Etiology of acute febrile illness in the peruvian amazon as determined by modular formatted quantitative PCR: a protocol for RIVERA, a health facility-based case-control study.

BACKGROUND: The study of the etiology of acute febrile illness (AFI) has historically been designed as a prevalence of pathogens detected from a case series. This strategy has an inherent unrealistic assumption that all pathogen detection allows for causal attribution, despite known asymptomatic carriage of the principal causes of acute febrile illness in most low- and middle-income countries (LMICs). We designed a semi-quantitative PCR in a modular format to detect bloodborne agents of acute febrile illness that encompassed common etiologies of AFI in the region, etiologies of recent epidemics, etiologies that require an immediate public health response and additional pathogens of unknown endemicity. We then designed a study that would delineate background levels of transmission in the community in the absence of symptoms to provide corrected estimates of attribution for the principal determinants of AFI. METHODS: A case-control study of acute febrile illness in patients ten years or older seeking health care in Iquitos, Loreto, Peru, was planned. Upon enrollment, we will obtain blood, saliva, and mid-turbinate nasal swabs at enrollment with a follow-up visit on day 21-28 following enrollment to attain vital status and convalescent saliva and blood samples, as well as a questionnaire including clinical, socio-demographic, occupational, travel, and animal contact information for each participant. Whole blood samples are to be simultaneously tested for 32 pathogens using TaqMan array cards. Mid-turbinate samples will be tested for SARS-CoV-2, Influenza A and Influenza B. Conditional logistic regression models will be fitted treating case/control status as the outcome and with pathogen-specific sample positivity as predictors to attain estimates of attributable pathogen fractions for AFI. DISCUSSION: The modular PCR platforms will allow for reporting of all primary results of respiratory samples within 72 h and blood samples within one week, allowing for results to influence local medical practice and enable timely public health responses. The inclusion of controls will allow for a more accurate estimate of the importance of specific prevalent pathogens as a cause of acute illness. STUDY REGISTRATION: Project 1791, Registro de Proyectos de Investigación en Salud Pública (PRISA), Instituto Nacional de Salud, Perú.

Population Attributable Fraction of Nonvaccination of Child and Adolescent Vaccines Attributed to Parental Vaccine Hesitancy, 2018-2019.

Understanding the role of vaccine hesitancy in undervaccination or nonvaccination of childhood vaccines is important for increasing vaccine confidence and uptake. We used data from April to June interviews in the 2018 and 2019 National Immunization Survey-Flu (n = 78,725, United States), a nationally representative cross-sectional household cellular telephone survey. We determined the adjusted population attributable fraction (PAF) for each recommended childhood vaccine to assess the contribution of vaccine hesitancy to the observed nonvaccination level. Hesitancy is defined as being somewhat or very hesitant toward childhood vaccines. Furthermore, we assessed the PAF of nonvaccination for influenza according to sociodemographic characteristics, Department of Health and Human Services region, and state. The proportion of nonvaccination attributed to parental vaccine hesitancy was lowest for hepatitis B birth dose vaccine (6.5%) and highest for ≥3-dose diphtheria and tetanus toxoids and acellular pertussis vaccine (31.3%). The PAF of influenza nonvaccination was highest for non-Hispanic Black populations (15.4%), households with high educational (17.7%) and income (16.5%) levels, and urban areas (16.1%). Among states, PAF ranged from 25.4% (New Hampshire) to 7.5% (Louisiana). Implementing strategies to increase vaccination confidence and uptake are important, particularly during the coronavirus disease 2019 pandemic.

Attributable Mortality of Ventilator-associated Pneumonia Among Patients with COVID-19.

Rationale: Patients with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are at higher risk of ventilator-associated pneumonia (VAP) and may have an increased attributable mortality (increased or decreased risk of death if VAP occurs in a patient) and attributable fraction (proportion of deaths that are attributable to an exposure) of VAP-related mortality compared with subjects without coronavirus disease (COVID-19). Objectives: Estimation of the attributable mortality of the VAP among patients with COVID-19. Methods: Using the REA-REZO surveillance network, three groups of adult medical ICU patients were computed: control group (patients admitted between 2016 and 2019; prepandemic patients), pandemic COVID-19 group (PandeCOV+), and pandemic non-COVID-19 group (PandeCOV-) admitted during 2020. The primary outcome was the estimation of attributable mortality and attributable fraction related to VAP in these patients. Using multistate modeling with causal inference, the outcomes related to VAP were also evaluated. Measurements and Main Results: A total of 64,816 patients were included in the control group, 7,442 in the PandeCOV- group, and 1,687 in the PandeCOV+ group. The incidence of VAP was 14.2 (95% confidence interval [CI], 13.9 to 14.6), 18.3 (95% CI, 17.3 to 19.4), and 31.9 (95% CI, 29.8 to 34.2) per 1,000 ventilation-days in each group, respectively. Attributable mortality at 90 days was 3.15% (95%, CI, 2.04% to 3.43%), 2.91% (95% CI, -0.21% to 5.02%), and 8.13% (95% CI, 3.54% to 12.24%), and attributable fraction of mortality at 90 days was 1.22% (95% CI, 0.83 to 1.63), 1.42% (95% CI, -0.11% to 2.61%), and 9.17% (95% CI, 3.54% to 12.24%) for the control, PandeCOV-, and PandeCOV+ groups, respectively. Except for the higher risk of developing VAP, the PandeCOV- group shared similar VAP characteristics with the control group. PandeCOV+ patients were at lower risk of death without VAP (hazard ratio, 0.62; 95% CI, 0.52 to 0.74) than the control group. Conclusions: VAP-attributable mortality was higher for patients with COVID-19, with more than 9% of the overall mortality related to VAP.

