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Background: Large numbers of young people die yearly due to homicide, but little is known about homicide during adolescence. Research primarily focuses on youth violence among young men and masks important gender-related factors inherent in the adolescent age group. Although young women are less likely to be victims of homicide, violence against women is an important form of violence experienced during adolescence. In this paper, we describe the prevalence of and gender difference in adolescent homicide in South Africa in 2009. Methods: We conducted a retrospective national mortuary-based study to identify all child homicides (boys and girls < 18 years) in 2009 in a proportionate sample of mortuaries. Victim, perpetrator and crime data were collected in three phases: cases were identified from the mortuary register, the autopsy report and from police interviews. In this paper we focus on the adolescent group, aged 10 to 17 years. Findings: We identified 674 (95% CI: 520–823) adolescent homicides for 2009, with more male (520) than female (154) homicides. This gender disparity increased as children aged, with the male homicide rate 27.9/100 000 population (95% CI: 20.3–35.5), nearly 5 times the female rate (4.5:1) of 6.5/ 100 000 population (95% CI: 4.7–8.2) in older adolescents (15–17 year old). Adolescent males were significantly more likely (61.2%) to die in a public space compared to female adolescents (39.3%) but more adolescent females (48.4%) died at home compared to adolescent males (32.9%). Adolescent females (42.1%) were mainly killed by a family member or intimate partner while adolescent males were predominantly (58%) killed by an acquaintance. Conclusion: We found a distinct gender pattern for adolescent homicide in South Africa. This pattern appears to be driven by gender norms that support violence. South Africa requires an investment in developing evidence informed interventions to reduce violence. [ABSTRACT FROM AUTHOR]

Background: We describe CD4 count recovery among HIV positive individuals who initiated antiretroviral therapy (ART) with and without severe immune suppression using complete laboratory data from South Africa’s national HIV treatment programme between 2010 and 2014 and discuss implications for CD4 count monitoring. Methods: Retrospective analysis of routinely collected laboratory data from South Africa’s National Health Laboratory Service (NHLS). A probabilistic record linkage algorithm was used to create a cohort of HIV positive individuals who initiated ART between 2010 and 2014 based on timing of CD4 count and viral load measurements. A CD4 count < 50 copies/μl at ART initiation was considered severe immunosuppression. A multivariable piecewise mixed-effects linear regression model adjusting for age, gender, year of starting ART, viral suppression in follow up and province was used to predict CD4 counts during follow up. Results: 1,070,900 individuals had evidence of starting ART during 2010–2014 and met the criteria for inclusion in the cohort -46.6% starting ART with CD4 < 200 cells/μl and 10.1% with CD4 < 50 cells/ μl. For individuals with CD4 counts < 200 cells/μl, predicted CD4 counts > 200 cells/μl, >350 cells/μl and >500 cells/μl corresponded with mean follow up durations of 1.5 years (standard deviation [s.d] 1.1), 1.9years (s.d 1.2) and 2.1 years (s.d 1.3 years). For those with CD4 counts < 50 cells/μl, predicted CD4 count above these threshold corresponded with mean follow up durations of 2.5 years (s.d 0.9 years), 4.4 years (s.d 0.4 years) and 5.0 years (s.d 0.1years) for recovery to the same thresholds. CD4 count recovery varied mostly with duration on ART, CD4 count at the start of ART and gender. Conclusion: For individuals starting with ART with severe immunosuppression, CD4 recovery to 200cells/μl did not occur or took longer than 12 month for significant proportions. CD4 monitoring and interventions recommended for advanced HIV disease should continue until full recovery. