Tobacco use in South Africans during 1998: The first demographic and health survey

Objective To determine smoking patterns in South Africa, and to identify groups requiring culturally appropriate smoking cessation programmes. Methods A random sample of 13 826 people (415 years), was interviewed to identify tobacco use patterns and respiratory symptoms. Peak expiratory flow rates were measured. Multinomial regression analyses identified sociodemographic factors related to tobacco use, and the latter’s association with respiratory conditions. Results In 1998, 24.6% adults (44.2% of males and 11.0% of females) smoked regularly. Coloured women had a higher rate (39%) than African women (5.4%). About 24% of the regular smokers had attempted to quit, with only 9.9% succeeding. African women (13.2%) used smokeless tobacco more frequently than others. Of the nonsmokers 28% and 19% were exposed to environmental tobacco smoke in their homes and workplaces, respectively. The regression analysis showed that the demographic characteristics of light smokers (1–14 tobacco equivalents per day) and heavy smokers (>=15 tobacco equivalents per day) differed. Light smoking occurred significantly more frequently in the poorest, least educated and urban people. The relative risk for light smoking was 18 in Coloured women compared with African women. Heavy smoking occurred most frequently in the highest educated group. A dose–response was observed between the amount smoked and the presence of respiratory diseases. Conclusions Smoking in South Africa is decreasing and should continue with the recently passed tobacco control legislation. Culturally appropriate tobacco cessation programmes for the identified target groups need to be developed.

Demographics and discharge outcomes of dysvascular and non-vascular lower limb amputees at a subacute rehabilitation unit: A 7-year series

Objective To examine personal and social demographics, and rehabilitation discharge outcomes of dysvascular and non-vascular lower limb amputees. Methods In total, 425 lower limb amputation inpatient rehabilitation admissions (335 individuals) from 2005 to 2011 were examined. Admission and discharge descriptive statistics (frequency, percentages) were calculated and compared by aetiology. Results Participants were male (74%), aged 65 years (s.d. 14), born in Australia (72%), had predominantly dysvascular aetiology (80%) and a median length of stay 48 days (interquartile range (IQR): 25–76). Following amputation, 56% received prostheses for mobility, 21% (n = 89) changed residence and 28% (n = 116) required community services. Dysvascular amputees were older (mean 67 years, s.d. 12 vs 54 years, s.d. 16; P < 0.001) and recorded lower functional independence measure – motor scores at admission (z = 3.61, P < 0.001) and discharge (z = 4.52, P < 0.001). More nonvascular amputees worked before amputation (43% vs 11%; P < 0.001), were prescribed a prosthesis by discharge (73% vs 52%; P < 0.001) and had a shorter length of stay (7 days, 95% confidence interval: –3 to 17), although this was not statistically significant. Conclusions Differences exist in social and demographic outcomes between dysvascular and non-vascular lower limb amputees.

Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics

Context Cancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report ten unrelieved and co-occurring symptoms. Objectives To determine if subgroups of oncology outpatients receiving active treatment (n=582) could be identified based on their distinct experience with thirteen commonly occurring symptoms; to determine whether these subgroups differed on select demographic, and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes. Methods Demographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis (LCA) was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale (MSAS). Results Four distinct latent classes were identified (i.e., All Low (28.0%), Moderate Physical and Lower Psych (26.3%), Moderate Physical and Higher Psych (25.4%), All High (20.3%)). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the All High class had the worst QOL scores. Conclusion Findings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients are risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.

Summarising and reporting sugarcane locomotive run statistics from TOTools and GPS data

The majority of sugar mill locomotives are equipped with GPS devices from which locomotive position data is stored. Locomotive run information (e.g. start times, run destinations and activities) is electronically stored in software called TOTools. The latest software development allows TOTools to interpret historical GPS information by combining this data with run information recorded in TOTools and geographic information from a GIS application called MapInfo. As a result, TOTools is capable of summarising run activity details such as run start and finish times and shunt activities with great accuracy. This paper presents 15 reports developed to summarise run activities and speed information. The reports will be of use pre-season to assist in developing the next year's schedule and for determining priorities for investment in the track infrastructure. They will also be of benefit during the season to closely monitor locomotive run performance against the existing schedule.

