BACKGROUND: Patients hospitalised with influenza have heterogeneous clinical presentations and disease severity, which may complicate epidemiologic study design or interpretation. We applied latent class analysis to identify clinically distinct subgroups of adults hospitalised with influenza. METHODS: We analysed cross-sectional study data on adults (≥18 years) hospitalised with laboratory-confirmed influenza from the population-based U.S. Influenza Hospitalization Surveillance Network (FluSurv-NET) including 13 states during 2017-2018 and 2018-2019 influenza seasons (October 1 through April 30). Adults were included if they were residents of the FluSurv-NET catchment area, hospitalised with laboratory-confirmed influenza during these two seasons, and had both the main case report form and the supplemental disease severity case report form completed. We constructed a latent class model to identify subgroups from multiple observed variables including baseline characteristics (age and comorbidities) and clinical course (symptoms at admission, respiratory support requirement, and development of new complications and exacerbations of underlying conditions). FINDINGS: Among the 43,811 influenza-associated hospitalizations reported during the 2017-2018 and 2018-2019 influenza seasons, 15,873 (36.2%) were included in our analytic population: among them, 7069 (44.5%) were male and 8804 (55.5%) were female. We identified five subgroups. Subgroup A included persons of all ages with few comorbidities and 87.9% (255/290) of pregnant women. Subgroup B included older adults with comorbidities (cardiovascular disease (79.7% [3650/4581]) and diabetes (50.6% [2320/4581])). Almost all patients in subgroups C and D had asthma or chronic lung disease and high proportions with exacerbations of underlying conditions (59.7% [889/1489] and 65.1% [2274/3496], respectively). Subgroup E had the highest proportion with new complications (90.3% [1383/1531]). Subgroups D and E had the highest proportions with severe disease indicators: 21.0% (733/3496) and 50.4% (771/1531) required ICU admission, 7.2% (253/3496) and 28.0% (428/1531) required invasive mechanical ventilation, and 3.3% (116/3496) and 11.4% (174/1531) died in-hospital, respectively. INTERPRETATION: The five identified subgroups of adults hospitalised with influenza had varying distributions of age, comorbid conditions, and clinical courses characterized by new complications versus exacerbations of existing conditions. Stratifying by these subgroups may strengthen analyses that assess the impact of influenza vaccination and antiviral treatment on risk of severe disease. Limitations included that results were based on a convenience sample within FluSurv-NET sites and were likely not representative of all adults hospitalised with influenza in the United States. Influenza testing was also clinician-driven, likely leading to under-ascertainment. FUNDING: Centers for Disease Control and Prevention.

BACKGROUND: Seasonal influenza causes an estimated 120 000 to 710 000 hospitalizations annually in the United States. Treatment with antiviral medications, such as oseltamivir, can reduce risks of hospitalization among people with influenza-associated illness. The US Centers for Disease Control and Prevention recommends initiating antiviral treatment as soon as possible for outpatients with suspected or confirmed influenza who have severe or progressive illness or are at higher risk of influenza complications. METHODS: We developed a probabilistic model to estimate the impact of antiviral treatment in reducing hospitalizations among US outpatients with influenza. Parameters were informed by seasonal influenza surveillance platforms and stratified by age group and whether individuals had a condition associated with higher risk of influenza complications. We modeled different scenarios for influenza antiviral effectiveness and outpatient testing and prescribing practices, then compared our results with a baseline scenario in which antivirals were not used. RESULTS: Across the modeled scenarios, antiviral treatment resulted in 1215 to 14 184 fewer influenza-associated hospitalizations on average when compared with the baseline scenario (0.2%-2.7% reduction). The greatest effects occurred among adults aged ≥65 years and individuals with conditions associated with higher risk of influenza complications. Modeling 50% improvements in access to care, testing, prescribing, and treatment resulted in greater potential impacts, with over 71 000 (13.3%) influenza-associated hospitalizations averted on average compared to baseline. CONCLUSIONS: Our results support recommendations to prioritize outpatient antiviral treatment among older adults and others at higher risk of influenza complications. Improving access to prompt testing and treatment among outpatients with suspected influenza could reduce hospitalizations substantially.