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis (TB) incidence in South Africa is among the highest globally. Initial loss to follow-up (ILFU), defined as not starting on TB treatment within 28 days of testing positive, is undermining control efforts. We assessed the feasibility, acceptability, and potential of a mHealth application to reduce ILFU. Methods: An mHealth application was developed to capture patients TB investigation data, provide results and monitor treatment initiation. This was implemented in two primary health clinics (PHC) in inner-city Johannesburg. Feasibility was assessed by comparing documentation of personal details, specimen results for same individuals during implementation period (paper register and Mhealth application). Effectiveness was assessed by comparing proportion of patients with results within 48 hours, and proportion started on treatment within 28 days of testing TB positive during pre- implementation (paper register) and implementation (mHealth application) periods. In-depth interviews with patients and providers were conducted to assess acceptability of application. Results: Pre-implementation, 457 patients were recorded in paper registers [195 (42.7%) male, median age 34 years (interquartile range IQR (28–40), 45 (10.5%) sputum Xpert positive]. During implementation, 319 patients were recorded in paper register and the mHealth application [131 (41.1%) male, median age 32 years (IQR 27–38), 33 (10.3%) sputum Xpert positive]. The proportion with complete personal details: [mHealth 95.0% versus paper register 94.0%, (p = 0.54)] and proportion with documented results: [mHealth 97.4% versus paper register 97.8%, (p = 0.79)] were not different in the two methods. The proportion of results available within 48 hours: [mHealth 96.8% versus paper register 68.6%), (p <0.001)], and the proportion on treatment within 28 days [mHealth 28/33 (84.8%) versus paper register 30/44 (68.2%), (p = 0.08)] increased during implementation but was not statistically significant. In-depth interviews showed that providers easily integrated the mHealth application into routine TB investigation and patients positively received the delivery of results via text message. Time from sputum collection to TB treatment initiation decreased from 4 days (pre-implementation) to 3 days but was not statistically significant. Conclusions: We demonstrated that implementation of the mHealth application was feasible, acceptable to health care providers and patients, and has potential to reduce the time to TB treatment initiation and ILFU in PHC settings. [ABSTRACT FROM AUTHOR]

Background: Systematic reviews have described high rates of attrition in patients with HIV receiving antiretroviral therapy (ART). However, migration and clinical transfer may lead to an overestimation of attrition (death and loss to follow-up). Using a newly linked national laboratory database in South Africa, we assessed national retention in South Africa's national HIV program.Methods and Findings: Patients receiving care in South Africa's national HIV program are monitored through regular CD4 count and viral load testing. South Africa's National Health Laboratory Service has maintained a database of all public-sector CD4 count and viral load results since 2004. We linked individual laboratory results to patients using probabilistic matching techniques, creating a national HIV cohort. Validation of our approach in comparison to a manually matched dataset showed 9.0% undermatching and 9.5% overmatching. We analyzed data on patients initiating ART in the public sector from April 1, 2004, to December 31, 2006, when ART initiation could be determined based on first viral load among those whose treatment followed guidelines. Attrition occurred on the date of a patient's last observed laboratory measure, allowing patients to exit and reenter care prior to that date. All patients had 6 potential years of follow-up, with an additional 2 years to have a final laboratory measurement to be retained at 6 years. Data were censored at December 31, 2012. We assessed (a) national retention including all laboratory tests regardless of testing facility and (b) initiating facility retention, where laboratory tests at other facilities were ignored. We followed 55,836 patients initiating ART between 2004 and 2006. At ART initiation, median age was 36 years (IQR: 30-43), median CD4 count was 150 cells/mm3 (IQR: 81-230), and 66.7% were female. Six-year initiating clinic retention was 29.1% (95% CI: 28.7%-29.5%). After allowing for transfers, national 6-year retention was 63.3% (95% CI: 62.9%-63.7%). Results differed little when tightening or relaxing matching procedures. We found strong differences in retention by province, ranging from 74.2% (95% CI: 73.2%-75.2%) in Western Cape to 52.2% (95% CI: 50.6%-53.7%) in Mpumalanga at 6 years. National attrition was higher among patients initiating at lower CD4 counts and higher viral loads, and among patients initiating ART at larger facilities. The study's main limitation is lack of perfect cohort matching, which may lead to over- or underestimation of retention. We also did not have data from KwaZulu-Natal province prior to 2010.Conclusions: In this study, HIV care retention was substantially higher when viewed from a national perspective than from a facility perspective. Our results suggest that traditional clinical cohorts underestimate retention. [ABSTRACT FROM AUTHOR]

