Projecting the lives saved by continuing the historical scale-up of child and maternal health interventions in Mozambique until 2030.

BACKGROUND: Over the past 20 years, Mozambique has achieved substantial reductions in maternal, neonatal, and child mortality. However, mortality rates are still high, and to achieve the Sustainable Development Goals (SDGs) for maternal and child health, further gains are needed. One technique that can guide policy makers to more effectively allocate health resources is to model the coverage increases and lives saved that would be achieved if trends continue as they have in the past, and under differing alternative scenarios. METHODS: We used historical coverage data to project future coverage levels for 22 child and maternal interventions for 2015-2030 using a Bayesian regression model. We then used the Lives Saved Tool (LiST) to estimate the additional lives saved by the projected coverage increases, and the further child lives saved if Mozambique were to achieve universal coverage levels of selected individual interventions. RESULTS: If historical trends continue, coverage of all interventions will increase from 2015 to 2030. As a result, 180 080 child lives (0-59 months) and 3640 maternal lives will be saved that would not be saved if coverage instead stays constant from 2015 to 2030. Most child lives will be saved by preventing malaria deaths: 40.9% of the mortality reduction will come from increased coverage of artemisinin-based compounds for malaria treatment (ACTs) and insecticide treated bednets (ITNs). Most maternal lives will be saved from increased labor and delivery management (29.4%) and clean birth practices (17.1%). The biggest opportunity to save even more lives, beyond those expected by historical trends, is to further invest in malaria treatment. If coverage of ACTs was increased to 90% in 2030, rather than the anticipated coverage of 68.4% in 2030, an additional 3456 child lives would be saved per year. CONCLUSIONS: Mozambique can expect to see continued reductions in mortality rates in the coming years, although due to population growth the absolute number of child deaths will decrease only marginally, the absolute number of maternal deaths will continue to increase, and the country will not achieve current SDG targets for either child or maternal mortality. Significant further health investments are needed to eliminate all preventable child and maternal deaths in the coming decades.

Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection.

INTRODUCTION: Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi-assay algorithms (MAAs) for incidence estimation in subtype C settings. METHODS: We analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1-4 of the following assays: Limiting Antigen Avidity assay (LAg-Avidity), BioRad-Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was <1 year. This MAA was compared to the LAg-Avidity and BioRad-Avidity assays alone, a widely used LAg algorithm (LAg-Avidity <1.5 OD-n + VL >1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross-sectional incidence estimates to observed incidence in an independent longitudinal cohort. RESULTS: The optimal MAA was LAg-Avidity <2.8 OD-n  +  BioRad-Avidity <95% + VL >400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort. CONCLUSIONS: An optimal MAA was identified for cross-sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.

Cross-Sectional HIV Incidence Estimation with Missing Biomarkers.

Considerable progress has been made in the development of approaches for HIV incidence estimation based on a cross-sectional survey for biomarkers of recent infection. Multiple biomarkers when used in combination can increase the precision of cross-sectional HIV incidence estimates. Multi-assay algorithms (MAAs) for cross-sectional HIV incidence estimation are hierarchical stepwise algorithms for testing the biological samples with multiple biomarkers. The objective of this paper is to consider some of the statistical challenges for addressing the problem of missing biomarkers in such testing algorithms. We consider several methods for handling missing biomarkers for (1) estimating the mean window period, and (2) estimating HIV incidence from a cross sectional survey once the mean window period has been determined. We develop a conditional estimation approach for addressing the missing data challenges and compare that method with two naïve approaches. Using MAAs developed for HIV subtype B, we evaluate the methods by simulation. We show that the two naïve estimation methods lead to biased results in most of the missing data scenarios considered. The proposed conditional approach protects against bias in all of the scenarios.

Short Communication: Comparison of Maxim and Sedia Limiting Antigen Assay Performance for Measuring HIV Incidence.

Accurate methods for cross-sectional incidence estimation are needed for HIV prevention research. The Limiting Antigen Avidity (LAg-Avidity) assay has been marketed by two vendors, Maxim Biomedical and Sedia BioSciences Corporation. Performance differences between the two versions of the assay are unknown. We tested a total 1,410 treatment-naive samples with both versions of the assay. The samples came from 176 seroconverters from the Zimbabwe Hormonal Contraception and HIV Study. The correlation between the two versions of the assay was 0.93 for the optical density (OD) and 0.86 for the normalized OD. As the difference was more pronounced for the normalized OD, the difference in assays can be attributed to the calibrators. The mean duration of recent infection (MDRI), the average time individuals infected <2 years appear recently infected, was determined for both versions using an assay cutoff of 1.5 OD-n alone or in combination with a viral load cutoff of >1,000 copies/ml. The MDRI was 137 days for Sedia and 157 days for Maxim, with a difference of 20 days (95% CI 11-30). The MDRIs decreased to 102 and 120 days with the inclusion of a viral load cutoff of >1,000 copies/ml. These results imply that use of the Sedia LAg-Avidity will result in estimates of incidence ∼13% lower than those using the Maxim LAg-Avidity.

