Stationary Trend Research Articles

Climate drivers of the 2017 devastating fires in Portugal

A record 500,000 hectares burned in Portugal during the extreme wildfire season of 2017, with more than 120 human lives lost. Here we analyse the climatic factors responsible for the burned area (BA) from June to October series in Portugal for the period 1980–2017. Superposed onto a substantially stationary trend on BA data, strong oscillations on shorter time scales were detected. Here we show that they are significantly affected by the compound effect of summer (June-July-August) drought and high temperature conditions during the fire season. Drought conditions were calculated using the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI). Then the extent to which the burned area has diverged from climate-expected trends was assessed. Our results indicate that in the absence of other drivers, climate change would have led to higher BA values. In addition, the 2017 extreme fire season is well captured with the model forced with climate drivers only, suggesting that the extreme fire season of 2017 could be a prelude to future conditions and likewise events. Indeed, the expected further increase of drought and high temperature conditions in forthcoming decades, point at a potential increase of fire risk in this region. The climate-fire model developed in this study could be useful to develop more skilled seasonal predictions capable of anticipating potentially hazardous conditions.

Trend analysis of leprosy indicators in a hyperendemic Brazilian state, 2001-2015.

ABSTRACTOBJECTIVE To analyze the temporal trend of leprosy indicators in a hyperendemic state of Brazil, from 2001–2015.METHODS This is a time-series study of leprosy indicators in the state of Maranhão, Northeastern region of Brazil. The study used data from the Brazilian National System of Reportable Diseases, for the period between 2001 and 2015. The following indicators were evaluated: (i) detection coefficient in the general population; (ii) detection coefficient in people under 15 years old; (iii) rate of cases with grade 2 physical disability in the diagnosis; (iv) rate of examined contacts, and (v) proportion of healing . The Prais-Winsten regression model was used for trend analysis. Analyses were performed for the state and by each health region.RESULTS 77,697 leprosy cases were analyzed in the general population and 7,599 in individuals under 15 years old. The detection coefficient in the general population ranged from 80.7/100 thousand inhabitants in 2001 to 51.2/100 thousand inhabitants in 2015. The coefficient in the general population presented a downward trend (annual percentage variation [APV] = -2.98; 95%CI -4.15– -1.79). For the population under 15 years old, the rate was 24.9/100 thousand inhabitants in 2001, and 19.9/100 thousand inhabitants in 2015, with downward trend (APV = -3.07; 95%CI -4.95– -1.15). It was observed upward trend in rate of contacts examined (APV = 2.35; 95%CI 0.58–4.15) and rate of cases with grade 2 disability (APV = 2.19; 95%CI 0.23–4.19). Stationary trend was observed in the proportion of healing (APV = -0.10; 95%CI -0.50–0.30). Regional differences were found in the performance of the indicators.CONCLUSIONS A downward trend for the detection coefficients in the general population and in individuals under 15 years old was found in Maranhão. Despite this result, the rates are still very high, demanding efforts from all spheres of public administration and health professionals to reduce the burden of the disease in the state.

Assessment of extreme precipitation events and their teleconnections to El Niño Southern Oscillation, a case study in the Wei River Basin of China

