Background Air pollution and meteorological factors exert complex nonlinear effects on acute symptoms in the population, with intricate interactions among these factors. Traditional statistical methods struggle to simultaneously address complex nonlinear relationships and multicollinearity issues. Objective To delineate the dynamic effects of air pollutants and meteorological parameters on acute symptoms in three distinct populations with the multicollinearity being addressed and to generate reliable scientific evidence for prevention and control of health risk factors. Methods A time-series study design was employed to collect data on air pollution (daily mean temperature, daily precipitation, daily mean relative humidity, and daily mean wind speed), meteorological factors [Air Quality Index (AQI), fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and 8-hour maximum ozone (O₃)], and acute symptoms such as fever, cough, and sore throat in Jinan from June to December 2023. Key variables were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by generalized additive mixed modeling (GAMM) to analyze the health effects of combined environmental exposures to air pollution and meteorological factors. Linear variables were modeled using linear mixed-effects function, nonlinear variables were smoothed using thin-plate regression splines, and variables with interaction effects were smoothed using low-rank scale-invariant tensor product splines. Fluctuations in independent variables following a normal distribution were treated as sampling errors and incorporated as random effects in the GAMM. Results For fever, the daily mean temperature, daily mean relative humidity, daily mean wind speed, and ambient SO₂ were statistically significant (P<0.05), with daily mean wind speed being a linear influencing factor. When the daily mean temperature was below 3 °C, each 10 °C increase corresponded to a relative risk (RR) of 2.64 (95%CI: 2.50, 2.79). When the daily mean temperature was ≥3 °C, each 10 °C increase corresponded to an RR of 0.86 (95%CI: 0.83, 0.89). Each 10% increase in daily mean relative humidity was associated with an RR of 0.93 (95%CI: 0.89, 0.97). Each 1 m·s⁻¹ increase in daily mean wind speed corresponded to an RR of 1.06 (95%CI: 1.02, 1.10). Within the concentration ranges of <10 μg·m⁻³, 10–<12.5 μg·m⁻³, and ≥12.5 μg·m⁻³, each 1 μg·m⁻³ increase in ambient SO₂ corresponded to RR values of 1.01 (95%CI: 0.98, 1.05), 1.21 (95%CI: 1.17, 1.24), and 0.97 (95%CI: 0.94, 0.99), respectively. For cough, the daily mean temperature, daily mean relative humidity, PM₁₀, and SO₂ were statistically significant (P<0.001), with PM₁₀ being a linear influencing factor. When the daily mean temperature was below 1 °C, each 10 °C increase corresponded to an RR of 1.47 (95%CI: 1.42, 1.52). When the daily mean temperature was ≥1 °C, each 10 °C increase corresponded to an RR of 0.85 (95%CI: 0.82, 0.87). Each 10% increase in daily mean relative humidity was associated with an RR of 0.95 (95%CI: 0.92, 0.98). Each 50 μg·m⁻³ increase in PM₁₀ concentration corresponded to an RR of 1.05 (95%CI: 1.02, 1.08). Within the concentration ranges of <10 μg·m⁻³, 10–<12.5 μg·m⁻³, and ≥ 12.5 μg·m⁻³, each 1 μg·m⁻³ increase in ambient SO₂ corresponded to RR values of 1.00 (95%CI: 0.97, 1.03), 1.12 (95%CI: 1.09, 1.16), and 0.98 (95%CI: 0.95, 1.00), respectively. For sore throat, the daily mean temperature, daily mean relative humidity, daily mean wind speed, PM₁₀, and SO₂ were statistically significant (P<0.05), with daily mean wind speed and PM₁₀ being linear influencing factors. When the daily mean temperature was below 2 °C, each 10 °C increase corresponded to an RR of 1.82 (95%CI: 1.69, 1.96). When the daily mean temperature was ≥2 °C, each 10 °C increase corresponded to an RR of 0.81 (95%CI: 0.77, 0.87). Each 10% increase in daily mean relative humidity was associated with an RR of 0.94 (95%CI: 0.88, 1.00). Within the concentration ranges of <10 μg·m⁻³, 10–<12.5 μg·m⁻³, and ≥12.5 μg·m⁻³, each 1 μg·m⁻³ increase in ambient SO₂ corresponded to RR values of 1.02 (95%CI: 0.97, 1.08), 1.13 (95%CI: 1.08, 1.19), and 0.98 (95%CI: 0.94, 1.02), respectively. Each 1 m·s⁻¹ increase in daily mean wind speed and each 50 μg·m⁻³ increase in PM₁₀ concentration were associated with RR values of 1.06 (95%CI: 1.00, 1.12) and 1.04 (95%CI: 0.98, 1.10), respectively. An interaction effect was observed between daily mean wind speed and PM₁₀: increasing daily mean wind speed non-linearly reduced the impact of PM₁₀, on sore throat whereas PM₁₀ had no significant effect on wind speed. Conclusion This study, by combining LASSO and GAMM, largely eliminates the multicollinearity among selected variables. It reveals complex non-linear effects and interactions between air pollutants, meteorological factors, and acute symptoms in different population groups in Jinan. The symptoms like fever, cough, and sore throat are non-linearly associated with daily mean temperature and SO₂ concentration, while PM₁₀ and wind speed show a linear relationship or interactive effects. These findings provide a new basis for the precise prevention and control of health risk factors.

