scholarly journals Optimal accounting for age and time structure of HIV incidence estimates based on cross-sectional survey data with ascertainment of ‘recent infection’

2021 ◽  
Author(s):  
Laurette Mhlanga ◽  
Grebe Eduard ◽  
Alex Welte
Keyword(s):  
Survey Data ◽  
Age Structure ◽  
Estimation Procedure ◽  
Time Dependent ◽  
Data Sets ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Cross Sectional ◽  
Incidence Estimation ◽  
Dependent Mortality

Abstract BackgroundMany surveys have attempted to estimate HIV incidence from cross-sectional data which includes ascertainment of ‘recent infection’, but the inevitable age and time structure of this data has never been systematically explored – no doubt partly because statistical precision in such estimates is often insufficient to allow for satisfactory disaggregation. Given the non-trivial age structure of HIV incidence and prevalence, and the enormous investments that have been made in such data sets, it is important to understand effective ways to extract valid age structure from these precious data sets. MethodsUsing a comprehensive demographic/epidemiological simulation platform developed for this, and some wider, purposes (documented in more detail separately) we simulated a complex ‘South Africa inspired’ HIV epidemic, with explicitly specified 1) age/time dependent incidence, 2) age/time dependent mortality for uninfected individuals, and 3) age/time/time-since-infection dependent mortality for infected individuals. In this simulated world, we conducted cross-sectional surveys at various times, and applied variants of the recent infection based incidence estimation methodology of Kassanjee et al. We analysed in considerable detail how to smooth, and average over, the age structure in these surveys to produce the incidence estimates, paying attention to the fundamental trade-off between bias and statistical error.ResultsWe summarise our detailed observations about incidence estimates, generated by various age smoothing or age disaggregation procedures, into a straightforward fully specified ‘one size fits most’ algorithm for processing the survey data into age-specific incidence estimates: 1) generalised linear regression to turn observations into ‘prevalence’ of ‘infection’ and ‘recent infection’ (logit, and complementary log log, link functions, respectively; fitting coefficients of up to cubic terms in age/time); 2) a ‘moving window’ data inclusion recipe which handles each age/time point of interest separately; 3) post hoc age averaging of resulting pseudo continuously fitted incidence; 4) bootstrapping as a generic variance/significance estimation procedure.ConclusionsAs far as we are aware, this is the first analysis of several fine details of how age structure in cross-sectional surveys interacts with recency-based incidence estimation. Our proposed default estimation procedure generates incidence estimates with negligible bias and near-optimal precision, and can be readily applied to complex survey data sets by any group in possession of such data. Our code is available, in part freely through the R computing platform, and in part upon request.

scholarly journals Optimal accounting for age and time structure of HIV incidence estimates based on cross-sectional survey data with ascertainment of ‘recent infection’

2021 ◽  
Author(s):  
Laurette Mhlanga ◽  
Grebe Eduard ◽  
Alex Welte
Keyword(s):  
Survey Data ◽  
Age Structure ◽  
Estimation Procedure ◽  
Time Dependent ◽  
Data Sets ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Cross Sectional ◽  
Incidence Estimation ◽  
Dependent Mortality

Abstract BackgroundMany surveys have attempted to estimate HIV incidence from cross-sectional data which includes ascertainment of ‘recent infection’, but the inevitable age and time structure of this data has never been systematically explored – no doubt partly because statistical precision in such estimates is often insufficient to allow for satisfactory disaggregation. Given the non-trivial age structure of HIV incidence and prevalence, and the enormous investments that have been made in such data sets, it is important to understand effective ways to extract valid age structure from these precious data sets. MethodsUsing a comprehensive demographic/epidemiological simulation platform developed for this, and some wider, purposes (documented in more detail separately) we simulated a complex ‘South Africa inspired’ HIV epidemic, with explicitly specified 1) age/time dependent incidence, 2) age/time dependent mortality for uninfected individuals, and 3) age/time/time-since-infection dependent mortality for infected individuals. In this simulated world, we conducted cross-sectional surveys at various times, and applied variants of the recent infection based incidence estimation methodology of Kassanjee et al. We analysed in considerable detail how to smooth, and average over, the age structure in these surveys to produce the incidence estimates, paying attention to the fundamental trade-off between bias and statistical error.ResultsWe summarise our detailed observations about incidence estimates, generated by various age smoothing or age disaggregation procedures, into a straightforward fully specified ‘one size fits most’ algorithm for processing the survey data into age-specific incidence estimates: 1) generalised linear regression to turn observations into ‘prevalence’ of ‘infection’ and ‘recent infection’ (logit, and complementary log log, link functions, respectively; fitting coefficients of up to cubic terms in age/time); 2) a ‘moving window’ data inclusion recipe which handles each age/time point of interest separately; 3) post hoc age averaging of resulting pseudo continuously fitted incidence; 4) bootstrapping as a generic variance/significance estimation procedure.ConclusionsAs far as we are aware, this is the first analysis of several fine details of how age structure in cross-sectional surveys interacts with recency-based incidence estimation. Our proposed default estimation procedure generates incidence estimates with negligible bias and near-optimal precision, and can be readily applied to complex survey data sets by any group in possession of such data. Our code is available, in part freely through the R computing platform, and in part upon request.

