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Many studies have reported a relationship between climate factors and malaria. However, results were inconsistent across the areas. We examined associations between climate factors and malaria in two geographically different areas: lowland (lakeside area) and highland in Western Kenya. Associations between climate factors (rainfall, land surface temperature (LST), and lake water level (LWL)) and monthly malaria cases from 2000 to 2013 in six hospitals (two in lowland and four in highland) were analyzed using time-series regression analysis with a distributed lag nonlinear model (DLNM) and multivariate meta-analysis. We found positive rainfall–malaria overall associations in lowland with a peak at 120 mm of monthly rainfall with a relative risk (RR) of 7.32 (95% CI: 2.74, 19.56) (reference 0 mm), whereas similar associations were not found in highland. Positive associations were observed at lags of 2 to 4 months at rainfall around 100–200 mm in both lowland and highland. The RRs at 150 mm rainfall were 1.42 (95% CI: 1.18, 1.71) in lowland and 1.20 (95% CI: 1.07, 1.33) in highland (at a lag of 3 months). LST and LWL did not show significant association with malaria. The results suggest that geographical characteristics can influence climate–malaria relationships.

BACKGROUND: Although interest in assessing the impacts of temperature on mortality has increased,few studies have used a case-crossover design to examine nonlinear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature—mortality relationship in China or on what temperature measure is the best predictor of mortality, OBJECTIVES: Our objectives were to use a distributed lag nonlinear model (DLNM) as a part of case crossover design to examine the nonlinear and distributed lag effects of temperature on mortality in Tianjin, China and to explore which temperature measure is the best predictor of mortality. METHODS: We applied die DLNM to a case-crossover design to assess die nonlinear and delayed effects of temperatures (maximum, mean, and minimum) on deaths (nonaccidental, cardiopulmonary,cardiovascular, and respiratory). RESULTS: A U-shaped relationship was found consistently between temperature and mortality. Cold effects (i.e., significantly increased mortality associated with low temperatures) were delayed by 3 days and persisted for 10 days. Hot effects (i.e., significantly increased mortality associated with high temperatures) were acute and lasted for 3 days and were followed by mortality displacement for nonaccidental, cardiopulmonary, and cardiovascular deatiis. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. CONCLUSIONS: In Tianjin, extreme cold and hot temperatures increased the risk of mortality. The effects of cold last longer than the effects of heat. Combining the DLNM and the case-crossover design allows the case-crossover design to flexibly estimate the nonlinear and delayed effects of temperature(or air pollution) while controlling for season.

Previous studies have identified various factors affecting dengue fever, but most focus on correlations within specific regions, not establishing causality. This study uses Convergent Cross Mapping (CCM) to explore the causal relationships between nine meteorological factors and reported dengue fever cases in 14 Chinese provinces with the highest incidence. Results show that temperature and pressure have causal links with case numbers in more provinces. In Guangdong, which has the most reported cases, Partial Cross Mapping (PCM) reveals a direct causal relationship only between GDP and reported dengue fever cases, while meteorological factors influence dengue fever via their impact on mosquito populations. Principal Component Analysis (PCA) from 30 provinces further confirms the importance of temperature and pressure. Given the significant negative correlation between temperature and pressure, separate models were developed for each province using the Distributed Lag Nonlinear Model (DLNM) combined with the Generalized Additive Model (GAM), with GDP as a covariate. The results indicate that the Relative Risk (RR) increases significantly under high temperatures and low pressure within a shorter lag period. GDP significantly promotes case numbers in all provinces.

