M. Shafiqur Rahman

A systematic review and meta-analysis of genotype–phenotype associations in patients with hypertrophic cardiomyopathy caused by sarcomeric protein mutations

Background The genetic basis of familial hypertrophic cardiomyopathy (HCM) is well described, but the relation between genotype and clinical phenotype is still poorly characterised. Objective To summarise and critically review the current literature on genotype–phenotype associations in patients with HCM and to perform a meta-analysis on selected clinical features. Data sources PubMed/Medline was searched up to January 2013. Retrieved articles were checked for additional publications. Selection criteria Observational, cross-sectional and prospectively designed English language human studies that analysed the relationship between the presence of mutations in sarcomeric protein genes and clinical parameters. Data extraction and analysis The pooled analysis was confined to studies reporting on cohorts of unrelated and consecutive patients in which at least two sarcomere genes were sequenced. A random effect meta-regression model was used to determine the overall prevalence of predefined clinical features: age at presentation, gender, family history of HCM, family history of sudden cardiac death (SCD), and maximum left ventricular wall thickness (MLVWT). The I2 statistic was used to estimate the proportion of total variability in the prevalence data attributable to the heterogeneity between studies. Results Eighteen publications (corresponding to a total of 2459 patients) were selected for the pooled analysis. The presence of any sarcomere gene mutation was associated with a younger age at presentation (38.4 vs 46.0 years, p<0.0005), a family history of HCM (50.6% vs 23.1%, p<0.0005), a family history of SCD (27.0% vs 14.9%, p<0.0005) and greater MLVWT (21.0 vs 19.3 mm, p=0.03). There were no differences when the two most frequently affected genes, MYBPC3 and MYH7, were compared. A total of 53 family studies were also included in the review. These were characterised by pronounced variability and the majority of studies reporting on outcomes analysed small cross-sectional cohorts and were unsuitable for pooled analyses. Conclusions The presence of a mutation in any sarcomere gene is associated with a number of clinical features. The heterogeneous nature of the disease and the inconsistency of study design precludes the establishment of more precise genotype–phenotype relationships. Large scale studies examining the relation between genotype, disease severity, and prognosis are required.

Prospects of mHealth services in Bangladesh: recent evidence from Chakaria.

Introduction Bangladesh has a serious shortage of qualified health workforce. The limited numbers of trained service providers are based in urban areas, which limits access to quality healthcare for the rural population. mHealth provides a new opportunity to ensure access to quality services to the population. A recent review suggested that there are 19 mHealth initiatives in the country. This paper reports findings on peoples knowledge, perception, use, cost and compliance with advice received from mHealth services from a study carried out during 2012–13 in Chakaria, a rural sub-district in Bangladesh. Methods A total of 4,915 randomly-chosen respondents aged 18 years and above were interviewed. Results Household ownership of mobile phones in the study area has increased from 2% in 2004 to 81% in 2012; 45% of the respondents reported that they had mobile phones. Thirty-one percent of the respondents were aware of the use of mobile phones for healthcare. Very few people were aware of the available mHealth services. Males, younger age group, better educated, and those from richer households were more knowledgeable about the existing mHealth services. Among the respondents who sought healthcare in the preceding two weeks of the survey, only 2% used mobile phones for healthcare. Adherence to the advice from the healthcare providers in terms of purchasing and taking the drugs was somewhat similar between the patients who used mobile phone for consultation versus making a physical visit. Conclusions The high penetration of mobile phones into the society provides a unique opportunity to use the mHealth technology for consulting healthcare providers. Although knowledge of the existence of mHealth services was low, it was encouraging that the compliance with the prescriptions was almost similar for advice received through mobile phone and physical visits. The study revealed clear indications that society is looking forward to embracing the mHealth technology.

Association of Low-Birth Weight with Malnutrition in Children under Five Years in Bangladesh: Do Mother's Education, Socio-Economic Status, and Birth Interval Matter?

