Temporal trends of the association between extreme temperatures and hospitalisations for schizophrenia in Hefei, China from 2005 to 2014

2021 ◽  
Vol 78 (5) ◽  
pp. 364-370
Author(s):  
Rubing Pan ◽  
Qizhi Wang ◽  
Weizhuo Yi ◽  
Qiannan Wei ◽  
Jian Cheng ◽  
...  
Keyword(s):  
Time Series Data ◽  
Temporal Trends ◽  
Extreme Temperature ◽  
Series Data ◽  
Extreme Temperatures ◽  
Distributed Lag ◽  
Increasing Trend ◽  
Related Hospitalisation ◽  
Extreme Cold ◽  
And Gender

ObjectiveWe aimed to examine the temporal trends of the association between extreme temperature and schizophrenia (SCZ) hospitalisations in Hefei, China.MethodsWe collected time-series data on SCZ hospitalisations for 10 years (2005–2014), with a total of 36 607 cases registered. We used quasi-Poisson regression and distributed lag non-linear model (DLNM) to assess the association between extreme temperature (cold and heat) and SCZ hospitalisations. A time-varying DLNM was then used to explore the temporal trends of the association between extreme temperature and SCZ hospitalisations in different periods. Subgroup analyses were conducted by age (0–39 and 40+ years) and gender, respectively.ResultsWe found that extreme cold and heat significantly increased the risk of SCZ hospitalisations (cold: 1st percentile of temperature 1.19 (95% CI 1.04 to 1.37) and 2.5th percentile of temperature 1.16 (95% CI 1.03 to 1.31); heat: 97.5th percentile of temperature 1.37 (95% CI 1.13 to 1.66) and 99th percentile of temperature 1.38 (95% CI 1.13 to 1.69)). We found a slightly decreasing trend in heat-related SCZ hospitalisations and a sharp increasing trend in cold effects from 2005 to 2014. However, the risk of heat-related hospitalisation has been rising since 2008. Stratified analyses showed that age and gender had different modification effects on temporal trends.ConclusionsThe findings highlight that as temperatures rise the body’s adaptability to high temperatures may be accompanied by more threats from extreme cold. The burden of cold-related SCZ hospitalisations may increase in the future.

scholarly journals Characteristics and trend of extreme temperature in Spatio-temporal context, South Xinjiang China.

2021 ◽  
Author(s):  
Ahmad Ali Khan ◽  
Yuanjie Zhao ◽  
Jamil Khan ◽  
Ghani Rahman ◽  
Muhammad Rafiq
Keyword(s):  
Time Series Data ◽  
Focal Point ◽  
Extreme Temperature ◽  
Climatic Conditions ◽  
Trend Detection ◽  
Series Data ◽  
Estimation Model ◽  
Upward Trend ◽  
Sen’S Slope ◽  
Increasing Trend

Abstract Evaluation of climate change study is vital for appropriate management of hydrological resources and future planning. South Xinjiang is comprising various sort of climatic conditions. The focal point of this study is to assess the dissemination and pattern of temperature for as far back as 39 years in south Xinjiang China. The time series data recorded as maximum, minimum and mean monthly temperature at different metrological stations. For trend detection Mann-Kendall tests and Sen's slope estimation model were applied for appropriate results. The statistical analysis of the study indicates a significant upward trend in three types mean max. min. and average temperature on seasonal & monthly scale. Change points find out in the four decades show an increasing trend of temperature. Results found from Sen's slope magnitudes vary from 0.010ºC to 0.070ºC in Tmax per annum. Further, Sen's slope from − 0.150ºC to 0.080ºC and − 0.080ºC to 0.060ºC every year for both Tmin and Tmean. So for the increasing trend in all temperature is a get way to a dangerous atmospheric devastation and environmental change. Seasonal evaluation of temperature (JJA) June, July and August detected upward trend of temperature while the rainfall occurring months (NDJF), November, December, January and February found significantly dry. The seasonal changeability of temperature is straightforwardly responsible for desertification in the area. The conclusion of the research study that southern Xinjiang facing severe dry conditions are essential to highlight this burning issue for further development and sustainability of water resources.

Macro-Economic Disequilibrium and Educational Development in Nigeria between 1980-2017

2019 ◽  
Vol 10 (08) ◽  
pp. 20592-21600
Author(s):  
Gbadebo Salako ◽  
Adejumo Musibau Ojo ◽  
Jaji Ayobami Francis
Keyword(s):  
Time Series Data ◽  
Educational Development ◽  
Negative Relationship ◽  
Series Data ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Stationarity Test ◽  
Level I

This study empirically investigates the effects of macroeconomic disequilibrium on educational development in Nigeria. The study employed time series data between 1980 and 2017. Autoregressive Distributed Lag method of estimation was employed. The result revealed that the variables stationarity test were mixed between the first difference I(I) and level I(0). The cointegration result shows that there exist long run relationship between the variables. The result revealed that Balance of payment, Poverty, Debt rate inflation and unemployment exhibited negative relationship with educational development. The estimation result showed that all explanatory variables account for 88% variation of educational development in Nigeria. It is therefore recommended that government should fast track policies that can stabilize inflation and exchange rate in the country. Also, Policies must be formulated to reduce poverty and unemployment.

