Association of longitudinal pet ownership with wheezing in 3-year-old children using the distributed lag model: the Japan Environment and Children's Study.

BACKGROUND: Time-varying exposures like pet ownership pose challenges for identifying critical windows due to multicollinearity when modeled simultaneously. The Distributed Lag Model (DLM) estimates critical windows for time-varying exposures, which are mainly continuous variables. However, applying complex functions such as high-order splines and nonlinear functions within DLMs may not be suitable for situations with limited time points or binary exposure, such as in questionnaire surveys. OBJECTIVES: (1) We examined the estimation performance of a simple DLM with fractional polynomial function for time-varying binary exposures through simulation experiments. (2) We evaluated the impact of pet ownership on childhood wheezing onset and estimate critical windows. METHODS: (1) We compared logistic regression including time-varying exposure in separate models, in one model simultaneously, and using DLM. For evaluation, we employed bias, empirical standard error (EmpSE), and mean squared error (MSE). (2) The Japan Environment and Children's Study (JECS) is a prospective birth cohort study of approximately 100,000 parent-child pairs, registered across Japan from 2011 to 2014. We applied DLM to the JECS data up to age 3. The estimated odds ratios (OR) were considered to be within critical windows when they were significant at the 5% level. RESULTS: (1) DLM and the separate model exhibited lower bias compared to the simultaneously model. Additionally, both DLM and the simultaneously model demonstrated lower EmpSEs than the separate model. In all scenarios, DLM had lower MSEs than the other methods. Specifically, where critical windows is clearly present and exposure correlation is high, DLM showed MSEs about 1/2 to 1/200 of those of other models. (2) Application of DLM to the JECS data showed that, unlike other models, a significant exposure effect was observed only between the ages of 0 and 6 months. During that periods, the highest ORs were 1.07 (95% confidence interval, 1.01 to 1.14) , observed between the ages of 2 and 5 months. CONCLUSIONS: (1) A simple DLM improves the accuracy of exposure effect and critical windows estimation. (2) 0-6 months may be the critical windows for the effect of pet ownership on the wheezing onset at 3 years.

Analysis of N-of-1 trials using Bayesian distributed lag model with autocorrelated errors.

An N-of-1 trial is a multi-period crossover trial performed in a single individual, with a primary goal to estimate treatment effect on the individual instead of population-level mean responses. As in a conventional crossover trial, it is critical to understand carryover effects of the treatment in an N-of-1 trial, especially when no washout periods between treatment periods are instituted to reduce trial duration. To deal with this issue in situations where a high volume of measurements are made during the study, we introduce a novel Bayesian distributed lag model that facilitates the estimation of carryover effects, while accounting for temporal correlations using an autoregressive model. Specifically, we propose a prior variance-covariance structure on the lag coefficients to address collinearity caused by the fact that treatment exposures are typically identical on successive days. A connection between the proposed Bayesian model and penalized regression is noted. Simulation results demonstrate that the proposed model substantially reduces the root mean squared error in the estimation of carryover effects and immediate effects when compared to other existing methods, while being comparable in the estimation of the total effects. We also apply the proposed method to assess the extent of carryover effects of light therapies in relieving depressive symptoms in cancer survivors.

Impact of heat on all-cause and cause-specific mortality: A multi-city study in Texas.

