The effects of daily meteorological perturbation on pregnancy outcome: follow-up of a cohort of young women undergoing IVF treatment

Abstract Background Human reproduction follows a seasonal pattern with respect to spontaneous conception, a phenomenon wherein the effect of meteorological fluctuations might not be unique. However, the effect of seasonal variations on patients who underwent in vitro fertilization (IVF) treatment is unclear. We aimed to evaluate the effects of meteorological variation on the pregnancy rate in a cohort undergoing IVF treatment by performing multivariable analyses. Methods We conducted a cohort study in a sub-tropical region with prominent seasonal variations (2005–2016). Women aged < 35 years who were treated with a long ovarian stimulation protocol and underwent fresh embryo transfer (ER) were included. Data on gonadotropin administration (CYCL), oocyte retrieval (OR), ER, and pregnancy outcomes were prospectively recorded. For each patient, the daily average of meteorological data (temperature, humidity, sunlight duration, solar radiation) was recorded from the date of CYCL to ER. Multiple logistic regression analysis adjusted for age, fertilization method, year of the cycle, gonadotropin dose, and transferred embryo grade was performed to determine the relationship between the meteorological parameters and clinical pregnancy. Patients with one successful cycle and one failed cycle were subtracted for a case-control subgroup analysis through mixed effect logistics regressions. Time-series analysis of data in the epidemic level was conducted using the distributed lag linear and non-linear models (DLNMs). Results There were 1029 fresh cycles in 860 women (mean age 31.9 ± 2.0 years). Higher mean temperature from CYCL to OR (adjusted odds ratio [aOR] 1.04; 95% confidence interval [CI] 1.01–1.07, P = 0.01) increased the odds of pregnancy, while OR to ER did not show any statistical significance. Compared to that in winter, the odds of becoming pregnant were higher during higher temperature seasons, summer and autumn (aOR 1.47, 95%CI 0.97–2.23, P = 0.07 (marginally significant) and aOR 1.73, 95%CI 1.12–2.68, P = 0.02, respectively). Humidity, sunlight duration, and solar radiation had no effect on the outcome. The subgroup analysis confirmed this finding. The time-series analysis revealed a positive association between temperature and relative risk for pregnancy. Conclusions In IVF treatment, the ambient temperature variation alters the pregnancy rates; this aspect must be considered when obtaining patient consent for assisted conception.

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Time-series analysis of tides and similar motions of the sea surface

Journal of Applied Probability ◽

10.2307/3212302 ◽

1967 ◽

Vol 4 (1) ◽

pp. 103-112 ◽

Cited By ~ 3

Author(s):

D. E. Cartwright

Keyword(s):

Time Series ◽

Solar Radiation ◽

Time Series Analysis ◽

Sea Level ◽

Sea Surface ◽

Survey Article ◽

Series Analysis ◽

Non Linear ◽

Exciting Forces ◽

Linear Effects

This survey article, most of whose results are described in greater detail in Munk and Cartwright (1966), which will hereafter be abbreviated to MC, describes methods which aim to separate the response of the sea level at a given place due to various exciting forces such as gravity, solar radiation, non-linear effects, and weather. In so doing, it provides predictors for sea level which are formally simpler and somewhat more accurate than those given by the classical methods.

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Time series analysis of hourly global horizontal solar radiation

Solar Energy ◽

10.1016/0038-092x(88)90016-3 ◽

1988 ◽

Vol 41 (5) ◽

pp. 423-429 ◽

Cited By ~ 31

Author(s):

J.M. Gordon ◽

T.A. Reddy

Keyword(s):

Time Series ◽

Solar Radiation ◽

Time Series Analysis ◽

Series Analysis

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Depth and seasonal variations in the thermal properties of Antarctic Dry Valley permafrost from temperature time series analysis

Journal of Geophysical Research Atmospheres ◽

10.1029/2002jb002364 ◽

2003 ◽

Vol 108 (B10) ◽

Cited By ~ 13

Author(s):

D. J. Pringle ◽

W. W. Dickinson ◽

H. J. Trodahl ◽

A. R. Pyne

Keyword(s):

Time Series ◽

Thermal Properties ◽

Time Series Analysis ◽

Seasonal Variations ◽

Temperature Time Series ◽

Series Analysis ◽

Dry Valley ◽

Temperature Time

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Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i8.122 ◽

2018 ◽

Vol 6 (8) ◽

pp. 85

Author(s):

Naresh Patnaik ◽

F Baliarsingh

Keyword(s):

Time Series ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Time Series Analysis ◽

Andhra Pradesh ◽

Maximum Temperature ◽

Climatic Parameters ◽

Series Analysis ◽

Annual Maximum Temperature

Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.

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Prescribing errors in electronic prescriptions for outpatients intercepted by pharmacists and the impact of prescribing workload on error rate in a Chinese tertiary-care women and children’s hospital

BMC Health Services Research ◽

10.1186/s12913-019-4843-1 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 1

Author(s):

Jian-hui Yang ◽

Yu-fang Liao ◽

Wu-bin Lin ◽

Wen Wu

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Error Rate ◽

Subgroup Analysis ◽

Tertiary Care ◽

Multiple Drug ◽

Prescribing Errors ◽

Series Analysis ◽

The Impact ◽

Electronic Prescriptions

Abstract Background Prescribing errors may, influenced by some risk factors, cause adverse drug events. Most studies in this field focus on errors in prescriptions for hospital inpatients, with only a few on those for outpatients. Our study aimed to explore the incidence of prescribing errors in electronic prescriptions and illustrate the trend of prescribing workload and error rate over time. Methods The cross-section study was performed between September, 2015 and November, 2015. Prescribing errors were intercepted by pharmacists using a prescription reviewing system under which prescriptions with errors were transferred to a specific computer and recorded by another pharmacist and the incidence of total prescribing errors and severe errors was then calculated. A subgroup analysis was conducted in accordance to the number of drug orders, the age group of patients, the seniority of physicians, the specialty of physicians, the working day when prescriptions were issued, and the prescribing workload of physicians. A time-series analysis was employed to analyze the trend of prescribing workload and error rate, and the correlation between them. Results Totally, 65,407 patients were included in this study and 150,611 prescriptions with 294,564 drug orders (including 584 different drugs) were reviewed for identification of errors. A total of 534 prescribing errors (an error rate of 0.34%) were identified. Severe errors accounted for 13.62% of total errors. The subgroup analysis showed prescriptions of multiple drug orders, for pediatric patients aged 29 days to 12 years, from physicians specializing in ophthalmology and otorhinolaryngology, or prescribing on weekdays were more susceptible to errors. A time-series analysis demonstrated no correlation between prescribing workload and error rate which increased at the end of each working shift while prescribing workload decreased. Conclusion Less than 1% of the studied prescriptions came with errors among which one in seven were severe ones. But prescribing errors were in no relation to workloads. What’s more, further studies are needed to investigate pharmacist-led intervention to reduce prescribing errors.

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