Modelled seasonal influenza mortality shows marked differences in risk by age, sex, ethnicity and socioeconomic position in New Zealand

Estimating mortality associated with seasonal influenza among adults aged 65 years and above in China from 2011 to 2016: A systematic review and model analysis

BACKGROUND

Estimation of influenza disease burden is crucial for optimizing intervention strategies against seasonal influenza. This study aimed to estimate influenza-associated excess respiratory and circulatory (R&C) and all-cause (AC) mortality among older adults aged 65 years and above in mainland China from 2011 to 2016.

METHODS

Through a systematic review, we collected influenza-associated excess R&C and AC mortality data of older adults aged 65 years and above for specific cities/provinces in mainland China. Generalized linear models were fitted to estimate the corresponding excess mortality for older adults by province and nationwide, accounting for the potential variables of influenza virus activity, demography, economics, meteorology, and health service. All statistical analyses were conducted using R software.

RESULTS

A total of 9154 studies were identified in English and Chinese databases, and 11 (0.1%) were included in the quantitative synthesis after excluding duplicates and screening the title, abstract, and full text. Using a generalized linear model, the estimates of annual national average influenza-associated excess R&C and AC mortality among older adults aged 65 years and above were 111.8 (95% CI: 92.8-141.1) and 151.6 (95% CI: 127.6-179.3) per 100,000 persons, respectively. Large variations in influenza-associated excess R&C and AC mortality among older adults were observed among 30 provinces.

CONCLUSIONS

Influenza was associated with substantial excess R&C and AC mortality among older adults aged 65 years and above in China from 2011 to 2016. This analysis provides valuable evidence for the introduction of the influenza vaccine into the National Immunization Program for the elderly in China.

Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783−0.789], compared with 0.694 [0.690−0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823−0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

The impact of ethnicity on clinical outcomes in COVID-19: A systematic review

BACKGROUND

The relationship between ethnicity and COVID-19 is uncertain. We performed a systematic review to assess whether ethnicity has been reported in patients with COVID-19 and its relation to clinical outcomes.

METHODS

We searched EMBASE, MEDLINE, Cochrane Library and PROSPERO for English-language citations on ethnicity and COVID-19 (1st December 2019-15th May 2020). We also reviewed: COVID-19 articles in NEJM, Lancet, BMJ, JAMA, clinical trial protocols, grey literature, surveillance data and preprint articles on COVID-19 in MedRxiv to evaluate if the association between ethnicity and clinical outcomes were reported and what they showed. PROSPERO:180654.

FINDINGS

Of 207 articles in the database search, five reported ethnicity; two reported no association between ethnicity and mortality. Of 690 articles identified from medical journals, 12 reported ethnicity; three reported no association between ethnicity and mortality. Of 209 preprints, 34 reported ethnicity - 13 found Black, Asian and Minority Ethnic (BAME) individuals had an increased risk of infection with SARS-CoV-2 and 12 reported worse clinical outcomes, including ITU admission and mortality, in BAME patients compared to White patients. Of 12 grey literature reports, seven with original data reported poorer clinical outcomes in BAME groups compared to White groups.

INTERPRETATION

Data on ethnicity in patients with COVID-19 in the published medical literature remains limited. However, emerging data from the grey literature and preprint articles suggest BAME individuals are at an increased risk of acquiring SARS-CoV-2 infection compared to White individuals and also worse clinical outcomes from COVID-19. Further work on the role of ethnicity in the current pandemic is of urgent public health importance.

FUNDING

NIHR.

Local deprivation status and seasonal influenza vaccination coverage in adults ≥ 65 years residing in the Foggia municipality, Italy, 2009-2016

Introduction

In Italy, vaccination against seasonal influenza has been recommended for the elderly since 1980, but coverage is still far below the WHO minimum target level of 75%. Effective interventions to improve influenza vaccination should take into account socioeconomic determinants of inequalities in vaccine uptake. This study aimed to assess differences in vaccination coverage, by socioeconomic status, among people ≥ 65 years of age residing in the Foggia municipality, Italy.

Methods

A Socio-Economic-Health Deprivation Index (SEHDI) was constructed by using a multivariate analysis model. The resident population, for census block, was classified in 5 deprivation groups. Differences in demographic and socioeconomic indicators, the standardized mortality ratios (SMRs), and the average vaccination coverage among deprivation groups were evaluated with the linear F-test. The association between census variables and influenza vaccination coverage, in each deprivation group, was assessed using the Pearson bivariate correlation.

Results

The SEHDI allowed to identify factors related to ageing, housing, household size and composition, and education. Forty percent of people residing in the Foggia municipality lived in conditions of socioeconomic and health deprivation. Belonging to families with 3 or 4 members was associated with increased coverage rates. In the most deprived group, vaccination uptake was positively associated with the dependency ratio.

Conclusions

The results of this study have shown that there is still large room for improving influenza vaccination coverage among subjects belonging to the most deprived areas. Surveillance of trends in influenza vaccine uptake by socioeconomic groups is a feasible contribution to implementing effective, tailored to the frail older persons, vaccine utilization programs.

Excess pneumonia and influenza mortality attributable to seasonal influenza in subtropical Shanghai, China

BACKGROUND

Disease burden attributable to influenza is substantial in subtropical regions. Our study aims to estimate excess pneumonia and influenza (P&I) mortality associated with influenza by subtypes/lineages in Shanghai, China, 2010-2015.

METHODS

Quasi-Poisson regression models were fitted to weekly numbers of deaths from causes coded as P&I for Shanghai general and registered population. Three proxies for influenza activity were respectively used as an explanatory variable. Long-term trend, seasonal trend and absolute humidity were adjusted for as confounding factors. The outcome measurements of excess P&I mortality associated with influenza subtypes/lineages were derived by subtracting the baseline mortality from fitted mortality.

RESULTS

Excess P&I mortality associated with influenza were 0.22, 0.30, and 0.23 per 100,000 population for three different proxies in Shanghai general population, lower than those in registered population (0.34, 0.48, and 0.36 per 100,000 population). Influenza B (Victoria) lineage did not contribute to excess P&I mortality (P = 0.206) while influenza B (Yamagata) lineage did (P = 0.044). Influenza-associated P&I mortality was high in the elderly population.

CONCLUSIONS

Seasonal influenza A virus had a higher P&I mortality than influenza B virus, while B (Yamagata) lineage is the dominant lineage attributable to P&I mortality.