Lagged Association between Climate Variables and Hospital Admissions for Pneumonia in South Africa

Impact of hot and cold nights on pneumonia hospitalisations in children under five years: Evidence from low-, middle-, and high-income countries

BACKGROUND

Studies have shown that abnormal temperature at night is a risk factor for respiratory health. However, there is limited evidence on the impact of hot and cold nights on cause-specific diseases such as pneumonia, which is a leading cause of morbidity and mortality in children.

METHODS

We collected daily data on pneumonia hospitalisations in children under five years from 2011 to 2017 in three low-, middle- and high-income countries (Bangladesh, China, and Australia). The intensity of hot and cold nights was measured by excess temperature. A space-time-stratified case-crossover analysis was used to estimate the association between hot and cold nights and childhood pneumonia hospitalisations. We further estimated the fraction of childhood pneumonia hospitalisations attributable to hot and cold nights.

RESULTS

Both hot and cold nights were associated with an increased risk of hospitalisations for childhood pneumonia in low-, middle-, and high-income countries, with a greater disease burden from hot nights. Specifically, the fraction of childhood pneumonia attributable to hot nights was the largest in Australia [21.2%, 95% confidence interval (CI): 11.8%-28.1%], followed by Bangladesh (15.2%, 95% CI: 4.1%-23.8%) and China (2.7%, 95% CI: 0.4%-4.7%). Additionally, the fraction of childhood pneumonia attributable to cold nights was 1.3% (95% CI: 0.4%-2.0%) in Bangladesh and 0.4% (95% CI: 0.1%-0.7%) in China.

CONCLUSION

This multi-country study suggests that hot and cold nights are not only associated with a higher risk of pneumonia hospitalisations in children but also responsible for substantial fraction of hospitalisations, with a greater impact from hot nights.

Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

Extreme temperatures increase the risk of pediatric pneumonia: a systematic review and meta-analysis

Introduction

The impact of climate change on ambient temperatures threatens to worsen pediatric pneumonia-related outcomes considerably. This study examined the associations of temperature variation and extreme temperature with pediatric pneumonia-related events using a meta-analysis.

Methods

We systematically searched PubMed, Medline, Embase, and Web of Science databases for relevant literature, and the quality of evidence was assessed. Fixed and random-effects meta-analyses were performed to calculate the pooled relative risks (RRs) of the associations with pneumonia-related events.

Results

We observed that a 1°C temperature variation increased the RR of pneumonia events by 1.06-fold (95% confidence interval (CI): 1.03-1.10). A 1°C temperature variation increased the RR by 1.10-fold of the pediatric pneumonia hospital admissions (95% CI: 1.00-1.21) and 1.06-fold of the pediatric pneumonia emergency department visits (95% CI: 1.01-1.10). Extreme cold increased the RR by 1.25-fold of the pediatric pneumonia events (95% CI: 1.07-1.45). A 1°C temperature variation increased the RR of pneumonia events in children by 1.19-fold (95% CI: 1.08-1.32), girls by 1.03-fold (95% CI: 1.02-1.05), and in temperate climate zones by 1.07-fold (95% CI: 1.03-1.11). Moreover, an increase in extreme cold increased the RR of pneumonia events in children by 2.43-fold (95% CI: 1.72-3.43), girls by 1.96-fold (95% CI: 1.29-2.98) and in temperate climate zones by 2.76-fold (95% CI: 1.71-4.47).

Conclusion

Our study demonstrated that pediatric pneumonia events are more prevalent among children, particularly girls, and individuals residing in temperate climate zones. Climate change represents an emergent public health threat, affecting pediatric pneumonia treatment and prevention..

Systematic Review Registration

PROSPERO (CRD42022378610).

An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals.

