The social construction of sexually transmitted infections (STIs) in South African communities

Since the medical link between sexually transmitted infections (STIs) and HIV/AIDS was established, there has been an increased focus on the spread of STIs in South Africa. The aim of this study was to provide an in-depth picture of the dynamics involved in sexuality and the spread of STIs and HIV/AIDS. The authors present the findings of a focus group study, which was a part of a larger, national project addressing the broad question of health-care seeking behavior for STIs. A discourse analysis carried out on 10 focus groups reveals complex and rich narratives on the way in which STIs are constructed in South African communities. The dominant discourses focused on the continuing stigmatization of STIs, causal explanations, and prevention strategies. The analysis raises important recommendations for both educational interventions and health services toward the challenge of halting the spread of STIs and HIV/AIDS.

Simulation-selection-extrapolation: Estimation in high-dimensional errors-in-variables models

Errors-in-variables models in high-dimensional settings pose two challenges in application. First, the number of observed covariates is larger than the sample size, while only a small number of covariates are true predictors under an assumption of model sparsity. Second, the presence of measurement error can result in severely biased parameter estimates, and also affects the ability of penalized methods such as the lasso to recover the true sparsity pattern. A new estimation procedure called SIMulation-SELection-EXtrapolation (SIMSELEX) is proposed. This procedure makes double use of lasso methodology. First, the lasso is used to estimate sparse solutions in the simulation step, after which a group lasso is implemented to do variable selection. The SIMSELEX estimator is shown to perform well in variable selection, and has significantly lower estimation error than naive estimators that ignore measurement error. SIMSELEX can be applied in a variety of errors-in-variables settings, including linear models, generalized linear models, and Cox survival models. It is furthermore shown in the Supporting Information how SIMSELEX can be applied to spline-based regression models. A simulation study is conducted to compare the SIMSELEX estimators to existing methods in the linear and logistic model settings, and to evaluate performance compared to naive methods in the Cox and spline models. Finally, the method is used to analyze a microarray dataset that contains gene expression measurements of favorable histology Wilms tumors.

Estimating Model-Based Oral Reading Fluency: A Bayesian Approach

Oral reading fluency (ORF), used by teachers and school districts across the country to screen and progress monitor at-risk readers, has been documented as a good indicator of reading comprehension and overall reading competence. In traditional ORF administration, students are given one minute to read a grade-level passage, after which the assessor calculates the words correct per minute (WCPM) fluency score by subtracting the number of incorrectly read words from the total number of words read aloud. As part of a larger effort to develop an improved ORF assessment system, this study expands on and demonstrates the performance of a new model-based estimate of WCPM based on a recently developed latent-variable psychometric model of speed and accuracy for ORF data. The proposed method was applied to a data set collected from 58 fourth-grade students who read four passages (a total of 260 words). The proposed model-based WCPM scores were also evaluated through a simulation study with respect to sample size and number of passages read.

Density estimation in the presence of heteroscedastic measurement error of unknown type using phase function deconvolution

It is important to properly correct for measurement error when estimating density functions associated with biomedical variables. These estimators that adjust for measurement error are broadly referred to as density deconvolution estimators. While most methods in the literature assume the distribution of the measurement error to be fully known, a recently proposed method based on the empirical phase function (EPF) can deal with the situation when the measurement error distribution is unknown. The EPF density estimator has only been considered in the context of additive and homoscedastic measurement error; however, the measurement error of many biomedical variables is heteroscedastic in nature. In this paper, we developed a phase function approach for density deconvolution when the measurement error has unknown distribution and is heteroscedastic. A weighted EPF (WEPF) is proposed where the weights are used to adjust for heteroscedasticity of measurement error. The asymptotic properties of the WEPF estimator are evaluated. Simulation results show that the weighting can result in large decreases in mean integrated squared error when estimating the phase function. The estimation of the weights from replicate observations is also discussed. Finally, the construction of a deconvolution density estimator using the WEPF is compared with an existing deconvolution estimator that adjusts for heteroscedasticity but assumes the measurement error distribution to be fully known. The WEPF estimator proves to be competitive, especially when considering that it relies on minimal assumption of the distribution of measurement error.

Penalized likelihood methods for modeling count data

The paper considers parameter estimation in count data models using penalized likelihood methods. The motivating data consists of multiple independent count variables with a moderate sample size per variable. The data were collected during the assessment of oral reading fluency (ORF) in school-aged children. A sample of fourth-grade students were given one of ten available passages to read with these differing in length and difficulty. The observed number of words read incorrectly (WRI) is used to measure ORF. Three models are considered for WRI scores, namely the binomial, the zero-inflated binomial, and the beta-binomial. We aim to efficiently estimate passage difficulty, a quantity expressed as a function of the underlying model parameters. Two types of penalty functions are considered for penalized likelihood with respective goals of shrinking parameter estimates closer to zero or closer to one another. A simulation study evaluates the efficacy of the shrinkage estimates using Mean Square Error (MSE) as metric. Big reductions in MSE relative to unpenalized maximum likelihood are observed. The paper concludes with an analysis of the motivating ORF data.

