Sociodemographic, climatic variability and lower respiratory tract infections: a systematic literature review

Climate and human health: a review of publication trends in the International Journal of Biometeorology

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Association between extreme rainfall and acute respiratory infection among children under-5 years in sub-Saharan Africa: an analysis of Demographic and Health Survey data, 2006-2020

OBJECTIVE

Despite an increase in the number of studies examining the association between extreme weather events and infectious diseases, evidence on respiratory infection remains scarce. This study examined the association between extreme rainfall and acute respiratory infection (ARI) in children aged <5 years in sub-Saharan Africa.

SETTING

Study data were taken from recent (2006-2020) Demographic and Health Survey data sets from 33 countries in sub-Saharan Africa.

PARTICIPANTS

280 157 children aged below 5 years were included.

OUTCOME MEASURES

The proportions of ARI according to individual, household and geographical characteristics were compared using the χ² test. The association between extreme rainfall (≥90th percentile) and ARI was examined using multivariate logistic regression for 10 of 33 countries with an adequate sample size of ARI and extreme rainfall events. The model was adjusted for temperature, comorbidity and sociodemographic factors as covariates. Stratification analyses by climate zone were also performed.

RESULTS

The prevalence of ARI in children aged <5 years ranged from 1.0% to 9.1% across sub-Saharan Africa. By country, no significant association was observed between extreme rainfall and ARI, except in Nigeria (OR: 2.14, 95% CI 1.06 to 4.31). Larger effect estimates were observed in the tropical zone (OR: 1.13, 95% CI 0.69 to 1.84) than in the arid zone (OR: 0.72, 95% CI 0.17 to 2.95), although the difference was not statistically significant.

CONCLUSION

We found no association between extreme rainfall and ARI in sub-Saharan Africa. Effect estimates tended to be larger in the tropical zone where intense rainfall events regularly occur. Comprehensive studies to investigate subsequent extreme climate events, such as flooding, are warranted in the future.

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections

Background

The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI.

Results

Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the ‘bacterial’ patients and 82% of the ‘viral’ patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus).

Conclusions

We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.

Can El Niño-Southern Oscillation Increase Respiratory Infectious Diseases in China? An Empirical Study of 31 Provinces

Respiratory infectious diseases (RID) are the major form of infectious diseases in China, and are highly susceptible to climatic conditions. Current research mainly focuses on the impact of weather on RID, but there is a lack of research on the effect of El Niño–Southern Oscillation (ENSO) on RID. Therefore, this paper uses the system generalized method of moments (SYS-GMM) and the data of 31 provinces in China from 2007 to 2018 to construct a dynamic panel model to empirically test the causality between ENSO and RID morbidity. Moreover, this paper considers the moderating effects of per capita disposable income and average years of education on this causality. The results show that ENSO can positively and significantly impact RID morbidity, which is 5.842% higher during El Niño years than normal years. In addition, per capita disposable income and average years of education can effectively weaken the relationship between ENSO and RID morbidity. Thus, this paper is of great significance for improving the RID early climate warning system in China and effectively controlling the spread of RID.

Impact of the COVID-19 pandemic on infectious disease hospitalizations of neonates at a tertiary academic hospital: a cross-sectional study

Background

To investigate the impact of the coronavirus disease 2019 (COVID-19) pandemic on hospitalizations for neonatal infectious diseases.

Methods

We analyzed data for neonatal inpatients admitted at a tertiary academic hospital with a principal diagnosis of an infectious disease during January 2015 to December 2020. We compared hospitalizations in 2020 (COVID-19 cohort), corresponding with the impact of COVID-19 pandemic and associated containment measures, and the comparable 2015 to 2019 (pre-COVID-19 cohort).