Health Risks of Temperature Variability on Hospital Admissions in Cape Town, 2011-2016

Epidemiological studies have provided compelling evidence of associations between temperature variability (TV) and health outcomes. However, such studies are limited in developing countries. This study aimed to investigate the relationship between TV and hospital admissions for cause-specific diseases in South Africa. Hospital admission data for cardiovascular diseases (CVD) and respiratory diseases (RD) were obtained from seven private hospitals in Cape Town from 1 January 2011 to 31 October 2016. Meteorological data were obtained from the South African Weather Service (SAWS). A quasi-Poisson regression model was used to investigate the association between TV and health outcomes after controlling for potential effect modifiers. A positive and statistically significant association between TV and hospital admissions for both diseases was observed, even after controlling for the non-linear and delayed effects of daily mean temperature and relative humidity. TV showed the greatest effect on the entire study group when using short lags, 0-2 days for CVD and 0-1 days for RD hospitalisations. However, the elderly were more sensitive to RD hospitalisation and the 15-64 year age group was more sensitive to CVD hospitalisations. Men were more susceptible to hospitalisation than females. The results indicate that more attention should be paid to the effects of temperature variability and change on human health. Furthermore, different weather and climate metrics, such as TV, should be considered in understanding the climate component of the epidemiology of these (and other diseases), especially in light of climate change, where a wider range and extreme climate events are expected to occur in future.

The Association between Apparent Temperature and Hospital Admissions for Cardiovascular Disease in Limpopo Province, South Africa

Cardiovascular diseases (CVDs) have a high disease burden both globally and in South Africa. They have also been found to be temperature-sensitive globally. The association between temperature and CVD morbidity has previously been demonstrated, but little is known about it in South Africa. It is important to understand how changes in temperature in South Africa will affect CVD morbidity, especially in rural regions, to inform public health interventions and adaptation strategies. This study aimed to determine the short-term effect of apparent temperature (T_app) on CVD hospital admissions in Mopani District, Limpopo province, South Africa. A total of 3124 CVD hospital admissions records were obtained from two hospitals from 1 June 2009 to 31 December 2016. Daily T_app was calculated using nearby weather station measurements. The association was modelled using a distributed lag non-linear model with a negative binomial regression over a 21-day lag period. The fraction of morbidity attributable to non-optimal T_app, i.e., cold (6-25 °C) and warm (27-32 °C) T_app was reported. We found an increase in the proportion of admissions due to CVDs for warm and cold T_app cumulatively over 21 days. Increasing CVD admissions due to warm T_app appeared immediately and lasted for two to four days, whereas the lag-structure for the cold effect was inconsistent. A proportion of 8.5% (95% Confidence Interval (CI): 3.1%, 13.7%) and 1.1% (95% CI: -1.4%, 3.5%) of the total CVD admissions was attributable to cold and warm temperatures, respectively. Warm and cold T_app may increase CVD admissions, suggesting that the healthcare system and community need to be prepared in the context of global temperature changes.

Protective effect of pneumococcal conjugate vaccination on the short-term association between low temperatures and childhood pneumonia hospitalizations: Interrupted time-series and case-crossover analyses in Matlab, Bangladesh

Studies have shown that ambient extreme temperatures (heat and cold) were associated with an increased risk of childhood pneumonia, but the evidence is very limited in low-middle-income countries. It also remains unknown whether pneumococcal conjugate vaccine (PCV) could prevent temperature-related childhood pneumonia. This study collected data on ambient temperature and hospitalizations for childhood pneumonia in Matlab, Bangladesh from 2012 to 2016. Interrupted time series (ITS) analysis was employed to assess the impact of PCV (10-valent) intervention on childhood pneumonia hospitalizations. A time-stratified case-crossover analysis with a conditional logistic regression was performed to examine the association of childhood pneumonia hospitalizations with extreme temperatures and heatwaves before and after PCV10 intervention. Subgroup analyses were conducted to explore the modification effects of seasons, age, gender, and socioeconomic levels on temperature-related childhood pneumonia hospitalizations. We found that after PCV10 intervention, there was a sharp decrease in hospitalizations for childhood pneumonia (relative risk (RR): 0.59, 95% confidence interval (CI): 0.43-0.83). During the study period, heat effects on childhood pneumonia appeared immediately on the current day (odds ratio (OR): 1.28; 95% CI: 1.02-1.60, lag 0), while cold effects appeared 4 weeks later (OR: 1.53, 95% CI: 1.06-2.22, lag 28). Importantly, cold effects decreased significantly after PCV10 (p-value＜0.05), but heat and heatwave effects increased after PCV10 (p-value＜0.05). Particularly, children from families with a middle or low socioeconomic level, boys, and infants were more susceptible to heat-related pneumonia. This study suggests that PCV10 intervention in Bangladesh may help decrease cold-related not heat-related childhood pneumonia.