Objective

To use data from the Glasgow Alumni Cohort to investigate whether oral health in young adulthood is independently associated with later life cardiovascular disease (CVD) and cancer mortality.

Methods and results

Of the original cohort (n = 15 322), 12 631 subjects were traced through the National Health Service Central Register. Of these, 9569 men and 2654 women were 30 years or younger at baseline. During up to 57 years of follow‐up, 1432 deaths occurred among subjects with complete data, including 509 deaths from CVD and 549 from cancer. After adjusting for potential confounders, no substantial association was found between the number of missing teeth (as a continuous variable) and all‐cause mortality (hazard ratio (HR) for each extra missing tooth  = 1.01; 95% confidence interval (CI) 1.00 to 1.02), CVD mortality (HR = 1.01; 95% CI 0.99 to 1.03) or cancer mortality (HR = 1.00; 95% CI 0.98 to 1.02). When the number of missing teeth was treated as a categorical variable, there was evidence that students with nine or more missing teeth at baseline had an increased risk of CVD (HR = 1.35; 95% CI 1.03 to 1.77) compared with those with fewer than five missing teeth. When the number of missing teeth was transformed using fractional polynomials, there seemed to be a non‐linear relation between missing teeth and CVD mortality.

Conclusions

Although some evidence was found to support the relation between tooth loss and CVD mortality, causal mechanisms underlying this association remain uncertain.

In the last decade, a new statistical methodology, namely, network meta-analysis, has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of all available treatments. A further development in the network meta-analysis is to use a Bayesian statistical approach, which provides a more flexible modelling framework to take into account heterogeneity in the evidence and complexity in the data structure. The aim of this paper is therefore to provide a nontechnical introduction to network meta-analysis for dental research community and raise the awareness of it. An example was used to demonstrate how to conduct a network meta-analysis and the differences between it and traditional meta-analysis. The statistical theory behind network meta-analysis is nevertheless complex, so we strongly encourage close collaboration between dental researchers and experienced statisticians when planning and conducting a network meta-analysis. The use of more sophisticated statistical approaches such as network meta-analysis will improve the efficiency in comparing the effectiveness between multiple treatments across a set of trials.

Background

The effects of baseline and changes in blood pressure and low density lipoprotein (LDL) cholesterol on the carotid intima media thickness (IMT) have not been well documented.

Methods

A total of 2572 adults (mean age 53.8 years, 54.6% women) in a Taiwanese community undertook three blood pressure and LDL cholesterol examinations over 6 years. Latent growth curve modeling was used to investigate the effects of baseline and change in blood pressure and LDL cholesterol on IMT.

Results

Greater baseline LDL and blood pressure were associated with an increase in IMT (0.005 ± 0.002 mm per 1 mg/dL [p = 0.006] and 0.041 ± 0.004 mm mmHg [p <0.0001], respectively. Change in blood pressure was associated with a significant increase in IMT (0.047±0.016, P = 0.004), whilst the association between change in LDL and change in IMT was not statistically significant (0.008±0.006, P = 0.20).

Conclusions

Carotid IMT was associated with baseline blood pressure and LDL cholesterol, yet only changes of blood pressure, not LDL cholesterol, were related to carotid IMT during the 6-year observation.

Background

Exploratory factor analysis is a commonly used statistical technique in metabolic syndrome research to uncover latent structure amongst metabolic variables. The application of factor analysis requires methodological decisions that reflect the hypothesis of the metabolic syndrome construct. These decisions often raise the complexity of the interpretation from the output. We propose two alternative techniques developed from cluster analysis which can achieve a clinically relevant structure, whilst maintaining intuitive advantages of clustering methodology.

Methods

Two advanced techniques of clustering in the VARCLUS and matroid methods are discussed and implemented on a metabolic syndrome data set to analyze the structure of ten metabolic risk factors. The subjects were selected from the normative aging study based in Boston, Massachusetts. The sample included a total of 847 men aged between 21 and 81 years who provided complete data on selected risk factors during the period 1987 to 1991.

Results

Four core components were identified by the clustering methods. These are labelled obesity, lipids, insulin resistance and blood pressure. The exploratory factor analysis with oblique rotation suggested an overlap of the loadings identified on the insulin resistance and obesity factors. The VARCLUS and matroid analyses separated these components and were able to demonstrate associations between individual risk factors.

Conclusions

An oblique rotation can be selected to reflect the clinical concept of a single underlying syndrome, however the results are often difficult to interpret. Factor loadings must be considered along with correlations between the factors. The correlated components produced by the VARCLUS and matroid analyses are not overlapped, which allows for a simpler application of the methodologies and interpretation of the results. These techniques encourage consistency in the interpretation whilst remaining faithful to the construct under study.

Background

We investigate whether the changing environment caused by rapid economic growth yielded differential effects for successive Taiwanese generations on 8 components of metabolic syndrome (MetS): body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein (HDL), Low-density lipoproteins (LDL) and uric acid (UA).