Extreme precipitation index (EPI) is used to assess the extreme precipitation events, to further monitor and detect climate change impacts. The primary objective of the paper is to assess the extreme precipitation events and their spatiotemporal variability of 14 EPIs, and further investigate the linear and nonlinear relationships between typical EPIs and global climate anomalies El Niño Southern Oscillation (ENSO), using Niño 3.4 and SOI as climate indices, for the period of 1969–2016 in the Wei River Basin (WRB) of China. Observed daily precipitation data from 18 meteorological stations in the WRB were selected to calculate 14 EPIs derived or modified from core indices of the CCI/WCRP Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI). The trends of EPIs were estimated using Mann-Kendall non-parametric test. The teleconnections of ENSO to EPIs were revealed using principal component analysis (PCA), Pearson's correlation and wavelet analysis. Results indicate that: (1) The change patterns of extreme precipitation events varied for different EPIs and different meteorological stations for the period of 1969–2016 in the WRB. Four EPIs were negative trends dominated, and six EPIs were positive trends dominated, indicating that the extreme precipitation events had mainly increased for the past 48 years. (2) The spatial distributions of seven categories of trends were scattered and irregular for different EPIs and different areas of WRB. However, some of the EPIs had similar spatial distributions of trends. For example, the stations that had increasing trends mainly located at the northern part of WRB, especially in the Jing River Basin (JRB), for six precipitation intensity EPIs except R150, which was stationary trend dominated. (3) The nonlinear teleconnection relationships between typical EPIs and two climate anomalies revealed using wavelet analysis were consistent with linear relationships using correlation analysis. The Niño 3.4 and SOI had similar wavelet coherence but opposite phase difference with PC1 of typical EPIs. The phase differences between Niño 3.4 and PC1 of EPIs were anti-phase dominated, and in-phase dominated between SOI and PC1 of EPIs, indicating that the Niño 3.4 exerted negative climate impacts on the extreme precipitation events in the WRB and vice-versa for SOI. (4) The results should be beneficial to assess the extreme precipitation events, which provide references for the water resources planning and management in the WRB. It is possible to estimate and predict the future extreme precipitation events using climate models and global climate anomalies as potential predictors, which should be further studied in our future work.

Methane emission from pan-Arctic natural wetlands estimated using a process-based model, 1901–2016

The vast pan-Arctic wetlands appear to be a large source of methane (CH4), a potent greenhouse gas. Here, I simulated CH4 emission from pan-Arctic wetlands (above 60°N) over the period 1901–2016 using a process-based biogeochemical model (VISIT), including two different schemes of wetland CH4 production, that was forced by historical atmospheric composition, climate, and land-use conditions. The two schemes simulated mean wetland CH4 emission rates of 10.9 and 11.4 Tg CH4 yr−1 in the 2000s with slightly different spatial source distributions. Both simulations showed clear seasonal cycles of CH4 emission, but the two schemes had different amplitudes and peak months. The schemes differed markedly in their simulated long-term patterns (i.e., a stationary trend and an increasing decadal trend), making it difficult to relate them to global atmospheric trends in a consistent manner. Linear regression analysis clarified the agreements and differences in environmental sensitivity and biological control of CH4 production simulated by the schemes. The time-series of CH4 emission in northern North America, northern Europe, and Siberia showed different patterns of correlation with two major meteorological teleconnection indices. This model-based study implied that the high-latitude CH4 emission accounts for 5–7% of global wetland emission and would play increasingly important roles (e.g., positive feedback) under changing climate. To reduce estimation uncertainties, as demonstrated by the comparison of two schemes, we need further studies for improving biogeochemical models in collaboration with field studies.

Effect of pH on physicochemical properties of oil bodies from different oil crops.

The objective of this study was to determine the effects of pH on the physicochemical properties of soybean oil bodies (SBOBs), peanut oil bodies (PNOBs) and sunflower oil bodies (SFOBs). The mean particle diameter[4,3] (D[4,3]) of oil bodies (OBs) changed to a stationary trend with increased pH. The surface hydrophobicity (H0) of SBOBs, PNOBs and SFOBs significantly decreased with increasing pH 2-12. The emulsifying activity index of SBOBs, PNOBs and SFOBs decreased with increased pH from 2 to 10. The viscosity modulus (G″) value of SBOBs at pH 4 was significantly higher than at pH 7 and pH 9. The initial elastic modulus (G') and G″ values of PNOBs at pH 9 were significantly higher than at pH 4 and pH 7. The G″ values of SFOBs at pH 4 and pH 9 were significantly lower than at pH 7. The steroleosin protein bands of SBOBs significantly decreased at pH 12. The protein bands of PNOBs were reduced at pH 2-4 and pH 10-12, and protein bands decreased most obviously at pH 2. The enthalpy of denaturation (ΔH) values of the oil body (OB) protein at pH 9 were significantly higher than at pH 4 and pH 7. The results showed that the ζ-potential, D[4,3], emulsifying property and H0 of SBOBs, PNOBs and SFOBs were similar to the change of pH value. The three types of OBs have better stability away from the isoelectric point.

