Effect modification of environmental factors on influenza-associated mortality: a time-series study in two Chinese cities

The Impact of Weather and Air Pollution on Viral Infection and Disease Outcome Among Pediatric Pneumonia Patients in Chongqing, China, from 2009 to 2018: A Prospective Observational Study

BACKGROUND

For pediatric pneumonia, the meteorological and air pollution indicators have been frequently investigated for their association with viral circulation but not for their impact on disease severity.

METHODS

We performed a 10-year prospective, observational study in 1 hospital in Chongqing, China, to recruit children with pneumonia. Eight commonly seen respiratory viruses were tested. Autoregressive distributed lag (ADL) and random forest (RF) models were used to fit monthly detection rates of each virus at the population level and to predict the possibility of severe pneumonia at the individual level, respectively.

RESULTS

Between 2009 and 2018, 6611 pediatric pneumonia patients were included, and 4846 (73.3%) tested positive for at least 1 respiratory virus. The patient median age was 9 months (interquartile range, 4‒20). ADL models demonstrated a decent fitting of detection rates of R2 > 0.7 for respiratory syncytial virus, human rhinovirus, parainfluenza virus, and human metapneumovirus. Based on the RF models, the area under the curve for host-related factors alone was 0.88 (95% confidence interval [CI], .87‒.89) and 0.86 (95% CI, .85‒.88) for meteorological and air pollution indicators alone and 0.62 (95% CI, .60‒.63) for viral infections alone. The final model indicated that 9 weather and air pollution indicators were important determinants of severe pneumonia, with a relative contribution of 62.53%, which is significantly higher than respiratory viral infections (7.36%).

CONCLUSIONS

Meteorological and air pollution predictors contributed more to severe pneumonia in children than did respiratory viruses. These meteorological data could help predict times when children would be at increased risk for severe pneumonia and when interventions, such as reducing outdoor activities, may be warranted.

In the Subtropical Monsoon Climate High-Density City, What Features of the Neighborhood Environment Matter Most for Public Health?

Urbanization and climate change have been rapidly occurring globally. Evidence-based healthy city development is required to improve living quality and mitigate the adverse impact of the outdoor neighborhood environment on public health. Taking Guangzhou as an example to explore the association of neighborhood environment and public health and preferably to offer some implications for better future city development, we measured ten environmental factors (temperature (T), wind-chill index (WCI), thermal stress index (HSI), relative humidity (RH), average wind speed (AWS), negative oxygen ions (NOI), PM2.5, luminous flux (LF), and illuminance (I)) in four seasons in four typical neighborhoods, and the SF-36 health scale was employed to assess the physical and mental health of neighborhood residents in nine subscales (health transition(HT), physiological functions (PF), general health status (GH), physical pain (BP), physiological functions (RP), energy vitality (VT), mental health (MH), social function (SF), and emotional functions (RE)). The linear mixed model was used in an analysis of variance. We ranked the different environmental factors in relation to aspects of health and weighted them accordingly. Generally, the thermal environment had the greatest impact on both physical and mental health and the atmospheric environment and wind environment had the least impact on physical health and mental health, respectively. In addition, the physical health of the resident was more greatly affected by the environment than mental health. According to the results, we make a number of strategic suggestions for the renewal of the outdoor neighborhood environment in subtropical monsoon climate high-density cities and provide a theoretical basis for improving public health through landscape architecture at the neighborhood scale.

Association of Influenza Activity and Environmental Conditions With the Risk of Invasive Pneumococcal Disease

IMPORTANCE

Streptococcus pneumoniae is the most commonly identified cause of bacterial pneumonia, and invasive pneumococcal disease (IPD) has a high case fatality rate. The wintertime coseasonality of influenza and IPD in temperate countries has suggested that pathogen-pathogen interaction or environmental conditions may contribute to IPD risk.

OBJECTIVES

To evaluate the short-term associations of influenza activity and environmental exposures with IPD risk in temperate countries and to examine the generalizability of such associations across multiple jurisdictions.

DESIGN, SETTING, AND PARTICIPANTS

This case-crossover analysis of 19 566 individuals with IPD from 1998 to 2011 combined individual-level outcomes of IPD and population-level exposures. Participants lived in 12 jurisdictions in Canada (the province of Alberta and cities of Toronto, Vancouver, and Halifax), Australia (Perth, Sydney, Adelaide, Brisbane, and Melbourne), and the United States (Baltimore, Providence, and Philadelphia). Data were analyzed in 2019.

EXPOSURES

Influenza activity, mean temperature, absolute humidity, and UV radiation at delays of 1 to 3 weeks before case occurrence in each jurisdiction.