Results

14,468 cases admitted for neonatal infectious diseases were included in our study, with 1201 cases in the COVID-19 cohort and 13,267 cases in the pre-COVID-19 cohort. The leading causes of hospitalizations for neonatal infectious diseases remain being respiratory tract infections (median ratio = 0.461, 95% CI 0.335–0.551), sepsis (median ratio = 0.292, 95% CI 0.263–0.361), gastric intestinal infections (median ratio = 0.095, 95% CI 0.078–0.118) and dermatologic infections (median ratio = 0.058, 95% CI 0.047–0.083). The seasonality of neonatal infectious disease hospitalizations could be obviously observed, with the total number and the overall rate of hospitalizations for neonatal infectious diseases in the first and fourth quarters greater than that of hospitalizations for neonatal infectious diseases in the second and third quarters in each year (1362.67 ± 360.54 vs 1048.67 ± 279.23, P = 0.001; 8176/20020 vs 6292/19369, P < 0.001, respectively). Both the numbers and the proportions of hospitalizations for neonatal infectious diseases in different quarters of the COVID-19 cohort significantly decreased as compared with those forecasted with the data from the pre-COVID-19 cohort: the numbers per quarter (300.25 ± 57.33 vs 546.64 ± 100.43, P-value = 0.006), the first quarter (0.34 vs 0.40, P = 0.002), the second quarter (0.24 vs 0.30, P = 0.001), the third quarter (0.24 vs 0.28, P = 0.024), and the fourth quarter (0.29 vs 0.35, P = 0.003).

Conclusions

Despite the outbreak of the COVID-19 pandemic, the leading causes of hospitalizations for neonatal infectious diseases remain unchanged. The seasonality of neonatal infectious disease hospitalizations could be obviously observed. The numbers as well as the overall rates of hospitalizations for neonatal infectious diseases in the COVID-19 cohort dramatically declined with the impact of the COVID-19 pandemic and its mitigation measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07211-x.

Spatial modeling of mortality from acute lower respiratory infections in children under 5 years of age in 2000-2017: a global study

BACKGROUND

Over the past few decades, various goals have been defined to reduce the mortality of children caused by acute lower respiratory infections (ALRIs) worldwide. However, few spatial studies to date have reported on ALRI deaths.

PURPOSE

We aimed to assess the spatial modeling of mortality from ALRI in children under 5 years of age during 2000-2017 using a global data.

METHODS

The data on the mortality of children under 5 years old caused by ALRI were initially obtained from the official website of the World Health Organization. The income status of their home countries was also gathered from the Country Income Groups (World Bank Classification) website and divided into 5 categories. After that, in the ArcGIS 10.6 environment, a database was created and the statistical tests and related maps were extracted. The Global Moran's I statistic, Getis-Ord Gi statistic, and geographically weighted regression were used for the analyses. In this study, higher z scores indicated the hot spots, while lower z scores indicated the cold spots.

RESULTS

In 2000-2017, child mortality showed a downward trend from 17.6 per 100,000 children to 8.1 and had a clustered pattern. Hot spots were concentrated in Asia in 2000 but shifted toward African countries by 2017. A cold spot that formed in Europe in 2007 showed an ascending trend by 2017. Based on the results of geographically weighted regression test, the regions identified as the hot spots of mortality from ALRI in children under 5 years old were among the middle-income countries (R2=0.01, adjusted R2=8.77).

CONCLUSION

While the total number of child deaths in 2000-2017 has decreased, the number of hot spots has increased among countries. This study also concluded that, during the study period, Central and Western Africa countries became the main new hot spots of deaths from ALRI.

Association between meteorological variables and semen quality: a retrospective study

