Trends in COVID-19 Cases, Emergency Department Visits, and Hospital Admissions Among Children and Adolescents Aged 0-17 Years - United States, August 2020-August 2021.

Although COVID-19 generally results in milder disease in children and adolescents than in adults, severe illness from COVID-19 can occur in children and adolescents and might require hospitalization and intensive care unit (ICU) support (1-3). It is not known whether the B.1.617.2 (Delta) variant,* which has been the predominant variant of SARS-CoV-2 (the virus that causes COVID-19) in the United States since late June 2021,† causes different clinical outcomes in children and adolescents compared with variants that circulated earlier. To assess trends among children and adolescents, CDC analyzed new COVID-19 cases, emergency department (ED) visits with a COVID-19 diagnosis code, and hospital admissions of patients with confirmed COVID-19 among persons aged 0-17 years during August 1, 2020-August 27, 2021. Since July 2021, after Delta had become the predominant circulating variant, the rate of new COVID-19 cases and COVID-19-related ED visits increased for persons aged 0-4, 5-11, and 12-17 years, and hospital admissions of patients with confirmed COVID-19 increased for persons aged 0-17 years. Among persons aged 0-17 years during the most recent 2-week period (August 14-27, 2021), COVID-19-related ED visits and hospital admissions in the states with the lowest vaccination coverage were 3.4 and 3.7 times that in the states with the highest vaccination coverage, respectively. At selected hospitals, the proportion of COVID-19 patients aged 0-17 years who were admitted to an ICU ranged from 10% to 25% during August 2020-June 2021 and was 20% and 18% during July and August 2021, respectively. Broad, community-wide vaccination of all eligible persons is a critical component of mitigation strategies to protect pediatric populations from SARS-CoV-2 infection and severe COVID-19 illness.

Prevalence of dementia subtypes in United States Medicare fee-for-service beneficiaries, 2011-2013.

INTRODUCTION: Rapid growth of the older adult population requires greater epidemiologic characterization of dementia. We developed national prevalence estimates of diagnosed dementia and subtypes in the highest risk United States (US) population. METHODS: We analyzed Centers for Medicare & Medicaid administrative enrollment and claims data for 100% of Medicare fee-for-service beneficiaries enrolled during 2011-2013 and age ≥68 years as of December 31, 2013 (n = 21.6 million). RESULTS: Over 3.1 million (14.4%) beneficiaries had a claim for a service and/or treatment for any dementia subtype. Dementia not otherwise specified was the most common diagnosis (present in 92.9%). The most common subtype was Alzheimer's (43.5%), followed by vascular (14.5%), Lewy body (5.4%), frontotemporal (1.0%), and alcohol induced (0.7%). The prevalence of other types of diagnosed dementia was 0.2%. DISCUSSION: This study is the first to document concurrent prevalence of primary dementia subtypes among this US population. The findings can assist in prioritizing dementia research, clinical services, and caregiving resources.

Medicare claims versus beneficiary self-report for influenza vaccination surveillance.

BACKGROUND: Although self-reported influenza vaccination status is routinely used in surveillance to estimate influenza vaccine coverage, Medicare data are becoming a promising resource for influenza surveillance to inform vaccination program management and planning. PURPOSE: To evaluate the concordance between self-reported influenza vaccination and influenza vaccination claims among Medicare beneficiaries. METHODS: This study compared influenza vaccination based upon Medicare claims and self-report among a sample of Medicare beneficiaries (N=9,378) from the 2011 Medicare Current Beneficiary Survey, which was the most recent year of data at the time of analysis (summer 2013). Sensitivity, specificity, positive predictive value, and negative predictive value were calculated using self-reported data as the referent standard. Logistic regression was used to compute the marginal mean proportions for whether a Medicare influenza vaccination claim was present among beneficiaries who reported receiving the vaccination. RESULTS: Influenza vaccination was higher for self-report (69.4%) than Medicare claims (48.3%). For Medicare claims, sensitivity=67.5%, specificity=96.3%, positive predictive value=97.6%, and negative predictive value=56.7%. Among beneficiaries reporting receiving an influenza vaccination, the percentage of beneficiaries with a vaccination claim was lower for beneficiaries who were aged <65 years, male, non-Hispanic black or Hispanic, and had less than a college education. CONCLUSIONS: The classification of influenza vaccination status for Medicare beneficiaries can differ based upon survey and claims. To improve Medicare claims-based surveillance studies, further research is needed to determine the sources of discordance in self-reported and Medicare claims data, specifically for sensitivity and negative predictive value.

Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010.