BACKGROUND: Pneumonia is common in adults hospitalized with laboratory-confirmed influenza, but the association between timeliness of influenza antiviral treatment and severe clinical outcomes in patients with influenza-associated pneumonia is not well characterized. METHODS: We included adults aged ≥18 years hospitalized with laboratory-confirmed influenza and a discharge diagnosis of pneumonia over 7 influenza seasons (2012-2019) sampled from a multistate population-based surveillance network. We evaluated 3 treatment groups based on timing of influenza antiviral initiation relative to admission date (day 0, day 1, days 2-5). Baseline characteristics and clinical outcomes were compared across groups using unweighted counts and weighted percentages accounting for the complex survey design. Logistic regression models were generated to evaluate the association between delayed treatment and 30-day all-cause mortality. RESULTS: A total of 26 233 adults were sampled in the analysis. Median age was 71 years and most (92.2%) had ≥1 non-immunocompromising condition. Overall, 60.9% started antiviral treatment on day 0, 29.5% on day 1, and 9.7% on days 2-5 (median, 2 days). Baseline characteristics were similar across groups. Thirty-day mortality occurred in 7.5%, 8.5%, and 10.2% of patients who started treatment on day 0, day 1, and days 2-5, respectively. Compared to those treated on day 0, adjusted odds ratio for death was 1.14 (95% confidence interval [CI], 1.01-1.27) in those starting treatment on day 1 and 1.40 (95% CI, 1.17-1.66) in those starting on days 2-5. CONCLUSIONS: Delayed initiation of antiviral treatment in patients hospitalized with influenza-associated pneumonia was associated with higher risk of death, highlighting the importance of timely initiation of antiviral treatment at admission.

PROBLEM/CONDITION: Seasonal influenza accounts for 9.3 million-41 million illnesses, 100,000-710,000 hospitalizations, and 4,900-51,000 deaths annually in the United States. Since 2003, the Influenza Hospitalization Surveillance Network (FluSurv-NET) has been conducting population-based surveillance for laboratory-confirmed influenza-associated hospitalizations in the United States, including weekly rate estimations and descriptions of clinical characteristics and outcomes for hospitalized patients. However, a comprehensive summary of trends in hospitalization rates and clinical data collected from the surveillance platform has not been available. REPORTING PERIOD: 2010-11 through 2022-23 influenza seasons. DESCRIPTION OF SYSTEM: FluSurv-NET conducts population-based surveillance for laboratory-confirmed influenza-associated hospitalizations among children and adults. During the reporting period, the surveillance network included 13-16 participating sites each influenza season, with prespecified geographic catchment areas that covered 27 million-29 million persons and included an estimated 8.8%-9.5% of the U.S. population. A case was defined as a person residing in the catchment area within one of the participating states who had a positive influenza laboratory test result within 14 days before or at any time during their hospitalization. Each site abstracted case data from hospital medical records into a standardized case report form, with selected variables submitted to CDC on a weekly basis for rate estimations. Weekly and cumulative laboratory-confirmed influenza-associated hospitalization rates per 100,000 population were calculated for each season from 2010-11 through 2022-23 and stratified by patient age (0-4 years, 5-17 years, 18-49 years, 50-64 years, and ≥65 years), sex, race and ethnicity, influenza type, and influenza A subtype. During the 2020-21 season, only the overall influenza hospitalization rate was reported because case counts were insufficient to estimate stratified rates. RESULTS: During the 2010-11 to 2022-23 influenza seasons, laboratory-confirmed influenza-associated hospitalization rates varied significantly across seasons. Before the COVID-19 pandemic, hospitalization rates per 100,000 population ranged from 8.7 (2011-12) to 102.9 (2017-18) and had consistent seasonality. After SARS-CoV-2 emerged, the hospitalization rate for 2020-21 was 0.8, and the rate did not return to recent prepandemic