Delia North

Factors affecting first-month adherence to antiretroviral therapy among HIV-positive adults in South Africa.

This study explores the influence of baseline factors on first-month adherence to highly active antiretroviral therapy (HAART) among adults. The study design involved a review of routinely collected patient information in the CAPRISA AIDS Treatment (CAT) programme, at a rural and an urban clinic in KwaZulu-Natal Province, South Africa. The records of 688 patients enrolled in the CAT programme between June 2004 and September 2006 were analysed. Adherence was calculated from pharmacy records (pill counts) and patients were considered adherent if they had taken at least 95% of their prescribed drugs. Logistic regression was used to analyse the data and account for confounding factors. During the first month of therapy, 79% of the patients were adherent to HAART. HAART adherence was negatively associated with a higher baseline CD4 count. Women had better adherence if they attended voluntarily testing and counselling or if they had taken an HIV test because they were unwell, while men had higher adherence if they were tested due to perceived risk of HIV infection. HAART adherence was positively associated with higher age among patients who possessed cell phones and among patients who provided a source of income in the urban setting, but not in the rural setting. Though long-term data from this cohort is required to fully evaluate the impact of non-adherence in the first month of treatment, this study identifies specific groups of patients at higher risk for whom adherence counselling should be targeted and tailored. For example, first-month HAART adherence can be improved by targeting patients initiated on treatment with a high CD4 count.

Determinants of optimal adherence over time to antiretroviral therapy amongst HIV positive adults in South Africa: A longitudinal study

Highly active antiretroviral therapy (HAART) requires strict adherence to achieve optimal clinical and survival benefits. A study was done to explore the factors affecting HAART adherence among HIV positive adults by reviewing routinely collected patient information in the Centre for the AIDS Programme of Research in South Africa’s (CAPRISA) AIDS Treatment Programme. Records of 688 patients enrolled between 2004 and 2006 were analysed. Patients were considered adherent if they had taken at least 95% of their prescribed drugs. Generalized estimating equations were used to analyse the data. The results showed that HAART adherence increased over time, however, the rate of increase differed by some of the socio-demographic and behavioural characteristics of the patients. For instance, HAART adherence increased in both urban and rural treatment sites over time, but the rate of increase was higher in the rural site. This helped identify sub-populations, such as the urban population, that required ongoing adherence counseling.

The application of subset correspondence analysis to address the problem of missing data in a study on asthma severity in childhood

Non-response in cross-sectional data is not uncommon and requires careful handling during the analysis stage so as not to bias results. In this paper, we illustrate how subset correspondence analysis can be applied in order to manage the non-response while at the same time retaining all observed data. This variant of correspondence analysis was applied to a set of epidemiological data in which relationships between numerous environmental, genetic, behavioural and socio-economic factors and their association with asthma severity in children were explored. The application of subset correspondence analysis revealed interesting associations between the measured variables that otherwise may not have been exposed. Many of the associations found confirm established theories found in literature regarding factors that exacerbate childhood asthma. Moderate to severe asthma was found to be associated with needing neonatal care, male children, 8- to 9-year olds, exposure to tobacco smoke in vehicles and living in areas that suffer from extreme air pollution. Associations were found between mild persistent asthma and low birthweight, and being exposed to smoke in the home and living in a home with up to four people. The classification of probable asthma was associated with a group of variables that indicate low socio-economic status.

Model development including interactions with multiple imputed data

BackgroundMultiple imputation is a reliable tool to deal with missing data and is becoming increasingly popular in biostatistics. However, building a model with interactions that are not specified a priori, in the presence of missing data, presents a challenge. On the one hand, the interactions are needed to impute the data, while on the other hand, the data is needed to identify the interactions. The objective of this study was to present a way in which this challenge can be addressed.MethodsThis paper investigates two strategies in which model development with interactions is achieved using a single data set generated from the Expectation Maximization (EM) algorithm. Imputation using both the fully conditional specification approach and the multivariate normal approach is carried out and results are compared. The strategies are illustrated with data from a study of ambient pollution and childhood asthma in Durban, South Africa.ResultsThe different approaches to model building and imputation yielded similar results despite the data being mainly categorical. Both strategies investigated for building the model using the multivariate normal imputed data resulted in the identical set of variables and interactions being identified; while models built using data imputed by fully conditional specification were marginally different for the two strategies. It was found that, for both imputation approaches, model building with backward elimination applied to the initial EM data set was easier to implement, and produced good results, compared to those from a complete case analysis.ConclusionsDeveloping a predictive model including interactions with data that suffers from missingness is easily done by identifying significant interactions and then applying backward elimination to a single data set imputed from the EM algorithm. It is hoped that this idea can be further developed and, by addressing this practical dilemma, there will be increased adoption of multiple imputation in medical research when data suffers from missingness.

