A parametric approach for measuring the effect of the 10th revision of the international classification of diseases

Summary.  The World Health Organization revises the international classification of diseases about every 10 years to stay abreast of advances in medical science and to compare international health statistics. However, the new revision (i.e. the 10th revision) introduces discontinuities in mortality trends, making it impossible to compare the mortality statistics before and after the revision directly. The US National Center for Health Statistics published comparability ratios to correct the discontinuities between the two sets of mortality data: one coded by the ninth revision and the other by the 10th revision. We propose a parametric two-stage model to produce new comparability ratios and use these ratios to correct the discontinuities. The asymptotic behaviour of the comparability ratios is investigated. Our model not only measures the extent of discontinuities in trends in mortality but also can be used to forecast future mortality. Comparing with the National Center for Health Statistics's ratios, our comparability ratios smooth out the discontinuities better for most causes.     

Nonparametric estimation of waiting time distributions in a Markov model based on current status data

We construct nonparametric estimators of state waiting time distribution functions in a Markov multistate model using current status data. This is a particularly difficult problem since neither the entry nor the exit times of a given state are directly observed. These estimators are obtained, using the Markov property, from estimators of counting processes of state entry and exit times, as well as, the size of “at risk” sets of state entry and transitions out of that state. Consistency of our estimators is established. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on current status data compare well with those based on complete data. We also illustrate our method using a pubertal development data set obtained from the NHANES III [1997. NHANES III Reference Manuals and Reports (CD-ROM). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD] study.     

Modelling childhood malnutrition in Zambia: an adaptive Bayesian splines approach

Nutritional status of a child is an important determinant factor of his/her health and survival. Evaluation of child nutritional status is usually based on three anthropometric indices of height-for-age (stunting), weight-for-height (wasting) and weight-for-age (underweight). This paper is a case study that focuses on a new approach of estimating nonlinear effects. It models the dependence of probability of underweight children in Zambia on some covariates, some of which are metrical whose effects are assumed to be nonlinear and estimated nonparametrically through a Bayesian B-spline basis function approach with adaptive knot selection using the data set from the 1992 Zambia Demographic and Health Survey (ZDHS). For all the unknown functions, the number and location of knots as well as the unknown coefficients of the basis functions are estimated simultaneously using reversible jump Markov chain Monte Carlo (RJMCMC) techniques.     

Modeling low birth weights using threshold regression: results for U. S. birth data

Babies born live under 2,500 g or with a gestational age under 37 weeks are often inadequately developed and have elevated risks of infant mortality, congenital malformations, mental retardation, and other physical and neurological impairments. In this paper, we model birth weight as a first hitting time (FHT) of a birthing boundary in a Wiener process representing fetal development. We associate the parameters of the process and boundary with covariates describing maternal characteristics and the birthing environment using a relatively new regression methodology called threshold regression. Two FHT models for birth weight are developed. One is a mixture model and the other a competing risks model. These models are tested in a case demonstration using a 4%-systematic sample of the more than four million live births in the United States in 2002. An extensive data set for these births was provided by the National Center for Health Statistics. The focus of this paper is on the conceptual framework, models and methodology. A full empirical study is deferred to a later occasion.     

Establishing the nadir of the body mass index-mortality relationship: a case study

The relationship between body weight and mortality is examined using U.S. data from the National Health and Nutrition Examination Survey I (NHANES I) Epidemiologic Follow-up Study concerning 13,242 individuals, the emphasis being on identifying the body mass index associated with the lowest levels of mortality. Factors such as smoking status, sex, race, and age are taken into consideration. The results suggest that only the interaction between race and body mass index is significant.     

International Statistical Activities

The growth of the health care industry places increasing strain on available resources. As in other areas of social policy, health statisticians and health data are increasingly expected to provide keys to rational decision making. To accomplish this goal, the statistician and decision-maker need to interact to an increasing degree. The current issues in health policy and the statistician's contribution to policy analysis are discussed in the context of the National Center for Health Statistics experience. While the article focuses on health, it has implications for statisticians and policy formation in other fields.     

Combining census and registration data to estimate detailed elderly migration flows in England and Wales

Summary.  A log-linear model is developed to estimate detailed elderly migration flows by combining data from the 2001 UK census and National Health Services patient register. After showing that the census and National Health Service migration flows can be reasonably combined, elderly migration flows between groupings of local authority districts by age, sex and health status for the 2000–2001 and 2003–2004 periods are estimated and then analysed to show how the patterns have changed. By combining registration data with census data, we can provide recent estimates of detailed elderly migration flows, which can be used for improvements in social planning or policy.     

