A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases

Different effects of air pollutant concentrations on influenza A and B in Sichuan, China

BACKGROUND

The detrimental effects of air pollution on the respiratory system are well documented. Previous research has established a correlation between air pollutant concentration and the frequency of outpatient visits for influenza-like illness. However, studies investigating the variations in infection among different influenza subtypes remain sparse. We aimed to determine the correlation between air pollutant levels and different influenza subtypes in Sichuan Province, China.

METHODS

A generalized additive model and distributed lag nonlinear model were employed to assess the association between air pollutants and influenza subtypes, utilizing daily influenza data obtained from 30 hospitals across 21 cities in Sichuan Province. The analysis considered the temporal effects and meteorological factors. The study spanned from January 1, 2017, to December 31, 2019. To provide a more precise evaluation of the actual impact of air pollution on different subtypes of influenza, we also performed subgroup analyses based on factors such as gender, age, and geography within the population.

RESULTS

During the investigation, 17,462 specimens from Sichuan Province tested positive for influenza. Among these, 12,607 and 4855 were diagnosed with Flu A and B, respectively. The related risk of influenza A infection significantly increased following exposure to PM2.5 on Lag2 days (RR=1.008, 95 % confidence interval [CI]: 1.000-1.016), SO2 and CO on Lag1 days (RR=1.121, 95 % CI: 1.032-1.219; RR=1.151, 95 % CI: 1.030-1.289), and NO2 on Lag0 day (RR=1.089, 95 % CI: 1.035-1.145). PM10 and SO2 levels on Lag0 day, PM2.5 levels on Lag1 day, and CO levels on Lag6 day, with a reduced risk of influenza B (RR=0.987, 95 % CI: 0.976-0.997; RR=0.817, 95 % CI: 0.676-0.987; RR=0.979, 95 % CI: 0.970-0.989; RR=0.814, 95 % CI: 0.561-0.921).

CONCLUSION

The findings from the overall population and subgroup analyses indicated that the impact of air pollutant concentrations on influenza A and B is inconsistent, with influenza A demonstrating greater susceptibility to these pollutants. Minimizing the levels of SO2, CO, NO2, and PM2.5 can significantly decrease the likelihood of contracting influenza A. Analyzing the influence of environmental contaminants on different influenza subtypes can provide insights into seasonal influenza trends and guide the development of preventive and control strategies.

Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network Links

Driven by the variety of available measures intended to estimate predictability of diverse objects such as time series and network links, this paper presents a comprehensive overview of the existing literature in this domain. Our overview delves into predictability from two distinct perspectives: the intrinsic predictability, which represents a data property independent of the chosen forecasting model and serves as the highest achievable forecasting quality level, and the realized predictability, which represents a chosen quality metric for a specific pair of data and model. The reviewed measures are used to assess predictability across different objects, starting from time series (univariate, multivariate, and categorical) to network links. Through experiments, we establish a noticeable relationship between measures of realized and intrinsic predictability in both generated and real-world time series data (with the correlation coefficient being statistically significant at a 5% significance level). The discovered correlation in this research holds significant value for tasks related to evaluating time series complexity and their potential to be accurately predicted.

Nonlinear and Multidelayed Effects of Meteorological Drivers on Human Respiratory Syncytial Virus Infection in Japan

In this study, we aimed to characterize the nonlinear and multidelayed effects of multiple meteorological drivers on human respiratory syncytial virus (HRSV) infection epidemics in Japan. The prefecture-specific weekly time-series of the number of newly confirmed HRSV infection cases and multiple meteorological variables were collected for 47 Japanese prefectures from 1 January 2014 to 31 December 2019. We combined standard time-series generalized linear models with distributed lag nonlinear models to determine the exposure-lag-response association between the incidence relative risks (IRRs) of HRSV infection and its meteorological drivers. Pooling the 2-week cumulative estimates showed that overall high ambient temperatures (22.7 °C at the 75th percentile compared to 16.3 °C) and high relative humidity (76.4% at the 75th percentile compared to 70.4%) were associated with higher HRSV infection incidence (IRR for ambient temperature 1.068, 95% confidence interval [CI], 1.056-1.079; IRR for relative humidity 1.045, 95% CI, 1.032-1.059). Precipitation revealed a positive association trend, and for wind speed, clear evidence of a negative association was found. Our findings provide a basic picture of the seasonality of HRSV transmission and its nonlinear association with multiple meteorological drivers in the pre-HRSV-vaccination and pre-coronavirus disease 2019 (COVID-19) era in Japan.

Text as signal. A tutorial with case studies focusing on social media (Twitter)

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.

Effects of different levels of non-pharmaceutical interventions on hand, foot and mouth disease in Guangzhou, China

BACKGROUND

Non-pharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) may have suppressed the transmission of other infectious diseases. This study aimed to evaluate the impact of different degrees of NPIs during the COVID-19 pandemic on hand, foot and mouth disease (HFMD) in Guangzhou, China.

METHODS

Weekly reported HFMD cases and pathogens information during 2015-2021 in Guangzhou were collected from the China National Notifiable Disease Reporting System. The observed number of HFMD cases in 2020 and 2021 was compared to the average level in the same period during 2015-2019. Then, an interrupted time-series segmented regression analysis was applied to estimate the impact of NPIs on HFMD, such as social distancing, suspension of schools, community management and mask wearing. The effects across different subgroups stratified by gender, children groups and enterovirus subtype of HFMD were also examined.

RESULTS

A total of 13,224 and 36,353 HFMD cases were reported in 2020 and 2021, which decreased by 80.80% and 15.06% respectively compared with the average number of cases in the same period during 2015-2019. A significant drop in the number of HFMD cases during time when strict NPIs were applied (relative change: 69.07% [95% confidence interval (CI): 68.84%-69.30%]). The HFMD incidence rebounded to historical levels in 2021 as the lockdown eased. The slightest reduction of HFMD cases was found among children at kindergartens or childcare centres among the three children groups (children at kindergartens or childcare centres: 55.50% [95% CI: 54.96%-56.03%]; children living at home: 72.64% [95% CI: 72.38%-72.89%]; others: 74.06% [95% CI: 73.19%-74.91%]).