MAIN OUTCOMES AND MEASURES

Matched odds ratios (ORs) for IPD associated with changes in exposure variables, estimated using multivariable conditional logistic regression models. Heterogeneity in effects across jurisdictions were evaluated using random-effects meta-analytic models.

RESULTS

This study included 19 566 patients: 9629 from Australia (mean [SD] age, 42.8 [30.8] years; 5280 [54.8%] men), 8522 from Canada (only case date reported), and 1415 from the United States (only case date reported). In adjusted models, increased influenza activity was associated with increases in IPD risk 2 weeks later (adjusted OR [aOR] per SD increase, 1.07; 95% CI, 1.01-1.13). Increased humidity was associated with decreased IPD risk 1 week later (aOR per 1 g/m3, 0.98; 95% CI, 0.96-1.00). Other associations were heterogeneous; metaregression suggested that combinations of environmental factors might represent unique local risk signatures. For example, the heterogeneity in effects of UV radiation and humidity at a 2-week lag was partially explained by variation in temperature (UV index: coefficient, 0.0261; 95% CI, 0.0078 to 0.0444; absolute humidity: coefficient, -0.0077; 95% CI, -0.0125 to -0.0030).

CONCLUSIONS AND RELEVANCE

In this study, influenza was associated with increased IPD risk in temperate countries. This association was not explained by coseasonality or case characteristics and appears generalizable. Absolute humidity was associated with decreased IPD risk in the same jurisdictions. The generalizable nature of these associations has important implications for influenza control and advances the understanding of the seasonality of this important disease.

The burden of influenza and pneumonia mortality attributable to absolute humidity among elderly people in Chongqing, China, 2012-2018

OBJECTIVE

To examine the association between absolute humidity (AH) and influenza and pneumonia (P&I) mortality, and to quantify P&I mortality burden attributable to non-optimum AHs among elderly people aged ≥65 years in Chongqing, the largest municipality of China.

METHODS

Daily data of P&I mortality from 2012 to 2018, and the contemporaneous meteorological data in the study area were collected. Distributed lag non-linear model (DLNM) was applied to estimate the non-linear and delayed effects of absolute humidity (AH) on P&I mortality. Then, attributable deaths were calculated for the dry and moist AH, defined as AH below and above the minimum mortality AH (MMAH), respectively. Moderate and extreme AHs were defined using cutoffs at the 2.5th and 97.5th percentiles of AH.

RESULTS

The relationship between AH and P&I mortality was a U-shaped curve. The MMAH was 11.5 g/m³ (46.4th percentile). In total, 25.7% (95% confidence interval: 10.0-38.2) of P&I mortality (4673 deaths) was attributed to non-optimum AHs. Low AHs were responsible for 12.7% of the P&I death burden (95%CI: 0.2-20.1), while high AHs for 13.0% (95%CI: -9.4-25.7). Extreme low and high AHs accounted for 3.7% (95%CI: 0.1-6.8) and 3.0% (95%CI: 0-5.4) of P&I mortality.

CONCLUSIONS

Our study showed that both low AHs and high AHs are responsible for considerable AH-related P&I mortality burden among elderly people. Our results may have important public health implications for the development of relevant intervention policies to reduce P&I deaths among the elderly.

Individual- and community-level shifts in mortality patterns during the January 2016 East Asia cold wave associated with a super El Niño event: Empirical evidence in Hong Kong

Despite the fact that cold weather has been widely documented as a major factor that can elevate the mortality in a subtropical population due to a lack of adaptability, the disastrous impacts from a major cold event in East Asia caused by a super El Niño event in January 2016 have passed largely unreported. In order to minimize the catastrophic risk from such events given ongoing concerns about climate change, as also noted in the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), it is important to evaluate the individual- and community-level shifts in mortality patterns during such cold waves, in order to develop health protocols for surveillance and disaster planning. This study evaluated the impacts of the 2016 cold wave on mortality patterns in Hong Kong because this city has been highlighted as a city with severe negative impacts from the disaster by social media. Based on a sensitivity analysis, we found significantly higher daily mortality for up to ten weeks during this cold wave compared to the same calendar days between 2007 and 2015. We also found that the short-term impact of the cold wave was prolonged and fatal, with the potential to increase the mortality across the city for up to five weeks compared to the pre-disaster period. An examination of the individual- and community-level shifts in mortality patterns reveals that the unmarried and economically inactive were most vulnerable during the 2016 cold wave, and respiratory diseases were the greatest medical problems, while age and gender effects as well as cardiovascular diseases did not enhance the fatal effect. The excessive mortality was citywide, and not limited to particular locations or specific characteristics of a community within the city. Based on the results, disaster education as well as social and health services should be provided to all local people for an extended period in order to minimize the fatal and prolonged effects of future cold waves.