Times to key events in Zika virus infection and implications for blood donation: a systematic review.

OBJECTIVE: To estimate the timing of key events in the natural history of Zika virus infection. METHODS: In February 2016, we searched PubMed, Scopus and the Web of Science for publications containing the term Zika. By pooling data, we estimated the incubation period, the time to seroconversion and the duration of viral shedding. We estimated the risk of Zika virus contaminated blood donations. FINDINGS: We identified 20 articles on 25 patients with Zika virus infection. The median incubation period for the infection was estimated to be 5.9 days (95% credible interval, CrI: 4.4-7.6), with 95% of people who developed symptoms doing so within 11.2 days (95% CrI: 7.6-18.0) after infection. On average, seroconversion occurred 9.1 days (95% CrI: 7.0-11.6) after infection. The virus was detectable in blood for 9.9 days (95% CrI: 6.9-21.4) on average. Without screening, the estimated risk that a blood donation would come from an infected individual increased by approximately 1 in 10 000 for every 1 per 100 000 person-days increase in the incidence of Zika virus infection. Symptom-based screening may reduce this rate by 7% (relative risk, RR: 0.93; 95% CrI: 0.89-0.99) and antibody screening, by 29% (RR: 0.71; 95% CrI: 0.28-0.88). CONCLUSION: Neither symptom- nor antibody-based screening for Zika virus infection substantially reduced the risk that blood donations would be contaminated by the virus. Polymerase chain reaction testing should be considered for identifying blood safe for use in pregnant women in high-incidence areas.

Sample size methods for estimating HIV incidence from cross-sectional surveys.

Understanding HIV incidence, the rate at which new infections occur in populations, is critical for tracking and surveillance of the epidemic. In this article, we derive methods for determining sample sizes for cross-sectional surveys to estimate incidence with sufficient precision. We further show how to specify sample sizes for two successive cross-sectional surveys to detect changes in incidence with adequate power. In these surveys biomarkers such as CD4 cell count, viral load, and recently developed serological assays are used to determine which individuals are in an early disease stage of infection. The total number of individuals in this stage, divided by the number of people who are uninfected, is used to approximate the incidence rate. Our methods account for uncertainty in the durations of time spent in the biomarker defined early disease stage. We find that failure to account for this uncertainty when designing surveys can lead to imprecise estimates of incidence and underpowered studies. We evaluated our sample size methods in simulations and found that they performed well in a variety of underlying epidemics. Code for implementing our methods in R is available with this article at the Biometrics website on Wiley Online Library.

A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation.

BACKGROUND: Multi-assay algorithms (MAAs) can be used to estimate HIV incidence in cross-sectional surveys. We compared the performance of two MAAs that use HIV diversity as one of four biomarkers for analysis of HIV incidence. METHODS: Both MAAs included two serologic assays (LAg-Avidity assay and BioRad-Avidity assay), HIV viral load, and an HIV diversity assay. HIV diversity was quantified using either a high resolution melting (HRM) diversity assay that does not require HIV sequencing (HRM score for a 239 base pair env region) or sequence ambiguity (the percentage of ambiguous bases in a 1,302 base pair pol region). Samples were classified as MAA positive (likely from individuals with recent HIV infection) if they met the criteria for all of the assays in the MAA. The following performance characteristics were assessed: (1) the proportion of samples classified as MAA positive as a function of duration of infection, (2) the mean window period, (3) the shadow (the time period before sample collection that is being assessed by the MAA), and (4) the accuracy of cross-sectional incidence estimates for three cohort studies. RESULTS: The proportion of samples classified as MAA positive as a function of duration of infection was nearly identical for the two MAAs. The mean window period was 141 days for the HRM-based MAA and 131 days for the sequence ambiguity-based MAA. The shadows for both MAAs were <1 year. Both MAAs provided cross-sectional HIV incidence estimates that were very similar to longitudinal incidence estimates based on HIV seroconversion. CONCLUSIONS: MAAs that include the LAg-Avidity assay, the BioRad-Avidity assay, HIV viral load, and HIV diversity can provide accurate HIV incidence estimates. Sequence ambiguity measures obtained using a commercially-available HIV genotyping system can be used as an alternative to HRM scores in MAAs for cross-sectional HIV incidence estimation.

HIV diversity as a biomarker for HIV incidence estimation: including a high-resolution melting diversity assay in a multiassay algorithm.