Objective: Using three models too estimate excess mortality associated with influenza of Shanxi Province during 2013-2017. Methods: Mortality data and influenza surveillance data of 11 cities of Shanxi Province from the 2013-2014 through 2016-2017 were used to estimate influenza-associated all cause deaths, circulatory and respiratory deaths and respiratory deaths. Three models were used: (i) Serfling regression, (ii)Poisson regression, (iii)General line model. Results: The total reported death cases of all cause were 157 733, annual death cases of all cause were 39 433, among these cases, male cases 93 831 (59.50%), cases above 65 years old 123 931 (78.57%). Annual influenza-associated excess mortality, for all causes, circulatory and respiratory deaths, respiratory deaths were 8.62 deaths per 100 000, 6.33 deaths per 100 000 and 0.68 deaths per 100 000 estimated by Serfling model, respectively; and 21.30 deaths per 100 000, 16.89 deaths per 100 000 and 2.14 deaths per 100 000 estimated by General line model, respectively; and 21.76 deaths per 100 000, 17.03 deaths per 100 000 and 2.05 deaths per 100 000, estimated by Poisson model, respectively. Influenza-related excess mortality was higher in people over 75 years old; influenza-associated excess mortalityfor all causes, circulatory and respiratory deaths, respiratory deaths were 259.67 deaths per 100 000, 229.90 deaths per 100 000 and 32.63 deaths per 100 000, estimated by GLM model, respectively; and 269.49 deaths per 100 000, 233.69 deaths per 100 000 and 31.27 deaths per 100 000, estimated by Poisson model,respectively. Conclusion Excess mortality associated with influenza mainly caused by A (H3N2), Influenza caused the most associated death amongold people.

Objective Using three models too estimate excess mortality associated with influenza of Shanxi Province during 2013-2017. Methods Mortality data and influenza surveillance data of 11 cities of Shanxi Province from the 2013-2014 through 2016-2017 were used to estimate influenza?associated all cause deaths, circulatory and respiratory deaths and respiratory deaths. Three models were used: (i) Serfling regression, (ii)Poisson regression, (iii)General line model. Results The total reported death cases of all cause were 157 733, annual death cases of all cause were 39 433, among these cases, male cases 93 831 (59.50%), cases above 65 years old 123 931 (78.57%). Annual influenza?associated excess mortality, for all causes, circulatory and respiratory deaths, respiratory deaths were 8.62 deaths per 100 000, 6.33 deaths per 100 000 and 0.68 deaths per 100 000 estimated by Serfling model, respectively; and 21.30 deaths per 100 000, 16.89 deaths per 100 000 and 2.14 deaths per 100 000 estimated by General line model, respectively; and 21.76 deaths per 100 000, 17.03 deaths per 100 000 and 2.05 deaths per 100 000, estimated by Poisson model, respectively. Influenza?related excess mortality was higher in people over 75 years old; influenza?associated excess mortalityfor all causes, circulatory and respiratory deaths, respiratory deaths were 259.67 deaths per 100 000, 229.90 deaths per 100 000 and 32.63 deaths per 100 000, estimated by GLM model, respectively; and 269.49 deaths per 100 000, 233.69 deaths per 100 000 and 31.27 deaths per 100 000, estimated by Poisson model,respectively. Conclusion Excess mortality associated with influenza mainly caused by A (H3N2), Influenza caused the most associated death amongold people.