The evaluation of immediate behavioural outcomes of the syndromic case management approach for the treatment of patients with sexually transmitted infections at PHC centres in South Africa: knowledge, attitudes, beliefs and sexual behaviour

This study aimed to determine the immediate behavioural outcomes of the WHO syndromic case management model for STIs in the public health sector in South Africa, on the levels of knowledge, attitudes and beliefs, and behavioural practices (KABPs) concerning STIs. An outcomes evaluation was conducted using KABP methodology. Exit interviews were conducted with 126 STI and non-STI patients at 24 primary health care (PHC) centres in four provinces. Both groups were found to have equally high levels of knowledge about STIs and their attitudes towards and beliefs about STIs were mostly practical and slightly negative, with only promiscuity both stereotyped and stigmatised. However, both groups were found to engage in risky sexual behavioural practices although they also indicated very strong intentions to use condoms in future. Overall, no significant differences were found between the two groups on any of the variables investigated. The implications of these findings for the control and prevention of both classic STIs and HIV/AIDS in South Africa are discussed.

Intramedullary nail fixation versus open reduction and internal fixation for treatment of adult diaphyseal forearm fractures: a systematic review and meta-analysis

BACKGROUND

Diaphyseal radius and ulna fractures require surgical fixation in adults. Open reduction and internal fixation (ORIF) have been considered the gold standard of treatment. The recent development of an interlocking intramedullary nail (IMN) has provided an alternative treatment method for these fractures. The objective of this meta-analysis is to compare the outcomes and complications of IMN versus ORIF for diaphyseal forearm fractures in adults.

METHODS

MEDLINE and Embase were searched from January 1, 2000, through January 7, 2024. All English-language studies were included comparing radiographic and functional outcomes for interlocking IMN fixation and ORIF of diaphyseal forearm fractures in adults (age ≥ 18 years). Study demographics, fracture data, functional outcomes, radiographic outcomes, and complications were extracted. Study quality was determined using the ROBINS-I criteria for cohort studies and the Cochrane risk of bias 2.0 (RoB 2) tool for randomized controlled trials. Meta-analysis of included studies used odds ratios and standardized mean difference when appropriate. Data was analyzed using subgroups of all diaphyseal fractures (including isolated radius or ulna fractures) and those with BBFFs.

RESULTS

Nine studies were included for analysis. There were 42 isolated radius, 80 isolated ulna, and 116 both-bone fractures (BBFF) treated with IMN and 36 radius, 81 ulna, and 116 both-bone fractures treated with ORIF. Compared to ORIF, IMN of diaphyseal forearm fractures appeared to be associated with shorter operative times and a lower overall complication rate. Time-to-union and the rate of nonunion following IMN were similar to ORIF. According to the Grace-Eversmann score, functional outcomes tended to be better following IMN, but DASH scores were similar between fixation strategies.

CONCLUSIONS

Our findings suggest that interlocking IMN can be a safe and effective treatment option for simple and complex diaphyseal forearm fractures in adults. Further high-quality studies are needed to define indications for treating diaphyseal fractures with an interlocking IMN.

LEVEL OF EVIDENCE

Therapeutic Level IV.

Estimation in linear errors-in-variables models with unknown error distribution

Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known or estimable from replicate data. A generalized method of moments approach can be used to estimate model parameters in the absence of knowledge of the error distributions, but it requires the existence of a large number of model moments. In this paper, parameter estimation based on the phase function, a normalized version of the characteristic function, is considered. This approach requires the model covariates to have asymmetric distributions, while the error distributions are symmetric. Parameters are estimated by minimizing a distance function between the empirical phase functions of the noisy covariates and the outcome variable. No knowledge of the measurement error distribution is needed to calculate this estimator. Both asymptotic and finite-sample properties of the estimator are studied. The connection between the phase function approach and method of moments is also discussed. The estimation of standard errors is considered and a modified bootstrap algorithm for fast computation is proposed. The newly proposed estimator is competitive with the generalized method of moments, despite making fewer model assumptions about the moment structure of the measurement error. Finally, the proposed method is applied to a real dataset containing measurements of air pollution levels.

Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process

For the classical, homoscedastic measurement error model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to measurement error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects modeling process, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. This general model includes classical measurement errors, Berkson measurement errors, mixtures of Berkson and classical errors and problems that are not measurement error problems, but also cases where the data-generating process for true exposure is a complex, nonlinear random effects modeling process. The methods are illustrated using the National Institutes of Health-AARP Diet and Health Study where the latent variable is a dietary pattern score called the Healthy Eating Index-2005. We also show how our general model includes methods used in radiation epidemiology as a special case. Simulations are used to illustrate the methods.