scholarly journals Use of HIV Recency Assays for HIV Incidence Estimation and Non-Incidence Surveillance Use Cases: A systematic review

2021 ◽  
Author(s):  
Shelley N. Facente ◽  
Lillian Agyei ◽  
Andrew D. Maher ◽  
Mary Mahy ◽  
Shona Dalal ◽  
...  
Keyword(s):  
Systematic Review ◽  
Molecular Genetic ◽  
Field Performance ◽  
Use Cases ◽  
Surveillance Systems ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Validation Data ◽  
Incidence Estimation ◽  
Age And Sex

ABSTRACTIntroductionHIV assays designed to detect recent infection, also known as “recency assays,” are often used to estimate HIV incidence in a specific country, region, or subpopulation, alone or as part of recent infection testing algorithms (RITAs). Recently, many countries and organizations have become interested in using recency assays within case surveillance systems and routine HIV testing services, and in measuring other indicators beyond incidence, generally referred to as “non-incidence surveillance use cases.”MethodsTo identify best methodological and field implementation practices for the use of recency assays to estimate HIV incidence and trends in recent infections for key populations or specific geographic areas, we undertook: 1) a global Call for Information released from WHO/UNAIDS; and 2) a systematic review of the literature to: (a) assess the field performance characteristics of commercially available recency assays, (b) understand the use of recency testing for surveillance in programmatic and laboratory settings, and (c) review methodologies for implementing recency testing for both incidence estimation and non-incidence use cases.Results and discussionAmong the 90 documents ultimately reviewed, 65 (88%) focused on assay/algorithm performance or methodological descriptions, with high-quality evidence of accurate age- and sex- disaggregated HIV incidence estimation at national or regional levels in general population settings, but not at finer geographic levels for prevention prioritization. The remaining 25 documents described field-derived incidence (n=14) and non-incidence (n=11) use cases, including integrating RITAs into routine surveillance and assisting with molecular genetic analyses, but evidence was generally weaker or only reported on what was done, without validation data or findings related to effectiveness of recency assays when used for these purposes.ConclusionsHIV recency assays have been widely validated for estimating HIV incidence in age- and sex-specific populations at national and sub-national regional levels; however, there was a lack of evidence validating the accuracy and effectiveness of using recency assays for non-incidence surveillance use cases. The evidence identified through this review will be used in forthcoming technical guidance on the use of HIV recency assays for surveillance use cases by WHO and UNAIDS; further evidence on methodologies and effectiveness of non-incidence use cases is needed.

scholarly journals Cross‐sectional HIV incidence estimation in an evolving epidemic

2019 ◽  
Author(s):  
Doug Morrison ◽  
Oliver Laeyendecker ◽  
Ron Brookmeyer
Keyword(s):  
Hiv Incidence ◽  
Cross Sectional ◽  
Incidence Estimation

scholarly journals Cross-Sectional HIV Incidence Estimation with Missing Biomarkers

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Doug Morrison ◽  
Oliver Laeyendecker ◽  
Jacob Konikoff ◽  
Ron Brookmeyer
Keyword(s):  
Missing Data ◽  
Estimation Methods ◽  
Hiv Incidence ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Window Period ◽  
Subtype B ◽  
Incidence Estimation ◽  
Multiple Biomarkers ◽  
The Mean

Abstract 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.

scholarly journals Cross-Sectional HIV Incidence Estimation in HIV Prevention Research

2013 ◽  
Vol 63 ◽  
pp. S233-S239 ◽  
Author(s):  
Ron Brookmeyer ◽  
Oliver Laeyendecker ◽  
Deborah Donnell ◽  
Susan H. Eshleman
Keyword(s):  
Hiv Prevention ◽  
Prevention Research ◽  
Hiv Incidence ◽  
Cross Sectional ◽  
Incidence Estimation

scholarly journals Development of Methods for Cross-Sectional HIV Incidence Estimation in a Large, Community Randomized Trial

