Xi-Ling Wang

Model Selection in Time Series Studies of Influenza-Associated Mortality

Background Poisson regression modeling has been widely used to estimate influenza-associated disease burden, as it has the advantage of adjusting for multiple seasonal confounders. However, few studies have discussed how to judge the adequacy of confounding adjustment. This study aims to compare the performance of commonly adopted model selection criteria in terms of providing a reliable and valid estimate for the health impact of influenza. Methods We assessed four model selection criteria: quasi Akaike information criterion (QAIC), quasi Bayesian information criterion (QBIC), partial autocorrelation functions of residuals (PACF), and generalized cross-validation (GCV), by separately applying them to select the Poisson model best fitted to the mortality datasets that were simulated under the different assumptions of seasonal confounding. The performance of these criteria was evaluated by the bias and root-mean-square error (RMSE) of estimates from the pre-determined coefficients of influenza proxy variable. These four criteria were subsequently applied to an empirical hospitalization dataset to confirm the findings of simulation study. Results GCV consistently provided smaller biases and RMSEs for the influenza coefficient estimates than QAIC, QBIC and PACF, under the different simulation scenarios. Sensitivity analysis of different pre-determined influenza coefficients, study periods and lag weeks showed that GCV consistently outperformed the other criteria. Similar results were found in applying these selection criteria to estimate influenza-associated hospitalization. Conclusions GCV criterion is recommended for selection of Poisson models to estimate influenza-associated mortality and morbidity burden with proper adjustment for confounding. These findings shall help standardize the Poisson modeling approach for influenza disease burden studies.

Impact of the 2009 H1N1 Pandemic on Age-Specific Epidemic Curves of Other Respiratory Viruses: A Comparison of Pre-Pandemic, Pandemic and Post-Pandemic Periods in a Subtropical City.

Background The 2009 H1N1 influenza pandemic caused offseason peaks in temperate regions but coincided with the summer epidemic of seasonal influenza and other common respiratory viruses in subtropical Hong Kong. This study was aimed to investigate the impact of the pandemic on age-specific epidemic curves of other respiratory viruses. Methods Weekly laboratory-confirmed cases of influenza A (subtypes seasonal A(H1N1), A(H3N2), pandemic virus A(H1N1)pdm09), influenza B, respiratory syncytial virus (RSV), adenovirus and parainfluenza were obtained from 2004 to 2013. Age-specific epidemic curves of viruses other than A(H1N1)pdm09 were compared between the pre-pandemic (May 2004 – April 2009), pandemic (May 2009 – April 2010) and post-pandemic periods (May 2010 – April 2013). Results There were two peaks of A(H1N1)pdm09 in Hong Kong, the first in September 2009 and the second in February 2011. The infection rate was found highest in young children in both waves, but markedly fewer cases in school children were recorded in the second wave than in the first wave. Positive proportions of viruses other than A(H1N1)pdm09 markedly decreased in all age groups during the first pandemic wave. After the first wave of the pandemic, the positive proportion of A(H3N2) increased, but those of B and RSV remained slightly lower than their pre-pandemic proportions. Changes in seasonal pattern and epidemic peak time were also observed, but inconsistent across virus-age groups. Conclusion Our findings provide some evidence that age distribution, seasonal pattern and peak time of other respiratory viruses have changed since the pandemic. These changes could be the result of immune interference and changing health seeking behavior, but the mechanism behind still needs further investigations.

Age and Sex Differences in Rates of Influenza-Associated Hospitalizations in Hong Kong

Few studies have explored age and sex differences in the disease burden of influenza, although men and women probably differ in their susceptibility to influenza infections. In this study, quasi-Poisson regression models were applied to weekly age- and sex-specific hospitalization numbers of pneumonia and influenza cases in the Hong Kong SAR, Peoples Republic of China, from 2004 to 2010. Age and sex differences were assessed by age- and sex-specific rates of excess hospitalization for influenza A subtypes A(H1N1), A(H3N2), and A(H1N1)pdm09 and influenza B, respectively. We found that, in children younger than 18 years, boys had a higher excess hospitalization rate than girls, with the male-to-female ratio of excess rate (MFR) ranging from 1.1 to 2.4. MFRs of hospitalization associated with different types/subtypes were less than 1.0 for adults younger than 40 years except for A(H3N2) (MFR = 1.6), while all the MFRs were equal to or higher than 1.0 in adults aged 40 years or more except for A(H1N1)pdm09 in elderly persons aged 65 years or more (MFR = 0.9). No MFR was found to be statistically significant (P < 0.05) for hospitalizations associated with influenza type/subtype. There is some limited evidence on age and sex differences in hospitalization associated with influenza in the subtropical city of Hong Kong.