CONCLUSIONS

The strong NPIs during the COVID-19 epidemic may have a significant beneficial effect on mitigating HFMD. However, the incidence of HFMD rebounded as the NPIs became less stringent. Authorities should consider applying these NPIs during HFMD outbreaks and strengthening personal hygiene in routine prevention.

The influence of the environment and indoor residual spraying on malaria risk in a cohort of children in Uganda

Studies have estimated the impact of the environment on malaria incidence although few have explored the differential impact due to malaria control interventions. Therefore, the objective of the study was to evaluate the effect of indoor residual spraying (IRS) on the relationship between malaria and environment (i.e. rainfall, temperatures, humidity, and vegetation) using data from a dynamic cohort of children from three sub-counties in Uganda. Environmental variables were extracted from remote sensing sources and averaged over different time periods. General linear mixed models were constructed for each sub-counties based on a log-binomial distribution. The influence of IRS was analysed by comparing marginal effects of environment in models adjusted and unadjusted for IRS. Great regional variability in the shape (linear and non-linear), direction, and magnitude of environmental associations with malaria risk were observed between sub-counties. IRS was significantly associated with malaria risk reduction (risk ratios vary from RR = 0.03, CI 95% [0.03-0.08] to RR = 0.35, CI95% [0.28-0.42]). Model adjustment for this intervention changed the magnitude and/or direction of environment-malaria associations, suggesting an interaction effect. This study evaluated the potential influence of IRS in the malaria-environment association and highlighted the necessity to control for interventions when they are performed to properly estimate the environmental influence on malaria. Local models are more informative to guide intervention program compared to national models.

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers.

Spatial-temporal Distribution and Influencing Factors of Helicobacter pylori Infection in Chinese Mainland, 2001-2020: A Systematic Review and Meta-Analysis

BACKGROUND

The spatial-temporal distribution of Helicobacter pylori infection in China is poorly understood. We aimed to study the spatial-temporal distribution of H. pylori infection in Chinese mainland and to explore its influencing factors.

MATERIALS AND METHODS

We searched the relevant literature from 2001 to 2021 and applied meta-analysis to obtain the pooled prevalence estimates of all studies and subgroups. Then, we used the pooled prevalence as the dependent variable for the following analysis, including time series analysis, statistical mapping, spatial autocorrelation analysis, and influencing factor analysis based on generalized additive model and panel data model.

RESULTS

A total of 726 articles and 3,407,392 people were included. The pooled prevalence was 43.7% (95% confidence interval: 42.7%-44.8%). The prevalence decreased in the past 20 years, with high in the eastern and western regions and low in the central region. Qinghai Tibet Plateau and Guizhou Plateau were the high incidence areas of this disease. The intake of vegetable oil, aquatic products, meat, milk, per capita gross domestic product, and annual average humidity were significantly correlated with H. pylori.

CONCLUSIONS

The prevalence of H. pylori is decreasing in Chinese mainland, but still high in underdeveloped areas. Appropriate strategies for the prevention need greater attention.

Impact of Temperature and Rainfall on Typhoid/Paratyphoid Fever in Taizhou, China: Effect Estimation and Vulnerable Group Identification

The impact of temperature and rainfall on the occurrence of typhoid/paratyphoid fever are not fully understood. This study aimed to characterize the effect of daily ambient temperature and total rainfall on the incidence of typhoid/paratyphoid in a sub-tropical climate city of China and to identify the vulnerable groups for disease prevention. Daily notified typhoid/paratyphoid fever cases and meteorological data for Taizhou from 2005 to 2013 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. Distributed lag nonlinear model was used to quantify the association between daily mean temperature, total rainfall, and typhoid/paratyphoid fever. Subgroup analyses by gender, age, and occupation were conducted to identify the vulnerable groups. A total of 625 typhoid fever cases and 1,353 paratyphoid fever cases were reported during the study period. An increased risk of typhoid fever was detected with the increase of temperature (Each 2°C rise resulted in 6%, 95% [confidence interval] CI: 2-10% increase in typhoid cases), while the increased risk was associated with the higher temperature for paratyphoid (the highest cumulative risk of temperature was 33.40 [95% CI: 12.23-91.19] at 33°C). After the onset of mild precipitation, the relative risk of typhoid fever increased in a short-lasting and with a 13-26 days delay, and the risk was no significant after the continuous increase of precipitation (the highest cumulative risk of rainfall was 24.96 [95% CI: 4.54-87.21] at 100 mm). Whereas the risk of paratyphoid fever was immediate and long lasting, and increase rapidly with the increase of rainfall (each 100 mm increase was associated with 26% increase in paratyphoid fever cases). Significant temperature-typhoid/paratyphoid fever and rainfall-typhoid/paratyphoid fever associations were found in both genders and those aged 0-4 years old, 15-60 years old, farmers, and children. Characterized with a lagged, nonlinear, and cumulative effect, high temperature and rainfall could increase the risk of typhoid/paratyphoid fever in regions with a subtropical climate. Public health interventions such as early warning and community health education should be taken to prevent the increased risk of typhoid/paratyphoid fever, especially for the vulnerable groups.

Prevalence and factors associated with diabetes mellitus among tuberculosis patients in South India-a cross-sectional analytical study

OBJECTIVE

To determine the prevalence and determinants of diabetes mellitus (DM) among tuberculosis (TB) patients and to assess the additional yield and number needed to screen (NNS) to obtain a newly diagnosed DM among TB patients.

DESIGN

We undertook a cross-sectional analysis of the cohort data under Regional Prospective Observational Research for Tuberculosis-India consortium. Newly diagnosed TB patients recruited into the cohort between 2014 and 2018 were included. Pretested standardised questionnaires and tools were used for data collection. Prevalence of DM among TB patients was summarised as proportion with 95% CI. Type II DM was diagnosed if random blood sugar level was >200 mg/dL or if the participant had a documented history of DM. NNS by blood glucose testing to diagnose one new DM case among TB patients was also calculated.