Dynamic Bayesian network in infectious diseases surveillance: a simulation study

The surveillance of infectious diseases relies on the identification of dynamic relations between the infectious diseases and corresponding influencing factors. However, the identification task confronts with two practical challenges: small sample size and delayed effect. To overcome both challenges to imporve the identification results, this study evaluated the performance of dynamic Bayesian network(DBN) in infectious diseases surveillance. Specifically, the evaluation was conducted by two simulations. The first simulation was to evaluate the performance of DBN by comparing it with the Granger causality test and the least absolute shrinkage and selection operator (LASSO) method; and the second simulation was to assess how the DBN could improve the forecasting ability of infectious diseases. In order to make both simulations close to the real-world situation as much as possible, their simulation scenarios were adapted from real-world studies, and practical issues such as nonlinearity and nuisance variables were also considered. The main simulation results were: ① When the sample size was large (n = 340), the true positive rates (TPRs) of DBN (≥98%) were slightly higher than those of the Granger causality method and approximately the same as those of the LASSO method; the false positive rates (FPRs) of DBN were averagely 46% less than those of the Granger causality test, and 22% less than those of the LASSO method. ② When the sample size was small, the main problem was low TPR, which would be further aggravated by the issues of nonlinearity and nuisance variables. In the worst situation (i.e., small sample size, nonlinearity and existence of nuisance variables), the TPR of DBN declined to 43.30%. However, it was worth noting that such decline could also be found in the corresponding results of Granger causality test and LASSO method. ③ Sample size was important for identifying the dynamic relations among multiple variables, in this case, at least three years of weekly historical data were needed to guarantee the quality of infectious diseases surveillance. ④ DBN could improve the foresting results through reducing forecasting errors by 7%. According to the above results, DBN is recommended to improve the quality of infectious diseases surveillance.

Relationship of Meteorological and Air Pollution Parameters with Pneumonia in Elderly Patients

Background and Purpose

In this study, we aimed to evaluate the relationship between pneumonia and meteorological parameters (temperature, humidity, precipitation, airborne particles, sulfur dioxide (SO₂), carbon monoxide (CO), nitrogen dioxide (NO₂), nitrite oxide (NO), and nitric oxide (NOX)) in patients with the diagnosis of pneumonia in the emergency department.

Methods

Our study was performed retrospectively with patients over 65 years of age who were diagnosed with pneumonia. The meteorological variables in the days of diagnosing pneumonia were compared with the meteorological variables in the days without diagnosis of pneumonia. The sociodemographic characteristics, complete blood count of the patients, and meteorological parameters (temperature, humidity, precipitation, airborne particles, SO2, CO, NO2, NO, and NOX) were investigated.

Results

When the temperature was high and low, the number of days consulted due to pneumonia was related to low air temperature (p < 0.05). During the periods when PM 10, NO, NO2, NOX, and CO levels were high, the number of days referred for pneumonia was increased (p < 0.05).

Conclusion

As a result, climatic (temperature, humidity, pressure levels, rain, etc.) and environmental factors (airborne particles, CO, NO, and NOX) were found to be effective in the number of patients admitted to the hospital due to pneumonia.

Impact of Influenza on Outpatient Visits, Hospitalizations, and Deaths by Using a Time Series Poisson Generalized Additive Model

BACKGROUND

The disease burden associated with influenza in developing tropical and subtropical countries is poorly understood owing to the lack of a comprehensive disease surveillance system and information-exchange mechanisms. The impact of influenza on outpatient visits, hospital admissions, and deaths has not been fully demonstrated to date in south China.

METHODS

A time series Poisson generalized additive model was used to quantitatively assess influenza-like illness (ILI) and influenza disease burden by using influenza surveillance data in Zhuhai City from 2007 to 2009, combined with the outpatient, inpatient, and respiratory disease mortality data of the same period.

RESULTS

The influenza activity in Zhuhai City demonstrated a typical subtropical seasonal pattern; however, each influenza virus subtype showed a specific transmission variation. The weekly ILI case number and virus isolation rate had a very close positive correlation (r = 0.774, P < 0.0001). The impact of ILI and influenza on weekly outpatient visits was statistically significant (P < 0.05). We determined that 10.7% of outpatient visits were associated with ILI and 1.88% were associated with influenza. ILI also had a significant influence on the hospitalization rates (P < 0.05), but mainly in populations <25 years of age. No statistically significant effect of influenza on hospital admissions was found (P > 0.05). The impact of ILI on chronic obstructive pulmonary disease (COPD) was most significant (P < 0.05), with 33.1% of COPD-related deaths being attributable to ILI. The impact of influenza on the mortality rate requires further evaluation.

CONCLUSIONS

ILI is a feasible indicator of influenza activity. Both ILI and influenza have a large impact on outpatient visits. Although ILI affects the number of hospital admissions and deaths, we found no consistent influence of influenza, which requires further assessment.