BACKGROUND: Studies on the health effects of heat are particularly limited in Texas, a U.S. state in the top 10 highest number of annual heat-related deaths per capita from 2018 to 2020. This study assessed the effects of heat on all-cause and cause-specific mortality in 12 metropolitan statistical areas (MSAs) across Texas from 1990 to 2011. METHODS: First, we determined the heat thresholds for each MSA above which the relation between temperature and mortality is linear. We then conducted a distributed lag non-linear model for each MSA, followed by a random effects meta-analysis to estimate the pooled effects for all MSAs. We repeated this process for each mortality cause and age group to achieve the effect estimates. RESULTS: We found a 1 °C temperature increase above the heat threshold is associated with an increase in the relative risk of all-cause mortality of 0.60% (95%CI [0.39%, 0.82%]) and 1.10% (95%CI [0.65%, 1.56%]) for adults older than 75. For each MSA, the relative risk of mortality for a 1 °C temperature increase above the heat threshold ranges from 0.10% (95%CI [0.09%, 0.10%]) to 1.29% (95%CI [1.26%, 1.32%]). Moreover, elevated temperatures showed a slight decrease in cardiovascular mortality (0.37%, 95%CI [-0.35%, 1.09%]) and respiratory disease (1.97%, 95%CI [-0.11%, 4.08%]), however this effect was not considered statistically significant.. CONCLUSION: Our study found that high temperatures can significantly impact all-cause mortality in Texas, and effect estimates differ by MSA, age group, and cause of death. Our findings generate critical information on the impact of heat on mortality in Texas, providing insights for policymakers on resource allocation and strategic intervention to reduce heat-related health effects.

The influence of meteorological factors on the risk of tick-borne encephalitis infection.

OBJECTIVES: The aim of this work was to analyze the relationship between new cases of clinical tick-borne encephalitis (TBE) and various meteorological and seasonal predictors. MATERIAL AND METHODS: The modelling is based on national data from the Czech Republic for the period 2001-2016 in daily resolution, namely on average temperatures, average relative air humidity and the number of TBE cases classified according to the date of the first symptoms. Four variants of a negative binomial model from the generalized additive model class are used. The basic model relates the occurrence of TBE to the lagged ambient daily average temperature and daily average relative air humidity and their interaction with the lag reflecting the incubation period and other factors. The lag value was estimated via the optimization procedure based on Akaike information criterion. The model also includes the effect of the season and the effect of the day of the week. To increase the biological plausibility, the basic model has been expanded to account for possible time-varying effects of meteorological variables and to incorporate multiple lags. RESULTS: The most statistically significant effect is the within-year seasonality and then the interaction of the temperature and relative air humidity. The relationship of both meteorological factors and their interactions vary throughout the activities season of the hostquesting Ixodes ricinus. This also changes the conditions of occurrence of the new clinical cases of TBE. The time-varying effect of meteorological factors on the incidence of TBE shows non-trivial changes within a year. In the period before the middle of the calendar year (around the week 22) the effect decreases, then it is followed by an increase until the week 35. CONCLUSION: Flexible models were developed with quantitatively characterized effects of temperature, air humidity and their interaction, with the delay of the effect estimated through the optimization process. Performance of the model with multiple lags was checked using independent data to verify the possibility of using the results to improve the prediction of the risk of clinical cases of TBE uprise.

Can weather help explain 'why now?': The potential role of hourly temperature as a stroke trigger.

BACKGROUND: While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of ultra short-term temperature. METHODS: We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and identified any relevant lags of exposure among adult New York State residents from 2000 to 2015. Cases were identified via ICD-9 codes from the New York Department of Health Statewide Planning and Reearch Cooperative System. We estimated ambient temperature up to 36 h prior to estimated stroke onset based on patient residential ZIP Code. We applied a time-stratified case-crossover study design; control periods were matched to case periods by year, month, day of week, and hour of day. Additionally, we assessed effect modification by leading stroke risk factors hypertension and atrial fibrillation. RESULTS: We observed 578,181 ischemic and 164,755 hemorrhagic strokes. Among ischemic and hemorrhagic strokes respectively, the mean (standard deviation; SD) patient age was 71.8 (14.6) and 66.8 (17.4) years, with 55% and 49% female. Temperature ranged from -29.5 °C to 39.2 °C, with mean (SD) 10.9 °C (10.3 °C). We found linear relationships for both stroke types. Higher temperature was associated with ischemic stroke over the 7 h following exposure; a 10 °C increase over 7 h was associated with 5.1% (95% Confidence Interval [CI]: 3.8, 6.4%) increase in hourly stroke rate. In contrast, temperature was negatively associated with hemorrhagic stroke over 5 h, with a 5-h cumulative association of -6.2% (95% CI: 8.6, -3.7%). We observed suggestive evidence of a larger association with hemorrhagic stroke among patients with hypertension and a smaller association with ischemic stroke among those with atrial fibrillation. CONCLUSION: Hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather influences stroke risk and hypertension may confer vulnerability.