SETTING

Three districts of South India: Puducherry, Cuddalore and Villupuram SUBJECTS: Newly diagnosed sputum smear positive pulmonary TB patients aged ≥16 years RESULTS: In total, 1188 TB patients were included. Prevalence of DM among TB patients was 39% (95% CI: 36.2% to 41.8%). In unadjusted analysis, elderly TB, marital status, caste, gender, higher education level, household income and obesity had a significant association with DM. However, in adjusted analysis, only marital status (currently married aPR; 3.77 (95 CI: 2.20 to 6.49), widowed/separated/divorced aPR; 3.66 (95 CI: 1.96 to 6.83)) and body mass index category (normal weight aPR; 3.26 (95 CI: 2.55 to 4.16), overweight aPR; 3.86 (95 CI: 2.69 to 5.52), obesity aPR; 4.08 (95 CI: 2.81 to 5.94)) were found to be significant determinants. The number of TB patients needed to be screened to find a new DM case was 12.

CONCLUSION

We found that one in three TB patients had coexisting DM. The number of TB patients needed to be screened to obtain a newly diagnosed DM patients was also determined. The study supports and highlights the need of RNTCP's effort in bidirectional screening of TB and DM.

Impact of solar ultraviolet radiation on daily outpatient visits of atopic dermatitis in Shanghai, China

The potential roles of solar ultraviolet radiation (UVR) as an environmental risk factor in inducing atopic dermatitis (AD) have not been well quantified. To determine the short-term associations between UVR and AD outpatient visits, we obtained daily outpatient visits of AD in Shanghai Skin Disease Hospital from 2013 to 2018. Data of hourly ground UVR were collected. We applied overdispersed generalized additive model to explore its associations. We found that daily exposure to UVR-A rather than UVR-B was positively associated with AD outpatient visits. The visits increased on the present day (lag 0 days) and decreased appreciably with longer lags and became insignificant at lag 4 days. For 10 w/m² increase in daytime mean and noontime mean exposure to overall UVR and UVR-A from lag 0 to 6 days, the cumulative relative risk of AD was 1.12/1.13 and 1.08/1.08, respectively. Stronger effects of UVR exposure on AD occurred in patients aged 0-7 and > 45 years and in the cold seasons. This study contributed to the few epidemiological evidences that acute exposure to solar UVR may elevate the risks of AD.

Meteorological factors and incidence of COVID-19 during the first wave of the pandemic in Catalonia (Spain): A multi-county study

The transmission of coronaviruses can be affected by several factors, including the climate. Due to the rapid spread of COVID-19 and the urgent need for rapid responses to contain the pandemic, it is essential to understand the role that weather conditions on the transmission of SARS-CoV-2. We evaluate the influence of meteorological factors on the incidence of COVID-19 during the first wave of the epidemic in Catalonia. We conducted a geographical analysis at the county level to evaluate the association between mean temperature, absolute humidity, solar radiation, and the cumulative incidence of COVID-19. Next, we used a time-series design to assess the short-term effects of meteorological factors on the daily incidence of COVID-19. We found a geographical association between meteorological factors and the cumulative incidence of COVID-19, from the end of March to June 2020, and a lesser extent in the short-term on the daily incidence during the first wave of the epidemic in Spain. Our findings suggest that warm and wet climates may reduce the incidence of COVID-19 in Catalonia. However, policy makers must interpret with caution any COVID-19 risk predictions based on climate information alone.

A systematic review on lagged associations in climate-health studies

BACKGROUND

Lagged associations in climate-health studies have already been ubiquitously acknowledged in recent years. Despite extensive time-series models having proposed accounting for lags, few studies have addressed the question of maximum-lag specification, which could induce considerable deviations of effect estimates.

METHODS

We searched the PubMed and Scopus electronic databases for existing climate-health literature in the English language with a time-series or case-crossover study design published during 2000-2019 to summarize the statistical methodologies and reported lags of associations between climate variables and 14 common causes of morbidity and mortality. We also aggregated the results of the included studies by country and climate zone.

RESULTS

The associations between infectious-disease outcomes and temperatures were found to be lagged for ∼1-2 weeks for influenza, 3-6 weeks for diarrhoea, 7-12 weeks for malaria and 6-16 weeks for dengue fever. Meanwhile, the associations between both cardiovascular and respiratory diseases and hot temperatures lasted for <5 days, whereas the associations between cardiovascular diseases and cold temperatures were observed to be 10-20 days. In addition, rainfall showed a 4- to 10-week lagged association with infectious diarrheal diseases, whereas the association could be further delayed to 8-12 weeks for vector-borne diseases.

CONCLUSION

Our findings indicated some general patterns for possible lagged associations between some common health outcomes and climatic exposures, and suggested a necessity for a biologically plausible and reasonable definition of the effect lag in the modelling practices for future environmental epidemiological studies.

Forecast and early warning of hand, foot, and mouth disease based on meteorological factors: Evidence from a multicity study of 11 meteorological geographical divisions in mainland China

BACKGROUND

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China. Early warning and forecasting are one of the most cost-effective ways for HFMD control and prevention. However, relevant research is limited, especially in China with a large population and diverse climatic characteristics. This study aims to identify local specific HFMD epidemic thresholds and construct a weather-based early warning model for HFMD control and prevention across China.

METHODS

Monthly notified HFMD cases and meteorological data for 22 cities selected from different climate zones from 2014 to 2018 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) based on meteorological factors was conducted to forecast HFMD epidemics. The receiver operator characteristic curve (ROC) was generated to determine the value of optimal warning threshold.

RESULTS

The developed model was solid in forecasting the epidemic of HFMD with all R square (R²) in the 22 cities above 85%, and mean absolute percentage error (MAPE) less than 1%. The warning thresholds varied by cities with the highest threshold observed in Shenzhen (n = 7195) and the lowest threshold in Liaoyang (n = 12). The areas under the curve (AUC) was greater than 0.9 for all regions, indicating a satisfied discriminating ability in epidemics detection.

CONCLUSIONS

The weather-based HFMD forecasting and early warning model we developed for different climate zones provides needed information on occurrence time and size of HFMD epidemics. An effective early warning system for HFMD could provide sufficient time for local authorities to implement timely interventions to minimize the HFMD morbidity and mortality.