Introduction: Cryptococcal meningitis (CM) is an opportunistic fungal disease with a high mortality among HIV-positive patients with severe immunosuppression (CD4 count <100 cells/μl). Reflexed screening for cryptococcal antigen (CrAg) in remnant blood samples was initially piloted at selected CD4 testing laboratories of the National Health Laboratory Service (NHLS) prior to the implementation of a national screening programme using a lateral flow assay (LFA) (IMMY, Norman, OK, USA). The aim of this study was to assess CrAg positivity nationally, per province and district in combination with the percentage of CD4 samples tested with a CD4 count <100 cells/μl to identify areas with advanced HIV/CrAg disease burden. Methods: CrAg and CD4 laboratory result data were extracted from the NHLS corporate data warehouse. Monthly test volumes were used to assess CrAg test volumes and coverage, while bubble charts were used to display the relationship between CD4 <100 cells/μl, CrAg positivity and number of positive CrAg samples by district. ArcGIS software was used to spatially report CrAg positivity. Results: CrAg screening coverage was stable at around 96% after November 2016. Samples with a CD4 <100 cell/μl and CrAg positivity were also stable over the study period at 10% and ~5% respectively. The highest CrAg positivity was reported for the Kwa-Zulu Natal province (7.3%), which also had the lowest percentage of samples with a CD4 <100 cells/μl (7.2%). Uthungulu and Umkhanyakude districts had the highest CrAg positivity (9.3% and 8.9% respectively). Ethekwini and Johannesburg Metro districts contributed to 22% of the total number of CrAg-positive samples tested across South Africa for the period reported. Conclusion: Existing CD4 testing services were used to rapidly scale up CrAg reflex testing in South Africa. Districts with advanced HIV and CrAg disease burden were identified that need further investigation of patient management interventions. [ABSTRACT FROM AUTHOR]

Background: Samples submitted for HIV PCR testing that fail to yield a positive or negative result represent missed diagnostic opportunities. We describe HIV PCR test rejections and indeterminate results, and the associated delay in diagnosis, within South Africa’s early infant diagnosis (EID) program from 2010 to 2015. Methods: HIV PCR test data from January 2010 to December 2015 were extracted from the National Health Laboratory Service Corporate Data Warehouse, a central data repository of all registered test-sets within the public health sector in South Africa, by laboratory number, result, date, facility, and testing laboratory. Samples that failed to yield either a positive or negative result were categorized according to the rejection code on the laboratory information system, and descriptive analysis performed using Microsoft Excel. Delay in diagnosis was calculated for patients who had a missed diagnostic opportunity registered between January 2013 and December 2015 by means of a patient linking-algorithm employing demographic details. Results: Between 2010 and 2015, 2 178 582 samples were registered for HIV PCR testing of which 6.2% (n = 134 339) failed to yield either a positive or negative result, decreasing proportionally from 7.0% (n = 20 556) in 2010 to 4.4% (n = 21 388) in 2015 (p<0.001). Amongst 76 972 coded missed diagnostic opportunities, 49 585 (64.4%) were a result of pre-analytical error and 27 387 (35.6%) analytical error. Amongst 49 694 patients searched for follow-up results, 16 895 (34.0%) had at least one subsequent HIV PCR test registered after a median of 29 days (IQR: 13–57), of which 8.4% tested positive compared with 3.6% of all samples submitted for the same period. Conclusions: Routine laboratory data provides the opportunity for near real-time surveillance and quality improvement within the EID program. Delay in diagnosis and wastage of resources associated with missed diagnostic opportunities must be addressed and infants actively followed-up as South Africa works towards elimination of mother-to-child transmission. [ABSTRACT FROM AUTHOR]