Is the virus-laden standing water change the transmission intensity of SARS-CoV-2 after precipitation? A framework for empirical studies.

Understanding the relationship between precipitation and SARS-CoV-2 is significant for combating COVID-19 in the wet season. However, the causes for the variation of SARS-CoV-2 transmission intensity after precipitation is unclear. Starting from "the Zhengzhou event," we found that the virus-laden standing water formed after precipitation might trigger some additional routes for SARS-CoV-2 transmission and thus change the transmission intensity of SARS-CoV-2. Then, we developed an interdisciplinary framework to examine whether the health risk related to the virus-laden standing water needs to be a concern. The framework enables the comparison of the instant and lag effects of precipitation on the transmission intensity of SARS-CoV-2 between city clusters with different formation risks of the virus-laden standing water. Based on the city-level data of China between January 01, 2020, and December 31, 2021, we conducted an empirical study. The result showed that in the cities with a high formation risk of the virus-laden standing water, heavy rain increased the instant transmission intensity of SARS-CoV-2 by 6.2% (95%CI: 4.85-10.2%), while in the other cities, precipitation was uninfluential to SARS-CoV-2 transmission, revealing that the health risk of the virus-laden standing water should not be underestimated during the COVID-19 pandemic. To reduce the relevant risk, virus-laden water control and proper disinfection are feasible response strategies.

Photochemical oxidants and ambulance dispatches for asthmatic symptoms in Tokyo.

While photochemical oxidants (Ox = O3+ NO2) are known to increase asthma flare-ups, there is a paucity of studies of the Japanese population, especially for Tokyo residents. We used data on asthma cases (n = 7,455) from ambulance dispatches in Tokyo, 2015-2016. Variables included date and time of incidence, age, sex, occurrence location at the ward (ku) level, and the symptom/cause of dispatch as recorded by paramedics. Ox data were obtained from the nearest air quality monitoring station to the occurrence location, then linked them with the outcomes based on occurrence date. We directly incorporated a distributed lag model into a bi-directional case-crossover study design controlling for ambient temperature and day of week. A 10-ppb increase in Ox for lag days 0-3 was associated with a 5.51% (95% CI: 0.13 to 11.18) increase in ambulance dispatches related to asthma. The association was strongest on lag day 1 (4.67%, 95% CI: 0.51 to 9.00). Exposure to high levels of Ox was associated with increased ambulance dispatches related to asthma exacerbations in Tokyo, Japan.

Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.

Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed-lag model in order to investigate the association between mobility and the number of reported COVID-19 cases; we additionally include a weekly first-order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.

La prévision du nombre de cas quotidiens de COVID­19 est cruciale pour la planification à court terme de ressources hospitalières et d'autres ressources publiques. Les données de localisation des téléphones mobiles qui mesurent le temps passé à la maison peuvent constituer un élément d'information important pour prédire les cas de COVID­19. Les modèles de séries chronologiques endémiques­épidémiques sont des modèles auto­régressifs récents où le nombre moyen de cas en cours est modélisé comme une moyenne pondérée du nombre de cas antérieurs multipliée par un taux auto­régressif (reproductif), plus une composante endémique. Les auteurs de ce travail généralisent les modèles endémiques­épidémiques pour y inclure un modèle à décalage distribué, et ce, dans le but de tenir compte du lien entre la mobilité et le nombre de cas de COVID­19 enregistrés. Pour saisir les variations de temps supplémentaires, ils y incorporent une marche hebdomadaire aléatoire d'ordre supérieur. De plus, ils proposent un schéma de pondération binomiale négative décalée pour les dénombrements passés, qui est plus flexible que les schémas de pondération existants. Ils utilisent l'inférence bayésienne afin d'intégrer l'incertitude des paramètres aux prédictions du modèle et ils illustrent les méthodes proposées avec des données provenant de quatre comtés américains.