Impact of meteorological parameters on COVID-19 transmission in Bangladesh: a spatiotemporal approach

It has been more than 10 months since the first COVID-19 case was reported in Wuhan, China, still menacing the world with a possible second wave. This study aimed to analyze how meteorological variables can affect the spread of local COVID-19 transmission in Bangladesh. Nine spatial units were considered from a meteorological standpoint to characterize COVID-19 transmission in Bangladesh. The daily COVID-19 incidence and meteorological variable (e.g., mean temperature, relative humidity, precipitation, and wind speed) data from April 5 to September 20, 2020, were collected. The Spearman rank correlation, heat maps, and multivariate quasi-Poisson regression were employed to understand their association. The effect of meteorological variables on COVID-19 transmission was modeled using a lag period of 10 days. Results showed that mean temperature, relative humidity, and wind speed are substantially associated with an increased risk of COVID-19. On the other hand, daily precipitation is significantly associated with a decreased risk of COVID-19 incidence. The relative risks (RR) of mean temperature for daily COVID-19 incidences were 1.222 (95% confidence interval [CI], 1.214-1.232). For wind speed, the RR was 1.087 (95% CI, 1.083-1.090). For relative humidity, the RR was 1.027 (95% CI, 1.025-1.029). Overall, this study found the profound effect of meteorological parameters on COVID-19 incidence across selected nine areas in Bangladesh. This study is probably the first study to explore the impact of region-specific meteorological conditions on COVID-19 incidence in Bangladesh. Moreover, adjustments on the areal-aggregated and regional levels were made for three confounding factors, including lockdown, population density, and potential seasonal effects. The study's findings suggest that SARS-CoV-2 can be transmitted in high temperatures and humidity conditions, which contradicts many other countries' prior studies. The research outcomes will provide implications for future control and prevention measures in Bangladesh and other countries with similar climate conditions and population density.

Climate and Human coronaviruses 229E and Human coronaviruses OC43 Infections: Respiratory Viral Infections Prevalence in Hospitalized Children in Cheonan, Korea

The study of climate and respiratory viral infections using big data may enable the recognition and interpretation of relationships between disease occurrence and climatic variables. In this study, realtime reverse transcription quantitative PCR (qPCR) methods were used to identify Human respiratory coronaviruses (HCoV). infections in patients below 10 years of age with respiratory infections who visited Dankook University Hospital in Cheonan, South Korea, from January 1, 2012, to December 31, 2018. Out of the 9010 patients who underwent respiratory virus real-time reverse transcription qPCR test, 364 tested positive for HCoV infections. Among these 364 patients, 72.8% (n = 265) were below 10 years of age. Data regarding the frequency of infections was used to uncover the seasonal pattern of the two viral strains, which was then compared with local meteorological data for the same time period. HCoV-229E and HCoV-OC43 showed high infection rates in patients below 10 years of age. There was a negative relationship between HCoV-229E and HCoV-OC43 infections with air temperature and wind-chill temperatures. Both HCoV-229E and HCoV-OC43 rates of infection were positively related to atmospheric pressure, while HCoV-229E was also positively associated with particulate matter concentrations. Our results suggest that climatic variables affect the rate in which children below 10 years of age are infected with HCoV. These findings may help to predict when prevention strategies may be most effective.

Short-term effects of meteorological factors, air pollution, and sunspot on childhood hand, foot, and mouth disease in Tianjin, China: a new time series regression, 2014-2018

This study is aimed at defining the relationship between a set of environmental factors and childhood HFMD and then at estimating the related effect. The 16 environmental factors included meteorological, air pollution, and sunspot. A traditional TSR modified by using susceptible-infectious-recovery models and distribution lag nonlinear model was applied to estimate the short-term effects of daily environmental factors on children HFMD occurrence in 2014-2018 with adjustment of potential confounding factors. A total of 70,027 children aged 0-15 years with HFMD were enrolled. No significant effect was observed for daily sunspot numbers and average visibility. We found positive effects of the ambient average temperature, with an approximately m-shaped curve of the overall cumulative relationship, peaking at 25.6 °C with a relative risk (RR) of 1.45 (95% confidence intervals 1.21-1.73). The largest RR value of hot effect was achieved on the current day and then decreased by 2 days (total group, male group, and scatter group) or 1 day (female group and nursery group), and the effect lasted about 6 to 8 days from the lag 4 or lag 6 day. A greater association of temperature with HFMD for the female group and the scattered group was observed. This study suggests that ambient average temperature might be a risk factor for children HFMD in Tianjin. Further studies are warranted to confirm these findings.

How climate change can affect cholera incidence and prevalence? A systematic review

Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.

Epidemiological models for predicting Ross River virus in Australia: A systematic review

Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used time-series models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation.

Assessment of Risk Hospitalization due to Acute Respiratory Incidents Related to Ozone Exposure in Silesian Voivodeship (Poland)

The main aim of this work is the estimation of health risks arising from exposure to ozone or other air pollutants by different statistical models taking into account delayed health effects. This paper presents the risk of hospitalization due to bronchitis and asthma exacerbation in adult inhabitants of Silesian Voivodeship from 1 January 2016 to 31 August 2017. Data were obtained from the daily register of hospitalizations for acute bronchitis (code J20-J21, International Classification of Diseases, Tenth Revision - ICD-10) and asthma (J45-J46) which is governed by the National Health Fund. Meteorological data and data on tropospheric ozone concentrations were obtained from the regional environmental monitoring database of the Provincial Inspector of Environmental Protection in Katowice. The paper includes descriptive and analytical statistical methods used in the estimation of health risk with a delayed effect: Almon Distributed Lag Model, the Poisson Distributed Lag Model, and Distributed Lag Non-Linear Model (DLNM). A significant relationship has only been confirmed by DLNM for bronchitis and a relatively short period (1-3 days) from exposure above the limit value (120 µg/m³). The relative risk value was RR = 1.15 (95% CI 1.03-1.28) for a 2-day lag. However, conclusive findings require the continuation of the study over longer observation periods.

Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan

Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10–14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations.

The acute effects of ultraviolet radiation exposure on solar dermatitis in Shanghai, China

Ultraviolet radiation (UVR) has long been considered associated with solar dermatitis, but the associations have not been well quantified. To depict the full-range exposure-response association between daily UVR exposures and daily outpatient visits of solar dermatitis. We collected the daily number of outpatient visits of solar dermatitis and monitored hourly ground data of UVR (the sum of A- and B-band) from 1 January 2013 to 31 December 2017 in Shanghai, China. The data were analyzed using the time-series approach, in which overdispersed generalized additive model was used and time trends and weather conditions were controlled for. During the study period, we recorded a total of 15,051 outpatient visits of solar dermatitis. There was a consistently increasing risk of solar dermatitis associated with stronger UVR without a discernible threshold. The effects occurred on the present day, increased to the largest at lag 1 or 2 days, and attenuated to the null at lag 5 days or more. A unit (w/m²) increase in daily maximum-hour UVR was associated with 1.70% (95%CI: 1.19%, 2.20%) increase of outpatient visits of solar dermatitis. Stronger effects occurred among the young people, females, and in the warm season. The risks of solar dermatitis due to UVR exposure would be overestimated if ambient temperature was not adjusted. This study provides quantitative epidemiological estimates for the positive associations between short-term exposure to UVR and increased risks of solar dermatitis. The associations were more prominent among young people, females, and in warm seasons.