INTRODUCTION: The increase in chronic health conditions among Medicare beneficiaries has implications for the Medicare system. The objective of this study was to use the US Department of Health and Human Services Strategic Framework on multiple chronic conditions as a basis to examine the prevalence of multiple chronic conditions among Medicare beneficiaries. ANALYSIS: We analyzed Centers for Medicare and Medicaid Services administrative claims data for Medicare beneficiaries enrolled in the fee-for-service program in 2010. We included approximately 31 million Medicare beneficiaries and examined 15 chronic conditions. A beneficiary was considered to have a chronic condition if a Medicare claim indicated that the beneficiary received a service or treatment for the condition. We defined the prevalence of multiple chronic conditions as having 2 or more chronic conditions. RESULTS: Overall, 68.4% of Medicare beneficiaries had 2 or more chronic conditions and 36.4% had 4 or more chronic conditions. The prevalence of multiple chronic conditions increased with age and was more prevalent among women than men across all age groups. Non-Hispanic black and Hispanic women had the highest prevalence of 4 or more chronic conditions, whereas Asian or Pacific Islander men and women, in general, had the lowest. SUMMARY: The prevalence of multiple chronic conditions among the Medicare fee-for-service population varies across demographic groups. Multiple chronic conditions appear to be more prevalent among women, particularly non-Hispanic black and Hispanic women, and among beneficiaries eligible for both Medicare and Medicaid benefits. Our findings can help public health researchers target prevention and management strategies to improve care and reduce costs for people with multiple chronic conditions.

Multiple chronic conditions among Medicare beneficiaries: state-level variations in prevalence, utilization, and cost, 2011.

OBJECTIVES: Individuals with multiple (>2) chronic conditions (MCC) present many challenges to the health care system, such as effective coordination of care and cost containment. To assist health policy makers and to fill research gaps on MCC, we describe state-level variation of MCC among Medicare beneficiaries, with a focus on those with six or more conditions. METHODS: Using Centers for Medicare & Medicaid Services administrative data for 2011, we characterized a beneficiary as having MCC by counting the number of conditions from a set of fifteen conditions, which were identified using diagnosis codes on the claims. The study population included fee-for-service beneficiaries residing in the 50 U.S. states and Washington, DC. RESULTS: Among beneficiaries with six or more chronic conditions, prevalence rates were lowest in Alaska and Wyoming (7%) and highest in Florida and New Jersey (18%); readmission rates were lowest in Utah (19%) and highest in Washington, DC (31%); the number of emergency department visits per beneficiary were lowest in New York and Florida (1.6) and highest in Washington, DC (2.7); and Medicare spending per beneficiary was lowest in Hawaii ($24,086) and highest in Maryland, Washington, DC, and Louisiana (over $37,000). CONCLUSION: These findings expand upon prior research on MCC among Medicare beneficiaries at the national level and demonstrate considerable state-level variation in the prevalence, health care utilization, and Medicare spending for beneficiaries with MCC. State-level data on MCC is important for decision making aimed at improved program planning, financing, and delivery of care for individuals with MCC.

Estimating standard errors for life expectancies based on complex survey data with mortality follow-up: A case study using the National Health Interview Survey Linked Mortality Files.

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.

Flu shots and the characteristics of unvaccinated elderly Medicare beneficiaries.

KEY FINDINGS: Data from the Medicare Current Beneficiary Survey, 2009. • Overall, 73% of Medicare beneficiaries aged 65 years and older reported receiving a flu shot for the 2008 flu season, but vaccination rates varied by socio-demographic characteristics. Flu vaccination was lowest for beneficiaries aged 65-74 years old, who were non-Hispanic Blacks and Hispanics, were not married, had less than a high school education, or who were eligible for Medicaid (i.e., dual eligibles). • Healthcare utilization and personal health behavior were also related to vaccination rates, with current smokers and those with no hospitalizations or physician visits being less likely to be vaccinated. • Among those beneficiaries who reported receiving a flu shot, 59% received it in a physician's office or clinic, with the next most common setting being in the community (21%); e.g., grocery store, shopping mall, library, or church. • Among those beneficiaries who did not receive a flu shot, the most common reasons were beliefs that the shot could cause side effects or disease (20%), that they didn't think the shot could prevent the flu (17%), or that the shot wasn't needed (16%). Less than 1% reported that they didn't get the flu shot because of cost. Elderly persons (aged 65 years and older) are at increased risk of complications from influenza, with the majority of influenza-related hospitalizations and deaths occurring among the elderly (Fiore et al., 2010). Most physicians recommend their elderly patients get a flu shot each year, and many hospitals inquire about elderly patient's immunization status upon admission, providing a vaccination if requested. The importance of getting a flu shot is underscored by the Department of Health and Human Services' Healthy People initiative, which has set a vaccination goal of 90% for the Nation's elderly by the year 2020 (Department of Health and Human Services [DHHS], 2011). Although all costs related to flu shots are covered by Medicare, requiring no co-pay on the part of the beneficiary (Centers for Medicare and Medicaid Services, 2011), for the 2008 flu season, only 73% of non-institutionalized Medicare beneficiaries, aged 65 years and older, reported receiving one. This report presents the most recent data on flu vaccination rates among non-institutionalized elderly Medicare beneficiaries and their association with socio-demographic and personal health characteristics. The report also describes the places beneficiaries received their flu shot and, for those not getting vaccinated, the reasons reported for not doing so.