Population Attributable Fractions of Underlying Medical Conditions for Coronavirus Disease 2019 (COVID-19) Diagnosis and COVID-19 Hospitalizations, Ventilations, and Deaths Among Adults in the United States.

Background: Several underlying medical conditions have been reported to be associated with an increased risk of coronavirus disease 2019 (COVID-19) and related hospitalization and death. Population attributable fractions (PAFs) describing the proportion of disease burden attributable to underlying medical conditions for COVID-19 diagnosis and outcomes have not been reported. Methods: A retrospective population-based cohort study was conducted using Optum's de-identified Clinformatics Data Mart database. Individuals were followed up from 20 January 2020 to 31 December 2020 for diagnosis and clinical progression, including hospitalization, intensive care unit admission, intubation and mechanical ventilation or extracorporeal membrane oxygenation, and death. Adjusted rate ratios and PAFs of underlying medical conditions for COVID-19 diagnosis and disease progression outcomes were estimated by age (18-49, 50-64, 65-74, or ≥75 years), sex, and race/ethnicity. Results: Of 10 679 566 cohort members, 391 964 (3.7%) were diagnosed with COVID-19, of whom 87 526 (22.3%) were hospitalized. Of those hospitalized, 26 640 (30.4%) died. Overall, cardiovascular disease and diabetes had the highest PAFs for COVID-19 diagnosis and outcomes of increasing severity across age groups (up to 0.49 and 0.35, respectively). Among adults ≥75 years of age, neurologic disease had the second-highest PAFs (0.05‒0.27) after cardiovascular disease (0.26‒0.44). PAFs were generally higher in Black persons than in other race/ethnicity groups for the same conditions, particularly in the 2 younger age groups. Conclusions: A substantial fraction of the COVID-19 disease burden in the United States is attributable to cardiovascular disease and diabetes, highlighting the continued importance of COVID-19 prevention ( eg, vaccination, mask wearing, social distancing) and disease management of patients with certain underlying medical conditions.

Projected all-cause deaths attributable to COVID-19-related unemployment in Croatia in 2020.

OBJECTIVES: In 2020, Croatia reported the first increase in the unemployment rate after six consecutive years of reduction in the number of unemployed persons. Unemployment is associated with an increase in morbidity and mortality among unemployed persons. We estimated the number of potential excess deaths that could be associated with an increase in unemployment seen after the beginning of the COVID-19 pandemic in 2020. STUDY DESIGN: This was a cross-sectional analytic study. METHODS: We used previously published meta-analyzed hazard ratios for the unemployment-mortality association and unemployment and mortality data from the Croatian Bureau of Statistics to estimate 1-year age-standardized deaths potentially attributable to COVID-19-related unemployment for persons aged 20-64 in Croatia. RESULTS: In January 2021, we observed a 19% increase in unemployment among persons aged 20-64 years compared with February 2020 (prepandemic). This increase in unemployment could lead to 23 excess deaths among newly unemployed persons. This would constitute a 42% increase in the number of deaths and 29% of all deaths among this group. Deaths were disproportionately higher among men and those aged >40 years. CONCLUSIONS: To mitigate the negative impact of COVID-19-related unemployment on population health, interventions that will reduce the further spread of SARS-CoV-2 and policies that will ensure economic recovery and reduction of unemployment are needed. Job skills training and provision of legal and welfare advice programs for unemployed persons should be integrated with health interventions.

Quantifying and communicating the burden of COVID-19.

BACKGROUND: An essential aspect of preventing further COVID-19 outbreaks and to learn for future pandemics is the evaluation of different political strategies, which aim at reducing transmission of and mortality due to COVID-19. One important aspect in this context is the comparison of attributable mortality. METHODS: We give a comprehensive overview of six epidemiological measures that are used to quantify COVID-19 attributable mortality (p-score, standardized mortality ratio, absolute number of excess deaths, per capita rate, z-score and the population attributable fraction). RESULTS: By defining the six measures based on observed and expected deaths, we explain their relationship. Moreover, three publicly available data examples serve to illustrate the interpretational strengths and weaknesses of the various measures. Finally, we give recommendation which measures are suitable for an evaluation of public health strategies against COVID-19. The R code to reproduce the results is available as online supplementary material. CONCLUSION: The number of excess deaths should be always reported together with the population attributable fraction, the p-score or the standardized mortality ratio instead of a per capita rate. For a complete picture of COVID-19 attributable mortality, quantifying and communicating its relative burden also to a lay audience is of major importance.

The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies.

Many countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown. We used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the population attributable fraction (PAF) due to groups as the relative change in the basic reproduction number when groups are prevented. Groups of 50+ individuals accounted for 0.5% of reported contact events, and we estimate that the PAF due to groups of 50+ people is 5.4% (95% confidence interval 1.4%, 11.5%). The PAF due to groups of 20+ people is 18.9% (12.7%, 25.7%) and the PAF due to groups of 10+ is 25.2% (19.4%, 31.4%). Under normal circumstances with pre-COVID-19 contact patterns, large groups of individuals have a relatively small epidemiological impact; small- and medium-sized groups between 10 and 50 people have a larger impact on an epidemic. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.