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness.

Disease-causing microorganisms, including viruses, bacteria, protozoa and fungi, form complex communities within animals and plants. These microorganisms can coexist harmoniously or even beneficially, or they may competitively interact for host resources. Well-studied examples include interactions between viruses and bacteria in the respiratory tract. Whilst ecological studies have revealed that some pathogens do interact within their hosts, identifying interactions from available population scale data from health authorities is challenging. This is exacerbated by a lack of large-scale data describing the infection patterns of multiple pathogens within single populations over long time frames. Furthermore, methods for evaluating whether infection frequencies of different pathogens fluctuate together or not over time cannot readily account for alternative explanations. For example, human pathogens may have related seasonal patterns depending on the age groups they infect and the weather conditions they survive in, and not because they are interacting. We developed a robust statistical framework to identify pathogen-pathogen interactions from population scale diagnostic data. This framework serves as a crucial step in identifying such important interactions and will guide new studies to elucidate their underpinning mechanisms. This will have important consequences for public health preparedness and the design of effective disease control interventions.

Climatic Factors in Relation to Diarrhoea Hospital Admissions in Rural Limpopo, South Africa

Diarrheal disease is one of the leading causes of morbidity and mortality globally, particularly in children under 5 years of age. Factors related to diarrheal disease incidence include infection, malnutrition, and exposure to contaminated water and food. Climate factors also contribute to diarrheal disease. We aimed to explore the relationship between temperature, precipitation and diarrhoea case counts of hospital admissions among vulnerable communities living in a rural setting in South Africa. We applied ‘contour analysis’ to visually examine simultaneous observations in frequencies of anomalously high and low diarrhoea case counts occurring in a season, and assigning colours to differences that were statistically significant based on chi-squared test results. Children under 5 years of age were especially vulnerable to diarrhoea during very dry, hot conditions as well as when conditions were wetter than usual. We saw an anomalously higher number of diarrhoea cases during ‘warmer than usual’ conditions in the dry winter season, with average winter temperatures in Limpopo being from about 5 to 10 °C. As for ‘wetter than usual’ conditions, we saw an anomalously higher number of diarrhoea cases during ‘drier than usual’ conditions for the winter and spring. The lagged association seen in cumulative rainfall could not be distinguished in the same way for temperature-related variables (indicating rainfall had a larger impact on higher cases of diarrhoea), nor for the older age group of 5 years and older. Dry conditions were associated with diarrhoea in children under 5 years of age; such conditions may lead to increased water storage, raising the risks of water contamination. Reduced use of water for personal hygiene and cleaning of outdoor pit latrines also affect sanitation quality. Rural communities require adequate and uninterrupted water provision, and healthcare providers should raise awareness about potential diarrhoeal risks, especially during the dry season as well as during wintertime when conditions are warmer than usual.

Evaluating the impact of health reforms in the Netherlands: Assessing the impact of an alcohol ban on sexually transmitted infections in national surveillance data

BACKGROUND

On 1 January 2014, the minimum age to buy alcohol increased (16-18 years), accompanied by a public awareness campaign (NIX18). Decreases in alcohol consumption are associated with less risky sexual behaviour. This study analyzed the association between the health reforms andChlamydia trachomatis infections (chlamydia) among young heterosexual people.

METHODS

Chlamydia positivity rates, age, and gender from all STI-clinic attendees between 16 and 19 years old in the Netherlands of 2010 to 2016 were obtained. Interrupted time-series assessed immediate and gradual trends in chlamydia rates.

RESULTS

Among the control group (18-19 year olds) chlamydia rates increased 0.5% each post-ban month (95% Confidence Interval [CI] 1.002-1.008, p = .001). Among 16-17 year olds there was no monthly increase post-ban (Rate Ratio 1.000, 95% CI 0.993-1.007, p = .948). In terms of confounders, only controlling for partner notification dissolved these time trends.

CONCLUSIONS

We found that chlamydia rates after the alcohol ban differed between 16-17 year olds and 18-19 year olds. This demonstrates that the health reforms might have affected this secondary outcome, but obtaining certainty using national surveillance data is difficult. Specific studies should be designed, as now changes in chlamydia over time could be explained by STI-clinic policy changes, by changes on an individual level including reduced alcohol consumption or most likely by the combination of these factors.

Modeling Future Projections of Temperature-Related Excess Morbidity due to Infectious Gastroenteritis under Climate Change Conditions in Japan

BACKGROUND

Climate change has marked implications for the burden of infectious diseases. However, no studies have estimated future projections of climate change–related excess morbidity due to diarrhea according to climate change scenarios.

OBJECTIVES

We aimed to examine temperature-infectious gastroenteritis associations throughout Japan and project temperature-related morbidity concomitant with climate change for the 2090s.

METHODS

Weekly time series of average temperature and morbidity for infectious gastroenteritis cases in the period 2005-2015 were collated from the 47 Japanese prefectures. A two-stage time-series analysis was adopted to estimate temperature-infectious gastroenteritis relationships. Time series of present and future average daily temperature fluctuations were projected for the four climate change scenarios of representative concentration pathways (RCPs) according to five general circulation models. Excess morbidity for high and low temperatures and the net change in the period 1990–2099 were projected for each climate change scenario by assuming the absence of adaptation and population alterations.

