Biomarkers of extracellular matrix metabolism (MMP-9 and TIMP-1) and risk of stroke, myocardial infarction and cause-specific mortality : cohort study

Objective: Turnover of the extracellular matrix in all solid organs is governed mainly by a balance between the degrading matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs). An altered extracellular matrix metabolism has been implicated in a variety of diseases. We investigated relations of serum levels of MMP-9 and TIMP-1 to mortality risk from an etiological perspective. Design: The prospective Uppsala Longitudinal Study of Adult Men (ULSAM) cohort, followed from 1991–1995 for up to 18.1 years. A random population-based sample of 1,082 71-year-old men, no loss to follow-up. Endpoints were all-cause (n = 628), cardiovascular (n = 230), non-cardiovascular (n = 398) and cancer mortality (n = 178), and fatal or non-fatal myocardial infarction (n = 138) or stroke (n = 163). Results: Serum MMP-9 and TIMP-1 levels were associated with risk of all-cause mortality (Cox proportional hazard ratio [HR] per standard deviation 1.10, 95% confidence interval [CI] 1.03–1.19; and 1.11, 1.02–1.20; respectively). TIMP-1 levels were mainly related to risks of cardiovascular mortality and stroke (HR per standard deviation 1.22, 95% CI 1.09–1.37; and 1.18, 1.04–1.35; respectively). All relations except those of TIMP-1 to stroke risk were attenuated by adjustment for cardiovascular disease risk factors. Relations in a subsample without cardiovascular disease or cancer were similar to those in the total sample. Conclusion: In this community-based cohort of elderly men, serum MMP-9 and TIMP-1 levels were related to mortality risk. An altered extracellular matrix metabolism may be involved in several detrimental pathways, and circulating MMP-9 or TIMP-1 levels may be relevant markers thereof.

Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Assessing the physical environment of older people’s residential care facilities: development of the Swedish version of the Sheffield Care Environment Assessment Matrix (S-SCEAM)

Background There is emerging evidence that the physical environment is important for health, quality of life and care, but there is a lack of valid instruments to assess health care environments. The Sheffield Care Environment Assessment Matrix (SCEAM), developed in the United Kingdom, provides a comprehensive assessment of the physical environment of residential care facilities for older people. This paper reports on the translation and adaptation of SCEAM for use in Swedish residential care facilities for older people, including information on its validity and reliability. Methods SCEAM was translated into Swedish and back-translated into English, and assessed for its relevance by experts using content validity index (CVI) together with qualitative data. After modification, the validity assessments were repeated and followed by test-retest and inter-rater reliability tests in six units within a Swedish residential care facility that varied in terms of their environmental characteristics. Results Translation and back translation identified linguistic and semantic related issues. The results of the first content validity analysis showed that more than one third of the items had item-CVI (I-CVI) values less than the critical value of 0.78.  After modifying the instrument, the second content validation analysis resulted in I-CVI scores above 0.78, the suggested criteria for excellent content validity. Test-retest reliability showed high stability (96% and 95% for two independent raters respectively), and inter-rater reliability demonstrated high levels of agreement (95% and 94% on two separate rating occasions). Kappa values were very good for test-retest (κ= 0.903 and 0.869) and inter-rater reliability (κ= 0.851 and 0.832). Conclusions Adapting an instrument to a domestic context is a complex and time-consuming process, requiring an understanding of the culture where the instrument was developed and where it is to be used. A team, including the instrument’s developers, translators, and researchers is necessary to ensure a valid translation and adaption. This study showed preliminary validity and reliability evidence for the Swedish version (S-SCEAM) when used in a Swedish context. Further, we believe that the S-SCEAM has improved compared to the original instrument and suggest that it can be used as a foundation for future developments of the SCEAM model.

Productivity of nations: a stochastic frontier approach to TFP decomposition

This Paper Tackles the Problem of Aggregate Tfp Measurement Using Stochastic Frontier Analysis (Sfa). Data From Penn World Table 6.1 are Used to Estimate a World Production Frontier For a Sample of 75 Countries Over a Long Period (1950-2000) Taking Advantage of the Model Offered By Battese and Coelli (1992). We Also Apply the Decomposition of Tfp Suggested By Bauer (1990) and Kumbhakar (2000) to a Smaller Sample of 36 Countries Over the Period 1970-2000 in Order to Evaluate the Effects of Changes in Efficiency (Technical and Allocative), Scale Effects and Technical Change. This Allows Us to Analyze the Role of Productivity and Its Components in Economic Growth of Developed and Developing Nations in Addition to the Importance of Factor Accumulation. Although not Much Explored in the Study of Economic Growth, Frontier Techniques Seem to Be of Particular Interest For That Purpose Since the Separation of Efficiency Effects and Technical Change Has a Direct Interpretation in Terms of the Catch-Up Debate. The Estimated Technical Efficiency Scores Reveal the Efficiency of Nations in the Production of Non Tradable Goods Since the Gdp Series Used is Ppp-Adjusted. We Also Provide a Second Set of Efficiency Scores Corrected in Order to Reveal Efficiency in the Production of Tradable Goods and Rank Them. When Compared to the Rankings of Productivity Indexes Offered By Non-Frontier Studies of Hall and Jones (1996) and Islam (1995) Our Ranking Shows a Somewhat More Intuitive Order of Countries. Rankings of the Technical Change and Scale Effects Components of Tfp Change are Also Very Intuitive. We Also Show That Productivity is Responsible For Virtually All the Differences of Performance Between Developed and Developing Countries in Terms of Rates of Growth of Income Per Worker. More Important, We Find That Changes in Allocative Efficiency Play a Crucial Role in Explaining Differences in the Productivity of Developed and Developing Nations, Even Larger Than the One Played By the Technology Gap