Distributed lag effects and vulnerable groups of floods on bacillary dysentery in Huaihua, China

Infectious Diarrhea Risks as a Public Health Emergency in Floods; a Systematic Review and Meta-Analysis

Introduction

Infectious diarrhea, a significant global health challenge, is exacerbated by flooding, a consequence of climate change and environmental disruption. This comprehensive study aims to quantify the association between flooding events and the incidence of infectious diarrhea, considering diverse demographic, environmental, and pathogen-specific factors.

Methods

In this systematic review and meta-analysis, adhering to PROSPERO protocol (CRD42024498899), we evaluated observational studies from January 2000 to December 2023. The analysis incorporated global data from PubMed, Scopus, Embase, Web of Science, and ProQuest, focusing on the relative risk (RR) of diarrhea post-flooding. The study encompassed diverse variables like age, sex, pathogen type, environmental context, and statistical modeling approaches.

Results

The meta-analysis, involving 42 high-quality studies, revealed a substantial increase (RR = 1.40, 95% CI [1.29-1.52]) in the incidence of diarrhea following floods. Notably, bacterial and parasitic diarrheas demonstrated higher RRs (1.82 and 1.35, respectively) compared to viral etiologies (RR = 1.15). A significant sex disparity was observed, with women exhibiting a higher susceptibility (RR = 1.55) than men (RR = 1.35). Adults (over 15 years) faced a greater risk than younger individuals, highlighting age-dependent vulnerability.

Conclusion

This extensive analysis confirms a significant correlation between flood events and increased infectious diarrhea risk, varying across pathogens and demographic groups. The findings highlight an urgent need for tailored public health interventions in flood-prone areas, focusing on enhanced sanitation, disease surveillance, and targeted education to mitigate this elevated risk. Our study underscores the critical importance of integrating flood-related health risks into global public health planning and climate change adaptation strategies.

Hysteresis effects of different levels of storm flooding on susceptible enteric infectious diseases in a central city of China

BACKGROUND

Recently, attention has focused on the impact of global climate change on infectious diseases. Storm flooding is an extreme weather phenomenon that not only impacts the health of the environment but also worsens the spread of pathogens. This poses a significant challenge to public health security. However, there is still a lack of research on how different levels of storm flooding affect susceptible enteric infectious diseases over time.

METHODS

Data on enteric infectious diseases, storm flooding events, and meteorology were collected for Changsha, Hunan Province, between 2016 and 2020. The Wilcoxon Rank Sum Test was used to identify the enteric infectious diseases that are susceptible to storm flooding. Then, the lagged effects of different levels of storm flooding on susceptible enteric infectious diseases were analyzed using a distributed lag nonlinear model.

RESULTS

There were eleven storm flooding events in Changsha from 2016 to 2020, concentrated in June and July. 37,882 cases of enteric infectious diseases were reported. During non-flooding days, the daily incidence rates of typhoid/paratyphoid and bacillary dysentery were 0.3/100,000 and 0.1/100,000, respectively. During flooding days, the corresponding rates increased to 2.0/100,000 and 0.8/100,000, respectively. The incidence rates of both diseases showed statistically significant differences between non-flooding and flooding days. Correlation analysis shows that the best lags for typhoid/paratyphoid and bacillary dysentery relative to storm flooding events may be 1 and 3 days. The results of the distributed lag nonlinear model showed that typhoid/paratyphoid had the highest cumulative RR values of 2.86 (95% CI: 1.71-4.76) and 8.16 (95% CI: 2.93-22.67) after 4 days of general flooding and heavy flooding, respectively; and bacillary dysentery had the highest cumulative RR values of 1.82 (95% CI: 1.40-2.35) and 3.31 (95% CI: 1.97-5.55) after 5 days of general flooding and heavy flooding, respectively.