Background With complex changes in the global climate, it is critical to understand how ambient temperature affects health, especially in China. We aimed to assess the effects of temperature on daily mortality, including total non-accidental, cardiovascular disease (CVD), respiratory disease, cerebrovascular disease, and ischemic heart disease (IHD) mortality between 2016 and 2020 in Chengdu, China. Methods We obtained daily temperature and mortality data for the period 2016–2020. A Poisson regression model combined with a distributed-lag nonlinear model was used to examine the association between temperature and daily mortality. We investigated the effects of individual characteristics by sex, age, education level, and marital status. Results We found significant non-linear effects of temperature on total non-accidental, CVD, respiratory, cerebrovascular, and IHD mortality. Heat effects were immediate and lasted for 0–3 days, whereas cold effects persisted for 7–10 days. The relative risks associated with extreme high temperatures (99th percentile of temperature, 28 °C) over lags of 0–3 days were 1.22 (95% confidence interval [CI]: 1.17, 1.28) for total non-accidental mortality, 1.40 (95% CI: 1.30, 1.50) for CVD morality, 1.34 (95% CI: 1.24, 1.46) for respiratory morality, 1.33 (95% CI: 1.20, 1.47) for cerebrovascular mortality, and 1.38 (95% CI: 1.20, 1.58) for IHD mortality. The relative risks associated with extreme cold temperature (1st percentile of temperature, 3.0 °C) over lags of 0–14 days were 1.32 (95% CI: 1.19, 1.46) for total mortality, 1.45 (95% CI: 1.24, 1.68) for CVD morality, 1.28 (95% CI: 1.09, 1.50) for respiratory morality, 1.36 (95% CI: 1.09, 1.70) for cerebrovascular mortality, and 1.26 (95% CI: 0.95, 1.68) for IHD morality. We found that hot and cold affects were greater in those over 85 years of age, and that women, individuals with low education levels, and those who were widowed, divorced, or never married, were more vulnerable. Conclusions This study showed that exposure to hot and cold temperatures in Chengdu was associated with increased mortality, with people over 85 years old, women, those with low education levels, and unmarried individuals being more affected by hot and cold temperatures.

Background Hand, foot and mouth disease (HFMD) caused by a variety of enteroviruses remains a major public health problem in China. Previous studies have found that social factors may contribute to the inconsistency of the relationship patterns between meteorological factors and HFMD, but the conclusions are inconsistent. The influence of social factors on the association between meteorology and HFMD is still less well understood. We aimed to analyze whether social factors affected the effect of meteorological factors on HFMD in Sichuan Province. Method We collected daily data on HFMD, meteorological factors and social factors in Sichuan Province from 2011 to 2017. First, we used a Bayesian spatiotemporal model combined with a distributed lag nonlinear model to evaluate the exposure-lag-response association between meteorological factors and HFMD. Second, by constructing the interaction of meteorological factors and social factors in the above model, the changes in the relative risk (RR) under different levels of social factors were evaluated. Results The cumulative exposure curves for average temperature, relative humidity, and HFMD were shaped like an inverted “V” and a “U” shape. As the average temperature increased, the RR increased and peaked at 19 °C (RR 1.020 [95% confidence interval CI 1.004–1.050]). The urbanization rate, per capita gross domestic product (GDP), population density, birth rate, number of beds in health care centers and number of kindergartens interacted with relative humidity. With the increase in social factors, the correlation curve between relative humidity and HFMD changed from an “S” shape to a “U” shape. Conclusions Relative humidity and average temperature increased the risk of HFMD within a certain range, and social factors enhanced the impact of high relative humidity. These results could provide insights into the combined role of environmental factors in HFMD and useful information for regional interventions.

Relatively few studies investigated the effects of extreme temperatures (both heat and cold) on mental health (ICD-9: 290-319; ICD-10: F00-F99) and the potential effect modifications by individuals’ age, sex, and race. We aimed to explore the effect of extreme temperatures of both heat and cold on the emergency room (ER) visits for mental health disorders, and conducted a stratified analysis to identify possible susceptible population in Erie and Niagara counties, NY, USA. To assess the short-term impacts of daily maximum temperature on ER visits related to mental disorders (2009–2015), we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, long-term time trend, and seasonality. We found that there were positive associations between short-term exposure to extreme ambient temperatures and increased ER visits for mental disorders, and the effects can vary by individual factors. We found heat effect (relative risk (RR) = 1.16; 95% confidence intervals (CI), 1.06–1.27) on exacerbated mental disorders became intense in the study region and subgroup of population (the elderly) being more susceptible to extreme heat than any other age group. For extreme cold, we found that there is a substantial delay effect of 14 days (RR = 1.25; 95% CI = 1.08–1.45), which is particularly burdensome to the age group of 50–64 years old and African-Americans. Our findings suggest that there is a positive association between short-term exposure to extreme ambient temperature (heat and cold) and increased ER visits for mental disorders, and the effects vary as a function of individual factors, such as age and race.