Objective Using three models too estimate excess mortality associated with influenza of Shanxi Province during 2013-2017. Methods Mortality data and influenza surveillance data of 11 cities of Shanxi Province from the 2013-2014 through 2016-2017 were used to estimate influenza?associated all cause deaths, circulatory and respiratory deaths and respiratory deaths. Three models were used: (i) Serfling regression, (ii)Poisson regression, (iii)General line model. Results The total reported death cases of all cause were 157 733, annual death cases of all cause were 39 433, among these cases, male cases 93 831 (59.50%), cases above 65 years old 123 931 (78.57%). Annual influenza?associated excess mortality, for all causes, circulatory and respiratory deaths, respiratory deaths were 8.62 deaths per 100 000, 6.33 deaths per 100 000 and 0.68 deaths per 100 000 estimated by Serfling model, respectively; and 21.30 deaths per 100 000, 16.89 deaths per 100 000 and 2.14 deaths per 100 000 estimated by General line model, respectively; and 21.76 deaths per 100 000, 17.03 deaths per 100 000 and 2.05 deaths per 100 000, estimated by Poisson model, respectively. Influenza?related excess mortality was higher in people over 75 years old; influenza?associated excess mortalityfor all causes, circulatory and respiratory deaths, respiratory deaths were 259.67 deaths per 100 000, 229.90 deaths per 100 000 and 32.63 deaths per 100 000, estimated by GLM model, respectively; and 269.49 deaths per 100 000, 233.69 deaths per 100 000 and 31.27 deaths per 100 000, estimated by Poisson model,respectively. Conclusion Excess mortality associated with influenza mainly caused by A (H3N2), Influenza caused the most associated death amongold people.

Objective: To study the epidemiological characters and risk factors of primary intussusception among children who were ≤2 years old in Jinan city. Methods: A retrospective study was also applied to collect the disease information of inpatient cases through hospital information system of inpatient during 2011 and 2015. The attack rate (AR) was calculated in total and each year, respectively. 1∶₁ matched pared case-control study was conducted to explore the risk factors of primary intussusception. A total of new confirmed 93 residential cases were enrolled with ≤2 years old, diagnosed as primary intussusception or diseases coded as K56.1 according to the International Classification of Diseases, tenth version (ICD-10), and without any other reason for organic lesions. The 93 patients without primary intussusception inpatients were selected from the same hospital and departments as the controls, and matched with the cases as the same gender, ±3 months old, and disease onset within 7 days, visiting the hospital during January 1, 2016 and June 30, 2016. Clinical and epidemiological information was collected both from cases and controls. The difference of AR among different years was compared by Trend Chi-squiare test, and difference between gender, month old and regions was compared by Chi-squiare test, respectively. Multiple-variables factor analysis was applied to analysis the risk factors of cases compared with controls by conditional logistic regression model. Results: A total of 574 cases were found and the overall AR was 86.3 per 100 000. A significant decreasing trend (χ^2trend=23.43, P<0.001) of AR was showed between 2011 and 2015, with the highest AR in 2011 (117.6 per 100 000), and the lower AR in 2014 (66.0 per 100 000). The lowest AR was also observed in male (106.9 per 100 000) compared with the female (63.2 per 100 000) (χ²=13.76, P<0.001), and in ≤12 months old (102.1 per 100 000) compared with the >12 months old (70.4 per 100 000) (χ²=19.46, P<0.001). The higher proportion of 57.1% (328/574) was found in 4-12 months old cases. The cases with an overweight before illness (OR=4.98, 95%CI: 1.27-19.50), with mixed feeding of breast-milk and milk-powder (OR=16.90, 95%CI: 5.25-54.20), and with instruction of solid food before 6 months old (OR=16.50, 95%CI: 1.91-143.10) could increase the risk of illness compared with keeping a healthy weight, breast feeding, and instruction of solid food after 6 months old, separately. Conclusion A higher attack rate of primary intussusception in children with a ≤2 years old in Jinan was observed. Male and 4-12 months group had a higher risk. Body weight, breast feeding, and the instruction of solid food before and after 6 months old were related to the risk of primary intussusception among children who were ≤2 years old.