Background Malnutrition in children under five years remains a significant problem in Bangladesh, despite substantial socio-economic progress and a decade of interventions aimed at improving it. Although several studies have been conducted to identify the important risk factors of malnutrition, none of them assess the role of low birth weight (LBW) despite its high prevalence (36%). This study examines the association between LBW and malnutrition using data from the Bangladesh Demographic and Health Survey (BDHS) 2011 and provides practical guidelines for improving nutritional status of children. Methods Malnutrition in children is measured in terms of their height-for-age, weight-for-height, and weight-for-age. Children whose Z-scores for either of these indices are below two standard deviations (–2SD) from median of WHO’s reference population are considered as stunted, wasted or underweight, respectively. The association between malnutrition and LBW was investigated by calculating adjusted risk-ratio (RR), which controls for potential confounders such as child’s age and sex, mother’s education and height, length of preceding-birth-interval, access to food, area of residence, household socio-economic status. Adjusted RR was calculated using both Cochran-Mantel-Haenszel approach and multivariable logistic regression models controlling for confounder. Results The prevalence of malnutrition was markedly higher in children with LBW than those with normal birth-weights (stunting: 51% vs 39%; wasting: 25% vs 14% and underweight: 52% vs 33%). While controlling for the known risk factors, children with LBW had significantly increased risk of becoming malnourished compared to their counter part with RR 1.23 (95% CI:1.16–1.30), 1.71 (95% CI:1.53–1.92) and 1.47 (95% CI: 1.38–1.56) for stunting, wasting and underweight, respectively. The observed associations were not modified by factors known to reduce the prevalence of malnutrition, such as higher education of mother, better household socio-economic conditions and longer birth-interval. Conclusions Higher education of mother, better household socio-economic conditions and prolonged birth intervals alone are not sufficient in bringing about substantial reductions in prevalence of child malnutrition in Bangladesh. Targeted interventions should be designed to reduce prevalence of LBW in addition to improving mother’s education and other socio-demographic conditions.

Performance of Firth-and logF -type penalized methods in risk prediction for small or sparse binary data

BackgroundWhen developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commonly even if sample size is large but there is sufficient number of strong predictors. In the presence of separation, even if one develops the model, it produces overfitted model with poor predictive performance. Firth-and logF-type penalized regression methods are popular alternative to MLE, particularly for solving separation-problem. Despite the attractive advantages, their use in risk prediction is very limited. This paper evaluated these methods in risk prediction in comparison with MLE and other commonly used penalized methods such as ridge.MethodsThe predictive performance of the methods was evaluated through assessing calibration, discrimination and overall predictive performance using an extensive simulation study. Further an illustration of the methods were provided using a real data example with low prevalence of outcome.ResultsThe MLE showed poor performance in risk prediction in small or sparse data sets. All penalized methods offered some improvements in calibration, discrimination and overall predictive performance. Although the Firth-and logF-type methods showed almost equal amount of improvement, Firth-type penalization produces some bias in the average predicted probability, and the amount of bias is even larger than that produced by MLE. Of the logF(1,1) and logF(2,2) penalization, logF(2,2) provides slight bias in the estimate of regression coefficient of binary predictor and logF(1,1) performed better in all aspects. Similarly, ridge performed well in discrimination and overall predictive performance but it often produces underfitted model and has high rate of convergence failure (even the rate is higher than that for MLE), probably due to the separation problem.ConclusionsThe logF-type penalized method, particularly logF(1,1) could be used in practice when developing risk model for small or sparse data sets.

Prevalence, pattern and sociodemographic differentials in smokeless tobacco consumption in Bangladesh: evidence from a population-based cross-sectional study in Chakaria

Background The health hazards associated with the use of smokeless tobacco (SLT) are similar to those of smoking. However, unlike smoking, limited initiatives have been taken to control the use of SLT, despite its widespread use in South and Southeast Asian countries including Bangladesh. It is therefore important to examine the prevalence of SLT use and its social determinants for designing appropriate strategies and programmes to control its use. Objective To investigate the use of SLT in terms of prevalence, pattern and sociodemographic differentials in a rural area of Bangladesh. Design Population-based cross-sectional household survey. Setting and participants A total of 6178 individuals aged ≥13 years from 1753 households under the Chakaria HDSS area were interviewed during October–November 2011. Methods The current use of SLT, namely sadapatha (dried tobacco leaves) and zarda (industrially processed leaves), was used as the outcome variable. The crude and net associations between the sociodemographic characteristics of respondents and the outcome variables were examined using cross-tabular and multivariable logistic regression analysis, respectively. Results 23% of the total respondents (men: 27.0%, women: 19.3%) used any form of SLT. Of the respondents, 10.4% used only sadapatha,13.6% used only zarda and 2.2% used both. SLT use was significantly higher among men, older people, illiterate, ever married, day labourers and relatively poorer respondents. The odds of being a sadapatha user were 3.5-fold greater for women than for men and the odds of being a zarda user were 3.6-fold greater for men than for women. Conclusions The prevalence of SLT use was high in the study area and was higher among socioeconomically disadvantaged groups. The limitation of the existing regulatory measures for controlling the use of non-industrial SLT products should be understood and discussion for developing new strategies should be a priority.