RESULTS

In the period 2005–2015, 11,529,833 infectious gastroenteritis cases were reported. There were net reductions in temperature-induced excess morbidity under higher emission scenarios. The net change in the projection period 2090-2099 in comparison with 2010–2019 was [Formula: see text] (95% empirical confidence interval [eCI]: [Formula: see text], 0.5) for RCP2.6, [Formula: see text] (95% eCI: [Formula: see text], [Formula: see text]) for RCP4.5, [Formula: see text] (95% eCI: [Formula: see text], [Formula: see text]) for RCP6.0, and [Formula: see text] (95% eCI: [Formula: see text], [Formula: see text]) for RCP8.5, and the higher the emissions scenario, the larger the estimates reductions. Spatial heterogeneity in the temperature-morbidity relationship was observed among prefectures (Cochran Q test, [Formula: see text]; [Formula: see text]).

CONCLUSIONS

Japan may experience a net reduction in temperature-related excess morbidity due to infectious gastroenteritis in higher emission scenarios. These results might be because the majority of temperature-related diarrhea cases in Japan are attributable to viral infections during the winter season. Further projections of specific pathogen-induced infectious gastroenteritis due to climate change are warranted. https://doi.org/10.1289/EHP4731.

Estimating age-stratified influenza-associated invasive pneumococcal disease in England: A time-series model based on population surveillance data

BACKGROUND

Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral-bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups.

METHODS AND FINDINGS

We used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t-1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%-39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%-63.2%) of incidence in adults aged 15-44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%-28.2%, and 6.07%, CI 2.83%-9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%-3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%-3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates.

CONCLUSIONS

Our estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups.

Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China

BACKGROUND

Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics.

METHODS

A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed.

RESULTS

DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics.

CONCLUSIONS

The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.

Syndromic survey and molecular analysis of influenza viruses at the human-swine interface in two West African cosmopolitan cities suggest the possibility of bidirectional interspecies transmission

Influenza viruses are frequently transmitted between pigs and their handlers, and among pig handlers. However, reports on socio-environmental variables as potential risk factors associated with transmission of influenza in West African swine production facilities are very scarce. Syndromic survey for influenza was therefore conducted in Ibadan, Nigeria, and Kumasi, Ghana, in order to identify and elucidate selected socio-environmental variables that may contribute to the occurrence and distribution of influenza-like illness (ILI) among swine industry workers. In addition, molecular analyses were conducted to elucidate the nature of influenza viruses circulating at the human-swine interface in these cities and better understand the dynamics of their transmission. Influenza viruses were detected by type-specific and subtype-specific RT-PCR. Sequencing and phylogenetic analyses were carried out. Socio-environmental variables were tested by both univariable and multivariable regression methods for significance at p < 0.05. Three risk factors for ILI were identified in each city. These included "frequency of visit of pig handler to pig pen or lairage" (Ibadan: risk ratio [RR] = 1.54, 95% confidence interval [CI] = 1.36-1.79, p = 0.02; Kumasi: RR = 1.28, 95% CI = 1.11-1.71, p = 0.01) and "pig handler's awareness about biosecurity measures" (Ibadan: RR = 7.09, 95% CI = 2.36-21.32, p < 0.001; Kumasi: RR = 4.84, 95% CI = 1.98-11.80, p < 0.001). Influenza A(H1N1)pdm09 viruses, with M genes closely related to those which circulated among pigs in the two cities during the same period, were detected among Nigerian and Ghanaian pig industry workers. These findings suggest the possibility of bidirectional transmission of influenza at the human-swine interface in these cities and underscore the need for more extensive molecular studies. Risk factors identified may assist in the control of human-to-human and human-to-swine transmission of influenza in the West African swine industry.

Association between floods and typhoid fever in Yongzhou, China: Effects and vulnerable groups

BACKGROUND

Little information about the effects of floods on typhoid fever is available in previous studies. This study aimed to examine the relationships between floods and typhoid fever and to identify the vulnerable groups in Yongzhou, China.

METHODS

Weekly typhoid fever data, flood data and meteorological data during the flood season (April to September) from 2005 to 2012 were collected for this study. A Poisson generalized linear model combined with a distributed lag non-linear model was conducted to quantify the lagged and cumulative effects of floods on typhoid fever, considering the confounding effects of long-term trend, seasonality, and meteorological variables. The model was also used to calculate risk ratios of floods for weekly typhoid fever cases among various subpopulations.

RESULTS

After adjusting for long-term trend, seasonality, and meteorological variables, floods were associated with an increased number of typhoid fever cases with a risk ratio of 1.46 (95% CI: 1.10-1.92) at 1-week lag and a cumulative risk ratio of 1.76 (95% CI: 1.21-2.57) at lag 0-1 weeks. Males, people aged 0-4 years old, people aged 15-64 years old, farmers, and children appeared to be more vulnerable than the others.

CONCLUSIONS

Our study indicates that floods could significantly increase the risks of typhoid fever with lag effects of 1 week in the study areas. Precautionary measures should be taken with a focus on the identified vulnerable groups in order to control the transmission of typhoid fever associated with floods.

A method for evaluating and comparing immunisation schedules that cover multiple diseases: Illustrative application to the UK routine childhood vaccine schedule

BACKGROUND

In the UK, the childhood immunisation programme is given in the first 5 years of life and protects against 12 vaccine-preventable diseases. Recently, this programme has undergone changes with addition of vaccination against Meningitis B from September 2015 and the removal of the primary dose of protection against Meningitis C from July 2016. These hanges have direct impact on the associated diseases but in addition may induce indirect effects on the vaccines that are given simultaneously or later in the programme. In this work, we developed a novel formal method to evaluate the impact of vaccination changes to one aspect of the programme across an entire vaccine programme.

METHODS

Firstly, we combined transmission modelling (for four diseases) and historic data synthesis (for eight diseases) to project, for each disease, the disease burden at different levels of effective coverage against the associated disease. Secondly, we used a simulation model to determine the vector of effective coverage against each disease under three variations of the current childhood schedule. Combining these, we calculated the vector of disease burden across the programme under different scenarios, and assessed the direct and indirect effects of the schedule changes.

RESULTS

Through illustrative application of our novel framework to three scenarios of the current childhood immunisation programme in the UK, we demonstrated the feasibility of this unifying approach. For each disease in the programme, we successfully quantified the residual disease burden due to the change. For some diseases, the change was indirectly beneficial and reduced the burden, whereas for others the effect was adverse and the change increased the disease burden.

CONCLUSIONS

Our results demonstrate the potential benefit of considering the programme-wide impact of changes to an immunisation schedule, and our framework is an important step in the development of a means for systematically doing so.