How do experts think about statistics? Hints for improving undergraduate and postgraduate training

Experts are increasingly being called upon to quantify their knowledge, particularly in situations where data is not yet available or of limited relevance. In many cases this involves asking experts to estimate probabilities. For example experts, in ecology or related fields, might be called upon to estimate probabilities of incidence or abundance of species, and how they relate to environmental factors. Although many ecologists undergo some training in statistics at undergraduate and postgraduate levels, this does not necessarily focus on interpretations of probabilities. More accurate elicitation can be obtained by training experts prior to elicitation, and if necessary tailoring elicitation to address the expert’s strengths and weaknesses. Here we address the first step of diagnosing conceptual understanding of probabilities. We refer to the psychological literature which identifies several common biases or fallacies that arise during elicitation. These form the basis for developing a diagnostic questionnaire, as a tool for supporting accurate elicitation, particularly when several experts or elicitors are involved. We report on a qualitative assessment of results from a pilot of this questionnaire. These results raise several implications for training experts, not only prior to elicitation, but more strategically by targeting them whilst still undergraduate or postgraduate students.

Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis

Background The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. Methods Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. Results In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996–2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001–2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III. Conclusions Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.

Toward the multilevel older person's transportation and road safety model: A new perspective on the role of demographic, functional, and psychosocial factors

Objectives Self-regulation refers to the practice of using self-imposed restrictions to protect oneself from situations that are, or are perceived to be, unsafe. Within the driving context, self-regulation refers the compensatory practices that some older adults adopt to restrict their driving to situations in which they feel safe. However, the way in which demographic, functional, and psychosocial factors, and the interactions between these factors, influence older adults’ driving self-regulation is not well understood. Improving this understanding could lead to new ways of considering the mobility concerns faced by older drivers. Method A systematic review of the current literature was conducted to explore this issue. Twenty-nine empirical studies investigating the factors associated with older adults’ self-regulatory driving behaviors were examined. Results The review findings were used to construct the Multilevel Older Persons Transportation and Road Safety (MOTRS) model. The MOTRS model proposes that individual and environmental factors such as age, gender, and the availability of alternative transportation predict older adults’ practice of driving-related self-regulation. However, these variables influence self-regulation through psychosocial variables such as driving confidence, affective attitude, and instrumental attitude toward driving. Discussions The MOTRS model extends previous attempts to model older adults’ driving by focusing on a novel target, driving self-regulation, and by including a wider range of predictors identified on the basis of the systematic literature review. This focus enables consideration of broader mobility issues and may inform new strategies to support the mobility of older adults.

A two-arm cluster randomized control trial to determine the effectiveness of a pressure ulcer prevention bundle for critically ill patients

Purpose This study tested the effectiveness of a pressure ulcer (PU) prevention bundle in reducing the incidence of PUs in critically ill patients in two Saudi intensive care units (ICUs). Design A two-arm cluster randomized experimental control trial. Methods Participants in the intervention group received the PU prevention bundle, while the control group received standard skin care as per the local ICU policies. Data collected included demographic variables (age, diagnosis, comorbidities, admission trajectory, length of stay) and clinical variables (Braden Scale score, severity of organ function score, mechanical ventilation, PU presence, and staging). All patients were followed every two days from admission through to discharge, death, or up to a maximum of 28 days. Data were analyzed with descriptive correlation statistics, Kaplan-Meier survival analysis, and Poisson regression. Findings The total number of participants recruited was 140: 70 control participants (with a total of 728 days of observation) and 70 intervention participants (784 days of observation). PU cumulative incidence was significantly lower in the intervention group (7.14%) compared to the control group (32.86%). Poisson regression revealed the likelihood of PU development was 70% lower in the intervention group. The intervention group had significantly less Stage I (p = 002) and Stage II PU development (p = 026). Conclusions Significant improvements were observed in PU-related outcomes with the implementation of the PU prevention bundle in the ICU; PU incidence, severity, and total number of PUs per patient were reduced. Clinical Relevance Utilizing a bundle approach and standardized nursing language through skin assessment and translation of the knowledge to practice has the potential to impact positively on the quality of care and patient outcome.

The modified card sorting test: Test-retest stability and relationships with demographic variables in a healthy older adult sample

Objectives. To investigate the test-retest stability of a standardized version of Nelson's (1976) Modified Card Sorting Test (MCST) and its relationships with demographic variables in a sample of healthy older adults. Design. A standard card order and administration were devised for the MCST and administered to participants at an initial assessment, and again at a second session conducted a minimum of six months later in order to examine its test-retest stability. Participants were also administered the WAIS-R at initial assessment in order to provide a measure of psychometric intelligence. Methods. Thirty-six (24 female, 12 male) healthy older adults aged 52 to 77 years with mean education 12.42 years (SD = 3.53) completed the MCST on two occasions approximately 7.5 months (SD = 1.61) apart. Stability coefficients and test-retest differences were calculated for the range of scores. The effect of gender on MCST performance was examined. Correlations between MCST scores and age, education and WAIS-R IQs were also determined. Results. Stability coefficients ranged from .26 for the percent perseverative errors measure to .49 for the failure to maintain set measure. Several measures were significantly correlated with age, education and WAIS-R IQs, although no effect of gender on MCST performance was found. Conclusions. None of the stability coefficients reached the level required for clinical decision making. The results indicate that participants' age, education, and intelligence need to be considered when interpreting MCST performance. Normative studies of MCST performance as well as further studies with patients with executive dysfunction are needed.

