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Background Living in social disadvantage has been conceptualised as a chronic stressor, although this contradicts evidence from recent studies using hair cortisol as a measure of hypothalamus-pituitary-adrenal (HPA) axis activity. The methodological limitations of previous studies investigating the association between socioeconomic position (SEP) and hair cortisol and cortisone are taken into account in this study which examines if lower SEP is associated with higher levels of HPA axis activity as measured by hair cortisol and cortisone among older adults. Methods Cortisol and cortisone levels in hair samples from 2468 participants in the 6th wave of the English Longitudinal Study of Ageing (ELSA) are examined, in relation to educational attainment, wealth, social class, and employment status. Multivariable linear regression models were used to examine the association between socioeconomic position and cortisol and cortisone levels. Inverse probability weighting and multiple imputation were used to compensate for missing data. Interactions between social class and employment status were tested. All models were adjusted for gender, age, interaction between gender and age, ethnicity, marital status, hair treatment, hair colour, nurse visiting month, smoking status, body mass index, self-assessed health, number of medications, and depressive symptoms. Results We found significant differences between the most and least advantaged social classes in their levels of hair cortisol and cortisone. Participants in the lower supervisory social class and retired had increased levels of cortisol (0.71 log(pg/mg), 95% CI 0.14 to 1.28) and cortisone (0.73 log(pg/mg), 95% CI 0.29 to 1.16) compared to participants in the most advantaged social class and those still in work. Among the economically inactive, the most disadvantaged social classes clearly had increased levels of hair cortisol and cortisone. Further analyses that take missing data into account showed that the complete case estimates of hair cortisone in the most disadvantaged groups were underestimated compared to estimates accounting for missing data, such as inverse probability weighting and multiple imputation. Conclusion This study demonstrates that social disadvantage as measured by low SEP is correlated with increased HPA axis activity. The conceptualisation of social disadvantage as a chronic stressor may be valid and previous studies reporting no associations between SEP and hair cortisol may have some methodological limitations. Future analyses using biosocial data may need to take into account and adjust for missing data in biosocial analyses.

For decades, national statistical agencies and other data custodians have been publishing frequency tables based on census, survey and administrative data. In order to protect the confidentiality of individuals represented in the data, tables based on original data are modified before release. Recently, in response to user demand for more flexible and responsive table publication services, frequency table publication schemes have been augmented with on-line table generating servers such as the US Census Bureau FactFinder and the Australian Bureau of Statistics (ABS) TableBuilder. These systems allow users to build their own custom tables, and make use of automated perturbation routines to protect confidentiality. Motivated by the growing popularity of table generating servers, in this paper we study confidentiality protection for perturbed frequency tables, including the trade-off with analytical utility, focusing on a version of the ABS TableBuilder as a concrete example of a data release mechanism, and examining its properties. Confidentiality protection is assessed in terms of the differential privacy standard, and this paper can be used as a practical introduction to differential privacy, to calculations related to its application, to the relationship between confidentiality protection and utility, and to confidentiality in general.

Recent survey literature shows an increasing interest in survey designs that adapt data collection to characteristics of the survey target population. Given a specified quality objective function, the designs attempt to find an optimal balance between quality and costs. Finding the optimal balance may not be straightforward as corresponding optimisation problems are often highly non-linear and non-convex. In this paper, we discuss how to choose strata in such designs and how to allocate these strata in a sequential design with two phases. We use partial R-indicators to build profiles of the data units where more or less attention is required in the data collection. In allocating cases, we look at two extremes: surveys that are run only once, or infrequent, and surveys that are run continuously. We demonstrate the impact of the sample size in a simulation study and provide an application to a real survey, the Dutch Crime Victimisation Survey.

