5 research outputs found
Risk factors and classification of diabetes in South Africa.
Masters Degree. University of KwaZulu-Natal, Durban.Diabetes prevalence has been seen to be on the increase in recent years, globally and
in South Africa. The number of people with diabetes globally has risen from 108
million in 1980 to 442 million in 2014. It was estimated that, of the 1.8 million people
between 20 and 79 years old with diabetes in South Africa in 2017, 84.8% were undiagnosed.
Diabetes was the 2nd leading underlying cause of death in South Africa in
2016. Identifying risk factors for diabetes will assist in raising public awareness and
assist public authorities to develop prevention programs. This study aimed to investigate
the prevalence and risk factors associated with diabetes in the South African
population aged 15 years and older, as well as explore various statistical methods of
classifying a person’s diabetic status.
This study made use of the South African Demographic Health Survey 2016 data
which involved a two-stage sampling design. The study participants included 6442
individuals aged 15 years and older. Of the individuals sampled, 11%, 67% and 22%
were found to be non-diabetic, pre-diabetic and diabetic, respectively. Classification
methods, namely, a decision tree, random forest and Bayesian neural network, were
used to assess classification of diabetic status based on the risk factors. Of the classification
methods, the Bayesian neural network gave the highest accuracy (75.9%).
These methods however, failed to account for the complex survey design and sampling
weights. In addition, these methods are not able to provide the estimated effect
that a risk factor has on the diabetic status.
Regression models were employed to identify the significant risk factors. Due to
the ordinal nature of diabetic status, initially the proportional odds model was fit.
However, the proportional odds assumption was found to be violated. A multinomial
generalized linear mixed model was fitted to account for the complexity of
the design. However, the model’s residuals were found to be spatially autocorrelated.
Accordingly, a spatial generalized additive mixed model, which accounts for
the complexity of the survey structure as well as incorporates nonlinear spatial effects,
was adopted. The highest accuracy from the regression models considered
was obtained from this adjusted surface correlation model (accuracy = 70.8%). Individuals
of the Black/African race were more likely to be diabetic (OR = 1.429; 95%
CI: 1.032-1.978) than other races. Individuals taking high blood pressure medication
were 1.444 times more likely to be diabetic than pre-diabetic (95% CI: 1.167-1.786)
compared to those not taking high blood pressure medication.Author's note about publications is on page i
Assessment of prevalence and risk factors of diabetes and pre‑diabetes in South Africa
AVAILABILITY OF DATA AND MATERIALS : This study utilized existing survey datasets that are in the public domain and
freely available from https://www.dhsprogram.com/data/dataset_admin/
login_main.cfm with the permission from the DHS Program.BACKGROUND : Diabetes prevalence, as well as that of pre-diabetes, is rapidly increasing in South Africa. Individuals
with pre-diabetes have a high risk of developing type 2 diabetes, which is reversible with a change in lifestyle. If left
untreated, diabetes can lead to serious health complications. Our objective was to assess the prevalence of diabetes
and pre-diabetes, and to investigate the associated risk factors of each in the South African population.
METHOD : This study made use of the South African Demographic Health Survey 2016 data. The study participants
included 6442 individuals aged 15 years and older. A generalized additive mixed model was employed to account for
the complex survey design of the study as well as well spatial autocorrelation in the data.
RESULTS : The observed prevalence of pre-diabetes and diabetes was 67% and 22%, respectively. Among those who
had never been tested for diabetes prior to the survey, 10% of females and 6% of males were found to be diabetic,
and 67% of both males and females were found to be pre-diabetic. Thus, a large proportion of the South African
population remains undiagnosed. The model revealed both common and uncommon factors significantly associated
with pre-diabetes and diabetes. This highlights the importance of considering diabetic status as a three-level
categorical outcome, rather than binary. In addition, significant interactions between some of the lifestyle factors,
demographic factors and anthropometric measures were revealed, which indicates that the effects each these factors
have on the likelihood of an individual being pre-diabetic or diabetic is confounded by other factors.
CONCLUSION : The risk factors for diabetes and pre-diabetes are many and complicated. Individuals need to be aware
of their diabetic status before health complications arise. It is therefore important for all stakeholders in government
and the private sector of South Africa to get involved in providing education and creating awareness about diabetes.