Background: South Africa has a large burden of rifampicin-resistant tuberculosis (RR-TB), with 18,734 patients diagnosed in 2014. The number of diagnosed patients has increased substantially with the introduction of the Xpert MTB/RIF test, used for tuberculosis (TB) diagnosis for all patients with presumptive TB. Routine aggregate data suggest a large treatment gap (pre-treatment loss to follow-up) between the numbers of patients with laboratory-confirmed RR-TB and those reported to have started second-line treatment. We aimed to assess the impact of Xpert MTB/RIF implementation on the delay to treatment initiation and loss to follow-up before second-line treatment for RR-TB across South Africa.Methods and Findings: A nationwide retrospective cohort study was conducted to assess second-line treatment initiation and treatment delay among laboratory-diagnosed RR-TB patients. Cohorts, including approximately 300 sequentially diagnosed RR-TB patients per South African province, were drawn from the years 2011 and 2013, i.e., before and after Xpert implementation. Patients with prior laboratory RR-TB diagnoses within 6 mo and currently treated patients were excluded. Treatment initiation was determined through data linkage with national and local treatment registers, medical record review, interviews with health care staff, and direct contact with patients or household members. Additional laboratory data were used to track cases. National estimates of the percentage of patients who initiated treatment and time to treatment were weighted to account for the sampling design. There were 2,508 and 2,528 eligible patients in the 2011 and 2013 cohorts, respectively; 92% were newly diagnosed with RR-TB (no prior RR-TB diagnoses). Nationally, among the 2,340 and 2,311 new RR-TB patients in the 2011 and 2013 cohorts, 55% (95% CI 53%-57%) and 63% (95% CI 61%-65%), respectively, started treatment within 6 mo of laboratory receipt of their diagnostic specimen (p < 0.001). However, in 2013, there was no difference in the percentage of patients who initiated treatment at 6 mo between the 1,368 new RR-TB patients diagnosed by Xpert (62%, 95% CI 59%-65%) and the 943 diagnosed by other methods (64%, 95% CI 61%-67%) (p = 0.39). The median time to treatment decreased from 44 d (interquartile range [IQR] 20-69) in 2011 to 22 d (IQR 2-43) in 2013 (p < 0.001). In 2013, across the nine provinces, there were substantial variations in both treatment initiation (range 51%-73% by 6 mo) and median time to treatment (range 15-36 d, n = 1,450), and only 53% of the 1,448 new RR-TB patients who received treatment were recorded in the national RR-TB register. This retrospective study is limited by the lack of information to assess reasons for non-initiation of treatment, particularly pre-treatment mortality data. Other limitations include the use of names and dates of birth to locate patient-level data, potentially resulting in missed treatment initiation among some patients.Conclusions: In 2013, there was a large treatment gap for RR-TB in South Africa that varied significantly across provinces. Xpert implementation, while reducing treatment delay, had not contributed substantially to reducing the treatment gap in 2013. However, given improved case detection with Xpert, a larger proportion of RR-TB patients overall have received treatment, with reduced delays. Nonetheless, strategies to further improve linkage to treatment for all diagnosed RR-TB patients are urgently required. [ABSTRACT FROM AUTHOR]

