RESUMO
BACKGROUND: There are limited data on influenza vaccine effectiveness (IVE) in preventing laboratory-confirmed influenza illness among healthcare personnel (HCP). METHODS: HCP with direct patient contact working full-time in hospitals were followed during three influenza seasons in Israel (2016-2017 to 2018-2019) and Peru (2016 to 2018). Trivalent influenza vaccines were available at all sites, except during 2018-2019 when Israel used quadrivalent vaccines; vaccination was documented by electronic medical records, vaccine registries, and/or self-report (for vaccinations outside the hospital). Twice-weekly active surveillance identified acute respiratory symptoms or febrile illness (ARFI); self-collected respiratory specimens were tested by real-time reverse transcription polymerase chain reaction (PCR) assay. IVE was 100â¯×â¯1-hazard ratio (adjusted for sex, age, occupation, and hospital). RESULTS: Among 5,489 HCP who contributed 10,041 person-seasons, influenza vaccination coverage was 47% in Israel and 32% in Peru. Of 3,056 ARFIs in Israel and 3,538 in Peru, A or B influenza virus infections were identified in 205 (7%) in Israel and 87 (2.5%) in Peru. IVE against all viruses across seasons was 1% (95% confidence interval [CI]â¯=â¯-30%, 25%) in Israel and 12% (95% CIâ¯=â¯-61%, 52%) in Peru. CONCLUSION: Estimates of IVE were null using person-time models during six study seasons in Israel and Peru.
Assuntos
Vacinas contra Influenza , Influenza Humana , Atenção à Saúde , Humanos , Influenza Humana/epidemiologia , Influenza Humana/prevenção & controle , Israel/epidemiologia , Peru/epidemiologia , Estudos Prospectivos , Estações do Ano , Vacinação , Eficácia de VacinasRESUMO
OBJECTIVE: To evaluate body mass index (BMI) acceleration patterns in children and to develop a prediction model targeted to identify children at high risk for obesity before the critical time window in which the largest increase in BMI percentile occurs. STUDY DESIGN: We analyzed electronic health records of children from Israel's largest healthcare provider from 2002 to 2018. Data included demographics, anthropometric measurements, medications, diagnoses, and laboratory tests of children and their families. Obesity was defined as BMI ≥95th percentile for age and sex. To identify the time window in which the largest annual increases in BMI z score occurs during early childhood, we first analyzed childhood BMI acceleration patterns among 417 915 adolescents. Next, we devised a model targeted to identify children at high risk before this time window, predicting obesity at 5-6 years of age based on data from the first 2 years of life of 132 262 children. RESULTS: Retrospective BMI analysis revealed that among adolescents with obesity, the greatest acceleration in BMI z score occurred between 2 and 4 years of age. Our model, validated temporally and geographically, accurately predicted obesity at 5-6 years old (area under the receiver operating characteristic curve of 0.803). Discrimination results on subpopulations demonstrated its robustness across the pediatric population. The model's most influential predictors included anthropometric measurements of the child and family. Other impactful predictors included ancestry and pregnancy glucose. CONCLUSIONS: Rapid rise in the prevalence of childhood obesity warrant the development of better prevention strategies. Our model may allow an accurate identification of children at high risk of obesity.