Age-specific epidemic waves of influenza and respiratory syncytial virus in a subtropical city.

Both influenza and respiratory syncytial virus (RSV) are active throughout the year in subtropical or tropical regions, but few studies have reported on age-specific seasonal patterns of these viruses. We examined the age-specific epidemic curves of laboratory-confirmed cases of influenza A (subtyped into seasonal A(H1N1), A(H3N2), and pandemic virus A(H1N1)pdm09), influenza B and respiratory syncytial virus (RSV), in subtropical city Hong Kong from 2004 to 2013. We found that different types and subtypes of influenza showed similar two-peak patterns across age groups, with one peak in winter and another in spring/summer. Age differences were found in epidemic onset time and duration, but none could reach statistical significance (pu2009>u20090.05). Age synchrony was found in epidemic peak time for both cool and warm seasons. RSV showed less clear seasonal patterns and non-synchronized epidemic curves across age. In conclusion, age synchrony was found in influenza seasonal epidemics and the 2009 pandemic, but not in RSV. None of the age groups consistently appear as the driving force for seasonal epidemics of influenza and RSV in Hong Kong.

Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China

Background Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China. Methods Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen. Temporal coherence of these surveillance data with laboratory-confirmed influenza cases was evaluated by wavelet analysis and only the coherent data streams were entered into the model. Timeliness, sensitivity and specificity of these models were also evaluated to compare their performance. Results Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p<0.05) during the entire study period, with occasional deviations observed in some data streams. The forecasting models that combined multi-stream ILI surveillance data generally outperformed the models with single-stream ILI data, by providing more timely, sensitive and specific alerts. Conclusions Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.

Factors Associated with the Emergence of Highly Pathogenic Avian Influenza A (H5N1) Poultry Outbreaks in China: Evidence from an Epidemiological Investigation in Ningxia, 2012

In April 2012, highly pathogenic avian influenza virus of the H5N1 subtype (HPAIV H5N1) emerged in poultry layers in Ningxia. A retrospective case-control study was conducted to identify possible risk factors associated with the emergence of H5N1 infection and describe and quantify the spatial variation in H5N1 infection. A multivariable logistic regression model was used to identify risk factors significantly associated with the presence of infection; residual spatial variation in H5N1 risk unaccounted by the factors included in the multivariable model was investigated using a semivariogram. Our results indicate that HPAIV H5N1-infected farms were three times more likely to improperly dispose farm waste [adjusted ORxa0=xa00.37; 95% CI: 0.12-0.82] and five times more likely to have had visitors in their farm within the past month [adjusted ORxa0=xa05.47; 95% CI: 1.97-15.64] compared to H5N1-non-infected farms. The variables included in the final multivariable model accounted only 20% for the spatial clustering of H5N1 infection. The average size of a H5N1 cluster was 660xa0m. Bio-exclusion practices should be strengthened on poultry farms to prevent further emergence of H5N1 infection. For future poultry depopulation, operations should consider H5N1 disease clusters to be as large as 700xa0m.

Different responses of influenza epidemic to weather factors among Shanghai, Hong Kong, and British Columbia

Weather factors have long been considered as key sources for regional heterogeneity of influenza seasonal patterns. As influenza peaks coincide with both high and low temperature in subtropical cities, weather factors may nonlinearly or interactively affect influenza activity. This study aims to assess the nonlinear and interactive effects of weather factors with influenza activity and compare the responses of influenza epidemic to weather factors in two subtropical regions of southern China (Shanghai and Hong Kong) and one temperate province of Canada (British Columbia). Weekly data on influenza activity and weather factors (i.e., mean temperature and relative humidity (RH)) were obtained from pertinent government departments for the three regions. Absolute humidity (AH) was measured by vapor pressure (VP), which could be converted from temperature and RH. Generalized additive models were used to assess the exposure-response relationship between weather factors and influenza virus activity. Interactions of weather factors were further assessed by bivariate response models and stratification analyses. The exposure-response curves of temperature and VP, but not RH, were consistent among three regions/cities. Bivariate response model revealed a significant interactive effect between temperature (or VP) and RH (Pxa0<xa00.05). Influenza peaked at low temperature or high temperature with high RH. Temperature and VP are important weather factors in developing a universal model to explain seasonal outbreaks of influenza. However, further research is needed to assess the association between weather factors and influenza activity in a wider context of social and environmental conditions.