Multiassay algorithms (MAAs) can be used to estimate cross-sectional HIV incidence. We previously identified a robust MAA that includes the BED capture enzyme immunoassay (BED-CEIA), the Bio-Rad Avidity assay, viral load, and CD4 cell count. In this report, we evaluated MAAs that include a high-resolution melting (HRM) diversity assay that does not require sequencing. HRM scores were determined for eight regions of the HIV genome (2 in gag, 1 in pol, and 5 in env). The MAAs that were evaluated included the BED-CEIA, the Bio-Rad Avidity assay, viral load, and the HRM diversity assay, using HRM scores from different regions and a range of region-specific HRM diversity assay cutoffs. The performance characteristics based on the proportion of samples that were classified as MAA positive by duration of infection were determined for each MAA, including the mean window period. The cross-sectional incidence estimates obtained using optimized MAAs were compared to longitudinal incidence estimates for three cohorts in the United States. The performance of the HRM-based MAA was nearly identical to that of the MAA that included CD4 cell count. The HRM-based MAA had a mean window period of 154 days and provided cross-sectional incidence estimates that were similar to those based on cohort follow-up. HIV diversity is a useful biomarker for estimating HIV incidence. MAAs that include the HRM diversity assay can provide accurate HIV incidence estimates using stored blood plasma or serum samples without a requirement for CD4 cell count data.

Estimation of HIV incidence using multiple biomarkers.

The incidence of human immunodeficiency virus (HIV) is the rate at which new HIV infections occur in populations. The development of accurate, practical, and cost-effective approaches to estimation of HIV incidence is a priority among researchers in HIV surveillance because of limitations with existing methods. In this paper, we develop methods for estimating HIV incidence rates using multiple biomarkers in biological samples collected from a cross-sectional survey. An advantage of the method is that it does not require longitudinal follow-up of individuals. We use assays for BED, avidity, viral load, and CD4 cell count data from clade B samples collected in several US epidemiologic cohorts between 1987 and 2010. Considering issues of accuracy, cost, and implementation, we identify optimal multiassay algorithms for estimating incidence. We find that the multiple-biomarker approach to cross-sectional HIV incidence estimation corrects the significant deficiencies of currently available approaches and is a potentially powerful and practical tool for HIV surveillance.

HIV incidence determination in the United States: a multiassay approach.

BACKGROUND: Accurate testing algorithms are needed for estimating human immunodeficiency virus (HIV) incidence from cross-sectional surveys. METHODS: We developed a multiassay algorithm (MAA) for HIV incidence that includes the BED capture enzyme immunoassay (BED-CEIA), an antibody avidity assay, HIV load, and CD4(+) T-cell count. We analyzed 1782 samples from 709 individuals in the United States who had a known duration of HIV infection (range, 0 to >8 years). Logistic regression with cubic splines was used to compare the performance of the MAA to the BED-CEIA and to determine the window period of the MAA. We compared the annual incidence estimated with the MAA to the annual incidence based on HIV seroconversion in a longitudinal cohort. RESULTS: The MAA had a window period of 141 days (95% confidence interval [CI], 94-150) and a very low false-recent misclassification rate (only 0.4% of 1474 samples from subjects infected for >1 year were misclassified as indicative of recent infection). In a cohort study, annual incidence based on HIV seroconversion was 1.04% (95% CI, .70%-1.55%). The incidence estimate obtained using the MAA was essentially identical: 0.97% (95% CI, .51%-1.71%). CONCLUSIONS: The MAA is as sensitive for detecting recent HIV infection as the BED-CEIA and has a very low rate of false-recent misclassification. It provides a powerful tool for cross-sectional HIV incidence determination.

Performance of a limiting-antigen avidity enzyme immunoassay for cross-sectional estimation of HIV incidence in the United States.

BACKGROUND: A limiting antigen avidity enzyme immunoassay (HIV-1 LAg-Avidity assay) was recently developed for cross-sectional HIV incidence estimation. We evaluated the performance of the LAg-Avidity assay alone and in multi-assay algorithms (MAAs) that included other biomarkers. METHODS AND FINDINGS: Performance of testing algorithms was evaluated using 2,282 samples from individuals in the United States collected 1 month to >8 years after HIV seroconversion. The capacity of selected testing algorithms to accurately estimate incidence was evaluated in three longitudinal cohorts. When used in a single-assay format, the LAg-Avidity assay classified some individuals infected >5 years as assay positive and failed to provide reliable incidence estimates in cohorts that included individuals with long-term infections. We evaluated >500,000 testing algorithms, that included the LAg-Avidity assay alone and MAAs with other biomarkers (BED capture immunoassay [BED-CEIA], BioRad-Avidity assay, HIV viral load, CD4 cell count), varying the assays and assay cutoffs. We identified an optimized 2-assay MAA that included the LAg-Avidity and BioRad-Avidity assays, and an optimized 4-assay MAA that included those assays, as well as HIV viral load and CD4 cell count. The two optimized MAAs classified all 845 samples from individuals infected >5 years as MAA negative and estimated incidence within a year of sample collection. These two MAAs produced incidence estimates that were consistent with those from longitudinal follow-up of cohorts. A comparison of the laboratory assay costs of the MAAs was also performed, and we found that the costs associated with the optimal two assay MAA were substantially less than with the four assay MAA. CONCLUSIONS: The LAg-Avidity assay did not perform well in a single-assay format, regardless of the assay cutoff. MAAs that include the LAg-Avidity and BioRad-Avidity assays, with or without viral load and CD4 cell count, provide accurate incidence estimates.