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

The global increase in vector borne diseases has been linked to climate change. Seasonal vegetation changes are known to influence disease vector population. However, the relationship is more theoretical than quantitatively defined. There is a growing demand for understanding and prediction of climate sensitive vector borne disease risks especially in regions where meteorological data are lacking. This study aimed at analyzing and quantitatively assessing the seasonal and year-to-year association between climatic factors (rainfall and temperature) and vegetation cover, and its implications for malaria risks in Baringo County, Kenya. Remotely sensed temperature, rainfall, and vegetation data for the period 2004–2015 were used. Poisson regression was used to model the association between malaria cases and climatic and environmental factors for the period 2009–2012, this being the period for which all datasets overlapped. A strong positive relationship was observed between the Normalized Difference Vegetation Index (NDVI) and monthly total precipitation. There was a strong negative relationship between NDVI and minimum temperature. The total monthly rainfall (between 94 -181mm), average monthly minimum temperatures (between 16–21°C) and mean monthly NDVI values lower than 0.35 were significantly associated with malaria incidence rates. Results suggests that a combination of climatic and vegetation greenness thresholds need to be met for malaria incidence to be significantly increased in the county. Planning for malaria control can therefore be enhanced by incorporating these factors in malaria risk mapping.

Using the Baidu Search Index to Predict the Incidence of HIV/AIDS in China

Based on a panel of 30 provinces and a timeframe from January 2009 to December 2013, we estimate the association between monthly human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) incidence and the relevant Internet search query volumes in Baidu, the most widely used search engine among the Chinese. The pooled mean group (PMG) model show that the Baidu search index (BSI) positively predicts the increase in HIV/AIDS incidence, with a 1% increase in BSI associated with a 2.1% increase in HIV/AIDS incidence on average. This study proposes a promising method to estimate and forecast the incidence of HIV/AIDS, a type of infectious disease that is culturally sensitive and highly unevenly distributed in China; the method can be taken as a complement to a traditional HIV/AIDS surveillance system.

Association between floods and infectious diarrhea and their effect modifiers in Hunan province, China: A two-stage model

BACKGROUND

Understanding the potential links between floods and infectious diarrhea is important under the context of climate change. However, little is known about the risk of infectious diarrhea after floods and what factors could modify these effects in China.

OBJECTIVES

This study aims to quantitatively examine the relationship between floods and infectious diarrhea and their effect modifiers.

METHODS

Weekly number of infectious diarrhea cases from 2004 to 2011 during flood season in Hunan province were supplied by the National Notifiable Disease Surveillance System. Flood and meteorological data over the same period were obtained. A two-stage model was used to estimate a provincial average association and their effect modifiers between floods and infectious diarrhea, accounting for other confounders.

RESULTS

A total of 134,571 cases of infectious diarrhea were notified from 2004 to 2011. After controlling for seasonality, long-term trends, and meteorological factors, floods were significantly associated with infectious diarrhea in the provincial level with a cumulative RR of 1.22 (95% CI: 1.05, 1.43) with a lagged effect of 0-1 week. Geographic locations and economic levels were identified as effect modifiers, with a higher impact of floods on infectious diarrhea in the western and regions with a low economic level of Hunan.

CONCLUSIONS

Our study provides strong evidence of a positive association between floods and infectious diarrhea in the study area. Local control strategies for public health should be taken in time to prevent and reduce the risk of infectious diarrhea after floods, especially for the vulnerable regions identified.

The non-linear and lagged short-term relationship between rainfall and leptospirosis and the intermediate role of floods in the Philippines

Background

Leptospirosis is a worldwide bacterial zoonosis. Outbreaks of leptospirosis after heavy rainfall and flooding have been reported. However, few studies have formally quantified the effect of weather factors on leptospirosis incidence. We estimated the association between rainfall and leptospirosis cases in an urban setting in Manila, the Philippines, and examined the potential intermediate role of floods in this association.

Methods/Principal findings

Relationships between rainfall and the weekly number of hospital admissions due to leptospirosis from 2001 to 2012 were analyzed using a distributed lag non-linear model in a quasi-Poisson regression framework, controlling for seasonally varying factors other than rainfall. The role of floods on the rainfall–leptospirosis relationship was examined using an indicator. We reported relative risks (RRs) by rainfall category based on the flood warning system in the country. The risk of post-rainfall leptospirosis peaked at a lag of 2 weeks (using 0 cm/week rainfall as the reference) with RRs of 1.30 (95% confidence interval: 0.99–1.70), 1.53 (1.12–2.09), 2.45 (1.80–3.33), 4.61 (3.30–6.43), and 13.77 (9.10–20.82) for light, moderate, heavy, intense and torrential rainfall (at 2, 5, 16, 32 and 63 cm/week), respectively. After adjusting for floods, RRs (at a lag of 2 weeks) decreased at higher rainfall levels suggesting that flood is on the causal pathway between rainfall and leptospirosis.

Conclusions

Rainfall was strongly associated with increased hospital admission for leptospirosis at a lag of 2 weeks, and this association was explained in part by floods.

Leptospirosis is a worldwide bacterial zoonosis which is mainly transmitted through contact with water contaminated by rodents’ urine. It manifests with various symptoms, ranging from fever and muscle pain to a severe syndrome characterized by jaundice, renal failure and pulmonary hemorrhage. Outbreaks of leptospirosis after heavy rainfall and flooding have been reported, but few studies have evaluated the effect of weather factors on leptospirosis. We estimated the association between rainfall and leptospirosis cases in an urban setting in Manila, the Philippines, and examined the potential intermediate role of floods in this association. The risk of post-rainfall leptospirosis peaked at a lag of 2 weeks. After adjusting for floods, the effect of rainfall at lag 2 decreased at higher rainfall levels suggesting that flooding is on the causal pathway between heavy rainfall and leptospirosis. The results are useful for public health interventions to prepare hospitals and clinics for increased number of patients in case of an outbreak, which can help reduce the disease burden.

Prognostic factors among TB and TB/DM comorbidity among patients on short course regimen within Nairobi and Kiambu counties in Kenya

Background

The double burden of diabetes mellitus (DM) and pulmonary tuberculosis (TB) is one of the global health challenges. Studies done in different parts of the world indicate that 12%-44% of TB disease is associated with DM. In Kenya TB-DM co-morbidity data is scarce and is not readily available. In this study we set to determine the difference in treatment outcomes among TB and TB/DM comorbidity patients and their respective clinical and socio-demographic characteristics.