The gap in occupational earnings between children from parents of higher versus lower socioeconomic background widens over the life course. This gap can be at least partly explained by family background characteristics in childhood but the extent to which parental social mobility in childhood can provide additional explanatory power is less well researched. This study compares socially mobile and socially stable families in childhood with static measures of parental socioeconomic status with respect to the children’s life course occupational earnings using data from the 1958 National Child Development Study. In doing so, this study investigates the extent to which the children’s life course occupational earnings are constrained by their parents’ (lack of) social mobility during childhood. Furthermore, the strength of intergenerational socioeconomic transmission may depend on specific socioeconomic characteristics of the parents. Therefore, this study explores the influence of changes in childhood socioeconomic characteristics, namely father’s social class and housing ownership status, both separately and combined for assessing independent effects, on adult life course occupational earnings whilst also controlling for maternal education, sex and region of residence. Using life course step function multilevel models, the results indicate that parental social mobility predicts life course occupational earnings over and above socioeconomic variables measured when the child was 16. Moreover, the father’s social class mobility measure showcases greater intergenerational accumulation of social (dis)advantage. We also demonstrate social mobility during childhood appears to have long lasting and independent effects on life course occupational earnings.

Background Greater cumulative life-course exposure to socioeconomic disadvantage is associated with higher levels of inflammatory biomarkers, C-Reactive protein and fibrinogen which are associated with increased cardiovascular disease risk. However, in most existing studies, researchers often use complete case data for analyses and ignore the impact of missing data on inference for blood based biomarker data. The English Longitudinal Study of Ageing (ELSA) has considerable missing data but the impact of missingness on inference is seldom examined. This paper aims primarily to examine whether the levels of adulthood inflammatory biomarkers of C-Reactive protein and fibrinogen can be explained by life course socioeconomic position. We evaluate the typologies of missing data under Missing Completely at Random and Missing at Random mechanisms and methods for compensating for missing data under these mechanisms. Methods This paper uses cross-sectional data from Wave 2 of ELSA (2004) which includes 9432 men and women aged over 52 living in England. However, only 6000 people had data for C-Reactive protein and fibrinogen. Logistic Regression modelling is implemented to identify predictors of missingness in ELSA health examination and blood collection. We use multiple linear regression modelling for analysing the association between socioeconomic position and C-Reactive protein and fibrinogen after accounting for different mechanisms of non-response using the following methods: complete case analysis (listwise deletion), inverse probability weighting and multiple imputations. Results Participants who refused to respond to the health examination were more likely to be renters, White, single, living in London or with poor assessed health. Those who refused to give a blood sample were more likely to be older female, living East of England or London, had cancer/cardiovascular disease/stroke and poorer self-assessed health. Complete case analysis showed that people with lower education level [0.14(CI0.04–0.25) and working in lower supervisory position [0.17(CI0.04–0.30)] were more likely to have higher C-Reactive protein levels. While people in the highest wealth quintile were less likely to have higher C-Reactive protein [−0.26(CI-0.36,–0.14)] and fibrinogen levels [−0.02(CI-0.04,–0.004)]. These associations remained similar in inverse probability weighting and multiple imputations although there was some variation in the estimates from the different methods for compensating for missing data. Conclusion While associations between socioeconomic position and inflammatory markers were similar across different approaches for compensating for missing data, there were differences in the estimated coefficients suggesting that it is important to account for missing biomarker data for statistical inference.

End-of-life care (EOLC) is a key component in care of older people. However, evidence suggests that the oldest old (85 years) are less likely to access specialist EOLC.The study's objective was to explore experiences of EOLC among the oldest old and determine their reported preference for place of death.The study involved a self-completion postbereavement survey.A census was taken of deaths registered between October 2009 and April 2010 in two health districts, identified from death certificates. Views of Informal Carers-Evalution of Service (VOICES)-Short Form was sent to each informant (n=1422, usually bereaved relative) 6 to 12 months after the death.Of 473 (33%) who responded, 48% of decedents were age 85 or over. There were no age differences in reported care quality in the last three months, but in the last two days the oldest old were reported to receive poorer relief of nonpain symptoms and less emotional and spiritual support. Compared to people under age 85, the over 85s were less likely to be reported to know they were dying, to have a record of their preferences for place of death, to die in their preferred place, to have enough choice about place of death-and more likely to be reported to have had unwanted treatment decisions. Being over 85 years was associated with a reduction in the odds of home death (OR=0.36); failure to ascertain and record preference for place of death contributed to this.Age-associated disparity exists in care provided in the last two days and the realization of preferences.