levels until 2022-23. Inconsistent seasonality also was observed during 2020-21 through 2022-23, with influenza activity being very low during 2020-21, extending later than usual during 2021-22, and occurring early during 2022-23. Molecular assays, particularly multiplex standard molecular assays, were the most common influenza test type in recent seasons, increasing from 12% during 2017-18 for both pediatric and adult cases to 43% and 55% during 2022-23 for pediatric and adult cases, respectively. During each season, adults aged ≥65 years consistently had the highest influenza-associated hospitalization rate across all age groups, followed in most seasons by children aged 0-4 years. Black or African American and American Indian or Alaska Native persons had the highest age-adjusted influenza-associated hospitalization rates across these seasons. Among patients hospitalized with influenza, the prevalence of at least one underlying medical condition increased with increasing age, ranging from 36.9% among children aged 0-4 years to 95.4% among adults aged ≥65 years. Consistently across each season, the most common underlying medical conditions among children and adolescents were asthma, neurologic disorders, and obesity. The most common underlying medical conditions among adults were hypertension, obesity, chronic metabolic disease, chronic lung disease, and cardiovascular disease. The proportion of FluSurv-NET patients with acute respiratory signs and symptoms at hospital admission decreased from 90.6% during 2018-19 to 83.2% during 2022-23. Although influenza antiviral use increased during the 2010-11 through the 2017-18 influenza seasons, it decreased from 90.2% during 2018-19 to 79.1% during 2022-23, particularly among children and adolescents. Admission to the intensive care unit, need for invasive mechanical ventilation, and in-hospital death ranged from 14.1% to 22.3%, 4.9% to 11.1%, and 2.2% to 3.5% of patients hospitalized with influenza, respectively, during the reported surveillance period. INTERPRETATIONS: Influenza continues to cause severe morbidity and mortality, particularly in older adults, and disparities have persisted in racial and ethnic minority groups. Persons with underlying medical conditions represented a large proportion of patients hospitalized with influenza. Increased use of multiplex tests and other potential changes in facility-level influenza testing practices (e.g., influenza screening at all hospital admissions) could have implications for the detection of influenza infections among hospitalized patients. Antiviral use decreased in recent seasons, and explanations for the decrease should be further evaluated. PUBLIC HEALTH ACTION: Continued robust influenza surveillance is critical to monitor progress in efforts to encourage antiviral treatment and improve clinical outcomes for persons hospitalized with influenza. In addition, robust influenza surveillance can potentially reduce disparities by informing efforts to increase access to preventive measures for influenza and monitoring any subsequent changes in hospitalization rates.

BACKGROUND: Influenza burden varies across seasons, partly due to differences in circulating influenza virus types or subtypes. Using data from the US population-based surveillance system, Influenza Hospitalization Surveillance Network (FluSurv-NET), we aimed to assess the severity of influenza-associated outcomes in individuals hospitalised with laboratory-confirmed influenza virus infections during the 2010-11 to 2018-19 influenza seasons. METHODS: To evaluate the association between influenza virus type or subtype causing the infection (influenza A H3N2, A H1N1pdm09, and B viruses) and in-hospital severity outcomes (intensive care unit [ICU] admission, use of mechanical ventilation or extracorporeal membrane oxygenation [ECMO], and death), we used FluSurv-NET to capture data for laboratory-confirmed influenza-associated hospitalisations from the 2010-11 to 2018-19 influenza seasons for individuals of all ages living in select counties in 13 US states. All individuals had to have an influenza virus test within 14 days before or during their hospital stay and an admission date between Oct 1 and April 30 of an influenza season. Exclusion criteria