Stroke incidence in the young: evidence from a Norwegian register study

IntroductionWhile there is a general agreement that stroke incidence among the elderly is declining in the developed world, there is a concern that it may be increasing among the young. The present study investigates this issue for the Norwegian population for the years 2010–2015. Cerebrovascular accidents (CVAs) for patients younger than 55 years were identified through the Norwegian Patient Registry and the Norwegian Cause-of-death Registry.MethodsNegative binomial regression modelling was used to estimate temporal trends in the CVA incidence rates for the young, aged 15–54, with 10-year sub-intervals, and for children below the age of 18. The main outcomes were CVA incidence per 100,000 person-years at risk (PY), 30-day stroke mortality per 100,000 PY, and 30-day case-fatality rates.ResultsThe analysis showed a negative and non-significant temporal trend in the CVA incidence (p = 0.052) as well as for 30-day mortality (p = 0.074) for the age group 15–54. Overall, the inclusion of an interaction for age in the bracket 45–54 suggested that any temporal decline is restricted to this age bracket. The analyses of the 10-year age brackets 15–24, 25–34, and 34–45, provided evidence neither for an increase, nor for a decrease, in incidence. Among the children, the estimated temporal coefficients were positive, but non-significant, consistent with a stationary trend.ConclusionWeak statistical evidence was found for a decline in CVA incidence and for overall stroke 30-day case fatality for 15–54 year olds, but the decline was significant only for the 45–54 age band. All results considered, the study suggests a stationary or decreasing temporal trend in CVA incidence and stroke fatality for children (0–18) and young (15–54) in Norway. Even larger data sets are needed to estimate these temporal trends accurately.

Online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology

This paper proposes a novel robust prognostic approach that contains three phases for degradation prediction of proton exchange membrane fuel cell (PEMFC) performance and its remaining useful lifetime (RUL) estimation. In the first detrending phase, a physical aging model (PAM) is used to remove the non-stationary trend in the original fuel cell degradation data. In the second filtering phase, the order of autoregressive and moving average (ARMA) model is determined by autocorrelation function (ACF), partial ACF and Akaike information criterion. The linear component in the stationary time series is then filtered by the identified ARMA model. In the third prediction phase, the remaining nonlinear pattern is used to train the time delay neural network (TDNN), in order to provide the final prediction result. Since the proposed prognostic approach uses appropriate methods to analyze and preprocess the original degradation data (i.e., the PAM maintains stationary trend, and then the identified ARMA filters linear component), the remaining nonlinear pattern of stationary time series can thus guarantee a good convergence performance of TDNN. In order to experimentally demonstrate the robustness and prediction accuracy of the proposed approach, degradation tests are performed using two types of PEMFC stack.

Epidemiological trends of leprosy in an endemic state

Objective: to analyze the epidemiological trends of leprosy in an endemic state. Methods: ecological study of temporal trends. The data were taken from the National Disease Notification System and analyzed through a generalized linear regression using the Prais Winsten method. Results: a total of 12,134 new cases of leprosy were analyzed. There was a downward trend of -4.8% in the overall detection rate and of -6.7% in the detection rate in children under 15 years of age. The proportion of new cases of leprosy with grade 2 physical disability at diagnosis showed a stationary trend of 2.7%. It is noteworthy that the trends of epidemiological indicators of leprosy are heterogeneous among the macro-regions of the health system. Conclusion: the incidence of leprosy declined in the general population, in children under 15 years of age, and the proportion of grade 2 disability showed stability. Despite the decline, it is evident that diagnoses occur late and the transmission remains active.