Background/aim: Health policy places emphasis on enabling patients to die in their place of choice, and increasing the proportion of home deaths. In this article, we seek to explore reported preferences for place of death and experiences of care in a population-based sample of deaths from all causes. Design: Self-completion post-bereavement survey. Setting/Participants: Census of deaths registered in two health districts between October 2009 and April 2010. Views of Informal Carers – Evaluation of Services Short Form was sent to each informant (n = 1422; usually bereaved relative) 6–12 months post-bereavement. Results: Response was 33%. In all, 35.7% of respondents reported that the deceased said where they wanted to die, and 49.3% of these were reported to achieve this. Whilist 73.9% of those who were reported to have a preference cited home as the preferred place, only 13.3% of the sample died at home. Cancer patients were more likely to be reported to achieve preferences than patients with other conditions (p < .01). Being reported to have a record of preferences for place of death increased the likelihood of dying at home (odds ratio = 22.10). When rating care in the last 2 days, respondents were more likely to rate ‘excellent’ or ‘good’ for nursing care (p < .01), relief of pain (p < .01) and other symptoms (p < .01), emotional support (p < .01) and privacy of patient’s environment (p < .01) if their relative died in their preferred place. Conclusions: More work is needed to encourage people to talk about their preferences at the end of life: this should not be restricted to those known to be dying. Increasing knowledge and achievement of preferences for place of death may also improve end-of-life care.

A major challenge faced by basically all institutes that collect statistical data on persons, households or enterprises is that data may be missing in the observed data sets. The most common solution for handling missing data is imputation. Imputation is complicated owing to the existence of constraints in the form of edit restrictions that have to be satisfied by the data. Examples of such edit restrictions are that someone who is less than 16 years old cannot be married in the Netherlands, and that someone whose marital status is unmarried cannot be the spouse of the head of household. Records that do not satisfy these edits are inconsistent, and are hence considered incorrect. A further complication when imputing categorical data is that the frequencies of certain categories are sometimes known from other sources or have previously been estimated. In this article we develop imputation methods for imputing missing values in categorical data that take both the edit restrictions and known frequencies into account.

Summary Non-response is a common source of error in many surveys. Because surveys often are costly instruments, quality-cost trade-offs play a continuing role in the design and analysis of surveys. The advances of telephone, computers, and Internet all had and still have considerable impact on the design of surveys. Recently, a strong focus on methods for survey data collection monitoring and tailoring has emerged as a new paradigm to efficiently reduce non-response error. Paradata and adaptive survey designs are key words in these new developments. Prerequisites to evaluating, comparing, monitoring, and improving quality of survey response are a conceptual framework for representative survey response, indicators to measure deviations thereof, and indicators to identify subpopulationsthatneedincreasedeffort.Inthispaper,wepresentanoverviewofrepresentativeness indicators or R-indicators that are fit for these purposes. We give several examples and provide guidelines for their use in practice.