Socioeconomic status and risk of diabetes-related mortality in the U.S.

OBJECTIVE: We examined disparities in diabetes-related mortality for socioeconomic status (SES) groups in nationally representative U.S. samples. METHODS: We analyzed National Health Interview Survey respondents linked to their death records and included those eligible for mortality follow-up who were aged 25 years and older at the time of interview and not missing information on covariates (n=527,426). We measured SES by education and family income. There were 5,613 diabetes-related deaths. RESULTS: Having less than a high school education was associated with a twofold higher mortality from diabetes, after controlling for age, gender, race/ethnicity, marital status, and body mass index, compared with adults with a college degree or higher education level (relative hazard [RH] = 2.05, 95% confidence interval [CI] 1.78, 2.35). Having a family income below poverty level was associated with a twofold higher mortality after adjustments compared with adults with the highest family incomes (RH=2.41, 95% CI 2.05, 2.84). Approximately one-quarter of the excess risk among those in the lowest SES categories was explained by adjusting for potential confounders. CONCLUSION: Findings from this nationally representative cohort demonstrate a socioeconomic gradient in diabetes-related mortality, with both education and income being important determinants of the risk of death.

Mortality experience of the 1986-2000 National Health Interview Survey Linked Mortality Files participants.

The National Center for Health Statistics (NCHS) has produced the 1986-2000 National Health Interview Survey (NHIS) Linked Mortality Files by linking eligible adults in the 1986-2000 NHIS cohorts through probabilistic record linkage to the National Death Index to obtain mortality follow-up through December 31, 2002. The resulting files contain more than 120,000 deaths and an average of 9 years of survival time. To assess how well mortality was ascertained in the linked mortality files, NCHS has conducted a comparison of the mortality experience of the 1986-2000 NHIS cohorts with that of the U.S. population. This report presents the results of this comparative mortality assessment. Methods The survival of each annual NHIS cohort was compared with that of the U.S. population during the same period. Cumulative survival probabilities for each annual NHIS cohort were derived using the Kaplan-Meier product limit method, and corresponding cumulative survival probabilities were computed for the U.S. population using information from annual U.S. life tables. The survival probabilities were calculated at various lengths of follow-up for each age-race-sex group of each NHIS cohort and for the U.S. population. Results As expected, mortality tended to be underestimated in the NHIS cohorts because the sample includes only civilian, noninstitutionalized persons, but this underestimation generally was not statistically significant. Statistically significant differences increased with length of follow-up, occurred more often for white females than for the other race-sex groups, and occurred more often in the oldest age groups. In general, the survival experience of the age-race-sex groups of each NHIS cohort corresponds quite closely to that of the U.S. population, providing support that the ascertainment of mortality through the probabilistic record linkage accurately reflects the mortality experience of the NHIS cohorts.

The public-use National Health Interview Survey linked mortality files: methods of reidentification risk avoidance and comparative analysis.

The National Center for Health Statistics (NCHS) conducts mortality follow-up for its major population-based surveys. In 2004, NCHS updated the mortality follow-up for the 1986-2000 National Health Interview Survey (NHIS) years, which because of confidentiality protections was made available only through the NCHS Research Data Center. In 2007, NCHS released a public-use version of the NHIS Linked Mortality Files that includes a limited amount of perturbed information for decedents. The modification of the public-use version included conducting a reidentification risk scenario to determine records at risk for reidentification and then imputing values for either date or cause of death for a select sample of records. To demonstrate the comparability between the public-use and restricted-use versions of the linked mortality files, the authors estimated relative hazards for all-cause and cause-specific mortality risk using a Cox proportional hazards model. The pooled 1986-2000 NHIS Linked Mortality Files contain 1,576,171 records and 120,765 deaths. The sample for the comparative analyses included 897,232 records and 114,264 deaths. The comparative analyses show that the two data files yield very similar results for both all-cause and cause-specific mortality. Analytical considerations when examining cause-specific analyses of numerically small demographic subgroups are addressed.

Neighborhood differences in social capital: a compositional artifact or a contextual construct?