The short-term harvesting effects of ambient particulate matter on mortality in Taiyuan elderly residents: A time-series analysis with a generalized additive distributed lag model.

The evaluation on mortality displacement and distributed lag effects of airborne particulate matter (PM) on death risks is important to understand the positive association of short-term pollution from both ambient PM10 and PM2.5 with daily mortality. Herein, short-term influences of urban PM10 and PM2.5 exposure on the mortality of respiratory diseases (RD) and cardiovascular diseases (CVD) were studied at Taiyuan, China, a typical inland city suffering from heavy ambient PM loading and having high morbidity of RD and CVD. Using a time-series analysis with generalized additive distributed lag model (DLM), the potential mortality displacement was determined and the single-day and cumulative lag-day effects of PM on mortality were estimated after the daily mass concentrations of urban PM2.5 and PM10 from January 2013 to October 2015 and the daily number of non-accidental death (NAD) and cause-specific mortality in the residents aged more than 65 years old were obtained. Results showed there were significant associations of PM2.5 and PM10 with daily mortality on the current day and within one week. And a statistically significant increase (P < 0.05) in the cumulative effect estimates of PM2.5 and PM10 on CVD, ischemic heart disease (IHD), and myocardial infarction (MI) mortality (as well as PM2.5 on NAD) was observed, while the associations of PM2.5 with RD and pneumonia mortality, PM10 with NAD and RD mortality were not statistically significant, when the exposure window was extended to lag 0-30 days. It was concluded that there were harvesting effects and cumulative effects of ambient PM2.5 and PM10 on the elderly residents' mortality due to RD and CVD at Taiyuan and they could be estimated quantitatively when the broader time window was used, suggesting that the underestimation on the association of ambient PM with non-accidental death can be avoided using the present method in our study.

Identifying critical exposure windows for ambient air pollution and semen quality in Chinese men.

Emerging studies documented the association between ambient air pollution exposure and semen quality, but the critical exposure windows have not been comprehensively studied. To identify susceptible windows for associations of exposure to ambient respirable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) with sperm concentration, sperm count, total motility, and progressive motility, we recruited 1061 men attending an infertility clinic in Wuhan, China, between 2011 and 2013. We used a distributed lag multivariate linear regression to assess the exposure-lag-response relationship between semen quality and weekly air pollution exposure. The critical exposure windows were during the 6th to 12th sperm development weeks for PM10, 10th to 11th weeks for O3, and 0 to 5th weeks for SO2. Over the entire 12 weeks of spermatogenesis period, an interquartile range increase (IQR) increase in PM10 was associated with declined sperm concentration [-45.64% (95% CI: -59.97%, -26.18%) percent decrease], declined sperm count [-49.42% (95% CI: -64.42%, -28.09%) percent decrease], reduced total motility [-12.42 (95% CI: -20.47, -4.37)], and reduced progressive motility [-8.81 (95% CI: -16.00, -1.61)], SO2 per IQR increase was associated with reduced sperm concentration [-39.73% (95% CI: -55.96%, -17.51%) percent decrease] and total motility [-8.64 (95% CI: -16.90, -0.38)], but NO2 and O3 were not associated with any of the four sperm quality parameters. Our findings suggest that exposure to PM10 during spermatidogenesis period, exposure to SO2 during spermatocytogenesis period, and exposure to O3 during spermiogenesis period were associated with impaired semen quality, which implies air pollutants impair semen quality through varied pathways.

Extreme temperature and mortality: evidence from China.

