Prevalence of Low Physical Activity and its Relation to Social Environment in Deprived Areas in the London Borough of Redbridge

Achieving adequate levels of physical activity (PA) is important to maintain health and prevent chronic disease. The costs of inadequate physical activity to the NHS have been estimated at over a billion pounds annually. While socio-demographic characteristics such as age, sex and ethnicity have been reported to be associated with different levels of PA, there is evidence that people??s social environments may also be important in encouraging a physically active lifestyle. The objective of this paper is to describe associations between the social environment and physical activity among the poorer communities in an outer London Borough, when other socio-demographic factors are controlled for. A household survey was carried out in six poorer neighbourhoods in Redbridge in 2008 as part of a wider health needs assessment. The questionnaire included questions allowing estimation of levels of physical activity as well as extent of social environment [social network score (SNS) and social support score (SSS)]. A random sample of households was taken and a total of 799 completed responses from over 16 year old were obtained. The association between physical activity and the social environment controlling for socio-demographic characteristics was estimated using a logistic nested model. Higher SNS was significantly associated with younger age, Black ethnicity, higher education level, higher household income and higher levels of PA in bivariate analyses. Higher SSS was positively associated with Indian ethnicity, higher household income and area of residence. In multivariate analyses higher levels of PA were significantly associated with wider social networks and stronger social support, educational level and marital status. Despite its limitations, our findings confirm that the relationship between low physical activity and weak social networks and low social support, observed in general population studies, also occurs in deprived communities in London. The relationship merits further exploration given the limited evidence on the effectiveness of interventions to promote physical activity.     

Determinants of Income Generation and Financial Performance Through Self-Help Groups

ABSTRACT

This study examines the efficacy of a microcredit-linked self-help group (SHG) program in identifying the problems faced by group members such as income generation and financial performance. To examine this, 120 members in each of three selected blocks in Birbhum District in West Bengal, India, were invited to participate. A multiple regression equation focused on identifying the contributing factors toward explaining SHG income. Results indicated that income generation for all the blocks together was significantly influenced by factors like loan amount, amount of saving, years of existence of SHG, education level of the group leader, and availability of the training facility. However, SHG-wise efficiency scores varied across the blocks that might be related to different sociocultural dimensions. Implications of the analytical findings for future research are discussed at the end of the article.     

Pseudo-empirical Bayes estimation of small area means under a nested error linear regression model with functional measurement errors

Small area estimation is studied under a nested error linear regression model with area level covariate subject to measurement error. Ghosh and Sinha (2007) obtained a pseudo-Bayes (PB) predictor of a small area mean and a corresponding pseudo-empirical Bayes (PEB) predictor, using the sample means of the observed covariate values to estimate the true covariate values. In this paper, we first derive an efficient PB predictor by using all the available data to estimate true covariate values. We then obtain a corresponding PEB predictor and show that it is asymptotically “optimal”. In addition, we employ a jackknife method to estimate the mean squared prediction error (MSPE) of the PEB predictor. Finally, we report the results of a simulation study on the performance of our PEB predictor and associated jackknife MSPE estimator. Our results show that the proposed PEB predictor can lead to significant gain in efficiency over the previously proposed PEB predictor. Area level models are also studied.     

The Impact of Worker and Establishment‐level Characteristics on Male–Female Wage Differentials: Evidence from Danish Matched Employee–Employer Data

Abstract. This paper uses matched employer–employee data from Denmark to examine how gender segregation at the level of occupation, industry, establishment, and job‐cell impacts the gender wage differential of full‐time, private‐sector salaried and manual workers. Wage effects of gender segregation at the above four levels are estimated through fixed effects or through controls for the proportion females within these structures. We find that occupation has a much larger role than industry or establishment in accounting for the gender gap for salaried but not manual workers, and that for both groups there is a significant within‐job‐cell gender wage differential.     

First-order random coefficient integer-valued autoregressive processes

A first-order random coefficient integer-valued autoregressive (RCINAR(1)) model is introduced. Ergodicity of the process is established. Moments and autocovariance functions are obtained. Conditional least squares and quasi-likelihood estimators of the model parameters are derived and their asymptotic properties are established. The performance of these estimators is compared with the maximum likelihood estimator via simulation.     

Bayesian Estimators for Small Area Models when Auxiliary Information is Measured with Error

Small area estimators in linear models are typically expressed as a convex combination of direct estimators and synthetic estimators from a suitable model. When auxiliary information used in the model is measured with error, a new estimator, accounting for the measurement error in the covariates, has been proposed in the literature. Recently, for area‐level model, Ybarra & Lohr (Biometrika, 95, 2008, 919) suggested a suitable modification to the estimates of small area means based on Fay & Herriot (J. Am. Stat. Assoc., 74, 1979, 269) model where some of the covariates are measured with error. They used a frequentist approach based on the method of moments. Adopting a Bayesian approach, we propose to rewrite the measurement error model as a hierarchical model; we use improper non‐informative priors on the model parameters and show, under a mild condition, that the joint posterior distribution is proper and the marginal posterior distributions of the model parameters have finite variances. We conduct a simulation study exploring different scenarios. The Bayesian predictors we propose show smaller empirical mean squared errors than the frequentist predictors of Ybarra & Lohr (Biometrika, 95, 2008, 919), and they seem also to be more stable in terms of variability and bias. We apply the proposed methodology to two real examples.     

Testing equality of survival distributions when the population marks are missing

This paper introduces a nonparametric approach for testing the equality of two or more survival distributions based on right censored failure times with missing population marks for the censored observations. The standard log-rank test is not applicable here because the population membership information is not available for the right censored individuals. We propose to use the imputed population marks for the censored observations leading to fractional at-risk sets that can be used in a two sample censored data log-rank test. We demonstrate with a simple example that there could be a gain in power by imputing population marks (the proposed method) for the right censored individuals compared to simply removing them (which also would maintain the right size). Performance of the imputed log-rank tests obtained this way is studied through simulation. We also obtain an asymptotic linear representation of our test statistic. Our testing methodology is illustrated using a real data set.