Time series analysis of PM2.5 and PM10−2.5 mass concentration in the city of Sao Carlos, Brazil

2010 ◽  
Vol 41 (1/2) ◽  
pp. 90 ◽  
Author(s):  
Simone Andrea Pozza ◽  
Ed Pinheiro Lima ◽  
Tatiane Tagino Comin ◽  
Marcelino Luiz Gimenes ◽  
Jose Renato Coury
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Mass Concentration ◽  
Series Analysis ◽  
The City

scholarly journals Time series analysis of dengue cases reporting to a tertiary care hospital

2019 ◽  
Vol 6 (5) ◽  
pp. 2200
Author(s):  
Ravindra S. Kembhavi ◽  
Saurabha U. S.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
Arima Model ◽  
Public Health Problem ◽  
Major Public Health Problem ◽  
Care Hospital ◽  
Series Analysis ◽  
The City

Background: Dengue fever is a major public health problem, the concern is high as the disease is closely related to climate change.Methods: This was a retrospective study, conducted for 1 year in a tertiary care hospital in the city of Mumbai. Data of Dengue cases and climate for the city of Mumbai between 2011 and 2015 were obtained. Data was analysed using SPSS- time series analysis and forecasting model.Results: 33% cases belonged to the 21-30 years, proportion of men affected were more than women. A seasonal distribution of cases was observed. A strong correlation was noted between the total number of cases reported and (a) mean monthly rainfall and (b) number of days of rainfall. ARIMA model was used for forecasting.Conclusions: The trend analysis along with forecasting model helps in being prepared for the year ahead. 

scholarly journals Gentrification, mobility patterns and health in Lisbon: a time series’ analysis from 2008 to 2018

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R Sá ◽  
E Soares dos Santos ◽  
T Gabriel ◽  
A Moreira ◽  
P Giraldo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Low Income ◽  
Housing Prices ◽  
Geographical Area ◽  
Mobility Patterns ◽  
Ecological Data ◽  
Series Analysis ◽  
Causal Pathways ◽  
The City

Abstract Background Mobility patterns have a great impact on health. The use of cars is known to be related with increasing air pollution, noise and accidents, and less active transportation, leading to cardiovascular, oncological or respiratory diseases, among others. Gentrification is a process through which the rising value of a geographical area displaces low-income inhabitants, mostly due to rising rents, mortgages and property taxes. This change has the potential for relocating long-time residents and businesses. The aim of this study was to quantify the effect of gentrification in the car influx in the city of Lisbon. Methods A time series' analysis was performed using public ecological data, from 2008 to 2018, of habitation costs per square meter (as a proxy of gentrification) and the number of cars that entered Lisbon through accessing highways. The model was adjusted for confounding factors such as Lisbon's gross income and fuel prices. Results We verified the effect of seasonality in the car influx, with peaks before and after summer - july and october - and a downward trend until 2013 that then inflected and started an upward trend from 2014 to 2018. Habitation costs were positively correlated with car influx into the city (R2=0.773; p < 0.001). In the model, 1€/m2 of increment in housing prices corresponded to 200 more cars that entered the city. Conclusions In Lisbon, gentrification was associated with the increasing number of cars entering the city. These findings may have implications in future policies that regulate housing and mobility. Further research is needed to fully understand the causal pathways of this phenomenon. Key messages Mobility patterns have a great influence on health, and gentrification may influence them. The increase of 1€ per square meter in housing prices lead to an increase of the influx of cars of 200.

scholarly journals Relationship between respiratory diseases and environmental conditions: a time-series analysis in Eastern Amazon

2021 ◽  
Vol 56 (3) ◽  
pp. 398-412
Author(s):  
Mauricio Do Nascimento Moura ◽  
Maria Isabel Vitorino ◽  
Glauber Guimarães Cirino da Silva ◽  
Valdir Soares de Andrade Filho
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Rainy Season ◽  
Respiratory Diseases ◽  
Hospital Admissions ◽  
Dry Season ◽  
Climate Indices ◽  
Series Analysis ◽  
Decadal Scale ◽  
The City