CONCLUSIONS

Typhoid/paratyphoid and bacillary dysentery are sensitive enteric infectious diseases related to storm flooding in Changsha. There is a lagging effect of storm flooding on the onset of typhoid/paratyphoid and bacillary dysentery, with the best lagging periods being days 1 and 3, respectively. The cumulative risk of typhoid/paratyphoid and bacillary dysentery was highest at 4/5 days lag, respectively. The higher of storm flooding, the higher the risk of disease, which suggests that the authorities should take appropriate preventive and control measures before and after storm flooding.

The size of the susceptible pool differentiates climate effects on seasonal epidemics of bacillary dysentery

OBJECTIVES

At present, some studies have pointed out several possible climate drivers of bacillary dysentery. However, there is a complex nonlinear interaction between climate drivers and susceptible population in the spread of diseases, which makes it challenging to detect climate drivers at the size of susceptible population.

METHODS

By using empirical dynamic modeling (EDM), the climate drivers of bacillary dysentery dynamic were explored in China's five temperature zones.

RESULTS

We verified the availability of climate drivers and susceptible population size on bacillary dysentery, and used this information for bacillary dysentery dynamic prediction. Moreover, we found that their respective effects increased with the increase of temperature and relative humidity, and their states (temperature and relative humidity) were different when they reached their maximum effects, and the negative effect between the effect of temperature and disease incidence increased with the change of temperature zone (from temperate zone to warm temperate zone to subtropical zone) and the climate driving effect of the temperate zone (warm temperate zone) was greater than that of the colder (temperate zone) and warmer (subtropics) zones. When we viewed from single temperature zone, the climatic effect arose only when the size of the susceptible pool was large.

CONCLUSIONS

These results provide empirical evidence that the climate factors on bacillary dysentery are nonlinear, complex but dependent on the size of susceptible populations and different climate scenarios.

The impact of heavy precipitation and its impact modifiers on shigellosis occurrence during typhoon season in Taiwan: A case-crossover design

Because of climate change, heavy precipitation is likely to become frequent and intense, thereby increasing the risk of shigellosis occurrence. However, few studies examined the impact of heavy precipitation on shigellosis and its impact modifiers in developed countries. This study aims to analyze the association between heavy precipitation and shigellosis in Taiwan, and to identify the vulnerable population and impact modifiers. We adopted a case-crossover design, and used conditional logistic regression to estimate odds ratios (ORs) for shigellosis occurrence. Information were collected on the daily shigellosis cases, precipitation, temperature, and typhoons from 1994 to 2015, and yearly data of medical resources and environmental factors were obtained at the city level from 1998 to 2015. Stratification analyses were performed by age, sex, medical resource, and environmental factors. We discovered that heavy precipitation ≥80 mm/day considerably increased the risk of shigellosis occurrence. The ORs of heavy rain (80 to <200 mm/day) were 2.08-2.26 at lags 0-1. The ORs of extremely heavy rain (≥200 mm/day) increased to 2.17-4.73 at lags 5-8. Moreover, the effect of heavy precipitation was greater under high temperature condition (≥23.6 °C). Adults were more susceptible to heavy-precipitation-associated shigellosis, especially the elderly. Males experienced marginally higher effects than females did. Moreover, cities with more medical resources and forest cover and higher percentage of completed storm sewers had lower effects; however, dense population and higher pig density were the risk factors. Although the high water-supply penetration rate did not decrease Shigella infection after heavy precipitation, it did lower the risk of typhoon-related shigellosis. In conclusion, hot temperature could enhance the impact of heavy precipitation on shigellosis. Public health interventions should be introduced according to the lag period after heavy precipitation, particularly in areas with high population density, proportion of elderly people, and pig density. The improvement of medical resources and tree cover as well as the construction of storm sewers and piped water systems might be mitigation measures that can be considered.