Background Stroke, especially ischemic stroke (IS), has been a severe public health problem around the world. However, the association between air pollution and ischemic stroke remains ambiguous. Methods A total of 63, 997 IS cases aged 18 years or above in Shenzhen were collected from 2008 to 2014. We used the time-stratified case-crossover design combining with distributed lag nonlinear model (DLNM) to estimate the association between air pollution and IS onset. Furthermore, this study explored the variability across gender and age groups. Results The cumulative exposure-response curves were J-shaped for SO 2 , NO 2 and PM 10 , and V-shaped for O 3 , and crossed over the relative risk ( RR ) of one. The 99th, 50th (median) and 1st percentiles of concentration (μg/m 3 ) respectively were 37.86, 10.06, 3.71 for SO 2 , 116.26, 41.29, 18.51 for NO 2 , 145.94, 48.29, 16.14 for PM 10 , and 111.57, 49.82, 16.00 for O 3 . Extreme high-SO 2 , high-NO 2 , high-PM 10 , high-O 3 , and low-O 3 concentration increased the risk of IS, with the maximum RR values and 95% CI s: 1.50(1.22, 1.84) (99th vs median) at 0–12 lag days, 1.37(1.13, 1.67) (99th vs median) at 0–10 lag days, 1.26(1.04, 1.53) (99th vs median) at 0–12 lag days, 1.25(1.04, 1.49) (99th vs median) at 0–14 lag days, and 1.29(1.03, 1.61) (1st vs median) at 0–14 lag days, respectively. The statistically significant minimal RR value and 95% CI was 0.79(0.66,0.94) at 0–10 lag days for extreme low-PM 10 . The elderly aged over 65 years were susceptible to extreme pollution conditions. Difference from the vulnerability of males to extreme high-SO 2 , high-NO 2 and low-O 3 , females were vulnerable to extreme high-PM 10 and high-O 3 . Comparing with the elderly, adults aged 18–64 year were immune to extreme low-NO 2 and low-PM 10 . However, no association between CO and IS onset was found. Conclusions SO 2 , NO 2 , PM 10 and O 3 exerted non-linear and delayed influence on IS, and such influence varied with gender and age. These findings may have significant public health implications for the prevention of IS.

Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM 2.5 was the highest (621 μg/m 3 ), the relative risk was the highest ( RR : 2.39, 95% CI: 1.10–5.17). For extremely high concentration PM 2.5 (348 μg/m 3 ), analysis of cumulative lag effect showed statistical significance from cumulative lag0–1 to lag0–6 day, and the minimum cumulative lag effect appeared in lag0–2 ( RR : 0.760, 95% CI: 0.655–0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O 3 concentration was 310 μg/m 3 , and the RR was 1.65(1.26,2.15), when O 3 concentration was 0 μg/m 3 . The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0–7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.

Adverse mental health outcomes have been associated with high temperatures in studies worldwide. Few studies explore a broad range of mental health outcomes, and to our knowledge, none are specific to NC, USA. This ecological study explored the relationship between ambient temperature and mental health outcomes (suicide, self-harm and suicide ideation, anxiety and stress, mood disorders, and depression) in six urban counties across the state of NC, USA. We applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM) to examine the short-term effects of daily ambient temperature on emergency admissions for mental health conditions (2016 to 2018) and violent deaths (2004 to 2018). The results were predominately insignificant, with some key exceptions. The county with the greatest temperature range (Wake) displays higher levels of significance, while counties with the lowest temperature ranges (New Hanover and Pitt) are almost entirely insignificant. Self-harm and suicidal ideation peak in the warm months (July) and generally exhibit a protective effect at lower temperatures and shorter lag intervals. Whereas anxiety, depression, and major depressive disorders peak in the cooler months (May and September). Suicide is the only outcome that favored a 20-day lag period in the sensitivity analysis, although the association with temperature was insignificant. Our findings suggest additional research is needed across a suite of mental health outcomes to fully understand the effects of temperatures on mental health.

As one of the most common complications of early pregnancy, spontaneous abortion is associated with environmental factors, but reports estimating the effect of ambient temperature on spontaneous abortion are still inconclusive. Herein, a case–control study (1002 cases and 2004 controls) in Nanjing, China, from 2017 to 2021 was conducted to evaluate the association between temperature exposure and the risk of spontaneous abortion by using distributed lag nonlinear model (DLNM). As a result, daily mean temperature exposure and early spontaneous abortion showed a nonlinear relationship in 14-day lag periods. Moreover, taking the median temperature (17 °C) as a reference, gradually increased positive effects of high temperature on spontaneous abortion could be found during the 4 days prior to hospitalization, and the highest odds ratio (OR) of 2.07 (95% confidence interval (CI): 1.36, 3.16) at extremely hot temperature (33 °C) was observed at 1 lag day. The results suggested that high-temperature exposure in short times during early pregnancy might increase the risk of SAB. Thus, our findings highlight the potential risk of short-term high-temperature exposure during early pregnancy, and more evidence was given for the effects of climate change on maternal health.