The frequency, intensity, and duration of extreme temperature events are expected to rise in the future and increase the related health risks of human beings. Using a novel, nationwide dataset that links extreme temperature and mortality, we estimated the short-term and long-term effects of extreme temperature on mortality in China during 2002-2013. Both extreme hot and extreme cold had immediate and long-term effects on all-cause mortality. Annual deaths per 100,000 people due to extreme hot and cold in the long term were considerably larger compared to the short term. The change in cold spell duration indicator exhibited the greatest effects on annual deaths per 100,000 people among a set of extreme weather indicators. Furthermore, cities with low economic development levels were more vulnerable to extreme temperature, compared to cities with high economic development levels. Our results offer important policy implications for developing a regional-specific extreme weather plan to handle extreme temperature events in China.

Relationships between aeroallergen levels and hospital admissions for asthma in the Brussels-Capital Region: a daily time series analysis.

BACKGROUND: Outdoor pollen grain and fungal spore concentrations have been associated with severe asthma exacerbations at the population level. The specific impact of each taxon and the concomitant effect of air pollution on these symptoms have, however, still to be better characterized. This study aimed to investigate the short-term associations between ambient concentrations of various aeroallergens and hospitalizations related to asthma in the Brussels-Capital Region (Belgium), an area recording especially high rates of admissions. METHODS: Based on administrative records of asthma hospitalizations and regular monitoring of 11 tree/herbaceous pollen taxa and 2 fungal spore taxa, daily time series analyses covering the 2008-2013 period were performed. Effects up to 6 days after exposure were captured by combining quasi-Poisson regression with distributed lag models, adjusting for seasonal and long-term trends, day of the week, public holidays, mean temperature and relative humidity. Effect modification by age and air pollution (PM, NO2, O3) was tested. RESULTS: A significant increase in asthma hospitalizations was observed for an interquartile range increase in grass (5.9%, 95% CI: 0.0, 12.0), birch (3.2%, 95% CI: 1.1, 5.3) and hornbeam (0.7%, 95% CI: 0.2, 1.3) pollen concentrations. For several taxa including grasses, an age modification effect was notable, the hospitalization risk tending to be higher in individuals younger than 60 years. Air pollutants impacted the relationships too: the risk appeared to be stronger for grass and birch pollen concentrations in case of high PM10 and O3 concentrations respectively. CONCLUSIONS: These findings suggest that airborne grass, birch and hornbeam pollen are associated with severe asthma exacerbations in the Brussels region. These compounds appear to act in synergy with air pollution and to more specifically affect young and intermediate age groups. Most of these life-threatening events could theoretically be prevented with improved disease diagnosis/management and targeted communication actions.

Technological innovation, militarization, and environmental change: evidence from BRICS economies.

In the complex international society, the economic development and defense construction of BRICS countries have attracted increasing attention. This article incorporates technological innovation, militarization, and environmental change into a unified analytical framework to assess the potential impact of technological progress and defense military expenditure on the environment in BRICS countries. Based on CSD tests, unit root tests, and cointegration tests, this study constructs a CS-ARDL model to examine the long-term and short-term relationships among various variables from 1990 to 2021. The results show that technological innovation, military expenditure, and economic growth can significantly increase ecological footprint in the long run, while in the short term, technological innovation and economic growth significantly increase ecological footprint, and the impact of military expenditure is not significant. It is suggested that BRICS countries should focus on supporting low-carbon technology policies and research and development investment, while also considering the use of cutting-edge technology to improve military intelligence capabilities in order to reduce the negative impact of technological innovation and military activities on the environment.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model.

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = - 9.568%, 95% CI - 16.165 to - 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = - 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = - 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = - 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = - 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(-), ∆AP(+), ∆MWV(+), and ∆ASH(-) at 0-2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

Relative responses of trade in goods and services to currency devaluation: Evidence from Ethiopia.