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e78818 ◽  
Author(s):  
Oliver Laeyendecker ◽  
Michal Kulich ◽  
Deborah Donnell ◽  
Arnošt Komárek ◽  
Marek Omelka ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Hiv Incidence ◽  
Cross Sectional ◽  
Large Community ◽  
Incidence Estimation

scholarly journals Principles and uses of HIV incidence estimation from recent infection testing - a review

2008 ◽  
Vol 13 (36) ◽  
Author(s):  
S Le Vu ◽  
J Pillonel ◽  
C Semaille ◽  
P Bernillon ◽  
Y Le Strat ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Hiv Infections ◽  
Specific Aspect ◽  
Surveillance Systems ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Immunodeficiency Virus ◽  
Period Estimation ◽  
Incidence Estimation ◽  
The Relationship

Since the 1990s, the development of laboratory-based methods has allowed to estimate incidence of human immunodeficiency virus (HIV) infections on single samples. The tests aim to differentiate recent from established HIV infection. Incidence estimates are obtained by using the relationship between prevalence, incidence and duration of recent infection. We describe the principle of the methods and typical uses of these tests to characterise recent infection and derive incidence. We discuss the challenges in interpreting estimates and we consider the implications for surveillance systems. Overall, these methods can add remarkable value to surveillance systems based on prevalence surveys as well as HIV case reporting. The assumptions that must be fulfilled to correctly interpret the estimates are mostly similar to those required in prevalence measurement. However, further research on the specific aspect of window period estimation is needed in order to generalise these methods in various population settings.

scholarly journals The added value of recent-infection testing in population-based HIV surveys

2021 ◽  
Author(s):  
Laurette Mhlanga ◽  
Eduard Grebe ◽  
Alex Welte
Keyword(s):  
Sexual Debut ◽  
Survey Design ◽  
Value Added ◽  
Early Years ◽  
Added Value ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Cross Sectional ◽  
Marginal Benefit ◽  
Incidence Trends

Abstract Background: There is no clear consensus on how best to use increasingly available data derived from large populationbased surveys featuring HIV infection status ascertainment. In particular, for the purpose of estimating HIV incidence, there is considerable scope for better elucidation of the benefit of adding ‘recent infection’ ascertainment, which adds considerable additional cost and complexity to surveys which are already costly and complex. Methods: Using an epidemic/survey simulation tool developed for this and some closely related investigations, we explore the value added by ‘recent infection’ data from population surveys, to support HIV incidence estimation. This directly piggy-backs on to two companion pieces which have explored, independently, the use of the ‘synthetic cohort’ paradigm of Mahiane et al (analysing age/time structure of prevalence, in conjunction with estimates of mortality) and the paradigm of Kassanjee et al (focusing on ‘recent infection’ data). Results: Our headline findings are that: 1) Recent infection data adds marginal benefit to surveillance focused on the early years after sexual debut, which can reasonably be taken to be a core sentinel group in which surveillance is significantly more efficient than attempts to cover all ages; and 2) by contrast, recent infection data is crucial for the reliable estimation of incidence trends when only two cross sectional surveys are available. We detail numerous components of a general and robust approach to analysing data when both the Mahiane and Kassanjee analyses are in play. Conclusion: Our main results present non-trivial dilemmas for survey design, as recency data is crucial for stabilising the more timely estimates, but of marginal benefit for the most important sentinel group. We hope that adaptation of our analysis, to simulated scenarios closely aligned to specific contexts facing expensive choices, will support rational investments in, and use of, precious surveillance opportunities and data sets.

scholarly journals Using tests for recent infection to estimate incidence: problems and prospects for HIV

2010 ◽  
Vol 15 (24) ◽  
Author(s):  
A Welte ◽  
T A McWalter ◽  
O Laeyendecker ◽  
T B Hallett
Keyword(s):  
Future Development ◽  
Theoretical Work ◽  
Incidence Rates ◽  
Hiv Incidence ◽  
Unified Framework ◽  
Cross Sectional Survey ◽  
Recent Infection ◽  
Cross Sectional ◽  
Recent Theoretical Work ◽  
Survey Sample

Tests for recent infection (TRIs), such as the BED assay, provide a convenient way to estimate HIV incidence rates from cross-sectional survey data. Controversy has arisen over how the imperfect performance of a TRI should be characterised and taken into account. Recent theoretical work is providing a unified framework within which to work with a variety of TRI- and epidemic-specific assumptions in order to estimate incidence using imperfect TRIs, but suggests that larger survey sample sizes will be required than previously thought. This paper reviews the framework qualitatively and provides examples of estimator performance, identifying the characteristics required by a TRI to estimate incidence reliably that should guide the future development of TRIs.

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