Public expenditure in the UK: how measures matter

Summary.  Studying spending over time requires reliable data. It is not clear that such data exist in the UK, however. The two published sources of functional spending numbers—the Office for National Statistics's 'blue book' and Her Majesty's Treasury's Public Expenditure Statistical Analyses (PESA)—rely on estimates of past spending, using a link year method, rather than recalculating actual spending figures when functional definitions change. We assess the various measures of spending in the UK. Specifically, we do two things. First, we present a new, third, set of spending numbers applying temporally consistent functional definitions to PESA microdata. Second, we compare the three measures. Our analyses indicate that the Office for National Statistics and PESA data differ quite markedly, especially for certain functions, i.e. in some cases the two measures imply completely different histories. The differences between the original PESA data and our new measures are less pronounced on average, though significant differences are evident, especially year by year.     

A fortran subroutine for computing indicators of the nutritional status of children and adolescents

For the nutritional status of children in developing countries the anthropometric standardized scores (SD-scores) WEHE, WEAG, HEAG based on an international reference population are recommended. The evaluation of data in terms of SD-scores are widely used. This paper provides the FORTRAN-subroutine SDSC for the calculation of those scores directly refering to the reference data. The application of this subroutine is demonstrated.     

Combining information from multiple surveys for assessing health disparities

Despite advances in public health practice and medical technology, the disparities in health among the various racial/ethnic and socioeconomic groups remain a concern which has prompted the Department of Human and Health Services to designate the elimination of disparities in health as an overarching goal of Healthy People 2010. To assess the progress towards this goal, suitable measures are needed at the population level that can be tracked over time; Statistical inferential procedures have to be developed for these population level measures; and the data sources have to be identified to allow for such inferences to be conducted. Popular data sources for health disparities research are large surveys such the National Health and Interview Survey (NHIS) or the Behavior Risk Factor Surveillance System (BRFSS). The self-report disease status collected in these surveys may be inaccurate and the errors may be correlated with variables used in defining the groups. This article uses the National Health and Nutritional Examination Survey (NHANES) 99-00 to assess the extent of error in the self-report disease status; uses a Bayesian framework develop corrections for the self-report disease status in the National Health Interview Survey (NHIS) 99-00; and compares inferences about various measures of health disparities, with and without correcting for measurement error. The methodology is illustrated using the disease outcome hypertension, a common risk factor for cardiovascular disease. JEL classification C1 (C11, C13, C15), C4 (C42) and I3 (I31, I38)     

Combining information from multiple surveys for assessing health disparities

Despite advances in public health practice and medical technology, the disparities in health among the various racial/ethnic and socioeconomic groups remain a concern which has prompted the Department of Human and Health Services to designate the elimination of disparities in health as an overarching goal of Healthy People 2010. To assess the progress towards this goal, suitable measures are needed at the population level that can be tracked over time; Statistical inferential procedures have to be developed for these population level measures; and the data sources have to be identified to allow for such inferences to be conducted. Popular data sources for health disparities research are large surveys such the National Health and Interview Survey (NHIS) or the Behavior Risk Factor Surveillance System (BRFSS). The self-report disease status collected in these surveys may be inaccurate and the errors may be correlated with variables used in defining the groups. This article uses the National Health and Nutritional Examination Survey (NHANES) 99-00 to assess the extent of error in the self-report disease status; uses a Bayesian framework develop corrections for the self-report disease status in the National Health Interview Survey (NHIS) 99-00; and compares inferences about various measures of health disparities, with and without correcting for measurement error. The methodology is illustrated using the disease outcome hypertension, a common risk factor for cardiovascular disease.     

Census data issues for epidemiology and health risk assessment: experiences from the Small Area Health Statistics Unit

Summary.  Census data are vital components of epidemiological studies, but the issues that are involved in using these data in such studies are often not fully appreciated. The paper describes some of the problems and uncertainties that arise, and some of the approaches that can be used to address them, based on experience in the Small Area Health Statistics Unit at Imperial College London. Issues considered include the geography of census data (zone design systems, recasting and the role of postcodes), temporal aspects of census data (especially in relation to migration and population change) and information content (especially in relation to characterization of socio-economic status). In the light of these issues, opportunities to improve the resolution and utility of census data for epidemiological studies are discussed.     