Meteorological Factors Affecting Infectious Diarrhea in Different Climate Zones of China

Meteorological factors and the increase in extreme weather events are closely related to the incidence rate of infectious diarrhea. However, few studies have explored whether the impact of the same meteorological factors on the incidence rate of infectious diarrhea in different climate regions has changed and quantified these changes. In this study, the time series fixed-effect Poisson regression model guided by climate was used to quantify the relationships between the incidence rate of various types of infectious diarrhea and meteorological factors in different climate regions of China from 2004 to 2018, with a lag of 0-2 months. In addition, six social factors, including per capita Gross Domestic Product (GDP), population density, number of doctors per 1000 people, proportion of urbanized population, proportion of children aged 0-14 years old, and proportion of elderly over 65 years old, were included in the model for confounding control. Additionally, the intercept of each province in each model was analyzed by a meta-analysis. Four climate regions were considered in this study: tropical monsoon areas, subtropical monsoon areas, temperate areas and alpine plateau areas. The results indicate that the influence of meteorological factors and extreme weather in different climate regions on diverse infectious diarrhea types is distinct. In general, temperature was positively correlated with all infectious diarrhea cases (0.2 ≤ r ≤ 0.6, p < 0.05). After extreme rainfall, the incidence rate of dysentery in alpine plateau area in one month would be reduced by 18.7% (95% confidence interval (CI): -27.8--9.6%). Two months after the period of extreme sunshine duration happened, the incidence of dysentery in the alpine plateau area would increase by 21.9% (95% CI: 15.4-28.4%) in that month, and the incidence rate of typhoid and paratyphoid in the temperate region would increase by 17.2% (95% CI: 15.5-18.9%) in that month. The meta-analysis showed that there is no consistency between different provinces in the same climate region. Our study indicated that meteorological factors and extreme weather in different climate areas had different effects on various types of infectious diarrhea, particularly extreme rainfall and extreme sunshine duration, which will help the government develop disease-specific and location-specific interventions, especially after the occurrence of extreme weather.

A High-Resolution Earth Observations and Machine Learning-Based Approach to Forecast Waterborne Disease Risk in Post-Disaster Settings

Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches.

CRISPR/Cas12a-Enhanced Loop-Mediated Isothermal Amplification for the Visual Detection of Shigella flexneri

Shigella flexneri is a serious threat to global public health, and a rapid detection method is urgently needed. The CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated) system is widely used in gene editing, gene therapy, and in vitro diagnosis. Here, we combined loop-mediated isothermal amplification (LAMP) and CRISPR/Cas12a to develop a novel diagnostic test (CRISPR/Cas12a-E-LAMP) for the diagnosis of S. flexneri. The CRISPR/Cas12a-E-LAMP protocol conducts LAMP reaction for S. flexneri templates followed by CRISPR/Cas12a detection of predefined target sequences. LAMP primers and sgRNAs were designed to the highly conserved gene hypothetical protein (accession: AE014073, region: 4170556–4171,068) of S. flexneri. After the LAMP reaction at 60°C for 20 min, the pre-loaded CRISPR/Cas12a regents were mixed with the LAMP products in one tube at 37°C for 20 min, and the final results can be viewed by naked eyes with a total time of 40 min. The sensitivity of CRISPR/Cas12a-E-LAMP to detect S. flexneri was 4 × 10⁰ copies/μl plasmids and without cross-reaction with other six closely related non-S. flexneri. Therefore, the CRISPR/Cas12a-E-LAMP assay is a useful method for the reliable and quick diagnosis of S. flexneri and may be applied in other pathogen infection detection.

Meteorological and social conditions contribute to infectious diarrhea in China

Infectious diarrhea in China showed a significant pattern. Many researchers have tried to reveal the drivers, yet usually only meteorological factors were taken into consideration. Furthermore, the diarrheal data they analyzed were incomplete and the algorithms they exploited were inefficient of adapting realistic relationships. Here, we investigate the impacts of meteorological and social factors on the number of infectious diarrhea cases in China. A machine learning algorithm called the Random Forest is utilized. Our results demonstrate that nearly half of infectious diarrhea occurred among children under 5 years old. Generally speaking, increasing temperature or relative humidity leads to increased cases of infectious diarrhea in China. Nevertheless, people from different age groups or different regions own different sensitivities to meteorological factors. The weight of feces that are harmfully treated could be a possible reason for infectious diarrhea of the elderly as well as children under 5 years old. These findings indicate that infectious diarrhea prevention for children under 5 years old remains a primary task in China. Personalized prevention countermeasures ought to be provided to different age groups and different regions. It is essential to bring the weight of feces that are harmfully treated to the forefront when considering infectious diarrhea prevention.

