Optimizing the precision of case fatality ratio estimates under the surveillance pyramid approach

Development of influenza-associated disease burden pyramid in Shanghai, China, 2010-2017: a Bayesian modelling study

OBJECTIVES

Negative estimates can be produced when statistical modelling techniques are applied to estimate morbidity and mortality attributable to influenza. Based on the prior knowledge that influenza viruses are hazardous pathogens and have adverse health outcomes of respiratory and circulatory disease (R&C), we developed an improved model incorporating Bayes' theorem to estimate the disease burden of influenza in Shanghai, China, from 2010 to 2017.

DESIGN

A modelling study using aggregated data from administrative systems on weekly R&C mortality and hospitalisation, influenza surveillance and meteorological data. We constrained the regression coefficients for influenza activity to be positive by truncating the prior distributions at zero.

SETTING

Shanghai, China.

PARTICIPANTS

People registered with R&C deaths (450 298) and hospitalisations (2621 787, from 1 July 2013), and with influenza-like illness (ILI) outpatient visits (342 149) between 4 January 2010 and 31 December 2017.

PRIMARY OUTCOME MEASURES

Influenza-associated disease burden (mortality, hospitalisation and outpatient visit rates) and clinical severity (outpatient-mortality, outpatient-hospitalisation and hospitalisation-mortality risks).

RESULTS

Influenza was associated with an annual average of 15.49 (95% credibility interval (CrI) 9.06-22.06) excess R&C deaths, 100.65 (95% CrI 48.79-156.78) excess R&C hospitalisations and 914.95 (95% CrI 798.51-1023.66) excess ILI outpatient visits per 100 000 population in Shanghai. 97.23% and 80.24% excess R&C deaths and hospitalisations occurred in people aged ≥65 years. More than half of excess morbidity and mortality were associated with influenza A(H3N2) virus, and its severities were 1.65-fold to 3.54-fold and 1.47-fold to 2.16-fold higher than that for influenza A(H1N1) and B viruses, respectively.

CONCLUSIONS

The proposed Bayesian approach with reasonable prior information improved estimates of influenza-associated disease burden. Influenza A(H3N2) virus was generally associated with higher morbidity and mortality, and was relatively more severe compared with influenza A(H1N1) and B viruses. Targeted influenza prevention and control strategies for the elderly in Shanghai may substantially reduce the disease burden.

Socioeconomic bias in influenza surveillance

Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America's primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate.

Case Fatality Ratio Estimates for the 2013-2016 West African Ebola Epidemic: Application of Boosted Regression Trees for Imputation

BACKGROUND

The 2013-2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported.

METHODS

Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR.

RESULTS

The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5-75.6%); specificity, 69.8% (54.1-75.6%); percentage correctly classified, 69.9% (53.7-75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8-82.1%). The adjusted CFR estimates (95% CI) for the 2013-2016 West African epidemic were 82.8% (45.6-85.6%) overall and 89.1% (40.8-91.6%), 65.6% (61.3-69.6%), and 79.2% (45.4-84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR.

CONCLUSIONS

The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions.

Using Clinical Research Networks to Assess Severity of an Emerging Influenza Pandemic

Background

Early clinical severity assessments during the 2009 influenza A H1N1 pandemic (pH1N1) overestimated clinical severity due to selection bias and other factors. We retrospectively investigated how to use data from the International Network for Strategic Initiatives in Global HIV Trials, a global clinical influenza research network, to make more accurate case fatality ratio (CFR) estimates early in a future pandemic, an essential part of pandemic response.

Methods

We estimated the CFR of medically attended influenza (CFR_MA) as the product of probability of hospitalization given confirmed outpatient influenza and the probability of death given hospitalization with confirmed influenza for the pandemic (2009–2011) and post-pandemic (2012–2015) periods. We used literature survey results on health-seeking behavior to convert that estimate to CFR among all infected persons (CFR_AR).

Results

During the pandemic period, 5.0% (3.1%–6.9%) of 561 pH1N1-positive outpatients were hospitalized. Of 282 pH1N1-positive inpatients, 8.5% (5.7%–12.6%) died. CFR_MA for pH1N1 was 0.4% (0.2%–0.6%) in the pandemic period 2009–2011 but declined 5-fold in young adults during the post-pandemic period compared to the level of seasonal influenza in the post-pandemic period 2012–2015. CFR for influenza-negative patients did not change over time. We estimated the 2009 pandemic CFR_AR to be 0.025%, 16-fold lower than CFR_MA.

Conclusions

Data from a clinical research network yielded accurate pandemic severity estimates, including increased severity among younger people. Going forward, clinical research networks with a global presence and standardized protocols would substantially aid rapid assessment of clinical severity.

Clinical Trials Registration

NCT01056354 and NCT010561.

How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact.

Many data can be used to estimate the transmission potential of a pathogen, including descriptions of the transmission chains, human cluster sizes, sources of infection, and epidemic curves.

An important agenda in public health is understanding the impact of control methods. However, the dynamic nature of epidemics makes this task challenging. Models can disentangle the natural course of outbreaks from the effect of external factors.

In the absence of reliable surveillance data, models can reconstruct epidemic history by combining age-specific seroprevalence data with an understanding of the natural history of infection.

Mechanisms of immunity are hard to observe at an individual level, yet they affect population-level dynamics. Models can tease out such signatures.

Morbidity and mortality can be difficult to estimate when many infections are unobserved and severe infections are reported more often. Models can be used to correct for under-reporting and selection bias.

Key data for outbreak evaluation: building on the Ebola experience

Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.

Case-fatality risk of pregnant women with acute viral hepatitis type E: a systematic review and meta-analysis

It is of great concern that pregnant women with acute viral hepatitis (AVH) type E have serious consequences. This study aimed to estimate the case-fatality risk (CFR) and potential risk factors of pregnant women with AVH type E. We searched the PubMed, EMBASE, and Web of Science databases for studies containing data on CFR in pregnancy with AVH type E. A pooled estimate of CFR was calculated using a random-effects model. Potential sources of heterogeneity were explored using subgroup analysis, sensitivity analysis, and meta-regression. We identified 47 eligible studies with a total African and Asian population of 3968 individuals. The pooled CFRs of maternal and fetal outcomes were 20·8% [95% confidence interval (CI) 16·6-25·3] and 34·2% (95% CI 26·0-43·0), respectively. Compared with these, the pooled CFR was highest (61·2%) in women with fulminant hepatic failure (FHF). Community-based surveys had lower pooled CFR (12·2%, 95% CI 9·2-15·6) and heterogeneity (25·8%, 95% CI 20·1-32·0) than hospital-based surveys. Univariate analysis showed that hospital-based surveying (P = 0·007), and patients in the third trimester of pregnancy or with FHF (P < 0·05), were significantly associated with CFR. Intrauterine fetal mortality (27·0%) was statistically higher than neonatal mortality (3·9%). Control measures for HEV infection would reduce feto-maternal mortality in Asia and Africa.

Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks

Estimating the case-fatality risk (CFR)-the probability that a person dies from an infection given that they are a case-is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR-preferential ascertainment of severe cases and bias from reporting delays-and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases.