Estimating cardiovascular risk in patients with type 2 diabetes: a national multicenter study in Brazil

According to Brazilian National Data Survey diabetes is the fifth cause for hospitalization and is one of the ten major causes of mortality in this country. Aims to stratify the estimated cardiovascular risk (eCVR) in a population of type 2 diabetics (T2DM) according to the Framingham prediction equations as well as to determine the association between eCVR with metabolic and clinical control of the disease. Methods From 2000 to 2001 a cross-sectional multicenter study was conducted in 13 public out-patients diabetes/endocrinology clinics from 8 Brazilian cities. The 10-year risk of developing coronary heart disease (CHD) was estimated by the prediction equations described by Wilson et al (Circulation 1998). LDL equations were preferably used; when patients missed LDL data we used total cholesterol equations instead. Results Data from 1382 patients (59.0% female) were analyzed. Median and inter-quartile range (IQ) of age and duration of diabetes were 57.4 (51-65) and 8.8 (3-13) years, respectively without differences according to the gender. Forty-two percent of these patients were overweight and 35.4% were obese (the prevalence of higher BMI and obesity in this T2DM group was significantly higher in women than in men; p < 0.001). The overall estimated eCVR in T2DM patients was 21.4 (13.5-31.3). The eCVR was high (> 20%) in 738 (53.4%), intermediate in 202 (14.6%) and low in 442 (32%) patients. Men [25.1(15.4-37.3)] showed a higher eCVR than women [18.8 (12.4-27.9) p < 0.001]. The most common risk factor was high LDL-cholesterol (80.8%), most frequently found in women than in men (p = 0.01). The median of risk factors present was three (2-4) without gender differences. Overall we observed that 60 (4.3%) of our patients had none, 154(11.1%) one, 310 (22.4%) two, 385 (27.9%) three, 300 (21.7%) four, 149 (10.5%) five and six, (2%) six risk factors. A higher eCVR was noted in overweight or obese patients (p = 0.01 for both groups). No association was found between eCVR with age or a specific type of diabetes treatment. A correlation was found between eCVR and duration of diabetes (p < 0.001), BMI (p < 0.001), creatinine (p < 0.001) and triglycerides levels (p < 0.001) but it was not found with HbA1c, fasting blood glucose and postprandial glucose. A higher eCVR was observed in patients with retinopathy (p < 0.001) and a tendency in patients with microalbuminuria (p = 0.06). Conclusion: our study showed that in this group of Brazilian T2DM the eCVR was correlated with the lipid profile and it was higher in patients with microvascular chronic complications. No correlation was found with glycemic control parameters. These data could explain the failure of intensive glycemic control programs aiming to reduce cardiovascular events observed in some studies.

Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada

Grass reference evapotranspiration (ETo) is an important agrometeorological parameter for climatological and hydrological studies, as well as for irrigation planning and management. There are several methods to estimate ETo, but their performance in different environments is diverse, since all of them have some empirical background. The FAO Penman-Monteith (FAD PM) method has been considered as a universal standard to estimate ETo for more than a decade. This method considers many parameters related to the evapotranspiration process: net radiation (Rn), air temperature (7), vapor pressure deficit (Delta e), and wind speed (U); and has presented very good results when compared to data from lysimeters Populated with short grass or alfalfa. In some conditions, the use of the FAO PM method is restricted by the lack of input variables. In these cases, when data are missing, the option is to calculate ETo by the FAD PM method using estimated input variables, as recommended by FAD Irrigation and Drainage Paper 56. Based on that, the objective of this study was to evaluate the performance of the FAO PM method to estimate ETo when Rn, Delta e, and U data are missing, in Southern Ontario, Canada. Other alternative methods were also tested for the region: Priestley-Taylor, Hargreaves, and Thornthwaite. Data from 12 locations across Southern Ontario, Canada, were used to compare ETo estimated by the FAD PM method with a complete data set and with missing data. The alternative ETo equations were also tested and calibrated for each location. When relative humidity (RH) and U data were missing, the FAD PM method was still a very good option for estimating ETo for Southern Ontario, with RMSE smaller than 0.53 mm day(-1). For these cases, U data were replaced by the normal values for the region and Delta e was estimated from temperature data. The Priestley-Taylor method was also a good option for estimating ETo when U and Delta e data were missing, mainly when calibrated locally (RMSE = 0.40 mm day(-1)). When Rn was missing, the FAD PM method was not good enough for estimating ETo, with RMSE increasing to 0.79 mm day(-1). When only T data were available, adjusted Hargreaves and modified Thornthwaite methods were better options to estimate ETo than the FAO) PM method, since RMSEs from these methods, respectively 0.79 and 0.83 mm day(-1), were significantly smaller than that obtained by FAO PM (RMSE = 1.12 mm day(-1). (C) 2009 Elsevier B.V. All rights reserved.

New body fat prediction equations for severely obese patients

Background & aims: Severe obesity imposes physical limitations to body composition assessment. Our aim was to compare body fat (BF) estimations of severely obese patients obtained by bioelectrical impedance (BIA) and air displacement plethysmography (ADP) for development of new equations for BF prediction. Methods: Severely obese subjects (83 female/36 mate, mean age = 41.6 +/- 11.6 years) had BF estimated by BIA and ADP. The agreement of the data was evaluated using Bland-Altman`s graphic and concordance correlation coefficient (CCC). A multivariate regression analysis was performed to develop and validate new predictive equations. Results: BF estimations from BIA (64.8 +/- 15 kg) and ADP (65.6 +/- 16.4 kg) did not differ (p > 0.05, with good accuracy, precision, and CCC), but the Bland- Altman graphic showed a wide Limit of agreement (- 10.4; 8.8). The standard BIA equation overestimated BF in women (-1.3 kg) and underestimated BF in men (5.6 kg; p < 0.05). Two BF new predictive equations were generated after BIA measurement, which predicted BF with higher accuracy, precision, CCC, and limits of agreement than the standard BIA equation. Conclusions: Standard BIA equations were inadequate for estimating BF in severely obese patients. Equations developed especially for this population provide more accurate BF assessment. (C) 2008 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Sex-Specific Equations to Estimate Maximum Oxygen Uptake in Cycle Ergometry

AbstractBackground:Aerobic fitness, assessed by measuring VO2max in maximum cardiopulmonary exercise testing (CPX) or by estimating VO2max through the use of equations in exercise testing, is a predictor of mortality. However, the error resulting from this estimate in a given individual can be high, affecting clinical decisions.Objective:To determine the error of estimate of VO2max in cycle ergometry in a population attending clinical exercise testing laboratories, and to propose sex-specific equations to minimize that error.Methods:This study assessed 1715 adults (18 to 91 years, 68% men) undertaking maximum CPX in a lower limbs cycle ergometer (LLCE) with ramp protocol. The percentage error (E%) between measured VO2max and that estimated from the modified ACSM equation (Lang et al. MSSE, 1992) was calculated. Then, estimation equations were developed: 1) for all the population tested (C-GENERAL); and 2) separately by sex (C-MEN and C-WOMEN).Results:Measured VO2max was higher in men than in WOMEN: -29.4 ± 10.5 and 24.2 ± 9.2 mL.(kg.min)-1 (p < 0.01). The equations for estimating VO2max [in mL.(kg.min)-1] were: C-GENERAL = [final workload (W)/body weight (kg)] x 10.483 + 7; C-MEN = [final workload (W)/body weight (kg)] x 10.791 + 7; and C-WOMEN = [final workload (W)/body weight (kg)] x 9.820 + 7. The E% for MEN was: -3.4 ± 13.4% (modified ACSM); 1.2 ± 13.2% (C-GENERAL); and -0.9 ± 13.4% (C-MEN) (p < 0.01). For WOMEN: -14.7 ± 17.4% (modified ACSM); -6.3 ± 16.5% (C-GENERAL); and -1.7 ± 16.2% (C-WOMEN) (p < 0.01).Conclusion:The error of estimate of VO2max by use of sex-specific equations was reduced, but not eliminated, in exercise tests on LLCE.

Minu, Startu and all that:- Pitfalls in estimating the sensitivity of a worker’s wage to aggregate unemployment

In this paper we show that the inclusion of unemployment-tenure interaction variates in Mincer wage equations is subject to serious pitfalls. These variates were designed to test whether or not the sensitivity to the business cycle of a worker’s wage varies according to her tenure. We show that three canonical variates used in the literature - the minimum unemployment rate during a worker’s time at the firm(min u), the unemployment rate at the start of her tenure(Su) and the current unemployment rate interacted with a new hire dummy(δu) - can all be significant and "correctly" signed even when each worker in the firm receives the same wage, regardless of tenure (equal treatment). In matched data the problem can be resolved by the inclusion in the panel of firm-year interaction dummies. In unmatched data where this is not possible, we propose a solution for min u and Su based on Solon, Barsky and Parker’s(1994) two step method. This method is sub-optimal because it ignores a large amount of cross tenure variation in average wages and is only valid when the scaled covariances of firm wages and firm employment are acyclical. Unfortunately δu cannot be identified in unmatched data because a differential wage response to unemployment of new hires and incumbents will appear under both equal treatment and unequal treatment.

Method for Estimating the Magnitude and Frequency of Floods at Ungaged Sites on Unregulated Rural Streams in Iowa, HR-268, 1987

This report provides techniques and procedures for estimating the probable magnitude and frequency of floods at ungaged sites on Iowa streams. Physiographic characteristics were used to define the boundaries of five hydrologic regions. Regional regression equations that relate the size of the drainage area to flood magnitude are defined for estimating peak discharges having specified recurrence intervals of 2, 5, 10, 25, 50, and 100 years. Regional regression equations are applicable to sites on streams that have drainage areas ranging from 0.04 to 5,150 square miles provided that the streams are not affected significantly by regulation upstream from the sites and that the drainage areas upstream from the sites are not mostly urban areas. Flood-frequency characteristics for the mainstems of selected rivers are presented in graphs as a function of drainage area.

Estimating Design-Flood Discharges for Streams in Iowa Using Drainage-Basin and Channel-Geometry Characteristics, HR-322, 1993

Drainage-basin and channel-geometry multiple-regression equations are presented for estimating design-flood discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years at stream sites on rural, unregulated streams in Iowa. Design-flood discharge estimates determined by Pearson Type-III analyses using data collected through the 1990 water year are reported for the 188 streamflow-gaging stations used in either the drainage-basin or channel-geometry regression analyses. Ordinary least-squares multiple-regression techniques were used to identify selected drainage-basin and channel-geometry regions. Weighted least-squares multiple-regression techniques, which account for differences in the variance of flows at different gaging stations and for variable lengths in station records, were used to estimate the regression parameters. Statewide drainage-basin equations were developed from analyses of 164 streamflow-gaging stations. Drainage-basin characteristics were quantified using a geographic-information-system (GIS) procedure to process topographic maps and digital cartographic data. The significant characteristics identified for the drainage-basin equations included contributing drainage area, relative relief, drainage frequency, and 2-year, 24-hour precipitation intensity. The average standard errors of prediction for the drainage-basin equations ranged from 38.6% to 50.2%. The GIS procedure expanded the capability to quantitatively relate drainage-basin characteristics to the magnitude and frequency of floods for stream sites in Iowa and provides a flood-estimation method that is independent of hydrologic regionalization. Statewide and regional channel-geometry regression equations were developed from analyses of 157 streamflow-gaging stations. Channel-geometry characteristics were measured on site and on topographic maps. Statewide and regional channel-geometry regression equations that are dependent on whether a stream has been channelized were developed on the basis of bankfull and active-channel characteristics. The significant channel-geometry characteristics identified for the statewide and regional regression equations included bankfull width and bankfull depth for natural channels unaffected by channelization, and active-channel width for stabilized channels affected by channelization. The average standard errors of prediction ranged from 41.0% to 68.4% for the statewide channel-geometry equations and from 30.3% to 70.0% for the regional channel-geometry equations. Procedures provided for applying the drainage-basin and channel-geometry regression equations depend on whether the design-flood discharge estimate is for a site on an ungaged stream, an ungaged site on a gaged stream, or a gaged site. When both a drainage-basin and a channel-geometry regression-equation estimate are available for a stream site, a procedure is presented for determining a weighted average of the two flood estimates.