Introduction: Meningitis is a major cause of mortality in southern Africa. We aimed to describe the aetiologies and frequencies of laboratory-confirmed fungal and bacterial meningitis among adults in a South African province with an 11% HIV prevalence, over 4 years. Methods: We conducted a retrospective, observational study of secondary laboratory data, extracted on all cerebrospinal fluid (CSF) specimens submitted to public-sector laboratories in Gauteng province from 2009 through 2012. We calculated cause-specific incidence rates in the general and HIV-infected populations and used Poisson regression to determine if trends were significant. Results: We identified 11,891 (10.7%) incident cases of meningitis from 110,885 CSF specimens. Cryptococcal meningitis, tuberculous meningitis and pneumococcal meningitis accounted for 62.3% (n = 7,406), 24.6% (n = 2,928) and 10.1% (n = 1,197) of cases over the four-year period. The overall incidence (cases per 100,000 persons) of cryptococcal meningitis declined by 23% from 24.4 in 2009 to 18.7 in 2012 (p <0.001) and decreased by 19% among HIV-infected persons from 178.2 to 144.7 (p <0.001). Tuberculous meningitis decreased by 40% from 11.3 in 2009 to 6.8 in 2012 (p <0.001) and decreased by 36% among HIV-infected persons from 54.4 to 34.9 (p <0.001). Pneumococcal meningitis decreased by 41% from 4.2 in 2009 to 2.5 in 2012 (p <0.001) and decreased by 38% among HIV-infected persons from 28.0 to 17.5 (p <0.001). Among cases of other bacterial meningitis (248/11,891, 2.1%), Neisseria meningitidis (n = 93), Escherichia coli (n = 72) and Haemophilus influenzae (n = 20) were the most common organisms identified. Conclusions: In this high HIV-prevalence province, cryptococcal meningitis was the leading cause of laboratory-confirmed meningitis among adults. Over a 4-year period, there was a significant decrease in incidence of cryptococcal, tuberculous and pneumococcal meningitis. This coincided with expansion of the national antiretroviral treatment programme, enhanced tuberculosis control programme and routine childhood immunisation with pneumococcal conjugate vaccines. [ABSTRACT FROM AUTHOR]

Rapid diagnostic tools have been shown to improve linkage of patients to care. In the context of infectious diseases, assessing the impact and cost-effectiveness of such tools at the population level, accounting for both direct and indirect effects, is key to informing adoption of these tools. Point-of-care (POC) CD4 testing has been shown to be highly effective in increasing the proportion of HIV positive patients who initiate ART. We assess the impact and cost-effectiveness of introducing POC CD4 testing at the population level in South Africa in a range of care contexts, using a dynamic compartmental model of HIV transmission, calibrated to the South African HIV epidemic. We performed a meta-analysis to quantify the differences between POC and laboratory CD4 testing on the proportion linking to care following CD4 testing. Cumulative infections averted and incremental cost-effectiveness ratios (ICERs) were estimated over one and three years. We estimated that POC CD4 testing introduced in the current South African care context can prevent 1.7% (95% CI: 0.4% - 4.3%) of new HIV infections over 1 year. In that context, POC CD4 testing was cost-effective 99.8% of the time after 1 year with a median estimated ICER of US$4,468/DALY averted. In healthcare contexts with expanded HIV testing and improved retention in care, POC CD4 testing only became cost-effective after 3 years. The results were similar when, in addition, ART was offered irrespective of CD4 count, and CD4 testing was used for clinical assessment. Our findings suggest that even if ART is expanded to all HIV positive individuals and HIV testing efforts are increased in the near future, POC CD4 testing is a cost-effective tool, even within a short time horizon. Our study also illustrates the importance of evaluating the potential impact of such diagnostic technologies at the population level, so that indirect benefits and costs can be incorporated into estimations of cost-effectiveness. [ABSTRACT FROM AUTHOR]

Background: The BD-FACSPresto™ CD4 is a new, point-of-care (POC) instrument utilising finger-stick capillary blood sampling. This study evaluated its performance against predicate CD4 testing in South Africa. Methods: Phase-I testing: HIV+ patient samples (n = 214) were analysed on the Presto™ under ideal laboratory conditions using venous blood. During Phase-II, 135 patients were capillary-bled for CD4 testing on FACSPresto™, performed according to manufacturer instruction. Comparative statistical analyses against predicate PLG/CD4 method and industry standards were done using GraphPad Prism 6. It included Bland-Altman with 95% limits of agreement (LOA) and percentage similarity with coefficient of variation (%CV) analyses for absolute CD4 count (cells/μl) and CD4 percentage of lymphocytes (CD4%). Results: In Phase-I, 