Objective To estimate the influence of the ambient PM10 and PM2.5 pollution on the hospital outpatient department visit due to respiratory diseases in local residents in Jinan quantitatively.Methods Time serial analysis using generalized addictive model (GAM) was conducted.After controlling the confotmding factors,such as long term trend,weekly pattern and meteorological factors,considering lag effect and the influence of other air pollutants,the excess relative risks of daily hospital visits associated with increased ambient PM10 and PM2.5 levels were estimated by fitting a Poisson regression model.Results A 10 μtg/m3 increase of PM10 and PM2.5 levels was associated with an increase of 0.36％ (95％ CI:0.30％-0.43％) and 0.50％ (95％ CI:0.30％-0.70％) respectively for hospital visits due to respiratory diseases.Lag effect of 6 days was strongest,the excess relative risks were 0.65％ (95％ CI:0.58％-0.71％) and 0.54％ (95％ CI:0.42％-0.67％) respectively.When NO2 concentration was introduced,the daily hospital visits due to respiratory disease increased by 0.83％ as a 10 μg/m3 increase of PM10 concentration (95％CI:0.76％-0.91％).Conclusion The ambient PM10 and PM2.5 pollution was positively associated with daily hospital visits due to respiratory disease in Jinan,and ambient NO2 concentration would have the synergistic effect.

Objective: To investigate the hot spots and epidemiologic characteristics of hand, foot and mouth disease (HFMD) in Jinan municipality from 2009 to 2016. Methods: Disease reports of HFMD in Jinan from 2009-2016 were collected and analyzed with ArcGis 10.2 to show the hot spot in different villages and towns, as well as clustering analysis and descriptive epidemiology to show epidemiologic characteristics. Results: A total of 89 486 HFMD cases were reported and the reported annual incidence rate was 160.94/100000 during the 7-year period, which increased year by year, and within the whole city, each county was at a higher epidemic level; the curve of incidence is unimodal and the incidence peak occurred mostly between May and August, especially in June; 115 severe cases were reported and the ratio was 0.13%. Of the reported cases, 81.51% were between 1 to 4 years old; 60.36 % were children living scattered. The hot spots were like a circle surrounding the core areas, showing a tendency of increase; the proportion of EV71, CVA16 and other enteroviruses were 33.67%、37.22%and 29.09%, respectively, and they appeared in turn, but severe cases were mostly affected by EV71. Conclusions The HFMD in Jinan is in a highly prevalent level, with low ratio of severe cases. Seasonal(high in summer) and unimodal; more common among children between 1 to 4 years old, living scatted and in urban and rural linking areas, with the tendency of increasing of hot spots; prevalent pathogens appear in turn.

China ; Humans ; Medical Informatics ; Public Health

Objective To explore the epidemic trend of malaria in Jinan City so as to provide the evidence for improving the prevention and control of malaria. Methods The surveillance and annual report data of malaria were collected and analyzed epidemiologically in Jinan City from 1989 to 2013. Results The prevalence of malaria was low in Jinan City from 1989 to 2013. Totally 179 cases of malaria were reported and 14 cases 7.82% were locally infected and 165 cases 92.18% were imported. Conclusion Most malaria cases were imported since the disease was basically eliminated in Jinan City. The overseas workers from high prevalence areas of malaria should be well managed.

OBJECTIVETo investigate the effect of weather patterns on the incidence of hand foot and mouth disease (HFMD) in Jinan.

METHODSHFMD confirmed cases aged ≤5 years old for the period of 2012-2014 were collected from National Notifiable Disease Report System (NNDRS). Simultaneous meteorological data, including daily average temperature, relative humidity, atmospheric pressure, wind velocity, rainfall and duration of sunshine were obtained from the website of Jinan meteorological bureau. A negative binomial multivariable regression was used to identify the relationship between meteorological variables and HFMD.

RESULTSDuring the study period, a total of 40 405 HFMD cases aged ≤5 years old were reported. The incident peak occurred from May to July, a total of 22 254 cases were reported during this period, which accounted for 55.08% of whole cases. After adjusting by "year", each 1 °C rise of weekly average temperature corresponded to an increase of 6.70% (95%CI:6.35% to 7.06%) in the weekly number of HFMD cases, while a 1 kPa rise of weekly average atmospheric pressure corresponded to a decrease in the number of cases by 44.77% (95%CI: -46.23% to -41.91%). Likewise, a one percent rise in weekly average relative humidity corresponded to an increase of 1.96% (95%CI: 1.84% to 2.09%), a 1 m/h rise in weekly average wind velocity corresponded to an increase of 16.63% (95% CI:14.08% to 19.25%), a hour add in weekly duration of sunshine corresponded to a decrease of 0.82% (95% CI : -0.93% to -0.72%), in the weekly number of HFMD cases.

CONCLUSIONWeather factors including temperature, atmospheric pressure, relative humidity, wind velocity, and duration of sunshine had a significant influence on occurrence and transmission of HFMD in Jinan city.