Practice patterns of radiation therapy technology in Australia: Results of a national audit

This article presents the results of a single-day census of radiation therapy (RT) treatment and technology use in Australia. The primary aim of the study was to ascertain patterns of RT practice and technology in use across Australia. These data were primarily collated to inform curriculum development of academic programs, thereby ensuring that training is matched to workforce patterns of practice. Methods: The study design was a census method with all 59 RT centres in Australia being invited to provide quantitative summary data relating to patient case mix and technology use on a randomly selected but common date. Anonymous and demographic-free data were analysed using descriptive statistics. Results: Overall data were provided across all six Australian States by 29 centres of a possible 59, yielding a response rate of 49% and representing a total of 2743 patients. Findings from this study indicate the increasing use of emerging intensity-modulated radiotherapy (IMRT), image fusion and image-guided radiation therapy (IGRT) technology in Australian RT planning and delivery phases. IMRT in particular was used for 37% of patients, indicating a high uptake of the technology in Australia when compared to other published data. The results also highlight the resource-intensive nature of benign tumour radiotherapy. Conclusions: In the absence of routine national data collection, the single-day census method offers a relatively convenient means of measuring and tracking RT resource utilisation. Wider use of this tool has the potential to not only track trends in technology implementation but also inform evidence-based guidelines for referral and resource planning.

Introducing the practice of statistics: are we environmentally friendly?

The practice of statistics is the focus of the world in which professional statisticians live. To understand meaningfully what this practice is about, students need to engage in it themselves. Acknowledging the limitations of a genuine classroom setting, this study attempted to expose four classes of year 5 students (n=91) to an authentic experience of the practice of statistics. Setting an overall context of people’s habits that are considered environmentally friendly, the students sampled their class and set criteria for being environmentally friendly based on questions from the Australian Bureau of Statistics CensusAtSchool site. They then analysed the data and made decisions, acknowledging their degree of certainty, about three populations based on their criteria: their class, year 5 students in their school and year 5 students in Australia. The next step was to collect a random sample the size of their class from an Australian Bureau of Statistics ‘population’, analyse it and again make a decision about Australian year 5 students. At the end, they suggested what further research they might do. The analysis of students’ responses gives insight into primary students’ capacity to appreciate and understand decision making, and to participate in the practice of statistics, a topic that has received very little attention in the literature. Based on the total possible score of 23 from student workbook entries, 80 % of students achieved at least a score of 11.

Signs of driver sleepiness and risky sleepy driving behaviours: the associations with demographic, work and sleep-related factors

Driving while sleepy is regarded as a substantial crash risk factor. Reducing the risk of sleep-related crashes predominately rests with the driver’s awareness of experiencing signs that are common when sleepy; such as yawning, frequent eye blinks, and difficulty keeping eyes open. However the relationship between the signs of sleepiness and risky sleepy driving behaviours is largely unknown. The current study sought to examine the relationships between drivers’ experiences of the signs of sleepiness, risky sleepy driving behaviours, and the associations with demographic, work and sleep-related factors. In total 1,608 participants completed a questionnaire administered via a telephone interview that assessed their experiences and behaviours of driving while sleepy. The results revealed a number of demographic, work and sleep-related factors were associated with experiencing signs of sleepiness when driving. Signs of sleepiness were also found to mediate the relationship between continuing to drive while sleepy and having a sleep-related close call event. A subgroup analysis based on age (under 30 and 30 years or older) found younger drivers were more likely to continue to drive when sleepy despite experiencing more signs of sleepiness. The results suggest participants had considerable experience with the signs of sleepiness and driving while sleepy. Actions to be taken from this research include informing the content of driver education campaigns regarding the importance of the signs of sleepiness. Working together to educate all drivers about the dangerousness of driving when experiencing signs of sleepiness is an important road safety outcome.

Disease mapping using Bayesian hierarchical models

description and analysis of geographically indexed health data with respect to demographic, environmental, behavioural, socioeconomic, genetic, and infectious risk factors (Elliott andWartenberg 2004). Disease maps can be useful for estimating relative risk; ecological analyses, incorporating area and/or individual-level covariates; or cluster analyses (Lawson 2009). As aggregated data are often more readily available, one common method of mapping disease is to aggregate the counts of disease at some geographical areal level, and present them as choropleth maps (Devesa et al. 1999; Population Health Division 2006). Therefore, this chapter will focus exclusively on methods appropriate for areal data...

Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.