Spermatogenesis is a temperature-dependent process, and high summer temperatures have been linked to lower sperm concentration and count. However, reports describing the association between other meteorological variables and semen quality are scarce. This study evaluated the association between semen quality and temperature, humidity, pressure, apparent temperature (AT), temperature-humidity index (THI), simplified wet-bulb global temperature (sWBGT), and sunshine duration. Semen samples were obtained at the Laboratorio de Andrología y Reproducción (LAR, Argentina), from men undergoing routine andrology examination (n=11657) and computer-assisted sperm analysis (n=4705) following WHO 2010 criteria. Meteorological variables readings were obtained from the Sistema Meteorológico Nacional. Sperm quality parameters were negatively affected in summer when compared to winter. Additionally, there was a significant decrease in sperm kinematics between winter and spring. Branch and bound variable selection followed by multiple regression analysis revealed a significant association between semen quality and meteorological variables. Specifically, changes in sunshine duration and humidity reinforced the prognosis of semen quality. Highest/lowest sunshine duration and humidity quantiles resulted in decreased sperm concentration, count, motility, vitality and membrane competence, nuclear maturity, and sperm kinematics associated to highest sunshine duration and lowest humidity. Findings from this report highlight the relevance of environmental studies for predicting alterations in male reproductive health associated to variations in meteorological variables, especially considering the current climate changes around the planet due to global warming and its consequences for human health.

Perspectives on biometeorological research on the African continent

Since the first issue of the International Journal of Biometeorology in 1957, a total of 135 papers have reported on research in or of African countries. The majority of these have been on topics of animal biometeorology (36%), and the greatest proportion (24%) are situated in Nigeria. There has been a considerable increase in papers on African biometeorology since 2011, with those from this past decade accounting for 58% of all African papers in the journal. This occurs concurrent to an increase in the total number of papers published in the journal, driven by a move to the Editorial Manager system. While 66% of the papers on African biometeorology in the journal are authored by at least one person with an affiliation in the African continent, only 15 African countries are represented in the total authorship. As much of the African continent is projected to experience climatic changes exceeding the global mean, as much of the region is involved in animal and plant farming, and as seasonally-fluctuating and climatically affected diseases are common place, this low representation of work in Africa is surprising. This points to the need for greater awareness among African researchers of the discipline of biometeorology, greater involvement of African biometeorologists in International Society of Biometeorology and Commission meetings, and the inclusion of a greater number of African academics in the review process. This would be beneficial to the Society in increasing diversity and encouraging a more cosmopolitan engagement, and to the recognition of scientific development in African countries.

How Nanophotonic Label-Free Biosensors Can Contribute to Rapid and Massive Diagnostics of Respiratory Virus Infections: COVID-19 Case

The global sanitary crisis caused by the emergence of the respiratory virus SARS-CoV-2 and the COVID-19 outbreak has revealed the urgent need for rapid, accurate, and affordable diagnostic tests to broadly and massively monitor the population in order to properly manage and control the spread of the pandemic. Current diagnostic techniques essentially rely on polymerase chain reaction (PCR) tests, which provide the required sensitivity and specificity. However, its relatively long time-to-result, including sample transport to a specialized laboratory, delays massive detection. Rapid lateral flow tests (both antigen and serological tests) are a remarkable alternative for rapid point-of-care diagnostics, but they exhibit critical limitations as they do not always achieve the required sensitivity for reliable diagnostics and surveillance. Next-generation diagnostic tools capable of overcoming all the above limitations are in demand, and optical biosensors are an excellent option to surpass such critical issues. Label-free nanophotonic biosensors offer high sensitivity and operational robustness with an enormous potential for integration in compact autonomous devices to be delivered out-of-the-lab at the point-of-care (POC). Taking the current COVID-19 pandemic as a critical case scenario, we provide an overview of the diagnostic techniques for respiratory viruses and analyze how nanophotonic biosensors can contribute to improving such diagnostics. We review the ongoing published work using this biosensor technology for intact virus detection, nucleic acid detection or serological tests, and the key factors for bringing nanophotonic POC biosensors to accurate and effective COVID-19 diagnosis on the short term.

Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms

•

Lower respiratory infections (LRI) are the cause of a significant number of hospitalizations in the US.

•

No previous nationwide study examined geographic variations of LRI mortality rates and their association with underlying factors.

•

There was a shift in the location of LRI hotspots from west coast to southeast over time.

•

Decision tree classifiers could predict LRI mortality hotspots with high accuracies.

•

Higher spring temperature and increased precipitation during winter were among the most substantial predictors of presence or absence of LRI hotspots.