were individuals who did not have a complete chart review; cases from sites that contributed data for three or fewer seasons; hospital-onset cases; cases with unidentified influenza type; cases of multiple influenza virus type or subtype co-infection; or individuals younger than 6 months and ineligible for the influenza vaccine. Logistic regression models adjusted for influenza season, influenza vaccination status, age, and FluSurv-NET site compared odds of in-hospital severity by virus type or subtype. When missing, influenza A subtypes were imputed using chained equations of known subtypes by season. FINDINGS: Data for 122 941 individuals hospitalised with influenza were captured in FluSurv-NET from the 2010-11 to 2018-19 seasons; after exclusions were applied, 107 941 individuals remained and underwent influenza A virus imputation when missing A subtype (43·4%). After imputation, data for 104 969 remained and were included in the final analytic sample. Averaging across imputed datasets, 57·7% (weighted percentage) had influenza A H3N2, 24·6% had influenza A H1N1pdm09, and 17·7% had influenza B virus infections; 16·7% required ICU admission, 6·5% received mechanical ventilation or ECMO, and 3·0% died (95% CIs had a range of less than 0·1% and are not displayed). Individuals with A H1N1pdm09 had higher odds of in-hospital severe outcomes than those with A H3N2: adjusted odds ratios (ORs) for A H1N1pdm09 versus A H3N2 were 1·42 (95% CI 1·32-1·52) for ICU admission; 1·79 (1·60-2·00) for mechanical ventilation or ECMO use; and 1·25 (1·07-1·46) for death. The adjusted ORs for individuals infected with influenza B versus influenza A H3N2 were 1·06 (95% CI 1·01-1·12) for ICU admission, 1·14 (1·05-1·24) for mechanical ventilation or ECMO use, and 1·18 (1·07-1·31) for death. INTERPRETATION: Despite a higher burden of hospitalisations with influenza A H3N2, we found an increased likelihood of in-hospital severe outcomes in individuals hospitalised with influenza A H1N1pdm09 or influenza B virus. Thus, it is important for individuals to receive an annual influenza vaccine and for health-care providers to provide early antiviral treatment for patients with suspected influenza who are at increased risk of severe outcomes, not only when there is high influenza A H3N2 virus circulation but also when influenza A H1N1pdm09 and influenza B viruses are circulating. FUNDING: The US Centers for Disease Control and Prevention.

OBJECTIVES: National data on COVID-19 vaccination coverage among pregnant women are limited. We assessed COVID-19 vaccination coverage and intent, factors associated with COVID-19 vaccination, reasons for nonvaccination, and knowledge, attitudes, and beliefs related to COVID-19 illness and vaccination among pregnant women in the United States. METHODS: Data from an opt-in internet panel survey of pregnant women conducted March 31-April 16, 2021, assessed receipt of 1 dose of any COVID-19 vaccine during pregnancy. The sample included 1516 women pregnant any time during December 1, 2020-April 16, 2021, who were not fully vaccinated before pregnancy. We used multivariable logistic regression to determine variables independently associated with receipt of COVID-19 vaccine. RESULTS: As of April 16, 2021, 21.7% of pregnant women had received 1 dose of COVID-19 vaccine during pregnancy, 24.0% intended to receive a vaccine, 17.2% were unsure, and 37.1% did not intend to receive a vaccine. Pregnant women with (vs without) a health care provider recommendation (adjusted prevalence ratio [aPR] = 4.86), those who lived (vs not) with someone with a condition that could increase risk for serious medical complications of COVID-19 (aPR = 2.11), and those who had received (vs not) an influenza vaccination (aPR = 2.35) were more likely to receive a COVID-19 vaccine. Common reasons for nonvaccination included concerns about safety risk to baby (37.2%) or self (34.6%) and about rapid vaccine development (29.7%) and approval (30.9%). CONCLUSIONS: Our findings indicate a continued need to emphasize the benefits of COVID-19 vaccination during pregnancy and to widely disseminate the recommendations of the Centers for Disease Control and Prevention and other clinical professional societies for all pregnant women to be vaccinated.