This paper provides a review of common statistical disclosure control (SDC) methods implemented at statistical agencies for standard tabular outputs containing whole population counts from a census (either enumerated or based on a register). These methods include record swapping on the microdata prior to its tabulation and rounding of entries in the tables after they are produced. The approach for assessing SDC methods is based on a disclosure risk–data utility framework and the need to find a balance between managing disclosure risk while maximizing the amount of information that can be released to users and ensuring high quality outputs. To carry out the analysis, quantitative measures of disclosure risk and data utility are defined and methods compared. Conclusions from the analysis show that record swapping as a sole SDC method leaves high probabilities of disclosure risk. Targeted record swapping lowers the disclosure risk, but there is more distortion of distributions. Small cell adjustments (rounding) give protection to census tables by eliminating small cells but only one set of variables and geographies can be disseminated in order to avoid disclosure by differencing nested tables. Full random rounding offers more protection against disclosure by differencing, but margins are typically rounded separately from the internal cells and tables are not additive. Rounding procedures protect against the perception of disclosure risk compared to record swapping since no small cells appear in the tables. Combining rounding with record swapping raises the level of protection but increases the loss of utility to census tabular outputs. For some statistical analysis, the combination of record swapping and rounding balances to some degree opposing effects that the methods have on the utility of the tables. Resume Cet article propose une revue des methodes de controle de la divulgation statistique (CDS) mises en place par les agences statistiques lors de production de tableaux statistiques derives de donnees des recensements. Ceci inclue des techniques de pre-traitements du type hybridation—echange partiel d'information entre individus—ou des methodes d'arrondis effectuees apres la production des tableaux. L'approche des methodes CDS presentee insiste sur la necessite de trouver un equilibre entre la gestion du risque de divulgation tout en maximisant la quantite d'information qui peut etre fournie aux utilisateurs. Des mesures quantitatives de risques et de degre d'utilite sont proposes et comparees. Les conclusions des analyses montrent que la technique d'hybridation peut conduire a des cas de divulgations pour les tableaux presentant des cellules a faibles effectifs. La meme technique utilisee sur des individus "cibles" diminue le risque mais au detriment des distributions statistiques. La methode de l'arrondi protege les tableaux en eliminant les cellules a faibles effectifs mais un seul type de variables et geographie doivent etre publies pour eviter le risque de divulgation par differenciation quand les tableaux sont lies les uns aux autres. L'arrondi aleatoire donne plus de protection contre le risque par differenciation mais certaines cellules peuvent etre reconstruites par comparaison avec les marges. Les techniques d'arrondis protegent contre la perception du risque mieux que l'hybridation. Combiner hybridation et arrondi augmente le niveau de protection mais augmente la perte de qualite quant a l'utilite des sorties statistiques. Dans certaines analyses statistiques, les deux approches utilisees simultanement peuvent cependant produire un effet equilibre.

Statistical agencies are making increased use of the internet to disseminate census tabular outputs through web-based flexible table-generating servers that allow users to define and generate their own tables. The key questions in the development of these servers are: (1) what data should be used to generate the tables, and (2) what statistical disclosure control (SDC) method should be applied. To generate flexible tables, the server has to be able to measure the disclosure risk in the final output table, apply the SDC method and then iteratively reassess the disclosure risk. SDC methods may be applied either to the underlying data used to generate the tables and/or to the final output table that is generated from original data. Besides assessing disclosure risk, the server should provide a measure of data utility by comparing the perturbed table to the original table. In this article, we examine aspects of the design and development of a flexible table-generating server for census tables and demonstrate a disclosure risk-data utility analysis for comparing SDC methods. We propose measures for disclosure risk and data utility that are based on information theory.

To protect individuals in microdata from the risk of re-identification, a general perturbative method called PRAM (the Post-Randomization Method) is sometimes used for masking records. This method adds “noise” to categorical variables by changing values of categories for a small number of records according to a prescribed probability matrix and a stochastic process based on the outcome of a random multinomial draw. Changing values of categorical variables, however, will cause fully edited and clean records in microdata to start failing edit constraints resulting in data of low utility. In addition, an inconsistent record pinpoints to a potential attacker that the record was perturbed and attempts can be made to unmask the data. Therefore, the perturbation process must take into account micro edit constraints which will ensure that perturbed microdata satisfy all edits. Macro edit constraints which take the form of information loss measures also need to be defined in order to ensure that the overall utility of the data will not be badly compromised given an acceptable level of disclosure risk. This paper will discuss methods for perturbing microdata using PRAM while minimizing micro and macro edit failures. (Updated 10th August 2005)

This paper contains an analytic summary of the content of the session Editing by respondents and data suppliers of the UNECE Work Session on Statistical Data Editing (SDE) held at the National Statistical Institute of Spain, Madrid in October 2003. The aim of the session was to collect information about recent trends in data editing. Statistical agencies are changing their traditional view on editing as a post-data collection phase under the control of survey methodologists. Instead, they are moving some of the editing processes to the respondents and data suppliers to prevent and eliminate errors. The session focused on the integration and optimisation of data editing in the collection phase, and the harmonisation of editing processes performed at both the data capturing and data editing stages by balancing expected levels of quality, time and available resources. The session also considered data editing when administrative or external sources of data are used for statistical purposes. A new "mixed" perspective is emerging where editing and imputation is spread over the entire survey process. In particular, the paper concentrates on the following aspects: evaluating data editing effectiveness when editing is spread over different survey phases; managing survey processes when multiple sources of information (statistical or administative) are used; rationalizing survey processes when different modes of data collection are combined; and improving cooperation with data providers.