Objective

Although lower respiratory infections (LRI) are among the leading causes of mortality in the US, their association with underlying factors and geographic variation have not been adequately examined.

Methods

In this study, explanatory variables (n = 46) including climatic, topographic, socio-economic, and demographic factors were compiled at the county level across the continentalUS.Machine learning algorithms - logistic regression (LR), random forest (RF), gradient boosting decision trees (GBDT), k-nearest neighbors (KNN), and support vector machine (SVM) - were employed to predict the presence/absence of hotspots (P < 0.05) for elevated age-adjusted LRI mortality rates in a geographic information system framework.

Results

Overall, there was a historical shift in hotspots away from the western US into the southeastern parts of the country and they were highly localized in a few counties. The two decision tree methods (RF and GBDT) outperformed the other algorithms (accuracies: 0.92; F1-scores: 0.85 and 0.84; area under the precision-recall curve: 0.84 and 0.83, respectively). Moreover, the results of the RF and GBDT indicated that higher spring minimum temperature, increased winter precipitation, and higher annual median household income were among the most substantial factors in predicting the hotspots.

Conclusions

This study helps raise awareness of public health decision-makers to develop and target LRI prevention programs.

Meteorological factors affecting respiratory syncytial virus infection: A time-series analysis

INTRODUCTION

Respiratory syncytial virus (RSV) infection is a major cause of hospitalization in children. Meteorological factors are known to influence seasonal RSV epidemics, but the relationship between meteorological factors and RSV infection in children is not well understood. We aimed to explore the relationship between meteorological factors and RSV infections among hospitalized children, using different statistical models.

METHODS

We conducted a retrospective review concerning children with RSV infections admitted to a tertiary pediatric hospital in Wenzhou, China, between January 2008 and December 2017. The relationship between meteorological factors (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed) and the incidence of RSV in hospitalized children was analyzed using three time-series models, namely an autoregressive integrated moving average (ARIMA) model, a generalized additive model (GAM), and a least absolute shrinkage and selection operator (LASSO)-based model.

RESULTS

In total, 15 858 (17.6%) children tested positive for RSV infection. The ARIMA model revealed a marked seasonal pattern in the RSV detection rate, which peaked in winter and spring. The model was a good predictor of RSV incidence (R² : 83.5%). The GAM revealed that a lower temperature and higher wind speed preceded increases in RSV detection. The LASSO-based model revealed that temperature and relative humidity were negatively correlated with RSV detection.

CONCLUSIONS

Seasonality of RSV infection in hospitalized children correlated strongly with temperature. The LASSO-based model can be used to predict annual RSV epidemics using weather forecast data.

Changes in the etiology of viral lower respiratory tract infections in hospitalized children in Wenzhou, China: 2008-2017

This study investigated the seasonality and secular trends in the etiology of viral lower respiratory tract infections (LRTIs) among hospitalized children in Wenzhou, southeastern China. A retrospective review was conducted concerning viral LRTIs in children hospitalized at a university hospital between January 1, 2008 and December 31, 2017. Direct immunofluorescence was used to detect respiratory syncytial virus (RSV), adenovirus (AdV), influenza A virus (Inf A), influenza B virus (Inf B), and human parainfluenza virus types 1 to 3 (hPIV1-3). Of 89 898 children tested, at least one viral respiratory pathogen was identified in 25.6% and multiple pathogens were identified in 0.4%. RSV (17.6%), hPIV3 (4.0%), and AdV (2.2%) were the most frequently detected pathogens. The proportion of positive samples varied with age and was the highest in children aged  <6 months (36.2%). Seasonal differences were observed in RSV, AdV, Inf A, Inf B, hPIV1, and hPIV3 infections. There was a declining trend in the proportion of positive samples over time, primarily due to a decrease in RSV and hPIV3 infections. RSV, hPIV3, and AdV were the most common viral respiratory pathogens identified among hospitalized children with LRTIs. The distribution of viruses varied with age and season.