INTRODUCTION: Healthcare personnel are at increased risk for COVID-19 from workplace exposure. National estimates on COVID-19 vaccination coverage among healthcare personnel are limited. METHODS: Data from an opt-in Internet panel survey of 2,434 healthcare personnel, conducted on March 30, 2021-April 15, 2021, were analyzed to assess the receipt of ≥1 dose of a COVID-19 vaccine and vaccination intent. Multivariable logistic regression was used to assess the factors associated with COVID-19 vaccination and intent for vaccination. RESULTS: Overall, 68.2% of healthcare personnel reported a receipt of ≥1 dose of a COVID-19 vaccine, 9.8% would probably/definitely get vaccinated, 7.1% were unsure, and 14.9% would probably/definitely not get vaccinated. COVID-19 vaccination coverage was highest among physicians (89.0%), healthcare personnel working in hospitals (75.0%), and healthcare personnel of non-Hispanic White or other race (75.7%-77.4%). Healthcare personnel who received influenza vaccine in 2020-2021 (adjusted prevalence ratio=1.92) and those aged ≥60 years (adjusted prevalence ratio=1.37) were more likely to report a receipt of ≥1 dose of a COVID-19 vaccine. Non-Hispanic Black healthcare personnel (adjusted prevalence ratio=0.74), nurse practitioners/physician assistants (adjusted prevalence ratio=0.55), assistants/aides (adjusted prevalence ratio=0.73), and nonclinical healthcare personnel (adjusted prevalence ratio=0.79) were less likely to have received a COVID-19 vaccine. The common reasons for vaccination included protecting self (88.1%), family and friends (86.3%), and patients (69.2%) from COVID-19. The most common reason for nonvaccination was concern about side effects and safety of COVID-19 vaccine (59.7%). CONCLUSIONS: Understanding vaccination status and intent among healthcare personnel is important for addressing barriers to vaccination. Addressing concerns on side effects, safety, and effectiveness of COVID-19 vaccines as well as their fast development and approval may help improve vaccination coverage among healthcare personnel.

PURPOSE: The purpose of the study was to examine how gender was related to enrollment and number of sessions attended in the National Diabetes Prevention Program's Lifestyle Change Program (DPP LCP). METHODS: To better understand program uptake, a population of those who would be eligible for the LCP was compared to those who enrolled. Estimates of those eligible were computed using data from the National Health and Nutrition Examination Survey, whereas enrollment and sessions attended were computed using data from the Centers for Disease Control and Prevention's Diabetes Prevention Recognition Program. RESULTS: Results revealed that although similar numbers of males and females were eligible for the program, only 39 321 males versus 121 007 females had enrolled in the National DPP LCP by the end of 2017 (odds ratio = 3.20; 95% CI, 3.17-3.24). The gender differences persisted even when stratifying by age or race/ethnicity. In contrast, no significant gender differences were found between the average number of sessions attended for males (14.0) and females (13.8). DISCUSSION: Results of the study can help inform efforts to market and tailor programs to appeal more directly to men and other groups that are underrepresented in the National DPP LCP.

OBJECTIVE: To assess retention in the National Diabetes Prevention Program (DPP) lifestyle change program, which seeks to prevent type 2 diabetes in adults at high risk. RESEARCH DESIGN AND METHODS: We analyzed retention among 41,203 individuals who enrolled in Centers for Disease Control and Prevention (CDC)-recognized in-person lifestyle change programs at organizations that submitted data to CDC's Diabetes Prevention Recognition Program during January 2012-February 2017. RESULTS: Weekly attrition rates were typically <1-2% but were between 3.5% and 5% at week 2 and at weeks 17 and 18, where session frequency typically transitions from weekly to monthly. The percent of participants retained through 18 weeks varied by age (45.9% for 18-29 year olds, 53.4% for 30-44 year olds, 60.2% for 45-54 year olds, 66.7% for 55-64 year olds, and 67.6% for >/=65 year olds), race/ethnicity (70.5% for non-Hispanic whites, 60.5% for non-Hispanic blacks, 52.6% for Hispanics, and 50.6% for other), mean weekly percentage of body weight lost (41.0% for </=0% lost, 66.2% for >0 to <0.25% lost, 72.9% for 0.25 to <0.5% lost, and 73.9% for >/=0.5% lost), and mean weekly physical activity minutes (12.8% for 0 min, 56.1% for >0 to <60 min, 74.8% for 60 to <150 min, and 82.8% for >/=150 min) but not by sex (63.0% for men and 63.1% for women). CONCLUSIONS: Our results demonstrate the need to identify strategies to improve retention, especially among individuals who are younger or are members of racial/ethnic minority populations and among those who report less physical activity or less early weight loss. Strategies that address retention after the first session and during the transition from weekly to monthly sessions offer the greatest opportunity for impact.

