Cumulative effect of indoor temperature on cardiovascular disease-related emergency department visits among older adults in Taiwan

Emergency room visits (ERVs) among occupational groups associated with ambient conditions in Taiwan

OBJECTIVE

This population-based study explored emergency room visits (ERVs) from all-causes, circulatory and respiratory diseases among different occupational groups in Taiwan associated with ambient average temperature.

METHOD

Daily area-age-sex specific ERVs records were obtained from the Taiwan's Ministry of Health and Welfare from 2009 to 2018. Distributed lag-nonlinear model (DLNM) was used to estimate the exposure-response relationships between daily average temperature and ERVs for all-causes, circulatory and respiratory diseases by occupational groups. Random-effects meta-analysis was used to pool the overall cumulative relative risk (RR) and 95% confidence interval (CI).

RESULTS

The exposure-response curves showed ERVs of all-cause and respiratory diseases increased with rising temperature across all occupational groups. These effects were consistently stronger among younger (20-64 years old) and outdoor workers. In contrast, ERVs risk from circulatory diseases increased significantly during cold snaps, with a substantially higher risk for female workers. Interestingly, female workers, regardless of indoor or outdoor work, consistently showed a higher risk of respiratory ERVs during hot weather compared to males. Younger workers (20-64 years old) exhibited a higher risk of ERVs, likely due to job profiles with greater exposure to extreme temperatures. Notably, the highest risk of all-causes ERVs was observed in outdoor male laborers (union members), followed by farmers and private employees, with the lowest risk among indoor workers. Conversely, female indoor workers and female farmers faced the highest risk of respiratory ERVs. Again, female farmers with consistent outdoor exposure had the highest risk of circulatory ERVs during cold conditions.

CONCLUSION

Our findings highlighted the complexity of temperature-related health risks associated with different occupational contexts. The population-level insights into vulnerable occupational groups could provide valuable comprehension for policymakers and healthcare practitioners.

Association of indoor dampness indicators with rheumatic diseases/symptoms in older adults: A comparative cross-sectional study in Chongqing and Beijing

Dampness is strongly associated with rheumatic diseases, which particularly affect the older adults. Tackling dampness is therefore important, especially given that climate change is expected to exacerbate rheumatic diseases; however, limited studies have compared the risk of rheumatic diseases in older adults based on humidity levels across different regions. To explore this, a comparative cross-sectional study was conducted to collect information on the residential characteristics, lifestyles, and health outcomes of 2000 individuals aged 60-74 years from Chongqing and Beijing. From this data, we tested for an association between six indoor dampness indicators and rheumatic related diseases/symptoms. The results showed that the risk values for joint pain were higher in Chongqing than in Beijing. Moreover, the risk of joint stiffness increased more strongly in Chongqing than in Beijing as the cumulative number of dampness exposure indicators increased. The key indoor dampness indicators affecting rheumatic diseases were different for Chongqing and Beijing. Overall, this study compared the risk of rheumatic diseases in older adults in the north and south of China because of dampness exposure and, from these, provided suggestions for modifying the indoor environments to prevent or reduce rheumatic symptoms.

The effect of living environmental factors on cardiovascular diseases in Chinese adults: results from a cross-sectional and longitudinal study

AIMS

This study aimed to investigate the association between multiple living environmental factors and cardiovascular diseases (CVDs).

METHODS AND RESULTS

This study was conducted on the China Health and Retirement Longitudinal Study (CHARLS), with 12 489 subjects in the cross-sectional study and 7932 subjects in the 7-year follow-up. Living environmental factors included ambient fine particulate matter (PM2.5), indoor fuel use, tap water use, and residence type. Logistic regression and Cox proportional hazard regression models were applied to explore the association between living environmental risk factors and CVD events in a cross-sectional and a cohort analysis, respectively. Compared with subjects in the low-risk groups, those in the middle-risk (odd ratio [OR], 95% confidence interval [CI]: 1.203, 0.943-1.534) and high-risk groups (OR, 95% CI: 1.616, 1.259-2.074) showed increased risks of CVD prevalence when considering the combined effects of their living environment. During the follow-up, similar associations were observed (hazard ratio [HR], 1.541, 95% CI [1.142-2.080] for the high-risk group; HR 1.296, 95% CI [0.968-1.736] for the middle-risk group); P for trend = 0.003).

CONCLUSION

An overall poor living environmental quality is a potential risk factor for CVD. Future studies should focus more on the effects of exposure to multiple factors.