Trend of leprosy in individuals under the age of 15 in Mato Grosso (Brazil), 2001-2013.

ABSTRACTOBJECTIVE To identify the historical trend of leprosy epidemiological indicators in individuals under the age of 15 in the state of Mato Grosso.METHODS Descriptive study with trend analysis of leprosy indicators in individuals under the age of fifteen registered in the Mato Grosso’s System for Notifiable Diseases between 2001 and 2013. We used the Prais-Winsten procedure for analyzing generalized linear regression at a significance level of 5%. We considered as increasing time series when the annual percent change was positive, decreasing when negative and stationary when there was no significant difference between its value and zero.RESULTS We analyzed 2455 cases of leprosy and the average detection rate in individuals under the age of fifteen was 22.7 per 100 thousand inhabitants. The trend of the general coefficient of incidence was decreasing, with an average annual rate of -5.5% (95%CI -7.5–-3.5). Increasing trend was observed with an increase of 6.7% (95%CI 2.7–10.8) in the proportion of multibacillary cases, 9.4% (95%CI 4.4–14.7) of cases diagnosed with dimorphic clinical form and 14% (95%CI 7.9–20.4) of cases with physical disability level 2 at the time of diagnosis. There was an increasing trend in the average proportion of examined contacts, with a growth of 4.1% (95%CI 1.2–7.1) and average proportion of healing was precarious (39.7%), with stationary trend.CONCLUSIONS The historical trend of leprosy cases in individuals under the age of fifteen proved to be decreasing in the period, however the trends of epidemiological indicators such as the proportion of multibacillary cases, physical disability level 2 and healing, indicate late diagnosis with stay sources of transmission and consequent worsening of the disease in the state of Mato Grosso.

Impact of Global Financial Crisis on FDI Inflows in India and Bangladesh

The investment climate in India and Bangladesh has undoubtedly become friendlier and investing in these countries has been an attractive proposition today than in earlier years. According to A.T. Kearney's FDI Confidence Index (2014) India ranks 7th on the basis of FDI inflows in the world while Bangladesh ranks 3rdamong SAARC countries. The present analysis attempts to show that though the global financial crisis (2008) had adversely impacted the growth in GDP and employment opportunities and FDI flows throughout the world, India and Bangladesh both had shown considerable resilience to the global economic crisis by maintaining a high growth rate during this period in the world. It highlights the changes the policy regimes in the two countries. It also tries to examine empirically, using secondary time series data, the amount of FDI inflows, component-wise and sector-wise break-up in FDI inflows in both countries during the pre and post-crisis era, based on Exogenous Structural Break Model. The empirical analysis clearly reveals both FDI and FDI-GDP ratio exhibits stationary trend in India while they are difference stationary in case of Bangladesh. It also focuses on the crisis management policies in the two nations for smooth flow of FDI in the long run.

Hospitalization and hospital mortality of elderly people with mental and behavioral disorders in Brazil, 2008-2014.

to analyze the evolution of hospitalizations and hospital mortality of elderly people with mental and behavioral disorders in Brazil, from 2008 to 2014. this is a time series ecological study, with data from the Hospital Information System of the Brazilian National Health System (SIH/SUS), and analyzed through Prais-Winsten regression. during the studied period, 139,941 hospitalizations and 2,962 deaths were recorded; the hospitalization coefficient for mental and behavioral disorders decreased in Brazil, from 122.3 to 84.2 per 100 thousand inhabitants (-0.14%; 95%CI -0.25;-0.03), and its macroregions, except the South, which presented stationary trend (0.08%; 95%CI -0.11;0.27); the hospital mortality coefficients in Brazil, in 2008 and 2014, were, respectively, 1.73 and 2.38 per 100 thousand inhabitants; dementia was the main diagnosis in hospitalizations that registered deaths (32.3%). despite the reduction in hospitalization coefficient for mental and behavioral disorders during the studied period, the hospital mortality coefficient has increased.