A robust parameter estimation method for estimating disease burden of respiratory viruses

BACKGROUND

Poisson model has been widely applied to estimate the disease burden of influenza, but there has been little success in providing reliable estimates for other respiratory viruses.

METHODS

We compared the estimates of excess hospitalization rates derived from the Poisson models with different combinations of inference methods and virus proxies respectively, with the aim to determine the optimal modeling approach. These models were validated by comparing the estimates of excess hospitalization attributable to respiratory viruses with the observed rates of laboratory confirmed paediatric hospitalization for acute respiratory infections obtained from a population based study.

RESULTS

The Bayesian inference method generally outperformed the classical likelihood estimation, particularly for RSV and parainfluenza, in terms of providing estimates closer to the observed hospitalization rates. Compared to the other proxy variables, age-specific positive counts provided better estimates for influenza, RSV and parainfluenza, regardless of inference methods. The Bayesian inference combined with age-specific positive counts also provided valid and reliable estimates for excess hospitalization associated with multiple respiratory viruses in both the 2009 H1N1 pandemic and interpandemic period.

CONCLUSIONS

Poisson models using the Bayesian inference method and virus proxies of age-specific positive counts should be considered in disease burden studies on multiple respiratory viruses.

Susceptibility to mortality in weather extremes: effect modification by personal and small-area characteristics

BACKGROUND

Extremes of temperature have been associated with short-term increases in daily mortality. We identified subpopulations with increased susceptibility to dying during temperature extremes, based on personal demographics, small-area characteristics, and preexisting medical conditions.

METHODS

We examined Medicare participants in 135 US cities and identified preexisting conditions based on hospitalization records before their deaths, from 1985 to 2006. Personal characteristics were obtained from the Medicare records, and area characteristics were assigned based on zip code of residence. We conducted a case-only analysis of over 11 million deaths and evaluated modification of the risk of dying associated with extremely hot days and extremely cold days, continuous temperatures, and water vapor pressure. Modifiers included preexisting conditions, personal characteristics, zip code-level population characteristics, and land cover characteristics. For each effect modifier, a city-specific logistic regression model was fitted and then an overall national estimate was calculated using meta-analysis.

RESULTS

People with certain preexisting conditions were more susceptible to extreme heat, with an additional 6% (95% confidence interval = 4%-8%) increase in the risk of dying on an extremely hot day in subjects with previous admission for atrial fibrillation, an additional 8% (4%-12%) in subjects with Alzheimer disease, and an additional 6% (3%-9%) in subjects with dementia. Zip code level and personal characteristics were also associated with increased susceptibility to temperature.

CONCLUSIONS

We identified several subgroups of the population who are particularly susceptible to temperature extremes, including persons with atrial fibrillation.

Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach

BACKGROUND

Official statistics under-estimate influenza deaths. Time series methods allow the estimation of influenza-attributable mortality. The methods often model background, non-influenza mortality using a cyclic, harmonic regression model based on the Serfling approach. This approach assumes that the seasonal pattern of non-influenza mortality is the same each year, which may not always be accurate.

AIM

To estimate Australian seasonal and pandemic influenza-attributable mortality from 2003 to 2009, and to assess a more flexible influenza mortality estimation approach.

METHODS

We used a semi-parametric generalized additive model (GAM) to replace the conventional seasonal harmonic terms with a smoothing spline of time ('spline model') to estimate influenza-attributable respiratory, respiratory and circulatory, and all-cause mortality in persons aged <65 and ≥ 65 years. Influenza A(H1N1)pdm09, seasonal influenza A and B virus laboratory detection time series were used as independent variables. Model fit and estimates were compared with those of a harmonic model.

RESULTS

Compared with the harmonic model, the spline model improved model fit by up to 20%. In <65 year-olds, the estimated respiratory mortality attributable to pandemic influenza A(H1N1)pdm09 was 0.5 (95% confidence interval (CI), 0.3, 0.7) per 100,000; similar to that of the years with the highest seasonal influenza A mortality, 2003 and 2007 (A/H3N2 years). In ≥ 65 year-olds, the highest annual seasonal influenza A mortality estimate was 25.8 (95% CI 22.2, 29.5) per 100,000 in 2003, five-fold higher than the non-statistically significant 2009 pandemic influenza estimate in that age group. Seasonal influenza B mortality estimates were negligible.

CONCLUSIONS

The spline model achieved a better model fit. The study provides additional evidence that seasonal influenza, particularly A/H3N2, remains an important cause of mortality in Australia and that the epidemic of pandemic influenza A (H1N1)pdm09 virus in 2009 did not result in mortality greater than seasonal A/H3N2 influenza mortality, even in younger age groups.