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

Background

We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014–2018.

Methods

A distributed lag non-linear model (DLNM) combined with a generalized additive mixed model (GAMM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders.

Results

A total of 6316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different relative risk (RR) of air pollution degrees [1.172, 95% confidence interval (CI): 1.038–1.323 in mild air pollution; 1.374, 95% CI 1.078–1.749 in moderate air pollution; 1.610, 95% CI 1.163–2.314 in severe air pollution; 1.887, 95% CI 1.163–3.061 in most severe air pollution].

Conclusions

Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06674-8.

Association between floods and the risk of dysentery in China: a meta-analysis

The association between floods and the risk of dysentery remain controversial. Therefore, we performed a meta-analysis to clarify this relationship. A literature search was performed in PubMed, Web of science, and Embase for relevant articles published up to November 2019. Random-effects model was used to pool relative risks with 95% confidence intervals. The sensitivity analysis was carried out to evaluate the stability of the results. Publication bias was estimated using Egger's test. Eleven studies from 10 articles evaluated the association between floods and the risk of dysentery in China. The pooled RR (95% CI) of dysentery for the flooded time versus non-flooded period was 1.48 (95% CI: 1.14-1.91). Significant association was found in subgroup analysis stratified by dysentery styles [dysentery: 1.61 (95% CI: 1.34-1.93) and bacillary dysentery: 1.46 (95% CI: 1.06-2.01)]. The pooled RR (95%CI) of sensitivity analysis for dysentery was 1.26 (95% CI: 1.05-1.52). No significant publication bias was found in our meta-analysis. This meta-analysis confirms that floods have significantly increased the risk of dysentery in China. Our findings will provide more evidence to reduce negative health outcomes of floods in China.

Effect of temperature and its interaction with other meteorological factors on bacillary dysentery in Jilin Province, China

Bacterial dysentery (BD) brings a major disease burden to developing countries. Exploring the influence of temperature and its interaction with other meteorological factors on BD is significant for the prevention and early warning of BD in the context of climate change. Daily BD cases and meteorological data from 2008 to 2018 were collected in all nine prefecture-level cities in Jilin Province. A one-stage province-level model and a two-stage city-specific multivariate meta-pooled level distributed lag non-linear model were established to explore the correlation between temperature and BD, then the weather-stratified generalised additive model was used to test the interaction. During the study period, a total of 26 971 cases of BD were developed. The one-stage and two-stage cumulative dose-response ‘J’ curves overlapped, and results showed a positive correlation between temperature and BD with a 1–6 days lag effect. Age group ⩾5 years was found to be more sensitive to the effects. Moreover, there was a significant interaction between temperature, humidity and precipitation (P = 0.004, 0.002, respectively) on BD under high temperature (>0 °C), reminding residents and policymakers to pay attention to the prevention of BD in situations with both high temperature and humidity, high temperature and precipitation during the temperate monsoon climate.

Associations between floods and bacillary dysentery cases in main urban areas of Chongqing, China, 2005-2016: a retrospective study

BACKGROUND

Understanding the association between floods and bacillary dysentery (BD) incidence is necessary for us to assess the health risk of extreme weather events. This study aims at exploring the association between floods and daily bacillary dysentery cases in main urban areas of Chongqing between 2005 and 2016 as well as evaluating the attributable risk from floods.

METHODS

The association between floods and daily bacillary dysentery cases was evaluated by using distributed lag non-linear model, controlling for meteorological factors, long-term trend, seasonality, and day of week. The fraction and number of bacillary dysentery cases attributable to floods was calculated. Subgroup analyses were conducted to explore the association across age, gender, and occupation.