Completeness of notifications of violence perpetrated against adolescents in the State of Pernambuco, Brazil

The scope of this study was to analyze the trend of completeness of the data on violence perpetrated against adolescents registered in the State of Pernambuco between 2009 and 2012. This involved a cross-sectional survey of 5,259 adolescents, who were the victims of violence reported in SINAN-VIVA of the Pernambuco State Health Department. Simple linear regression was used to investigate the trend of completeness of the variables. The percentages of completeness were considered to be dependent variables (Y) and the number of years as independent variables (X). The results show a significant increase of 204% in the number of notifications. However, of the 34 variables analyzed, 27 (79.4%) showed a stationary trend, 6 (17.6%) a downward trend, and only one variable (2.9%) an upward trend. Completeness was considered 'Very Poor' for the variables: Education (47.3%), Full Address (21.3%), Occurrence Time (38%) and Use of Alcohol by the Attacker (47%). Therefore, despite the large increase in the number of notifications, data quality continued to be compromised, hampering a more realistic analysis of this group.

Aids mortality trends according to sociodemographic characteristics in Rio Grande do Sul State and Porto Alegre City, Brazil: 2000-2011.

to analyze AIDS mortality trends in Rio Grande do Sul State (RS) and Porto Alegre City (POA) according to sociodemographic characteristics in the period 2000-2011. this was an ecological time series study of AIDS mortality rates; Prais-Winsten regression model was used. the standardized mortality AIDS rate showed a stationary trend in RS (1.3%; 95%CI: -0..;6.7) and in POA (-0.3%; 95%CI: -5.1;3.9); there was an increasing trend in the following categories: women in RS (4.1%; 95%CI: 3.0; 5.3) and in POA (2.7%; 95%CI: 1.8; 3.5), people with brown skin color in RS (4.5%; 95%CI: 1.9; 7.2) and in POA (4.6%; 95%CI: 1.5; 7.9), the 40-49 age group in RS (4.0%; 95%CI: 1.3; 6.7), the 50-59 age group in RS (5.8%; 95%CI: 1.9; 9.9) and in POA (6.0%; 95%CI: 2.1; 9.9), and the >60 age group in RS (4.0%; 95%CI: 1.1; 6.9). AIDS mortality increased among women, individuals with brown skin color and older age groups in RS and POA.

Effect of Variable Salinity and Phosphorus Culture Conditions on Growth and Pigment Content of <i>Chlorella vulgaris</i>

Background/Objective: Chlorella vulgaris, microalgae belonging to Chlorellaceae family is widely used as food supplement and as feedstock for biodiesel production. Hence, it is needed for clean and cost-effective algal production with a high yield of biomass. This study will determine the algal response under various growth conditions for high growth, biomass yield with high pigment content. Methods/ Statistical Analysis: In our study, experiments in controlled conditions with variable NaCl concentration (0.0M, 0.1M, 0.15M, 0.2M and 0.25 M) and variable phosphorus concentration (0.04 g/l, 0.02g/l, 0.01g/l and 0 g/l K 2 HPO 4 ) were conducted to study the response of Chlorella vulgaris in respect to growth and pigment components. All experiments were conducted in triplicates and the results were studied statistically by SPSS PASW 18.0 for two ways ANOVA. Findings: Under variable phosphorus conditions in respect of days the significant increase in growth was observed between 14th day to 21st day after which stationary trend was observed. Also, when phosphorus concentration was decreased from 0.04g/l to 0.02 g/l, 0.01g/l and 0.0g/l the growth, biomass and pigment composition showed a declining trend. Culture growing at 0.04g/l of K 2 HPO 4 concentration in medium showed highest growth (1.281 ± 0.003 d-1,, where d is number of days) with variable NaCl concentrations the cell growth was favored at minimum salt concentration and with the increase of NaCl from 0.0M to 0.25M the growth and pigment components showed a decline. In respect of days with variable NaCl, the growth showed a significant increase from zero-day to 21st day thereafter only slight increase was seen on the 28th day. This result goes in consensus with previous findings. Hence, Chlo isolated from Rajasthan region is adaptable to grow at low salinity andrella vulgaris control phosphorus (0.04 g/l) conditions for high biomass with high pigment content which can be used for high nutritive purposes. Improvement/Application: Chlorella vulgaris yields high biomass and pigments both at 0.0 Molar salinity and 0.04 g/l phosphorus concentration. These conditions were successfully applied to grow Chlorella vulgaris in lab conditions to get the highest yield of biomass with high pigment content.