This study examines the relationship between the time-series analysis of climate, deforestation, wildfire, Aerosol Optical Depth (AOD), and hospital admissions for respiratory diseases in the Eastern Amazon. Through a descriptive study with an ecological approach of an 18-year time-series analysis, we made a statistical analysis of two pre-established periods, namely, the rainy season and the dry season. On a decadal scale, analyzing the signals of climate indices [i.e., the Southern Oscillation Index (SOI) and the Atlantic Meridional Mode (AMM)], the city of Marabá presents correlations between hospital admissions, wildfire, and AOD. This is not observed with the same accuracy in Santarém. On a seasonal scale, our analysis demonstrated how both cities in this research presented an increase in the number of hospital admissions during the dry season: Marabá, 3%; Santarém, 5%. The same season also presented a higher number of fire outbreaks, AOD, and higher temperatures. The AOD monthly analysis showed that the atmosphere of Marabá may be under the influence of other types of aerosols, such as those from mining activities. There is a time lag of approximately 2 months in the records of wildfire in the city. Such lag is not found in Santarém. The linear regression analysis shows that there is a correlation above 64% (Marabá) and 50% (Santarém), which is statistically significant because it proves that the number of hospital admissions for respiratory diseases is dependable on the AOD value. From the cities in the study, Marabá presents the highest incidence of wildfire, with an average of 188.5— the average in Santarém is 68.7—, and therefore the highest AOD value, with an average of 0.66 (Santarém, 0.47), both during the dry season. It is evident that the climate component has a relevant contribution to the increase in the number of hospital admissions, especially during the rainy season, where there are few or no records of wildfires.

scholarly journals Social Inequalities in Heat-attributable Mortality in the City of Turin, Northwest of Italy: A Time Series Analysis From 1982 to 2018

2020 ◽  
Author(s):  
Marta Ellena ◽  
Joan Ballester ◽  
Paola Mercogliano ◽  
Elisa Ferracin ◽  
Giuliana Barbato ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Social Inequalities ◽  
Attributable Mortality ◽  
Series Analysis ◽  
The City

Abstract The authors have withdrawn this preprint due to author disagreement.

scholarly journals Intervention targeted at physicians’ treatment of musculoskeletal disorders and sickness certification: an interrupted time series analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e047018
Author(s):  
Johanna Kausto ◽  
Tom Henrik Rosenström ◽  
Jenni Ervasti ◽  
Olli Pietiläinen ◽  
Leena Kaila-Kangas ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sick Leave ◽  
Cost Savings ◽  
Interrupted Time Series ◽  
Healthcare Services ◽  
Gradual Change ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The City

ObjectiveAn intervention was carried out at the occupational healthcare services (OHS) of the City of Helsinki beginning in 2016. We investigated the association between the intervention and employee sick leaves using interrupted time series analysis.DesignRegister-based cohort study with a quasi-experimental study design.SettingEmployees of the City of Helsinki.ParticipantsWe analysed individual-level register-based data on all employees who were employed by the city for any length of time between 2013 and 2018 (a total 86 970 employees and 3 014 075 sick leave days). Sick leave days and periods that were OHS-based constituted the intervention time series and the rest of the sick leave days and periods contributed to the comparison time series.InterventionRecommendations provided to physicians on managing pain and prescribing sick leave for low back, shoulder and elbow pain.Outcome measuresNumber of sick leave days per month and sick leave periods per year.ResultsFor all sick leave days prescribed at OHS, there was no immediate change in sick leave days, whereas a gradual change showing decreasing number of OHS-based sick leave days was detected. On average, the intervention was estimated to have saved 2.5 sick leave days per year per employee. For other sick leave days, there was an immediate increase in the level of sick leave days after the intervention and a subsequent gradual trend showing decreasing number of sick leave days.ConclusionsThe intervention may have reduced employee sick leaves and therefore it is possible that it had led to direct cost savings. However, further evidence for causal inferences is needed.

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