Review and evaluation of performance measures for survival prediction models in external validation settings

BackgroundWhen developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice.MethodsAn extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software.ResultsMost of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell’s concordance measure which tended to increase as censoring increased.ConclusionsWe recommend that Uno’s concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller’s measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston’s D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive accuracy curves. In addition, we recommend to investigate the characteristics of the validation data such as the level of censoring and the distribution of the prognostic index derived in the validation setting before choosing the performance measures.

A bivariate Bernoulli model for analyzing malnutrition data

Multivariate binary responses from the same subject are usually correlated. For example, malnutrition of children are usually measured using ‘stunting’ (low height-for-age) and ‘wasting’ (low weight-for-age) calculated from their height, weight and age, and hence the status of being stunted may depend on the status of being wasted and vice-versa. For analyzing such malnutrition data, one needs special statistical models allowing for dependence between the responses to avoid misleading inference. The problem of dependence in multivariate binary responses is generally addressed by using marginal models with generalized estimating equation. However, using the marginal models alone, it is difficult to specify the measures of dependence between the responses precisely. Islam et al. (J Appl Stat 40(5):1064–1075, 2013) proposed a joint modeling approach for bivariate binary responses using both the conditional and marginal models where the dependence between the responses can be measured and tested using a link function of the models. However, the author didn’t examine the properties of the regression coefficient except for the dependence parameter. This paper has given further insight into the joint model and investigated the properties of regression coefficients using an extensive simulation study. The simulation results showed that the maximum likelihood estimators (MLEs) of the regression coefficients of the joint model showed well performance in terms of bias, mean squared error and coverage probability particularly when sample size large. Generally speaking, the MLEs of the parameters associated with joint models possessed the same asymptotic properties as the MLEs of those associated with standard generalized linear models, except for the interpretations. Further the paper provided an application of joint model for analyzing malnutrition data from Bangladesh demographic and health survey 2011. The results revealed that the estimates of the both marginal and condition regression coefficients of the joint model have meaningful interpretation and explanation, which will in turn help the policy makers for designing appropriate policies for improving nutrition status.

Knowledge, attitudes and intention regarding mHealth in generation Y: evidence from a population based cross sectional study in Chakaria, Bangladesh

Background and objectives mHealth offers a new opportunity to ensure access to qualified healthcare providers. Therefore, to better understand its potential in Bangladesh, it is important to understand how young people use mobile phones for healthcare. Here we examine the knowledge, attitudes and intentions to use mHealth services among young population. Design Population based cross sectional household survey. Setting and participants A total of 4909 respondents, aged 18 years and above, under the Chakaria Health and Demographic Surveillance System (HDSS) area, were interviewed during the period November 2012 to April 2013. Methods Participants younger than 30 years of age were defined as young (or generation Y). To examine the level of knowledge about and intention towards mHealth services in generation Y compared with their older counterparts, the percentage of the respective outcome measure from a 2×2 contingency table and adjusted odds ratio (aOR), which controls for potential confounders such as mobile ownership, sex, education, occupation and socioeconomic status, were estimated. The aOR was estimated using both the Cochran–Mantel–Haenszel approach and multivariable logistic regression models controlling for confounders. Results Generation Y had significantly greater access to mobile phones (50%vs40%) and better knowledge about its use for healthcare (37.8%vs27.5%;aOR 1.6 (95% CI1.3 to 2.0)). Furthermore, the level of knowledge about two existing mHealth services in generation Y was significantly higher compared with their older counterparts, with aOR values of 3.2 (95% CI 2.6 to 5.5) and 1.5 (95% CI 1.1 to 1.8), respectively. Similarly, generation Y showed significantly greater intention towards future use of mHealth services compared with their older counterparts (aOR 1.3 (95% CI 1.1 to 1.4)). The observed associations were not modified by sociodemographic factors. Conclusion There is a greater potential for mHealth services in the future among young people compared with older age groups. However, given the low overall use of mHealth, appropriate policy measures need to be formulated to enhance availability, access, utilisation and effectiveness of mHealth services.