Excess out-of-hospital cardiac arrests due to ambient temperatures in South Korea from 2008 to 2018

Out-of-hospital cardiac arrest (OHCA) is a notable public health issue with negative outcomes, such as high mortality and aftereffects. Additionally, the adverse effects of extreme temperatures on health have become more important under climate change; however, few studies have investigated the relationship between temperature and OHCA. In this study, we examined the association between temperature and OHCA and its underlying risk factors. We conducted a two-stage time-series analysis using a Poisson regression model with a distributed lag non-linear model (DLNM) and meta-analysis, based on a nationwide dataset from South Korea (2008-2018). We found that 17.4% of excess OHCA was attributed to cold, while 0.9% was attributed to heat. Based on central estimates, excess OHCA attributed to cold were more prominent in the population with hypertension comorbidity (31.0%) than the populations with diabetes (24.3%) and heart disease (17.4%). Excess OHCA attributed to heat were larger in the populations with diabetes (2.7%) and heart disease comorbidity (2.7%) than the population with hypertension (1.2%) based on central estimates. Furthermore, the time-varying excess OHCA attributed to cold have decreased over time, and although those of heat did not show a certain pattern during the study period, there was a weak increasing tendency since 2011. In conclusion, we found that OHCAs were associated with temperature, and cold temperatures showed a greater impact than that of hot temperatures. The effects of cold and hot temperatures on OHCA were more evident in the populations with hypertension, diabetes, and heart diseases, compared to the general population. In addition, the impacts of heat on OHCA increased in recent years, while those of cold temperatures decreased. Our results provide scientific evidence for policymakers to mitigate the OHCA burden attributed to temperature.

Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach

Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.

A Longitudinal Study on the Impact of Indoor Temperature on Heat-Related Symptoms in Older Adults Living in Non-Air-Conditioned Households

BACKGROUND

Both chronic and acute heat result in a substantial health burden globally, causing particular concern for at-risk populations, such as older adults. Outdoor temperatures are often assessed as the exposure and are used for heat warning systems despite individuals spending most of their time indoors. Many studies use ecological designs, with death or hospitalizations rates. Individual-level outcomes that are directly related to heat-symptoms should also be considered to refine prevention efforts.

OBJECTIVES

In this longitudinal study, we assessed the association between indoor temperature and proximal symptoms in individuals ≥60 years of age living in non-air-conditioned households in Montérégie, Quebec, during the 2017-2018 summer months.

METHODS

We gathered continuously measured indoor temperature and humidity from HOBO sensors and repeated health-related questionnaires about health-related symptoms administered across three periods of increasing outdoor temperatures, where the reference measurement (T1) occurred during a cool period with a target temperature of 18-22°C and two measurements (T2 and T3) occurred during warmer periods with target temperatures of 28-30°C and 30-33°C, respectively. We used generalized estimating equations with Poisson regression models and estimated risk ratios (RRs) between temperature, humidity, and each heat-related symptom.

RESULTS

Participants (n=277) had an average age (mean±standard deviation) of 72.8±7.02y. Higher indoor temperatures were associated with increased risk of dry mouth (T3 RR=2.5; 95% CI: 1.8, 3.5), fatigue (RR=2.3; 95% CI: 1.8, 3.0), thirst (RR=3.4; 95% CI: 2.5, 4.5), less frequent urination (RR=3.7; 95% CI: 1.8, 7.3), and trouble sleeping (RR=2.2; 95% CI: 1.6, 3.2) compared with T1. We identified a nonlinear relationship with indoor temperatures across most symptoms of interest.

DISCUSSION

This study identified that increasing indoor temperatures were associated with various health symptoms. By considering the prevalence of these early stage outcomes and indoor temperature exposures, adaptation strategies may be improved to minimize the burden of heat among vulnerable communities. https://doi.org/10.1289/EHP10291.

Influence of Indoor Temperature Exposure on Emergency Department Visits Due to Infectious and Non-Infectious Respiratory Diseases for Older People

Previous studies have demonstrated that outdoor temperature exposure was an important risk factor for respiratory diseases. However, no study investigates the effect of indoor temperature exposure on respiratory diseases and further assesses cumulative effect. The objective of this study is to study the cumulative effect of indoor temperature exposure on emergency department visits due to infectious (IRD) and non-infectious (NIRD) respiratory diseases among older adults. Subjects were collected from the Longitudinal Health Insurance Database in Taiwan. The cumulative degree hours (CDHs) was used to assess the cumulative effect of indoor temperature exposure. A distributed lag nonlinear model with quasi-Poisson function was used to analyze the association between CDHs and emergency department visits due to IRD and NIRD. For IRD, there was a significant risk at 27, 28, 29, 30, and 31 °C when the CDHs exceeded 69, 40, 14, 5, and 1 during the cooling season (May to October), respectively, and at 19, 20, 21, 22, and 23 °C when the CDHs exceeded 8, 1, 1, 35, and 62 during the heating season (November to April), respectively. For NIRD, there was a significant risk at 19, 20, 21, 22, and 23 °C when the CDHs exceeded 1, 1, 16, 36, and 52 during the heating season, respectively; the CDHs at 1 was only associated with the NIRD at 31 °C during the cooling season. Our data also indicated that the CDHs was lower among men than women. We conclude that the cumulative effects of indoor temperature exposure should be considered to reduce IRD risk in both cooling and heating seasons and NIRD risk in heating season and the cumulative effect on different gender.

Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.