Methods

To assess the impact of age, birth year and year of examination on MetS components, we used partial least squares regression to analyze data collected by Mei-Jaw clinics in Taiwan in years 1996 and 2006. Confounders, such as the number of years in formal education, alcohol intake, smoking history status, and betel-nut chewing were adjusted for.

Results

As the age of individuals increased, the values of components generally increased except for UA. Men born after 1970 had lower FPG, lower BMI, lower DBP, lower TG, Lower LDL and greater HDL; women born after 1970 had lower BMI, lower DBP, lower TG, Lower LDL and greater HDL and UA. There is a similar pattern between the trend in levels of metabolic syndrome components against birth year of birth and economic growth in Taiwan.

Conclusions

We found cohort effects in some MetS components, suggesting associations between the changing environment and health outcomes in later life. This ecological association is worthy of further investigation.

Due to a problem of identification, how to estimate the distinct effects of age, time period and cohort has been a controversial issue in the analysis of trends in health outcomes in epidemiology. In this study, we propose a novel approach, partial least squares (PLS) analysis, to separate the effects of age, period, and cohort. Our example for illustration is taken from the Glasgow Alumni cohort. A total of 15,322 students (11,755 men and 3,567 women) received medical screening at the Glasgow University between 1948 and 1968. The aim is to investigate the secular trends in blood pressure over 1925 and 1950 while taking into account the year of examination and age at examination. We excluded students born before 1925 or aged over 25 years at examination and those with missing values in confounders from the analyses, resulting in 12,546 and 12,516 students for analysis of systolic and diastolic blood pressure, respectively. PLS analysis shows that both systolic and diastolic blood pressure increased with students' age, and students born later had on average lower blood pressure (SBP: −0.17 mmHg/per year [95% confidence intervals: −0.19 to −0.15] for men and −0.25 [−0.28 to −0.22] for women; DBP: −0.14 [−0.15 to −0.13] for men; −0.09 [−0.11 to −0.07] for women). PLS also shows a decreasing trend in blood pressure over the examination period. As identification is not a problem for PLS, it provides a flexible modelling strategy for age-period-cohort analysis. More emphasis is then required to clarify the substantive and conceptual issues surrounding the definitions and interpretations of age, period and cohort effects.

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results.

Background

Hospital mortality rates are one of the most frequently selected indicators for measuring the performance of NHS Trusts. A recent article in a national newspaper named the hospital with the highest or lowest mortality in the 2005/6 financial year; a report by the organization Dr Foster Intelligence provided information with regard to the performance of all NHS Trusts in England.

Methods

Basic statistical theory and computer simulations were used to explore the relationship between the variations in the performance of NHS Trusts and the sizes of the Trusts. Data of hospital standardised mortality ratio (HSMR) of 152 English NHS Trusts for 2005/6 were re-analysed.

Results

A close examination of the information reveals a pattern which is consistent with a statistical phenomenon, discovered by the French mathematician de Moivre nearly 300 years ago, described in every introductory statistics textbook: namely that variation in performance indicators is expected to be greater in small Trusts and smaller in large Trusts. From a statistical viewpoint, the number of deaths in a hospital is not in proportion to the size of the hospital, but is proportional to the square root of its size. Therefore, it is not surprising to note that small hospitals are more likely to occur at the top and the bottom of league tables, whilst mortality rates are independent of hospital sizes.

Conclusion

This statistical phenomenon needs to be taken into account in the comparison of hospital Trusts performance, especially with regard to policy decisions.

Background

General practitioners' remuneration is now linked directly to the scores attained in the Quality and Outcomes Framework (QOF). The success of this approach depends in part on designing a robust and clinically meaningful set of indicators. The aim of this study was to assess the extent to which measures of health observed in practice populations are correlated with their QOF scores, after accounting for the established associations between health outcomes and socio-demographics.

Methods

QOF data for the period April 2004 to March 2005 were obtained for all general practices in two English Primary Care Trusts. These data were linked to data for emergency hospital admissions (for asthma, cancer, chronic obstructive pulmonary disease, coronary hear disease, diabetes, stroke and all other conditions) and all cause mortality for the period September 2004 to August 2005. Multilevel logistic regression models explored the association between health outcomes (hospital admission and death) and practice QOF scores (clinical, additional services and organisational domains), age, sex and socio-economic deprivation.

Results

Higher clinical domain scores were generally associated with lower admission rates and this was significant for cancer and other conditions in PCT 2. Higher scores in the additional services domain were associated with higher admission rates, significantly so for asthma, CHD, stroke and other conditions in PCT 1 and cancer in PCT 2. Little association was observed between the organisational domain scores and admissions. The relationship between the QOF variables and mortality was less clear. Being female was associated with fewer admissions for cancer and CHD and lower mortality rates. Increasing age was mainly associated with an increased number of events. Increasing deprivation was associated with higher admission rates for all conditions and with higher mortality rates.

Conclusion

The associations between QOF scores and emergency admissions and mortality were small and inconsistent, whilst the impact of socio-economic deprivation on the outcomes was much stronger. These results have implications for the use of target-based remuneration of general practitioners and emphasise the need to tackle inequalities and improve the health of disadvantaged groups and the population as a whole.