Lack of hard currency is one of the key growth barriers in emerging countries, as imports vastly outstrip exports. In response, governments in these countries usually undertake a variety of policy packages, including the devaluation of the domestic currency. Despite these efforts, there have been no discernible adjustments to foreign balance. This study examines whether the response to devaluation differs significantly between the goods and services trade in Ethiopia. We estimate the long- and short-run elasticities of the disaggregated trade indicators using an autoregressive distributed lag (ARDL) and error correction mechanisms. The empirical results confirm a significant difference between the goods and service sectors in terms of their responses to the devaluation policy in the long and short run. The estimated long-run elasticities of devaluation are only statistically significant for service imports and trade balances with negative signs. The remaining sectors did not show any significant relationships. In addition, we obtain meaningful short-run elasticities for service imports, goods exports, and total exports, all of which have a negative sign. Domestic inflation accounted for a large portion of the short-run import dynamics, output growth, and FDI, which contributed significantly to long-term export performance. The current study reveals that the government should not rely exclusively on the devaluation policy to bridge its external imbalances, and should see alternative and more effective policy mixes to alter the demand and supply sides of foreign trade.

Determinants of the personal savings rate in the Kingdom of Saudi Arabia using time savings deposits, 2012-2022.

Personal saving is an important indicator of future economic prosperity. Despite the growth in gross domestic product per capita (GDPPC) from 2012 to 2022 in the Kingdom of Saudi Arabia (KSA), the personal savings rate remains low. Only a few studies have explored savings in KSA, but no study that used time savings deposits (TSD) to measure personal savings exists. Thus, this study aims to investigate the determinants of personal savings employing TSD. Using data from the Saudi Central Bank from 2012 to 2022, this study empirically examines the determinants of TSD. The autoregressive distributed lag cointegration technique determines the long-run relationship between the study's variables. The study finds that GDPPC, deposit interest rates (DIR), consumer loans (CLs), and real estate loans (RELs) significantly impact personal savings. Only GDPPC and RELs have a significant negative impact on personal savings. The study is among the few to examine savings in KSA but, unlike other studies, used TSD as a measure of personal savings. In conclusion, several implications and recommendations for policymakers and financial institutions were presented.

Testing the impact of renewable energy and oil price on carbon emission intensity in China's transportation sector.

As the largest carbon emitter in the world, with its transportation sector contributing the largest shares of its emission, the need for a low-carbon transition economy has become a policy agenda for China because in order to reach carbon neutrality by 2050, lowering the intensity of carbon emissions in the transportation sector will be crucial. In this regard, we used the "bootstrap autoregressive distributed lag model" to explore the impact of clean energy and oil prices on the intensity of carbon emissions in China's transportation sector. The study found that an increase in oil prices decreases the intensity of carbon emissions in the short and long run. Similarly, an increase in the level of renewable energy and economic complexity declines the intensity of carbon emissions in the transportation sector. On the contrary, the research demonstrates that non-renewable energy contributes positively to carbon emission intensity. Therefore, the authorities must promote green technology to neutralize the transportation system's detrimental effects on China's environmental quality. The implications for successfully promoting carbon emission intensity mitigation in the transportation sector are examined in the conclusion.

Ambient temperature and ambulatory blood pressure: An hourly-level, longitudinal panel study.