Assessment of social capital at the neighborhood level is often based on aggregating individual perceptions of trust and reciprocity. Individual perceptions, meanwhile, are influenced through a range of individual attributes. This paper examines the socioeconomic and demographic attributes that systematically correlate with individual perception of social capital and determines the extent to which such attributes account for neighborhood differences in social capital. Using improved multilevel modeling procedures, we ascertain the extent to which differences in social capital perception can be ascribed to true neighborhood-level variations. The analysis is based on the 1994-95 Community Survey of the Project on Human Development in Chicago Neighborhoods (PHDCN). The response measure is based on survey respondent's perceptions of whether people in their neighborhood can be trusted. The results suggest that even after accounting for individual demographic (age, sex, race, marital status) and socioeconomic characteristics (income, education), significant neighborhood differences remain in individual perceptions of trust, substantiating the notion of social capital as a true contextual construct.

Social capital and neighborhood mortality rates in Chicago.

Several empirical studies have suggested that neighborhood characteristics influence health, with most studies having focused on neighborhood deprivation or aspects of the physical environment, such as services and amenities. However, such physical characteristics are not the only features of neighborhoods that potentially affect health. Neighborhoods also matter because of the nature of their social organization. This study examined social capital as a potential neighborhood characteristic influencing health. Using a cross-sectional study design which linked counts of death for persons 45-64 years by race and sex to neighborhood indicators of social capital and poverty for 342 Chicago neighborhoods in the USA, we tested the ecological association between neighborhood-level social capital and mortality rates, taking advantage of the community survey data collected as part of the Project on Human Development in Chicago Neighborhoods. We estimated a hierarchical generalized linear model to examine the association of race and sex specific mortality rates to social capital. Overall, neighborhood social capital-as measured by reciprocity, trust, and civic participation-was associated with lower neighborhood death rates, after adjustment for neighborhood material deprivation. Specifically, higher levels of neighborhood social capital were associated with lower neighborhood death rates for total mortality as well as death from heart disease and "other" causes for White men and women and, to a less consistent extent, for Blacks. However, there was no association between social capital and cancer mortality. Although, the findings from this study extend the state-level findings linking social capital to health to the level of neighborhoods, much work remains to be carried out before social capital can be widely applied to improve population health, including establishing standards of measurement, and exploring the potential "downsides" of social capital.

Future directions in residential segregation and health research: a multilevel approach.

The authors examine the research evidence on the effect of residential segregation on health, identify research gaps, and propose new research directions. Four recommendations are made on the basis of a review of the sociological and social epidemiology literature on residential segregation: (1) develop multilevel research designs to examine the effects of individual, neighborhood, and metropolitan-area factors on health outcomes; (2) continue examining the health effects of residential segregation among African Americans but also initiate studies examining segregation among Hispanics and Asians; (3) consider racial/ethnic segregation along with income segregation and other metropolitan area factors such as poverty concentration and metropolitan governance fragmentation; and (4) develop better conceptual frameworks of the pathways that may link various segregation dimensions to specific health outcomes.

Book Review Essay. Social determinants of health: Meeting at the crossroads

This paper describes how social conditions influence health and the behavior across the life cycle and what emerges from reading is an articulation of five common themes, by more than 100 researchers, relating to the central importance of social conditions in shaping the health of the public: (1) a population perspective on health determinants, (2) the complex interactions among multiple levels of influence, (3) the social context that shapes behavior, (4) a life course and developmental perspective, and (5) identifying biological mechanisms.

Metropolitan area income inequality and self-rated health--a multi-level study.

We examined the association of income inequality measured at the metropolitan area (MA) and county levels with individual self-rated health. Individual-level data were drawn from 259,762 respondents to the March Current Population Survey in 1996 and 1998. Income inequality and average income were calculated from 1990 census data, the former using Gini coefficients. Multi-level logistic regression models were used. Controlling for sex, age, race, and individual-level household income, respondents living in high, medium-high, and medium-low income inequality MAs had odds ratios of fair/poor self-rated health of 1.20 (95% confidence interval 1.04-1.38), 1.07 (0.95-1.21), and 1.02 (0.91-1.15), respectively, compared to people living in the MAs with the lowest income inequality. However, we found only a small association of MA-level income inequality with fair/poor health when controlling further for average MA household income: odds ratios were 1.10 (0.95-1.28), 1.01 (0.89-1.14), and 1.00 (0.89-1.12), respectively. Likewise, we found only a small association of county-level income inequality with self-rated health although only 40.7% of the sample had an identified county on CPS data. Regarding the association of state-level income inequality with fair/poor health, we found the association to be considerably stronger among non-metropolitan (i.e. rural) compared to metropolitan residents.