Cervical cancer mortality in the state of Rio Grande do Norte, Brazil, 1996-2010: time trends and projections up to 2030

to analyze cervical cancer mortality trends in the state of Rio Grande do Norte and its health micro-regions from 1996 to 2010, as well as to make projections for five-year periods from 2011 to 2030. this was an ecological time series study; negative binomial regression was used to analyze trends and projections. rates above 5.0 deaths per 100,000 women were observed in all the micro-regions, with a stationary trend in the state as a whole and an upward trend in the micro-regions with the worst socioeconomic conditions; projections indicated reduction in mortality rates in the state, from 5.95/100,000 women (2006-2010) to 3.67 (2026-2030), although a 22% increase in the absolute number of deaths is expected. although a reduction in mortality rates is projected, they continue to be high, indicating the need for review and strengthening of the state's cervical cancer control program.

Dynamics of a recovering Arctic bird population: the importance of climate, density dependence, and site quality.

Intrinsic and extrinsic factors affect vital rates and population-level processes, and understanding these factors is paramount to devising successful management plans for wildlife species. For example, birds time migration in response, in part, to local and broadscale climate fluctuations to initiate breeding upon arrival to nesting territories, and prolonged inclement weather early in the breeding season can inhibit egg-laying and reduce productivity. Also, density-dependent regulation occurs in raptor populations, as territory size is related to resource availability. Arctic Peregrine Falcons (Falco peregrinus tundrius; hereafter Arctic peregrine) have a limited and northern breeding distribution, including the Colville River Special Area (CRSA) in the National Petroleum Reserve-Alaska, USA. We quantified influences of climate, topography, nest productivity, prey habitat, density dependence, and interspecific competition affecting Arctic peregrines in the CRSA by applying the Dail-Madsen model to estimate abundance and vital rates of adults on nesting cliffs from 1981 through 2002. Arctic peregrine abundance increased throughout the 1980s, which spanned the population's recovery from DDT-induced reproductive failure, until exhibiting a stationary trend in the 1990s. Apparent survival rate (i.e., emigration; death) was negatively correlated with the number of adult Arctic peregrines on the cliff the previous year, suggesting effects of density-dependent population regulation. Apparent survival and arrival rates (i.e., immigration; recruitment) were higher during years with earlier snowmelt and milder winters, and apparent survival was positively correlated with nesting season maximum daily temperature. Arrival rate was positively correlated with average Arctic peregrine productivity along a cliff segment from the previous year and initial abundance was positively correlated with cliff height. Higher cliffs with documented higher productivity (presumably indicative of higher-quality habitat), are a priority for continued protection from potential nearby development and disturbance to minimize population-level impacts. Climate change. may affect Arctic peregrines in multiple ways, including through access to more snow-free nest sites and a lengthened breeding season that may increase likelihood of nest success. Our work provides insight into factors affecting a population during and after recovery, and demonstrates how the Dail-Madsen model can be used for any unmarked population with multiple years of abundance data collected through repeated surveys.