Statistical agencies release heath data collected in surveys, censuses and registers. In this chapter, statistical disclosure limitation (SDL) from the perspective of statistical agencies is presented. Traditional outputs in the form of survey microdata and tabular outputs are first presented with respect to quantifying disclosure risk, common SDL techniques for protecting the data, and measuring information loss. In recent years, however, there is greater demand for data including government ‘open data’ initiatives, which have led statistical agencies to examine additional forms of disclosure risks, related to the concept of differential privacy in the computer science literature. A discussion on whether SDL practices carried out at statistical agencies for traditional outputs are differentially private, is provided in the chapter. The chapter concludes with the presentation of some innovative data dissemination strategies that are currently being assessed by statistical agencies, where stricter privacy guarantees are necessary.

A common problem faced by statistical institutes is that data may be missing from collected data sets. The typical way to overcome this problem is to impute the missing data. The problem of imputing missing data is complicated by the fact that statistical data often have to satisfy certain edit rules and that values of variables across units sometimes have to sum up to known totals. For numerical data, edit rules are most often formulated as linear restrictions on the variables. For example, for data on enterprises edit rules could be that the profit and costs of an enterprise should sum up to its turnover and that the turnover should be at least zero. The totals of some variables across units may already be known from administrative data (e.g., turnover from a tax register) or estimated from other sources. Standard imputation methods for numerical data as described in the literature generally do not take such edit rules and totals into account. In this article we describe algorithms for imputing missing numerical data that take edit restrictions into account and ensure that sums are calibrated to known totals. These algorithms are based on a sequential regression approach that uses regression predictions to impute the variables one by one. To assess the performance of the imputation methods, a simulation study is carried out as well as an evaluation study based on a real data set., Comment: Published in at http://dx.doi.org/10.1214/13-AOAS664 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Given a sample from a finite population partitioned into classes, we consider estimating the distribution of the class frequencies. We propose first to estimate certain moments of this distribution, assuming Poisson sampling with unequal inclusion probabilities, and then to adapt these estimates using modelling assumptions. A simulation study illustrates the bias-robustness of the approach to departures from these assumptions.

Nonresponse is a major source of estimation error in sample surveys. The response rate is widely used to measure survey quality associated with nonresponse, but is inadequate as an indicator because of its limited relation with nonresponse bias. Schouten et al. (2009) proposed an alternative indicator, which they refer to as an indicator of representativeness or R-indicator. This indicator measures the variability of the probabilities of response for units in the population. This paper develops methods for the estimation of this R-indicator assuming that values of a set of auxiliary variables are observed for both respondents and nonrespondents. We propose bias adjustments to the point estimator proposed by Schouten et al. (2009) and demonstrate the effectiveness of this adjustment in a simulation study where it is shown that the method is valid, especially for smaller sample sizes. We also propose linearization variance estimators which avoid the need for computer-intensive replication methods and show good coverage in the simulation study even when models are not fully specified. The use of the proposed procedures is also illustrated in an application to two business surveys at Statistics Netherlands.

Disclosure risk occurs when there is a high probability that an intruder can identify an individual in released sample microdata and confidential information may be revealed. For some social surveys, the population from which the sample is drawn is generally not known or only partially known through marginal distributions. The identification is made possible through the use of a key, which is a combination of indirectly identifying variables, such as age, sex, and place of residence. Disclosure risk measures are based on the notion of population uniqueness in the key. In order to quantify the disclosure risk, probabilistic models are defined based on distributional assumptions about the population counts according to the observed sample counts. The parameters for the distribution are estimated through log-linear models. The model selection criteria is based on a ‘minimum error’ test using a forward search algorithm. The methods are expanded to cover the case of complex survey designs and misclassification on the key variables, either arising from the survey process or as a result of perturbative disclosure control techniques that may have been applied to the data. Variance and confidence intervals of estimated disclosure risk measures are also addressed. The methods are demonstrated on real data drawn from extracts of the 2001 UK Census. Possible extensions to the probabilistic modeling are presented based on a local polynomial regression smoothing technique in neighborhoods of the cells of the key.