Implementasi Metode Winter untuk Forecasting Pertumbuhan Jumlah Penduduk: Studi Kasus Wilayah Provinsi NTB

2020 ◽  
Vol 3 (1) ◽  
pp. 51-61
Author(s):  
Syaharuddin ◽  
Abdul Adhiim Rizky ◽  
Lutfi Jauhari ◽  
Siti Fatimah ◽  
Wahyu Ningsih ◽  
...  
Keyword(s):  
Time Series Data ◽  
Optimal Parameter ◽  
Series Data ◽  
Percentage Error ◽  
Average Error ◽  
Ratio Data ◽  
Female Data ◽  
Forecasting System ◽  
Conducting Research ◽  
And Gender

This research aims to analyse the acceleration of population growth based on gender in West Nusa Tenggara Province (NTB) using the Forecasting system by constructing the winter's method in the shape of the Multiple Forecasting System (G-MFS) based on Matlab by calculating the period indicator for accuracy to find time series data in the year 2020-2029. At the simulation stage, researchers used the population and gender ratio data in NTB Province in 2009-2019. The method used in conducting research is to use the winter's method. The evaluation of Forecasting results is done by calculating the average error value using the Mean Absolute Percentage Error (MAPE) method. From this study obtained the most optimal parameter value on male data namely ʌ, β and γ sequential values of 0.9, 0.5 and 0.9 while in female data, the value of ʌ, β and γ respectively, 0.2, 0.1 and 0.5. Then with the value of the parameter obtained MAPE value in male data of 1.7785% and in female data of 0.89034%.

Fish Exports and Economic Growth: The Pakistan’s Experience

2019 ◽  
Vol 20 (2) ◽  
pp. 279-296 ◽  
Author(s):  
Syed Tehseen Jawaid ◽  
Mohammad Haris Siddiqui ◽  
Zeeshan Atiq ◽  
Usman Azhar
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Positive Impact ◽  
Impulse Response Function ◽  
Policy Implications ◽  
Sensitivity Analyses ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Short Run

This study attempts to explore first time ever the relationship between fish exports and economic growth of Pakistan by employing annual time series data for the period 1974–2013. Autoregressive distributed lag and Johansen and Juselius cointegration results confirm the existence of a positive long-run relationship among the variables. Further, the error correction model reveals that no immediate or short-run relationship exists between fish exports and economic growth. Different sensitivity analyses indicate that initial results are robust. Rolling window analysis has been applied to identify the yearly behaviour of fish exports, and it remains negative from 1979 to 1982, 1984 to 1988, 1993 to 1999, 2004 and from 2010 to 2013, and it shows positive impact from 1989 to 1992, 2000 to 2003 and from 2005 to 2009. Furthermore, the variance decomposition method and impulse response function suggest the bidirectional causal relationship between fish exports and economic growth. The findings are beneficial for policymakers in the area of export planning. This study also provides some policy implications in the final section.

Export-led Growth Hypothesis: Econometric Evidence from Malaysia

2013 ◽  
Vol 14 (2) ◽  
pp. 94-112
Author(s):  
Hassanudin Mohd Thas Thaker ◽  
Tan Siew Ee ◽  
Sushant Vaidik
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Series Data ◽  
Vector Error Correction ◽  
Distributed Lag ◽  
Long Run ◽  
Paper Cover ◽  
Econometric Evidence ◽  
Augmented Dickey Fuller ◽  
Growth Hypothesis

The objective of this paper is to test the validity of the Export-led Growth Hypothesis (ELGH) in the Malaysian economy. Malaysia has always been considered to have attained its growth primarily through exports (Okposin, Bassey, Hamid, Halim, and Boon, 1999; Mun, 2008; Mahathir, 1990). In the past, several studies on this topic have been conducted but their analyses were limited to relationships using Bound-testing, Autoregressive –Distributed Lag (ARDL) and the Toda Yamamoto analysis. Empirical data and analysis in our paper cover a 21 – year span and quarterly time-series data (1991:Q1 – 2012:Q4) are used to test this ELG hypothesis. Also, many dynamic econometric measures including the Augmented Dickey Fuller (ADF) and Phillip – Perron (PP) unit root tests, Cointegration test as well as the Vector Error Correction model (VEC) for the long run have been applied. Based on these generic models, both real exports and capital stock (productivity) are found to have stimulated positive adjustments to economic growth in the long run whereas real exchange rate is found to have influenced economic growth negatively. Overall, our conclusion is that the ELG hypothesis seems applicable to Malaysia in the long run.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Pantelis Samartsidis ◽  
Natasha N. Martin ◽  
Victor De Gruttola ◽  
Frank De Vocht ◽  
Sharon Hutchinson ◽  
...  
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Series Data ◽  
Temporal Trends ◽  
Epidemiological Studies ◽  
Series Data ◽  
Treatment As Prevention ◽  
Open Problems ◽  
Multiple Characteristics ◽  
Causal Impact