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

The most common procedure for analyzing multi-environmental trials is based on the assumption that the residual error variance is homogenous across all locations considered. However, this may often be unrealistic, and therefore limit the accuracy of variety evaluation or the reliability of variety recommendations. The objectives of this study were to show the advantages of mixed models with spatial variance–covariance structures, and direct implications of model choice on the inference of varietal performance, ranking and testing based on two multi-environmental data sets from realistic national trials. A model comparison with a chi-square test for the trials in the two data sets (wheat data set BW00RVTI and barley data set BW01RVII) suggested that selected spatial variance-covariance structures fitted the data significantly better than the ANOVA model. The forms of optimally-fitted spatial variance-covariance, ranking and consistency ratio test were not the same from one trial (location) to the other. Linear mixed models with single stage analysis including spatial variance-covariance structure with a group factor of location on the random model also improved the real estimation of genotype effect and their ranking. The model also improved varietal performance estimation because of its capacity to handle additional sources of variation, location and genotype by location (environment) interaction variation and accommodating of local stationary trend.

Do We Search on What We Tweet or Tweet on What We Search?

We investigate the properties of Twitter and search using a vector auto-regressive model. The VAR has two advantages. First it allows us to jointly decompose the time series into a stationary trend and random shocks. We investigate the relation between the shocks to gain further insights into the correlation between the shocks. Second, using the VAR model we perform a Granger causality test to see whether one variable Granger causes the other. An important implication of Granger causality is that the lagged versions of one variables can help predict the other variable. We tested our models on daily Twitter and search data. We found that Twitter and search can simultaneously Granger cause each other, Twitter Granger causes search, and neither search nor Twitter Granger causes the other. When Granger causality holds, the VAR model reduces the standard deviation of the out-of-sample prediction error by over 37% when compared to the best univariate model.

Exact local Whittle estimation of fractionally cointegrated systems

Semiparametric estimation of a bivariate fractionally cointegrated system is considered. We propose a two-step procedure that accommodates both (asymptotically) stationary (δ<1/2) and nonstationary (δ≥1/2) stochastic trend and/or equilibrium error. A tapered version of the local Whittle estimator of Robinson (2008) is used as the first-stage estimator, and the second-stage estimator employs the exact local Whittle approach of Shimotsu and Phillips (2005). The consistency and asymptotic distribution of the two-step estimator are derived. The estimator of the memory parameters has the same Gaussian asymptotic distribution in both the stationary and the nonstationary case. The convergence rate and the asymptotic distribution of the estimator of the cointegrating vector are affected by the difference between the memory parameters. Further, the estimator has a Gaussian asymptotic distribution when the difference between the memory parameters is less than 1/2.

Significance of Prediagnostic Thyroid Antibodies in Women with Autoimmune Thyroid Disease

Antibodies to thyroglobulin (Tg), thyroperoxidase (TPO), and TSH receptor (TSH-R) are prevalent in autoimmune thyroid diseases. We aimed to assess whether females with Graves disease or Hashimoto thyroiditis are more likely than age-matched controls to have thyroid antibodies before clinical diagnosis and to measure the timing of antibody seroconversion. This was a nested case-control study using the Department of Defense Serum Repository and the Defense Medical Surveillance System, 1998-2007. We assessed thyroid antibodies in the serum of 522 female, active-duty, military personnel including: 87 Graves disease cases, 87 Hashimoto thyroiditis cases, and 348 age matched controls. One serum sample was available at the time of the clinical diagnosis (±6 months); three additional samples were retrieved from the repository up to 7 yr before the clinical diagnosis, for a total of 2088 samples. In Hashimoto thyroiditis, TPO antibodies were found in about 66% of the cases at all time points. Tg antibodies showed a similar stationary trend, at a lower prevalence of about 53%at all time points. No TSH-R antibodies were found. In Graves disease, TPO antibodies gradually increased from 31% at 5-7 yr prior to diagnosis to 57% at diagnosis and Tg antibodies from 18 to 47%. TSH-R antibodies were present before diagnosis and showed an increasing prevalence from 2, 7, 20, to 55%. Antibodies to Tg, TPO, and TSH-R precede by years the development of the diagnostic autoimmune thyroid diseases phenotype. Overall, the presence of thyroid antibodies in apparently healthy individuals should not be neglected.