Modeling multiple‐response categorical data from complex surveys

Although “choose all that apply” questions are common in modern surveys, methods for analyzing associations among responses to such questions have only recently been developed. These methods are generally valid only for simple random sampling, but these types of questions often appear in surveys conducted under more complex sampling plans. The purpose of this article is to provide statistical analysis methods that can be applied to “choose all that apply” questions in complex survey sampling situations. Loglinear models are developed to incorporate the multiple responses inherent in these types of questions. Statistics to compare models and to measure association are proposed and their asymptotic distributions are derived. Monte Carlo simulations show that tests based on adjusted Pearson statistics generally hold their correct size when comparing models. These simulations also show that confidence intervals for odds ratios estimated from loglinear models have good coverage properties, while being shorter than those constructed using empirical estimates. Furthermore, the methods are shown to be applicable to more general problems of modeling associations between elements of two or more binary vectors. The proposed analysis methods are applied to data from the National Health and Nutrition Examination Survey. The Canadian Journal of Statistics © 2009 Statistical Society of Canada     

No islands: charting a course for UK official statistics

Our system of official statistics in the UK is one of the most decentralised and elaborate in the world. As well as the Office for National Statistics (ONS), it spans several sizeable centres of expertise in Whitehall departments and in the three devolved administrations; and it embraces agencies such as the Higher Education Statistics Agency and the Information Centre for Health and Social Care, which sit at arms length from central departments. In 2008, legislation establishing a new Statistics Board, with oversight not just of the ONS but of all official figures produced by these bodies, will come fully into force. Richard Alldritt of the Statistics Commission and Richard Laux of the ONS identify some of the challenges facing the statistical system at this time of transition and suggests a course that the Statistics Board might want to steer.     

Dependence in the 2001 one-number census project

Summary.  The one-number census approach was developed by the Office for National Statistics to adjust the counts from the 2001 census of England and Wales for underenumeration. The method is underpinned by an assumption of independence between the count of the population that was given by the 2001 census and the count that was given by the Census Coverage Survey. Some dependence was, however, detected, and the paper describes the strategy that was used to measure dependence and to adjust the 2001 census population estimates.     

行政记录和政府统计的多视角研究

在社会经济统计中,行政记录的重要性日益被统计部门重视,其开发使用也成为学术研究热点。通过研究现有文献,本文系统综述了行政记录和政府统计之间的渊源、行政记录的优点、使用行政记录应注意的问题、行政记录在国内外政府统计中的使用实践和趋势以及使用行政记录改善政府统计的方向.     

Does social mobility affect the size of the socioeconomic mortality differential?: evidence from the Office for National Statistics Longitudinal Study

The effect of social mobility on the socioeconomic differential in mortality is examined with data from the Office for National Statistics Longitudinal Study. The analyses involve 46 980 men aged 45–64 years in 1981. The mortality risk of the socially mobile is compared with the mortality risk of the socially stable after adjustment for their class of origin (their social class in 1971) and class of destination (their social class in 1981) separately. Among those in employment there is some evidence that movement out of their class of origin is in the direction predicted by the idea of health-related social mobility. This evidence, however, seems strongest for causes of death which are least likely to have been preceded by prolonged incapacity. Movement into the class of destination, however, shows the opposite relationship with mortality. Compared with the socially stable members of their class of destination, the upwardly mobile tend to have higher mortality and the downwardly mobile tend to have lower mortality. This relationship with the class of destination, it is suggested, may explain why socioeconomic mortality differentials do not widen with increasing age.     

Robust rank-based variable selection in double generalized linear models with diverging number of parameters under adaptive Lasso

We propose a robust rank-based estimation and variable selection in double generalized linear models when the number of parameters diverges with the sample size. The consistency of the variable selection procedure and asymptotic properties of the resulting estimators are established under appropriate selection of tuning parameters. Simulations are performed to assess the finite sample performance of the proposed estimation and variable selection procedure. In the presence of gross outliers, the proposed method is showing that the variable selection method works better. For practical application, a real data application is provided using nutritional epidemiology data, in which we explore the relationship between plasma beta-carotene levels and personal characteristics (e.g. age, gender, fat, etc.) as well as dietary factors (e.g. smoking status, intake of cholesterol, etc.).     

A comparison of procedures to correct for base-line differences in the analysis of continuous longitudinal data: a case-study

Summary.  The main advantage of longitudinal studies is that they can distinguish changes over time within individuals (longitudinal effects) from differences between subjects at the start of the study (base-line characteristics; cross-sectional effects). Often, especially in observational studies, subjects are very heterogeneous at base-line, and one may want to correct for this, when doing inferences for the longitudinal trends. Three procedures for base-line correction are compared in the context of linear mixed models for continuous longitudinal data. All procedures are illustrated extensively by using data from an experiment which aimed at studying the relationship between the post-operative evolution of the functional status of elderly hip fracture patients and their preoperative neurocognitive status.