Government statistical agencies often apply statistical disclosure limitation techniques to survey microdata to protect the confidentiality of respondents. There is a need for valid and practical ways to assess the protection provided. This paper develops some simple methods for disclosure limitation techniques which perturb the values of categorical identifying variables. The methods are applied in numerical experiments based upon census data from the United Kingdom which are subject to two perturbation techniques: data swapping (random and targeted) and the post randomization method. Some simplifying approximations to the measure of risk are found to work well in capturing the impacts of these techniques. These approximations provide simple extensions of existing risk assessment methods based upon Poisson log-linear models. A numerical experiment is also undertaken to assess the impact of multivariate misclassification with an increasing number of identifying variables. It is found that the misclassification dominates the usual monotone increasing relationship between this number and risk so that the risk eventually declines, implying less sensitivity of risk to choice of identifying variables. The methods developed in this paper may also be used to obtain more realistic assessments of risk which take account of the kinds of measurement and other nonsampling errors commonly arising in surveys., Comment: Published in at http://dx.doi.org/10.1214/09-AOAS317 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

This article considers the assessment of the risk of identification of respondents in survey microdata, in the context of applications at the United Kingdom (UK) Office for National Statistics (ONS). The threat comes from the matching of categorical “key“ variables between microdata records and external data sources and from the use of log-linear models to facilitate matching. While the potential use of such statistical models is well established in the literature, little consideration has been given to model specification or to the sensitivity of risk assessment to this specification. In numerical work not reported here, we have found that standard techniques for selecting log-linear models, such as chi-squared goodness-of-fit tests, provide little guidance regarding the accuracy of risk estimation for the very sparse tables generated by typical applications at ONS, for example, tables with millions of cells formed by cross-classifying six key variables, with sample sizes of 10 or 100,000. In this article we develop new criteria for assessing the specification of a log-linear model in relation to the accuracy of risk estimates. We find that, within a class of “reasonable“ models, risk estimates tend to decrease as the complexity of the model increases. We develop criteria that detect “underfitting“ (associated with overestimation of the risk). The criteria may also reveal “overfitting“ (associated with underestimation) although not so clearly, so we suggest employing a forward model selection approach. Our criteria turn out to be related to established methods of testing for overdispersion in Poisson log-linear models. We show how our approach may be used for both file-level and record-level measures of risk. We evaluate the proposed procedures using samples drawn from the 2001 UK Census where the true risks can be determined and show that a forward selection approach leads to good risk estimates. There are several “good“ models between which our approach provides little discrimination. The risk estimates are found to be stable across these models, implying a form of robustness. We also apply our approach to a large survey dataset. There is no indication that increasing the sample size necessarily leads to the selection of a more complex model. The risk estimates for this application display more variation but suggest a suitable upper bound.

When a sample frequency table is published, disclosure risk arises when some individuals can be identified on the basis of their values in certain attributes in the table called key variables, and then their values in other attributes may be inferred, and their privacy is violated. On the basis of the sample to be released, and possibly some partial knowledge of the whole population, an agency which considers releasing the sample, has to estimate the disclosure risk. Risk arises from non-empty sample cells which represent small population cells and from population uniques in particular. Therefore risk estimation requires assessing how many of the relevant population cells are likely to be small. Various methods have been proposed for this task, and we present a method in which estimation of a population cell frequency is based on smoothing using a local neighborhood of this cell, that is, cells having similar or close values in all attributes. We provide some preliminary results and experiments with this method. Comparisons are made to two other methods: 1. a log-linear models approach in which inference on a given cell is based on a ``neighborhood'' of cells determined by the log-linear model. Such neighborhoods have one or some common attributes with the cell in question, but some other attributes may differ significantly. 2 The Argus method in which inference on a given cell is based only on the sample frequency in the specific cell, on the sample design and on some known marginal distributions of the population, without learning from any type of ``neighborhood'' of the given cell, nor from any model which uses the structure of the table., Published at http://dx.doi.org/10.1214/074921707000000120 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)