Possible interference between seasonal epidemics of influenza and other respiratory viruses in Hong Kong, 2014–2017

BackgroundUnlike influenza viruses, little is known about the prevalence and seasonality of other respiratory viruses because laboratory surveillance for non-influenza respiratory viruses is not well developed or supported in China and other resource-limited countries. We studied the interference between seasonal epidemics of influenza viruses and five other common viruses that cause respiratory illnesses in Hong Kong from 2014 to 2017.MethodsThe weekly laboratory-confirmed positive rates of each virus were analyzed from 2014 to 2017 in Hong Kong to describe the epidemiological trends and interference between influenza viruses, respiratory syncytial virus (RSV), parainfluenza virus (PIV), adenovirus, enterovirus and rhinovirus. A sinusoidal model was established to estimate the peak timing of each virus by phase angle parameters.ResultsSeasonal features of the influenza viruses, PIV, enterovirus and adenovirus were obvious, whereas annual peaks of RSV and rhinovirus were not observed. The incidence of the influenza viruses usually peaked in February and July, and the summer peaks in July were generally caused by the H3 subtype of influenza A alone. When influenza viruses were active, other viruses tended to have a low level of activity. The peaks of the influenza viruses were not synchronized. An epidemic of rhinovirus tended to shift the subsequent epidemics of the other viruses.ConclusionThe evidence from recent surveillance data in Hong Kong suggests that viral interference during the epidemics of influenza viruses and other common respiratory viruses might affect the timing and duration of subsequent epidemics of a certain or several viruses.

Hospitalization risk of the 2009 H1N1 pandemic cases in Hong Kong

BackgroundReliable assessment for the severity of the 2009 H1N1 pandemic influenza is critical for evaluation of vaccination strategies for future pandemics. This study aims to estimate the age-specific hospitalization risks of the 2009 pandemic cases during the first wave in Hong Kong, by combining the findings from the serology and disease burden studies.MethodsExcess hospitalization rates associated with the pandemic H1N1 were estimated from Poisson regression models fitted to weekly total numbers of non-accidental hospitalization from 2005 to 2010. Age-specific infection-hospitalization risks were calculated as excess hospitalization rates divided by the attack rates in the corresponding age group, which were estimated from serology studies previously conducted in Hong Kong.ResultsExcess hospitalization rate associated with pandemic H1N1 was highest in the 0–4 age group (881.3 per 100,000 population), followed by the 5–14, 60+, 15–29, 50–59, 30–39 and 40–49 age groups. The hospitalization risk of the infected cases (i.e. infection-hospitalization risk) was found highest in the 60+ age group and lowest in the 15–29 age group, with the estimates of 17.5% and 0.7%, respectively.ConclusionsPeople aged 60 or over had a relatively high infection-hospitalization risk during the first wave of the 2009 H1N1 pandemic, despite of a low attack rate in this age group. The findings support the policy of listing older people as the priority group for pandemic vaccination.

Impact of weather factors on influenza hospitalization across different age groups in subtropical Hong Kong

Accumulating evidence demonstrates the significant influence of weather factors, especially temperature and humidity, on influenza seasonality. However, it is still unclear whether temperature variation within the same day, that is diurnal temperature range (DTR), is related to influenza seasonality. In addition, the different effects of weather factors on influenza seasonality across age groups have not been well documented in previous studies. Our study aims to explore the effects of DTR and humidity on influenza seasonality, and the differences in the association between weather factors and influenza seasonality among different age groups in Hong Kong, China. Generalized additive models were conducted to flexibly assess the impact of DTR, absolute humidity (vapor pressure, VP), and relative humidity on influenza seasonality in Hong Kong, China, from January 2012 to December 2016. Stratified analyses were performed to determine if the effects of weather factors differ across age groups (<u20095, 5–9, 10–64, and >u200964xa0years). The results suggested that DTR, absolute humidity, and relative humidity were significantly related to influenza seasonality in dry period (when VP is less than 20xa0mb), while no significant association was found in humid period (when VP is greater than 20xa0mb). The percentage changes of hospitalization rates due to influenza associated with per unit increase of weather factors in the very young children (age 0–4) and the elderly (age 65+) were higher than that in the adults (age 10–64). Diurnal temperature range is significantly associated with influenza seasonality in dry period, and the effects of weather factors differ across age groups in Hong Kong, China.