RESULTS

After controlling the impact of temperature, precipitation, relative humidity, long-term trend, and seasonality, a significant lag effect of floods on bacillary dysentery cases was found at 0-day, 3-day, and 4-day lag, and the cumulative relative risk (CRR) over a 7-lag day period was 1.393 (95%CI 1.216-1.596). Male had higher risk than female. People under 5 years old and people aged 15-64 years old had significantly higher risk. Students, workers, and children had significantly higher risk. During the study period, based on 7-lag days, the attributable fraction of bacillary dysentery cases due to floods was 1.10% and the attributable number was 497 persons.

CONCLUSIONS

This study confirms that floods can increase the risk of bacillary dysentery incidence in main urban areas of Chongqing within an accurate time scale, the risk of bacillary dysentery caused by floods is still serious. The key population includes male, people under 5 years old, students, workers, and children. Considering the lag effect of floods on bacillary dysentery, the government and public health emergency departments should advance to the emergency health response in order to minimize the potential risk of floods on public.

Effects of Floods on the Incidence of Acute Hemorrhagic Conjunctivitis in Mengshan, China, from 2005 to 2012

This study aimed to examine the association between floods and the morbidity of acute hemorrhagic conjunctivitis (AHC) in Mengshan, China. Relying on the longitudinal data, a generalized additive model (GAM) was applied to quantify the relationship between the morbidity of AHC and floods from 2005 to 2012, controlling for other meteorological variables. Years lived with disability (YLDs) and attributable YLDs were used as the measure of the burden of AHC because of the floods. Multivariable analysis showed that floods were significantly associated with an increased risk of the morbidity of AHC (rate ratio [RR] = 2.136, 95% CI: 2.109-2.163). The total YLDs per 1,000 in Mengshan was 0.2001, although the value in females was higher than that in males (0.2351 versus 0.1686). The YLD per 1,000 of AHC in Mengshan was highest between the ages of 5 and 14 years (0.6530), followed by the age of 0-4 years (0.3325). The attributable YLD per 1,000 of AHC due to the floods in Mengshan was 0.0434 (95% CI: 0.0425-0.0442). Our study confirms that floods have significantly increased the risks of AHC in the selected study area. Females and youngsters may be the vulnerable populations to develop the flood-related disease.

Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

Background

This study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD.

Methods

In this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence.

Results

In total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R²) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively.

Conclusion

From 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

Insights into the Bacterial Profiles and Resistome Structures Following the Severe 2018 Flood in Kerala, South India

Extreme flooding is one of the major risk factors for human health, and it can significantly influence the microbial communities and enhance the mobility of infectious disease agents within the affected areas. The flood crisis in 2018 was one of the severe natural calamities recorded in the southern state of India (Kerala) that significantly affected its economy and ecological habitat. We utilized a combination of shotgun metagenomics and bioinformatics approaches to understand the bacterial profile and the abundance of pathogenic and antibiotic-resistant bacteria in extremely flooded areas of Kuttanad, Kerala (4-10 feet below sea level). Here we report the bacterial profiles of flooded sites that are abundant with virulent and resistant bacteria. The flooded sites were heavily contaminated with faecal contamination indicators such as Escherichia coli and Enterococcus faecalis and multidrug-resistant strains of Pseudomonas aeruginosa, Salmonella typhi/typhimurium, Klebsiella pneumoniae, Vibrio cholerae. The resistome of the flooded sites contains 103 known resistant genes, of which 38% are plasmid-encoded, where most of them are known to be associated with pathogenic bacteria. Our results reveal an overall picture of the bacterial profile and resistome of sites following a devastating flood event, which might increase the levels of pathogens and its associated risks.

