prevalence estimates. Counties in close proximity with an estimated prevalence of diagnosed diabetes ≥11.0% were considered to define the diabetes belt. Prevalence of risk factors in the diabetes belt was compared to that in the rest of the U.S. The fraction of the excess risk associated with living in the diabetes belt associated with selected risk factors, both modifiable (sedentary lifestyle, obesity) and nonmodifiable (age, gender, race/ethnicity, education), was calculated. RESULTS: A diabetes belt consisting of 644 counties in 15 mostly southern states was identified. People in the diabetes belt were more likely to be non-Hispanic African-American, lead a sedentary lifestyle, and be obese than in the rest of the U.S. Thirty percent of the excess risk was associated with modifiable risk factors, and 37% with nonmodifiable factors. CONCLUSIONS: Nearly one third of the difference in diabetes prevalence between the diabetes belt and the rest of the U.S. is associated with sedentary lifestyle and obesity. Culturally appropriate interventions aimed at decreasing obesity and sedentary lifestyle in counties within the diabetes belt should be considered.

Knowledge and perceived risk of tuberculosis: US racial and regional differences.

The purpose of this study is to report the findings of the 2004 National Health Interview Survey (NHIS) questions on tuberculosis (TB) knowledge and perceived risk of contracting TB. Tuberculosis (TB) continues to be a major health threat in the United States, but minimal effort is made on public education to increase knowledge about TB. Using data from the 2004 NHIS, this study examined knowledge and perceived risk of TB of 26,136 US respondents. Results showed that nationally, how much a respondent knew about tuberculosis, knowing someone with tuberculosis, being 18-34 years old, and being Black were most strongly associated with perceived high to medium risk of getting TB. Black respondents were nearly twice as likely to perceive a high to moderate risk compared to other races in the Northeast and South. Knowing someone with tuberculosis or having a lot or some knowledge of the disease was strongly associated with perceived risk in all regions of the nation. Conclusions were to increase efforts targeted toward broad health promotion education activities on TB risk.

Self-reported physical activity among South Carolina adults trying to maintain or lose weight.

OBJECTIVES: The obesity epidemic is related to widespread physical inactivity in the United States. This study determined the proportion of South Carolinians trying to maintain or lose weight and within that subpopulation, the number who practiced a restricted diet and engaged in physical activity. METHODS: Data from the 2002 South Carolina Behavioral Risk Factor Surveillance System survey were used to classify adults who were trying to maintain weight or lose weight. Self-reported prevalence of restricted diet and participation in physical activity were investigated. Of those who reported weight control practices, levels of physical activity were analyzed to determine if those trying to maintain weight or lose weight were meeting the national guidelines for moderate or vigorous physical activity. RESULTS: More than 70% of South Carolina adults reported trying to control their weight and the majority reported using physical activity for weight control. Though the majority reported use of restricted diet and physical activity, more than one-half of those individuals did not meet the minimum standards for physical activity designed for heart health. CONCLUSIONS: Although most adults who are trying to maintain or lose weight are participating in physical activity, public health efforts need to focus on encouraging these adults to increase their levels of physical activity to meet the minimum standards for health benefits. Health care providers have an opportunity to educate and encourage patients about the recommended levels of physical activity to obtain maximum health benefits.