Techniques for Estimating Flood-Frequency Discharges for Streams in Iowa, HR-395A, 2001

A statewide study was conducted to develop regression equations for estimating flood-frequency discharges for ungaged stream sites in Iowa. Thirty-eight selected basin characteristics were quantified and flood-frequency analyses were computed for 291 streamflow-gaging stations in Iowa and adjacent States. A generalized-skew-coefficient analysis was conducted to determine whether generalized skew coefficients could be improved for Iowa. Station skew coefficients were computed for 239 gaging stations in Iowa and adjacent States, and an isoline map of generalized-skew-coefficient values was developed for Iowa using variogram modeling and kriging methods. The skew map provided the lowest mean square error for the generalized-skew- coefficient analysis and was used to revise generalized skew coefficients for flood-frequency analyses for gaging stations in Iowa. Regional regression analysis, using generalized least-squares regression and data from 241 gaging stations, was used to develop equations for three hydrologic regions defined for the State. The regression equations can be used to estimate flood discharges that have recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for ungaged stream sites in Iowa. One-variable equations were developed for each of the three regions and multi-variable equations were developed for two of the regions. Two sets of equations are presented for two of the regions because one-variable equations are considered easy for users to apply and the predictive accuracies of multi-variable equations are greater. Standard error of prediction for the one-variable equations ranges from about 34 to 45 percent and for the multi-variable equations range from about 31 to 42 percent. A region-of-influence regression method was also investigated for estimating flood-frequency discharges for ungaged stream sites in Iowa. A comparison of regional and region-of-influence regression methods, based on ease of application and root mean square errors, determined the regional regression method to be the better estimation method for Iowa. Techniques for estimating flood-frequency discharges for streams in Iowa are presented for determining ( 1) regional regression estimates for ungaged sites on ungaged streams; (2) weighted estimates for gaged sites; and (3) weighted estimates for ungaged sites on gaged streams. The technique for determining regional regression estimates for ungaged sites on ungaged streams requires determining which of four possible examples applies to the location of the stream site and its basin. Illustrations for determining which example applies to an ungaged stream site and for applying both the one-variable and multi-variable regression equations are provided for the estimation techniques.

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

Estimating parameters for generalized mass action models using constraint propagation.

As modern molecular biology moves towards the analysis of biological systems as opposed to their individual components, the need for appropriate mathematical and computational techniques for understanding the dynamics and structure of such systems is becoming more pressing. For example, the modeling of biochemical systems using ordinary differential equations (ODEs) based on high-throughput, time-dense profiles is becoming more common-place, which is necessitating the development of improved techniques to estimate model parameters from such data. Due to the high dimensionality of this estimation problem, straight-forward optimization strategies rarely produce correct parameter values, and hence current methods tend to utilize genetic/evolutionary algorithms to perform non-linear parameter fitting. Here, we describe a completely deterministic approach, which is based on interval analysis. This allows us to examine entire sets of parameters, and thus to exhaust the global search within a finite number of steps. In particular, we show how our method may be applied to a generic class of ODEs used for modeling biochemical systems called Generalized Mass Action Models (GMAs). In addition, we show that for GMAs our method is amenable to the technique in interval arithmetic called constraint propagation, which allows great improvement of its efficiency. To illustrate the applicability of our method we apply it to some networks of biochemical reactions appearing in the literature, showing in particular that, in addition to estimating system parameters in the absence of noise, our method may also be used to recover the topology of these networks.

Estimating the Price and Demand Development of Some Paperboard Grades

Työn tavoitteena oli luoda malli, jonka avulla voitaisiin ennustaa kartonkituotteiden hinnan ja kysynnän kehitystä. Työssä kerättiin aluksi tietoa kartonkimarkkinoista haastattelujen ja tilastotietojen avulla. Näiden perusteella luotiin malli, joka kuvaa kartonkimarkkinoiden ja -teollisuuden rakennetta. Erityisesti kiinnitettiin huomiota asiakkaiden tilauskäyttäytymiseen, tuotannonohjaukseen sekä hinnan muodostumiseen. Työssä käytettiin ennustemenetelmänä systeemidynamiikkaa. Oleellista oli löytää systeemissä esiintyvät tärkeimmät takaisinkytkennät ja systeemin avainmuuttujat. Kun kaikille mallin muuttujille oli määritetty yhtälöt ja vakioille annettu arvot, voitiin mallia simuloida, ja saada ennusteet halutuille muuttujille. Työssä esitettiin ennusteet kartonkimarkkinoiden tärkeimmille parametreille kahden vuoden päähän. Työssä tarkasteltiin myös, miten muutokset mallin käyttäytymistä säätelevissä muuttujissa vaikuttavat tuloksiin. Jotta pystyttäisiin paremmin selvittämään koko kartonkiteollisuuden dynamiikka, lisätutkimusta tarvittaisiin vielä eri kartonkilajien substituutiomahdollisuuksista ja hintojen riippuvuuksista. Mielenkiintoista olisi myös tietää, miten tuotannon käyttöasteiden muutokset ja hinnan vaihtelut vaikuttavat liiketoiminnan kannattavuuteen valmistajien näkökulmasta.