Density deconvolution for generalized skew-symmetric distributions

The density deconvolution problem is considered for random variables assumed to belong to the generalized skew-symmetric (GSS) family of distributions. The approach is semiparametric in that the symmetric component of the GSS distribution is assumed known, and the skewing function capturing deviation from the symmetric component is estimated using a deconvolution kernel approach. This requires the specification of a bandwidth parameter. The mean integrated square error (MISE) of the GSS deconvolution estimator is derived, and two bandwidth estimation methods based on approximating the MISE are also proposed. A generalized method of moments approach is also developed for estimation of the underlying GSS location and scale parameters. Simulation study results are presented including a comparing the GSS approach to the nonparametric deconvolution estimator. For most simulation settings considered, the GSS estimator is seen to have performance superior to the nonparametric estimator.

Equating Oral Reading Fluency Scores: A Model-Based Approach

Words read correctly per minute (WCPM) is the reporting score metric in oral reading fluency (ORF) assessments, which is popularly utilized as part of curriculum-based measurements to screen at-risk readers and to monitor progress of students who receive interventions. Just like other types of assessments with multiple forms, equating would be necessary when WCPM scores are obtained from multiple ORF passages to be compared both between and within students. This article proposes a model-based approach for equating WCPM scores. A simulation study was conducted to evaluate the performance of the model-based equating approach along with some observed-score equating methods with external anchor test design.

Incorporating calibration errors in oral reading fluency scoring

Oral reading fluency (ORF) assessments are commonly used to screen at-risk readers and evaluate interventions' effectiveness as curriculum-based measurements. Similar to the standard practice in item response theory (IRT), calibrated passage parameter estimates are currently used as if they were population values in model-based ORF scoring. However, calibration errors that are unaccounted for may bias ORF score estimates and, in particular, lead to underestimated standard errors (SEs) of ORF scores. Therefore, we consider an approach that incorporates the calibration errors in latent variable scores. We further derive the SEs of ORF scores based on the delta method to incorporate the calibration uncertainty. We conduct a simulation study to evaluate the recovery of point estimates and SEs of latent variable scores and ORF scores in various simulated conditions. Results suggest that ignoring calibration errors leads to underestimated latent variable score SEs and ORF score SEs, especially when the calibration sample is small.

Vaccination in the control of bluetongue in endemic regions: the South African experience

The eradication of bluetongue virus (BTV) from endemic regions of Africa is virtually impossible due to the role played by the widely distributed Culicoides spp. of midge vectors and the ubiquitous distribution of reservoir species. In endemic areas attempts can only be made to limit the occurrence of bluetongue (BT) disease and its economic impact through vaccination. Despite several potential problems (teratogenicity, risk of reassortment and reversion to virulence of the attenuated viral strains), epidemiological and recent molecular data support the fact that the live attenuated vaccine that has been used for decades in enzootic regions, provides a safe and efficacious means to control the disease in regions of southern Africa, as well as other areas of the world.

Incidence of venous thromboembolism in fracture below the knee with and without chemical thromboprophylaxis: a systematic review and meta-analysis

INTRODUCTION

Low rates of venous thromboembolism (VTE) have been found in patients with isolated orthopaedic trauma below the knee. Many surgeons routinely provide chemical thromboprophylaxis in these injuries, however. This is not without inherent risks, and this remains a controversial topic in perioperative care in orthopaedic trauma. This systematic review and meta-analysis was performed to look at rates of VTE in patients with isolated orthopaedic fractures below the knee, grouped by whether they received chemical prophylaxis versus no chemical prophylaxis.

METHODS

A systematic review was performed comparing VTE with and without chemical thromboprophylaxis following isolated orthopaedic fracture below the knee. A chi-square analysis was then performed on data including patients who received chemical prophylaxis versus those who did not from all 25 included articles. The articles were grouped according to type of study, such as observational versus randomized controlled trial (RCT), and then further subdivided according to surgical intervention status, and whether routine screening for thromboembolism was utilized to diagnose. Risk of bias assessment was performed using the ROBINS-I criteria for cohort studies and the Cochrane RoB 2 tool for randomized controlled trials. A random effects pooled logistic regression and Fisher's exact tests were then performed.

RESULTS

222,188 patients were found from 25 articles. Chemical prophylaxis was given to 8,666 patients, and VTE was reported in 347 cases (4.0%). 213,522 patients did not receive chemical prophylaxis, and VTE was reported in 2,185 (1.02%) (χ2 (1, n = 222,188) = 656.8, p < .00001). Pooled logistic regression revealed that patients receiving prophylaxis were 0.5 times less likely to develop VTE. With a calculated population baseline risk of 1.5% for developing VTE, the number needed to treat (NNT) with chemical prophylaxis is 134 to prevent 1 VTE after fracture below the knee.

CONCLUSIONS

In patients with isolated orthopaedic trauma below the knee, indiscriminate use of chemical VTE prophylaxis is not recommended due to the lack of significant benefit and high NNT.

LEVEL OF EVIDENCE

Level III.