The Role of Statistical Offices and Associations in Supporting the Teaching of Statistics at School Level

In many countries mathematics curricula for primary and secondary schools have been reformed to include statistics. At the same time, national statistics offices have recognised that statistics, if taught meaningfully at school-level, would promote statistical literacy and lead to a better understanding of national statistics office activities, such as census. A number of national statistics offices and statistical associations have thus embarked on projects that develop materials for use in the classroom and/or assist school teachers to engage more meaningfully with the statistics content of the school syllabus. This chapter gives a few specific examples of the roles that national statistics offices and statistical associations around the world are playing in supporting the teaching of statistics at school-level.

Remainder modified systematic sampling in the presence of linear trend

ABSTRACT If the population size is not a multiple of the sample size, then the usual linear systematic sampling design is unattractive, since the sample size obtained will either vary, or be constant and different to the required sample size. Only a few modified systematic sampling designs are known to deal with this problem and in the presence of linear trend, most of these designs do not provide favorable results. In this paper, a modified systematic sampling design, known as remainder modified systematic sampling (RMSS), is introduced. There are seven cases of RMSS and the results in this paper suggest that the proposed design is favorable, regardless of each case, while providing linear trend-free sampling results for three of the seven cases. To obtain linear trend-free sampling for the other cases and thus improve results, an end corrections estimator is constructed.

Exploring Teachers’ Descriptions of ‘Ways of Working with the Curriculum’ in Teaching Mathematics and Statistics

What teachers teach, how they teach it and when they teach it should be guided by the curriculum. This paper focuses on teachers’ reports about how they integrate the curriculum documentation in teaching mathematics and statistics concepts. Questionnaires containing both Likert scale and closed and open-ended questions were administered to 75 mathematics teachers who were teaching from Grade 4 to Grade 12 in schools in KwaZulu–Natal province in South Africa. Teachers were requested to indicate their level of the use of national curriculum statement. The findings revealed that they had little idea about how they could integrate the curriculum documentation in mathematics teaching and learning. It is encouraging that some teachers perceived links within the vertical as well as across the horizontal curriculum, both important drivers of quality education. However, it is a concern that most teachers were unable to perceive ways of working within and across the curriculum, suggesting that professional development interventions need to consider ways in which this can be addressed. A further concern is that only 60% of teachers indicated that they used the National Curriculum Statements Grades R–12. Teachers’ propensity to use the curriculum was moderated by the factors of age and participation in professional development activities.

The effect of the mechanism and amount of missingness on subset correspondence analysis

ABSTRACT The application of subset correspondence analysis is a relatively new technique to deal with the analysis of categorical data with missingness. A simulation study is used to test the effects of Little and Rubins missingness mechanisms, as well as missingness up to 50% on subset correspondence analysis. Missingness was simulated across 18 different scenarios and each scenario was repeated 10 times, with outcomes averaged across the 10 simulations. In this application, it was found that while missingness in excess of 30% has some effect on certain outcomes, there is no evidence to suggest that the missingness mechanism significantly affects results.

Multiple-start balanced modified systematic sampling in the presence of linear trend

ABSTRACT In this paper, we propose a sampling design termed as multiple-start balanced modified systematic sampling (MBMSS), which involves the supplementation of two or more balanced modified systematic samples, thus permitting us to obtain an unbiased estimate of the associated sampling variance. There are five cases for this design and in the presence of linear trend only one of these cases is optimal. To further improve results for the other cases, we propose an estimator that removes linear trend by applying weights to the first and last sampling units of the selected balanced modified systematic samples and is thus termed as the MBMSS with end corrections (MBMSSEC) estimator. By assuming a linear trend model averaged over a super-population model, we will compare the expected mean square errors (MSEs) of the proposed sample means, to that of simple random sampling (SRS), linear systematic sampling (LSS), stratified random sampling (STR), multiple-start linear systematic sampling (MLSS), and other modified MLSS estimators. As a result, MBMSS is optimal for one of the five possible cases, while the MBMSSEC estimator is preferred for three of the other four cases.

Transforming Statistics Education in South Africa

Challenges faced by Statistics Education in developing countries are similar, though often of larger magnitude, and of a more critical nature, than in more developed countries. This chapter focuses on the status of Statistics Education at school and tertiary level in South Africa. The authors give a historical overview, followed by a discussion of the current status, mentioning challenges and successes to building statistics capacity in the country, and emphasizing the importance of facing realities. Finally, mention is made of a few key projects, aimed at statistics capacity building, which have the potential to change the face of Statistics Education in a country grappling with legacies of the past, whilst balancing risks and opportunities of the future.