Abstract Objectives The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

scholarly journals Analysis of Budget Imbalance Dynamics in Kenya

2018 ◽  
Vol 4 (4) ◽  
pp. 352
Author(s):  
Alex Oguso ◽  
Francis M. Mwega ◽  
Nelson H. Wawire ◽  
Purna Samanta
Keyword(s):  
Public Sector ◽  
Time Series Data ◽  
Supply And Demand ◽  
Series Data ◽  
Fiscal Consolidation ◽  
The Public ◽  
Distributed Lag ◽  
Seeking Behavior ◽  
Development Goals ◽  
The Government

<p><em>Kenya needs substantial and sustained fiscal consolidation to create fiscal space for financing the government’s election pledges, the Vision 2030 development projects, and sustainable development goals. However, the government has found it hard to sustain its fiscal consolidation attempts. This study investigates the fiscal consolidation constraints that act through the budget imbalance dynamics in Kenya using the </em><em>Olivera-Tanzi effect approach.</em><em> The study covers the period 2000-2015</em><em> using time series data and employs three </em><em>Auto-regressive Distributed Lag (ARDL) error correction models</em><em> in the analysis. The study showed that a </em><em>rise in the general price levels in the economy, adjustment of minimum wages, rise</em><em> in perceived levels of corruption in the public sector and the political budget cycles (occurrence of a general election) worsen the budget imbalances (deficits) thus </em><em>constrain fiscal consolidation efforts in Kenya. The study also demonstrated that </em><em>budget imbalance dynamics in Kenya could partly be explained by the Olivera-Tanzi proposition. </em><em>The study rec</em><em>ommends measures to reduce the fiscal imbalance gap in Kenya, which include controlling both supply and demand side inflationary pressure and dealing with rent seeking behavior in the public sector.</em></p>

scholarly journals Consequences of Capital formation, Trade Liberalization on the Economic Growth: Pakistan’s Experience

2015 ◽  
Vol 2 (1) ◽  
pp. 1-4
Author(s):  
Nadia Bukhari ◽  
Anjum Iqbal
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Trade Openness ◽  
Capital Formation ◽  
Vital Role ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Auto Regressive ◽  
Adf Test

This study considers the long run relationship between the liberalization of trade, capital formation and the economic growth of Pakistan by using the time series data from 1975-2013. The main aim of this study is to examine that how much liberalization of trade and capital formation affects the economic growth of Pakistan in long run. The approach that has been used for empirical analysis is Auto Regressive Distributed Lag (ARDL) model. Under the ADF test capital formation (CF) is stationary at its first level but the trade openness (TO) and GDP is stationary at its first difference. Moreover, the granger casualty test is evident that there become a casual relationship between the trade openness and GDP. The result of this study shows that both the trade openness and the capital formation determined the economic growth in long run and they both have statistically significant effect on the GDP. Furthermore it has has been depicted from the study that the trade has a vital role to influence the economic growth.

scholarly journals Financial innovation and economic growth: evidence from Zimbabwe

2016 ◽  
Vol 13 (2) ◽  
pp. 65-75 ◽  
Author(s):  
Alex Bara ◽  
Calvin Mudzingiri
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Time Series Data ◽  
Financial Time Series ◽  
Financial Innovation ◽  
Dark Side ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Causality Tests

The role of financial innovation on economic growth in developing countries has not been actively pursued. Stemming from the finance-growth nexus, literature suggests that financial innovation has a relationship to growth, which could be either positive or negative. Implicitly, financial innovation has a good and a dark side that affects growth. This study establishes the causal relationship between financial innovation and economic growth in Zimbabwe empirically. Using the Autoregressive Distributed Lag (ARDL) bounds tests and Granger causality tests on financial time series data of Zimbabwe for the period 1980-2013, the study finds that financial innovation has a relationship to economic growth that varies depending on the variable used to measure financial innovation. A long-run, growth-driven financial innovationis confirmed, with causality running from economic growth to financial innovation. Bi-directional causality also exists after conditionally netting-off financial development. Policies that enhance economic growth inter-twined with financial innovation are essential, if developing countries, such as Zimbabwe, aim to maximize economic development

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