Objective

To determine prognostic factors among TB and TB/DM comorbidity among patients on short course regimen within Nairobi and Kiambu counties in Kenya.

Methods

We carried out a prospective cohort study of non-pregnant patients aged 15 years and above that tested positive for TB in two peri‑urban counties in Kenya between February 2014 and August 2015. Clinical and socio demographic data were obtained from a questionnaire and medical records of the National TB program patient data base at two, three, five and six months. The data consisted of TB status, HIV status, TB lineage, County, (Glucose, %HbA1c, creatinine) weight, height, BMI, regimen, sex, level of education, employment status, distance from health facility, number of cigarettes smoked, home size, and diet. Univariate analysis was then used to compare each potential risk factor in the TB and TB/DM patients by the Pearson x² test of proportions or fisher exact test, as appropriate.

Results

DM prevalence (HbA1c > 6%) among TB infected patients was 37.2%. Regimen, employment status, alcohol intake, smoking, age and household size were some of the factors associated with DM among TB patients at p-value < 0.05. The number of cigarettes smoked per day and the value of the BUN were significant risk factors of developing DM among TB patients (p values = 0.045). Mean time to conversion from positive to negative was slightly higher for the TB-DM patients compared to the TB patents, though not statistically significant (p = 0.365).

Conclusion

Patients regimen, employment status, alcohol intake, smoking, age and are associated with DM among TB patients.

Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead

Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.

Bayesian dynamic modeling of time series of dengue disease case counts

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review

Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally.

Association between dengue fever incidence and meteorological factors in Guangzhou, China, 2005-2014

This study aims to (1) investigate the associations between climatic factors and dengue; and (2) identify the susceptible subgroups. De-identified daily dengue cases in Guangzhou for 2005-2014 were obtained from the Chinese Center for Disease Control and Prevention. Weather data were downloaded from the China Meteorological Data Sharing Service System. Distributed lag non-linear models (DLNM) were used to graphically demonstrate the three-dimensional temperature-dengue association. Generalised estimating equation models (GEE) with piecewise linear spline functions were used to quantify the temperature-dengue associations. Threshold values were estimated using a broken-stick model. Middle-aged and older people, people undertaking household duties, retirees, and those unemployed were at high risk of dengue. Reversed U-shaped non-linear associations were found between ambient temperature, relative humidity, extreme wind velocity, and dengue. The optimal maximum temperature (T_max) range for dengue transmission in Guangzhou was 21.6-32.9°C, and 11.2-23.7°C for minimum temperature (T_min). A 1°C increase of T_max and T_min within these ranges was associated with 11.9% and 9.9% increase in dengue at lag0, respectively. Although lag effects of temperature were observed for up to 141 days for T_max and 150 days for T_min, the maximum lag effects were observed at 32 days and 39 days respectively. Average relative humidity was negatively associated with dengue when it exceeded 78.9%. Maximum wind velocity (>10.7m/s) inhibited dengue transmission. Climatic factors had significant impacts on dengue in Guangzhou. Lag effects of temperature on dengue lasted the local whole epidemic season. To reduce the likely increasing dengue burden, more efforts are needed to strengthen the capacity building of public health systems.

A Spatial Hierarchical Analysis of the Temporal Influences of the El Niño-Southern Oscillation and Weather on Dengue in Kalutara District, Sri Lanka

Dengue is the major public health burden in Sri Lanka. Kalutara is one of the highly affected districts. Understanding the drivers of dengue is vital in controlling and preventing the disease spread. This study focuses on quantifying the influence of weather variability on dengue incidence over 10 Medical Officer of Health (MOH) divisions of Kalutara district. Weekly weather variables and data on dengue notifications, measured at 10 MOH divisions in Kalutara from 2009 to 2013, were retrieved and analysed. Distributed lag non-linear model and hierarchical-analysis was used to estimate division specific and overall relationships between weather and dengue. We incorporated lag times up to 12 weeks and evaluated models based on the Akaike Information Criterion. Consistent exposure-response patterns between different geographical locations were observed for rainfall, showing increasing relative risk of dengue with increasing rainfall from 50 mm per week. The strongest association with dengue risk centred around 6 to 10 weeks following rainfalls of more than 300 mm per week. With increasing temperature, the overall relative risk of dengue increased steadily starting from a lag of 4 weeks. We found similarly a strong link between the Oceanic Niño Index to weather patterns in the district in Sri Lanka and to dengue at a longer latency time confirming these relationships. Part of the influences of rainfall and temperature can be seen as mediator in the causal pathway of the Ocean Niño Index, which may allow a longer lead time for early warning signals. Our findings describe a strong association between weather, El Niño-Southern Oscillation and dengue in Sri Lanka.

The short-term association between meteorological factors and mumps in Jining, China

BACKGROUND

An increasing trend of the incidence of mumps has been observed in a few developing countries in recent years, presenting a major threat to children's health. A few studies have examined the relationship between meteorological factors and mumps with inconsistent findings.

METHODS

The daily data of meteorological variables and mumps from 2009 to 2013 were obtained from Jining, a temperate inland city of China. A generalized additive model was used to quantify the association between meteorological factors and mumps based on the exposure-response relationship.

RESULTS

A total of 8520 mumps cases were included in this study. We found a nonlinear relationship of daily mean temperature, sunshine duration and relative humidity with mumps, with an approximately linear association for mean temperature above 4°C (excess risk (ER) for 1°C increase was 2.72%, 95% confidence interval (CI): 2.38%, 3.05% on the current day), for relative humidity above 54%, the ER for 1% increase was -1.86% (95% CI: -2.06%, -1.65%) at lag day 14; and for sunshine duration higher than 5h/d, the ER for per 1h/d increase was12.91% (95% CI: 11.38%, 14.47%) at lag day 1. While we found linear effects for daily wind speed (ER: 2.98%, 95% CI: 2.71%, 3.26% at lag day 13).

CONCLUSIONS

This study suggests that meteorological factors might be important predictors of incidence of mumps, and should be considered in its control and prevention.

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)₁₂ model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

Time series regression model for infectious disease and weather

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context.