The demand for a healthy diet: estimating the almost ideal demand system with infrequency of purchase

A Bayesian method of estimating multivariate sample selection models is introduced and applied to the estimation of a demand system for food in the UK to account for censoring arising from infrequency of purchase. We show how it is possible to impose identifying restrictions on the sample selection equations and that, unlike a maximum likelihood framework, the imposition of adding up at both latent and observed levels is straightforward. Our results emphasise the role played by low incomes and socio-economic circumstances in leading to poor diets and also indicate that the presence of children in a household has a negative impact on dietary quality.

Adjoint methods in data assimilation for estimating model error

Data assimilation aims to incorporate measured observations into a dynamical system model in order to produce accurate estimates of all the current (and future) state variables of the system. The optimal estimates minimize a variational principle and can be found using adjoint methods. The model equations are treated as strong constraints on the problem. In reality, the model does not represent the system behaviour exactly and errors arise due to lack of resolution and inaccuracies in physical parameters, boundary conditions and forcing terms. A technique for estimating systematic and time-correlated errors as part of the variational assimilation procedure is described here. The modified method determines a correction term that compensates for model error and leads to improved predictions of the system states. The technique is illustrated in two test cases. Applications to the 1-D nonlinear shallow water equations demonstrate the effectiveness of the new procedure.

Estimating age of mature adults from the degeneration of the sternal end of the clavicle

The sternal end of the clavicle has been illustrated to be useful in aging young adults, however, no studies have investigated what age-related changes occur to the sternal end post epiphyseal fusion. In this study, three morphological features (i.e., surface topography, porosity, and osteophyte formation) were examined and scored using 564 clavicles of individuals of European ancestry (n = 318 males; n = 246 females), with known ages of 40+ years, from four documented skeletal collections: Hamann-Todd, Pretoria, St. Bride's, and Coimbra. An ordinal scoring method was developed for each of the three traits. Surface topography showed the strongest correlation with age, and composite scores (formed by summing the three separate trait scores) indicated progressive degeneration of the surface with increasing chronological age. Linear regression analyses were performed on the trait scores to produce pooled-sample age estimation equations. Blind tests of the composite score method and regression formulae on 56 individuals, aged 40+ years, from Christ Church Spitalfields, suggest accuracies of 96.4% for both methods. These preliminary results display the first evidence of the utility of the sternal end of the clavicle in aging older adult individuals. However, in the current format, these criteria should only be applied to individuals already identified as over 40 years in order to refine the age ranges used for advanced age. These findings do suggest the sternal end of the clavicle has potential to aid age estimates beyond the traditional "mature adult" age category (i.e., 46+ years), and provides several suggestions for future research.

Estimating the aboveground biomass and wood volume of savanna woodlands in Brazil's Pantanal wetlands based on allometric correlations

The quantity and distribution of vegetal biomass are important aspects to consider in ecosystem studies. However, little information is available about Brazil's Pantanal woodland savannas. This work involved the development of regression equations of the aerial biomass and wood volume of native tree species in a region of woodland savanna on Rio Negro farm in the Pantanal of Nhecolandia, Brazil. Samples were taken from 10 trees of each of five species: Protium heptaphyllum (Aub1.) Marchand, Magonia pubescens A. St.-Hil., Diptychandra aurantiaca Tul., Terminalia argentea Mart. and Zucc. and Licania minutiflora (Sagot) Fritsch and from a miscellaneous group of I I different species. Linear and nonlinear regression analyses were developed relating the diameter at breast height to the dry weight of wood, branches and leaves, wood volume and total aerial biomass. All the regressions showed a significance of P < 0.05 and an R-2 close to or above 0.8. The biomass curve predicted by linear regression analysis of the studied species was similar to the nonlinear regression, with the exception of L. minutiflora and the miscellaneous group. The breast height diameter proved a good choice for estimating biomass and wood volume. The estimated wood volume and biomass of the Pantanal woodland savanna is crucial information for understanding the carbon cycle and for ensuring the region's conservation and sustainable use. (c) 2006 Elsevier B.V. All rights reserved.