PROBLEM/CONDITION: Diabetes self-management education (DSME) is a clinical practice intended to improve preventive practices and behaviors with a focus on decision-making, problem-solving, and self-care. The distribution and correlates of established DSME programs in nonmetropolitan counties across the United States have not been previously described, nor have the characteristics of the nonmetropolitan counties with DSME programs. REPORTING PERIOD: July 2016. DESCRIPTION OF SYSTEMS: DSME programs recognized by the American Diabetes Association or accredited by the American Association of Diabetes Educators (i.e., active programs) as of July 2016 were shared with CDC by both organizations. The U.S. Census Bureau's census geocoder was used to identify the county of each DSME program site using documented addresses. County characteristic data originated from the U.S. Census Bureau, compiled by the U.S. Department of Agriculture's Economic Research Service into the 2013 Atlas of Rural and Small-Town America data set. County levels of diagnosed diabetes prevalence and incidence, as well as the number of persons with diagnosed diabetes, were previously estimated by CDC. This report defined nonmetropolitan counties using the rural-urban continuum code from the 2013 Atlas of Rural and Small-Town America data set. This code included six nonmetropolitan categories of 1,976 urban and rural counties (62% of counties) adjacent to and nonadjacent to metropolitan counties. RESULTS: In 2016, a total of 1,065 DSME programs were located in 38% of the 1,976 nonmetropolitan counties; 62% of nonmetropolitan counties did not have a DSME program. The total number of DSME programs for nonmetropolitan counties with at least one DSME program ranged from 1 to 8, with an average of 1.4 programs. After adjusting for county-level characteristics, the odds of a nonmetropolitan county having at least one DSME program increased as the percentage insured increased (adjusted odds ratio [AOR] = 1.10, 95% confidence interval [CI] = 1.08-1.13), the percentage with a high school education or less decreased (AOR = 1.06, 95% CI = 1.04-1.07), the unemployment rate decreased (AOR = 1.19, 95% CI = 1.11-1.23), and the natural logarithm of the number of persons with diabetes increased (AOR = 3.63, 95% CI = 3.15-4.19). INTERPRETATION: In 2016, there were few DMSE programs in nonmetropolitan, socially disadvantaged counties in the United States. The number of persons with diabetes, percentage insured, percentage with a high school education or less, and the percentage unemployed were significantly associated with whether a DSME program was located in a nonmetropolitan county. PUBLIC HEALTH ACTION: Monitoring the distribution of DSME programs at the county level provides insight needed to strategically address rural disparities in diabetes care and outcomes. These findings provide information needed to assess lack of availability of DSME programs and to explore evidence-based strategies and innovative technologies to deliver DSME programs in underserved rural communities.

State and local vaccination requirements for school entry are implemented to maintain high vaccination coverage and protect schoolchildren from vaccine-preventable diseases. Each year, to assess state and national vaccination coverage and exemption levels among kindergartners, CDC analyzes school vaccination data collected by federally funded state, local, and territorial immunization programs. This report describes vaccination coverage in 49 states and the District of Columbia (DC) and vaccination exemption rates in 46 states and DC for children enrolled in kindergarten during the 2013-14 school year. Median vaccination coverage was 94.7% for 2 doses of measles, mumps, and rubella (MMR) vaccine; 95.0% for varying local requirements for diphtheria, tetanus toxoid, and acellular pertussis (DTaP) vaccine; and 93.3% for 2 doses of varicella vaccine among those states with a 2-dose requirement. The median total exemption rate was 1.8%. High exemption levels and suboptimal vaccination coverage leave children vulnerable to vaccine-preventable diseases. Although vaccination coverage among kindergartners for the majority of reporting states was at or near the 95% national Healthy People 2020 targets for 4 doses of DTaP, 2 doses of MMR, and 2 doses of varicella vaccine, low vaccination coverage and high exemption levels can cluster within communities. Immunization programs might have access to school vaccination coverage and exemption rates at a local level for counties, school districts, or schools that can identify areas where children are more vulnerable to vaccine-preventable diseases. Health promotion efforts in these local areas can be used to help parents understand the risks for vaccine-preventable diseases and the protection that vaccinations provide to their children.