The impact of the 2016 flood event in Anhui Province, China on infectious diarrhea disease: An interrupted time-series study

Climate change may bring more frequent and severe floods which will heighten public health problems, including an increased risk of infectious diarrhea in susceptible populations. Affected by heavy rainfall and an El Niño event, a destructive flood occurred in Anhui province, China on 18th June 2016. This study investigates the impact of this severe flood on infectious diarrhea at both city-level and provincial level, and further to identify modifying factor. We obtained information on infectious diarrheal cases during 2013-2017 from the National Disease Surveillance System. An interrupted time-series design was used to estimate effects of the flood event on diarrhea in 16 cities. Then we applied a meta-analysis to estimate the area-level pooled effects of the flood in both flooded areas and non-flooded areas. Finally, a meta-regression was applied to determine whether proximity to flood was a predictor of city-level risks. Stratified analyses by gender and age group were also conducted for flooded areas. A significant increase in infectious diarrhea risk (RR = 1.11, 95% CI: 1.01, 1.23) after the flood event was found in flooded area with variation in risks across cities, while there was no increase in non-flooded areas. Diarrheal risks post-flood was progressively higher in cities with greater proximity to the Yangtze River. Children aged 5-14 were at highest risk of diarrhea post-flood in the flooded areas. Our study provides strong evidence that the 2016 severe flood significantly increased infectious diarrheal risk in exposed populations. Local public health agencies are advised to develop intervention programs to prevent and control infectious diarrhea risk when a major flood occurs, especially in areas close to water bodies and among vulnerable populations.

Identifying different types of flood-sensitive diarrheal diseases from 2006 to 2010 in Guangxi, China

Floods may influence different types of diarrheal diseases and epidemiological studies of pathogen-specific diarrhea due to floods in China are still needed. In addition, few studies have been conducted to quantify the lag and cumulative risk of diarrheal disease due to floods in Guangxi, China. Our study aimed to identify different types of diarrheal diseases that were sensitive to floods and to quantify their lag and cumulative impact. A matched analysis based on time series data of floods and infectious disease from 2006 to 2010 was conducted in Guangxi, China. Each flood day was treated as an independent unit in our study. A simplified assumption that each day of the flood confers the same risk was adopted before analysis. Each flood day was matched to a non-flood day by city and time. Log-linear mixed-effects regression models were used to quantify the association between different types of diarrheal diseases and floods. Lag and cumulative effects were also calculated to get delayed and overall effects. A total of 45,131 diarrhea cases were notified in the study area over the study period. After controlling for the long-term trend, seasonality, and meteorological factors, floods caused a significantly increased risk of total diarrheal diseases. The RR was highest at lag 2 days (RR=1.24, 95% CI: 1.11-1.40). Floods caused a significantly increased risk in bacillary dysentery and in other infectious diarrhea, but not in typhoid fever and paratyphoid fever. Floods were significantly associated with total diarrheal diseases and other infectious diarrhea for both cumulative lag 0-7 and 0-14 days. Our study provides strong evidence of a positive association between floods and diarrheal diseases including bacillary dysentery and other infectious diarrhea in study area. Public health interventions should be taken to prevent a potential risk of these flood-sensitive diarrheal diseases according to the different lag period after floods.

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.

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.

Floods Increase the Risks of Hand-Foot-Mouth Disease in Qingdao, China, 2009-2013: A Quantitative Analysis

BACKGROUND

We aimed to quantify the impact of few times floods on hand-foot-mouth disease (HFMD) in Qingdao during 2009-2013.

METHODS

The Spearman correlation test was applied to examine the lagged effects of floods on monthly morbidity of HFMD during study period in Qingdao. We further quantified the effects of 5 flood events on the morbidity of HFMD using the time-series Poisson regression controlling for climatic factors, seasonality, and lagged effects among different populations.

RESULTS

A total of 55,920 cases of HFMD were reported in the study region over the study period. The relative risks of floods on the morbidity of HFMD among the total population, males, females, under 1-2 years old, and 3-5 years old were 1.178, 1.165, 1.198, 1.338, and 1.245, respectively.

CONCLUSIONS

This study has, for the first time, provided the positive evidence of the impact of floods on HFMD. It demonstrates that floods can significantly increase the risk of HFMD during study period. Additionally, among the different populations, the risks were higher among children under 1-5 years old. (Disaster Med Public Health Preparedness. 2018;12:723-729).