BACKGROUND: Variations of blood pressure (BP) related to air temperature have been reported previously; however, no evidence is available regarding the association of hourly ambient temperature with ambulatory blood pressure. METHODS: We conducted a longitudinal panel study among 1895 patients from an outpatient department who received repeated ambulatory blood pressure monitoring in Urumqi, China between July 2020 and December 2021. We obtained hourly ambient temperature from the nearest monitoring station to the residential address, and measured 4 ambulatory blood pressure indicators. Linear mixed-effect model combined with distributed lag models were applied to investigate the cumulative associations of hourly temperature with BP. RESULTS: A total of 97,466 valid blood pressure measurements were evaluated. We observed almost linear and monotonically decreasing relationships between temperature and blood pressure. The effects occurred in the same hour, attenuated thereafter and became insignificant approximately 36 h. A 10 °C decrease in temperature was significantly associated with increments of 0.84 mmHg in systolic blood pressure, 0.56 mmHg in diastolic blood pressure, 1.38 mmHg in mean arterial pressure, and 0.66 mmHg in pulse pressure over lag 0 to 36 h. Stronger associations were found among patients of female sex, age between 18 and 65 years, overweight or obesity, minority, less education or in the cold season, as well as those without hypertension or with coronary heart disease, or did not take anti-hypertension medication. CONCLUSION: Our study provides robust evidence that hourly ambient temperature is inversely associated with ambulatory blood pressure. It also highlights a linear relationship between decreased ambient temperature and elevated BP, which may have implications for the prevention and management of hypertension in susceptible populations.

Air pollution exposure and the risk of macrosomia: Identifying specific susceptible months.

Birth weight is an important indicator of future growth and development for newborns. Few studies investigated the potential effects of air pollutants on macrosomia and their susceptible windows. We included 38,971 singleton full-term births from Beijing HaiDian Maternal and Child Health Hospital between 2014 and 2018, and assessed the associations of air pollutants exposure during preconception and pregnancy with macrosomia as well as the corresponding susceptible windows. The concentrations of air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) for participants were calculated by the data from the nearest monitoring stations. Distributed lag models (DLM) incorporating logistic regression models were used to estimate the associations between air pollutants exposure during the 3 months before conception and pregnancy period and the risk of macrosomia, identifying susceptible windows of air pollutants. Weighted quantile sum (WQS) regression was applied to estimate the joint effect of air pollutants. A 10 µg/m3 increase in PM2.5 exposure from 3rd to 8th gestational month was positively associated with the risk of macrosomia, with the strongest effect in the 6th month (OR = 1.010, 95 % CI: 1.002-1.019). For a 10 µg/m3 increase in SO2, the windows of significant exposure were from the 1st preconception month to the 3rd gestational month, with the strongest effect in the 2nd month (OR = 1.030, 95 % CI: 1.010-1.049). We also observed the significant positive associations were in the 5th-8th gestational months for PM10, the 8th-9th gestational months for NO2 and the 3rd-7th gestational months for CO respectively. WQS regression also indicated a positive association between co-exposure to air pollutants and macrosomia. Our results suggest air pollution exposure is associated with increased risk of macrosomia. The windows of exposure for susceptibility to the risk of macrosomia vary between air pollutants. The susceptible exposure windows were middle and late pregnancy for PM, CO and NO2, while for SO2, early pregnancy is the window of vulnerability. Our findings provide the evidence that air pollution exposure is an independent risk factor for macrosomia and a basis for targeted environment policy.

How Does Climate Change Affect Tomato and Okra Production? Evidence from Nigeria.

This study examined the impacts of climate change on okra and tomato yields. Fertilizer consumption and credit to the crop sector were considered as covariates in the analysis. Time-series data, spanning a period of 40 years, were obtained from various sources. An autoregressive distributed lag model was applied to analyze short- and long-term impacts of climate change and agricultural inputs on okra and tomato yields. Not all variables were stationary at levels (order zero), but they were all significant at first difference, indicating the presence of cointegration. The Bound's test F-ratio was statistically significant and implied the presence of long- and short-term relationships among the variables studied. The mean temperatures had negative impacts on okra and tomato yields in both the short and long terms. Credit guaranteed to the crop sector had positive short- and long-term impacts on tomato yield; fertilizer consumption had a negative long-term impact on okra yield. Our study concludes that climate change, particularly rising temperature, impacts herbaceous fruit crop production in Nigeria. Therefore, we recommend that breeding and disseminating climate-smart tomato and okra varieties will help fruit crop farmers respond to rising temperatures.