Regular testing of diabetes, as well as leading a healthy lifestyle, should be encouraged.The South African Medical Research Council through its Division of Research Capacity Development under the Biostatistics Capacity Development partnership with the Belgian Development Agency (Enabel) under its framework of Building Academic Partnerships for Economic Development (BAPED).am2023Statistic
Normative Scores for CrossFit<sup>®</sup> Open Workouts: 2011–2022
To create normative scores for all CrossFit® Open (CFO) workouts and compare male and female performances, official scores were collected from the official competition leaderboard for all competitors of the 2011–2022 CFO competitions. Percentiles were calculated for athletes (18–54 years) who completed all workouts within a single year ‘as prescribed’ and met minimum scoring thresholds. Independent t-tests revealed significant (p small to large differences (d = 0.22–0.81), whereas women completed more repetitions in 6 workouts by small to medium differences (d = 0.36–0.77). When workouts were scored by time to completion, men were faster in 10 workouts by small to large differences (d = 0.23–1.12), while women were faster in 3 workouts by small differences (d = 0.46). In three workouts scored by load lifted, men lifted more weight by large differences (d = 2.00–2.98). All other differences were either trivial or not significant. Despite adjusted programming for men and women, the persistence of performance differences across all CFO workouts suggests that resultant challenges are not the same. These normative values may be useful for training and research in male and female CrossFit® athletes
Normative Scores for CrossFit® Open Workouts: 2011–2022
To create normative scores for all CrossFit® Open (CFO) workouts and compare male and female performances, official scores were collected from the official competition leaderboard for all competitors of the 2011–2022 CFO competitions. Percentiles were calculated for athletes (18–54 years) who completed all workouts within a single year ‘as prescribed’ and met minimum scoring thresholds. Independent t-tests revealed significant (p < 0.05) sex differences for 56 of 60 workouts. In workouts scored by repetitions completed, men completed more repetitions in 18 workouts by small to large differences (d = 0.22–0.81), whereas women completed more repetitions in 6 workouts by small to medium differences (d = 0.36–0.77). When workouts were scored by time to completion, men were faster in 10 workouts by small to large differences (d = 0.23–1.12), while women were faster in 3 workouts by small differences (d = 0.46). In three workouts scored by load lifted, men lifted more weight by large differences (d = 2.00–2.98). All other differences were either trivial or not significant. Despite adjusted programming for men and women, the persistence of performance differences across all CFO workouts suggests that resultant challenges are not the same. These normative values may be useful for training and research in male and female CrossFit® athletes
Outcomes and Adverse Effects of Baricitinib Versus Tocilizumab in the Management of Severe COVID-19∗
Objectives: The National Institutes of Health and Infectious Diseases Society of America guidelines recommend tocilizumab or baricitinib in the management of severe COVID-19. Despite clinical trials on the individual agents, there are no large-scale studies comparing the two agents to guide the selection of one versus the other. The purpose of this study was to compare the outcomes and adverse effects of baricitinib versus tocilizumab in the management of severe COVID-19. Design: Retrospective, observational cohort study. Setting: Eleven acute care hospitals in a large health system in Georgia. Patients: Adult patients with severe COVID-19 who received at least one dose of either baricitinib or tocilizumab between June 2021 and October 2021. Interventions: None. Measurements and Main Results: The primary outcome was in-hospital mortality. The key secondary outcome was occurrence rate of adverse effects. A total of 956 patients were identified. The median age was 57 years, and 53% were of male sex. The median body mass index was 33.5, and more than 94% of the population was unvaccinated. Propensity score matching by baseline characteristics resulted in a total of 582 patients, 291 in each group. There was no difference in mortality between the two groups; however, the occurrence rate of adverse effects was significantly higher in the tocilizumab group compared with baricitinib: secondary infections (32% vs 22%; p \u3c 0.01); thrombotic events (24% vs 16%; p \u3c 0.01); and acute liver injury (8% vs 3%; p \u3c 0.01). Conclusions: Our propensity score-matched, retrospective, observational study in patients hospitalized with severe COVID-19 showed no difference in mortality but significantly fewer adverse effects with baricitinib compared with tocilizumab. Our data suggest that baricitinib may be a better choice when treating patients with severe COVID-19, but additional prospective, randomized trials are needed to help clinicians choose the most optimal drug