179/217 samples yielded reportable results with Presto™ using venous blood filled cartridges. Compared to predicate, a mean bias of 40.4±45.8 (LOA of -49.2 to 130.2) and %similarity (%CV) of 106.1%±7.75 (7.3%) was noted for CD4 absolute counts. In Phase-2 field study, 118/135 capillary-bled Presto™ samples resulted CD4 parameters. Compared to predicate, a mean bias of 50.2±92.8 (LOA of -131.7 to 232) with %similarity (%CV) 105%±10.8 (10.3%), and 2.87±2.7 (LOA of -8.2 to 2.5) with similarity of 94.7±6.5% (6.83%) noted for absolute CD4 and CD4% respectively. No significant clinical differences were indicated for either parameter using two sampling methods. Conclusion: The Presto™ produced remarkable precision to predicate methods, irrespective of venous or capillary blood sampling. A consistent, clinically insignificant over-estimation (5–7%) of counts against PLG/CD4 and equivalency to FACSCount™ was noted. Further field studies are awaited to confirm longer-term use. [ABSTRACT FROM AUTHOR]

Data on neonatal group B streptococcal (GBS) invasive disease burden are needed to refine prevention policies. Differences in surveillance methods and investigating for cases can lead to varying disease burden estimates. We compared the findings of laboratory-based passive surveillance for GBS disease across South Africa, and for one of the provinces compared this to a real-time, systematic, clinical surveillance in a population-defined region in Johannesburg, Soweto. Passive surveillance identified a total of 799 early-onset disease (EOD, <7 days age) and 818 LOD (late onset disease, 7–89 days age) cases nationwide. The passive surveillance provincial incidence varied for EOD (range 0.00 to 1.23/1000 live births), and was 0.03 to 1.04/1000 live births for LOD. The passive surveillance rates for Soweto, were not significantly different compared to those from the systematic surveillance (EOD 1.23 [95%CI 1.06–1.43] vs. 1.50 [95%CI 1.30–1.71], respectively, rate ratio 0.82 [95%CI 0.67–1.01]; LOD 1.04 [95% CI 0.90–1.23] vs. 1.22 [95%CI 1.05–1.42], rate ratio 0.85 [95% CI 0.68–1.07]). A review of the few cases missed in the passive system in Soweto, suggested that missing key identifiers, such as date of birth, resulted in their omission during the electronic data extraction process. Our analysis suggests that passive surveillance provides a modestly lower estimate of invasive GBS rates compared to real time sentinel-site systematic surveillance, however, this is unlikely to be the reason for the provincial variability in incidence of invasive GBS disease in South Africa. This, possibly reflects that invasive GBS disease goes undiagnosed due to issues related to access to healthcare, poor laboratory capacity and varying diagnostic procedures or empiric antibiotic treatment of neonates with suspected sepsis in the absence of attempting to making a microbiological diagnosis. An efficacious GBS vaccine for pregnant women, when available, could be used as a probe to better quantify the burden of invasive GBS disease in low-middle resourced settings such as ours. From our study passive systems are important to monitor trends over time as long as they are interpreted with caution; active systems give better detailed information and will have greater representivity when expanded to other surveillance sites. [ABSTRACT FROM AUTHOR]

In South Africa, various point-of-care hemoglobin meters are used. However, the regulatory framework for approval, implementation and oversight of use of point-of-care hemoglobin meters is suboptimal. We assessed the diagnostic accuracy of the HemoCue Hb 301, STAT-Site MHgb and URIT-12 point-of-care hemoglobin meters, compared to a central laboratory based reference assay, in a central laboratory and a community based clinic in Durban, South Africa. Differences in performance of the point-of-care assays, compared to the reference assay, were more pronounced in the community based clinic. Results were reasonable for the HemoCue Hb 301, but poor for the STAT-Site MHgb and the URIT-12. Poor test performance of point-of-care hemoglobin meters, and inadequate evaluations and oversight in South Africa, leads to suboptimal clinical care and clinical research, and increased costs. There is a need for proper evaluation and quality assurance of point-of-care tests, the results of which should be made widely available to key stakeholders. [ABSTRACT FROM AUTHOR]