Background The End of Life Care Strategy highlighted a need to evaluate care experiences by accessing the views of those who use end of life care services. The Strategy identified the Views of Informal Carers – Evaluation of Services (VOICES) questionnaire, which is completed by bereaved relatives, as a potential method of evaluating these experiences. The DH commissioned this study to explore the feasibility of a national VOICES survey. Aims To develop the most appropriate methods for a national end of life care survey by considering recruitment, sampling, online methods, ethics, increasing participation and support for participants. Methods VOICES was re-designed following user/professional discussion groups and analysis of existing VOICES datasets. 1422 deaths registered in two PCTs were identified by the Office of National Statistics using stratified sampling methods. Coroner-registered deaths were excluded. Deaths were assigned to one of two trial groups to determine the most appropriate recruitment approach (opt-in vs opt-out). Online completion was offered to all informants. Local organisations representing minority ethnic groups collaborated in publicising the survey, interpreting services were provided and advertising posters were translated into five languages. A series of support structures was initiated. Results Response rate was 33% and response was significantly higher in the ‘opt-out’ trial group (40%, p Conclusions The opt-out method is the recommended recruitment approach. Experiences of minority groups should be gathered using alternative methods. Coroner-registered deaths should be included and data should be weighted. VOICES detected differences between PCTs. It will be used in 2011/2012 together with these methods in the first national end of life care survey.

Background A key aim of the End of Life Care Strategy is to enable more people to die in their preferred place; this is often their own home. However, hospital remains the most common place of death, particularly for people dying from causes other than cancer. Aims To explore end of life care preferences and determine predictors of achieving preferred place of death, home death and hospital death. Methods The Office for National Statistics drew a proportionally allocated stratified sample of deaths registered in two PCTs between October 2009 and April 2010. Coroner-registered deaths and deaths under the age of 18 years were excluded. The VOICES questionnaire was sent to each informant (n=1422, usually bereaved relative) 6–12 months after the death. As planned, data were weighted to account for response bias. Results 473 returned questionnaires (33%). 35% of respondents reported that the deceased had said where they wanted to die (mainly home 68%). This was apparently recorded for 36% of those expressing a preference. Only 1% was reported to have changed their mind about where they wanted to die. Cancer patients were significantly more likely to die at home than those who died from any other condition (p Conclusions Although, out of all causes, cancer deaths were most likely to occur at home, having a record of preferences was the only independent predictor of achieving preferred place of death.

Background Although in health services survey research we strive for a high response rate, this must be balanced against the need to recruit participants ethically and considerately, particularly in surveys with a sensitive nature. In survey research there are no established recommendations to guide recruitment approach and an ‘opt-in’ system that requires potential participants to request a copy of the questionnaire by returning a reply slip is frequently adopted. However, in observational research the risk to participants is lower than in clinical research and so some surveys have used an ‘opt-out’ system. The effect of this approach on response and distress is unknown. We sought to investigate this in a survey of end of life care completed by bereaved relatives. Methods Out of a sample of 1422 bereaved relatives we assigned potential participants to one of two study groups: an ‘opt in’ group (n=711) where a letter of invitation was issued with a reply slip to request a copy of the questionnaire; or an ‘opt out’ group (n=711) where the survey questionnaire was provided alongside the invitation letter. We assessed response and distress between groups. Results From a sample of 1422, 473 participants returned questionnaires. Response was higher in the ‘opt out’ group than in the ‘opt in’ group (40% compared to 26.4%: χ2 =29.79, p-value2=28.60, p-value Conclusion Given that the ‘opt out’ method of recruitment is associated with a higher response than the ‘opt in’ method, seems to have no impact on complaints or distress about the survey, and there are differences in the patterns of responses between groups, the ‘opt out’ method could be recommended as the most efficient way to recruit into surveys, even in those with a sensitive nature.