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Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.

Hund L, Pagano M - Emerg Themes Epidemiol (2013)

Bottom Line: : Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing.For those individuals, we suggest offering the option of being tested in a pool.We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Family and Community Medicine, University of New Mexico, 2400 Tucker NE, Albuquerque, NM 87106, USA. lhund@salud.unm.edu.

ABSTRACT
: Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing. One can ignore this problem, as is often done, but the resultant bias can be of sufficient magnitude to invalidate the results of the survey, especially if the number of non-responders is high and the reason for refusing to participate is related to the individual's HIV status. One reason for refusing to participate may be for reasons of privacy. For those individuals, we suggest offering the option of being tested in a pool. This form of testing is less certain than individual testing, but, if it convinces more people to submit to testing, it should reduce the potential for bias and give a cleaner answer to the question of prevalence. This paper explores the logistics of implementing a combined individual and pooled testing approach and evaluates the analytical advantages to such a combined testing strategy. We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach. Minimizing non-response is key for reducing bias, and, if pooled testing assuages privacy concerns, offering a pooled testing strategy has the potential to substantially improve HIV prevalence estimates.

No MeSH data available.


Comparing the asymptotic properties of the combined estimator to the individuals-only estimator. Ratio of the asymptotic mse for the combined estimator to the ratio of the asymptotic mse for the estimator using only individuals in the low, moderate, and high prevalence settings for two scenarios: (a) pooled testers have a higher prevalence than individual testers, m=1000; (b) the prevalence in the pooled testers equals that in the individual testers (this ratio is independent of m). The combined estimator always has lower mse than the individuals only estimator in these settings.
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Figure 1: Comparing the asymptotic properties of the combined estimator to the individuals-only estimator. Ratio of the asymptotic mse for the combined estimator to the ratio of the asymptotic mse for the estimator using only individuals in the low, moderate, and high prevalence settings for two scenarios: (a) pooled testers have a higher prevalence than individual testers, m=1000; (b) the prevalence in the pooled testers equals that in the individual testers (this ratio is independent of m). The combined estimator always has lower mse than the individuals only estimator in these settings.

Mentions: Since the combined estimator is asymptotically unbiased, the asymptotic mean-squared error of the estimator is identical to the variance of the estimator. The estimator using only individual testers has mse equal to the sum of the variance of and the square of the bias of the prevalence estimator when the pooled testers are excluded. The ratio of the mse using the pooled strategy versus the mse using individuals only is always less than one when the pool size is less than 7 for the low, moderate, and high prevalence settings (Figure 1), indicating that the combined estimator outperforms the estimator using only individuals. Indeed, in the situations in which pooled testers have a higher prevalence than individual testers, the mse ratio ranges between 0.1 and 0.4, and the combined estimator provides substantial improvement over the estimator ignoring pooled testers. Even when the prevalence is the same in the pooled and individual testing populations, the mse ratio ranges between 0.6 and 0.85, and the combined estimator still outperforms the individuals-only estimator.


Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.

Hund L, Pagano M - Emerg Themes Epidemiol (2013)

Comparing the asymptotic properties of the combined estimator to the individuals-only estimator. Ratio of the asymptotic mse for the combined estimator to the ratio of the asymptotic mse for the estimator using only individuals in the low, moderate, and high prevalence settings for two scenarios: (a) pooled testers have a higher prevalence than individual testers, m=1000; (b) the prevalence in the pooled testers equals that in the individual testers (this ratio is independent of m). The combined estimator always has lower mse than the individuals only estimator in these settings.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3649931&req=5

Figure 1: Comparing the asymptotic properties of the combined estimator to the individuals-only estimator. Ratio of the asymptotic mse for the combined estimator to the ratio of the asymptotic mse for the estimator using only individuals in the low, moderate, and high prevalence settings for two scenarios: (a) pooled testers have a higher prevalence than individual testers, m=1000; (b) the prevalence in the pooled testers equals that in the individual testers (this ratio is independent of m). The combined estimator always has lower mse than the individuals only estimator in these settings.
Mentions: Since the combined estimator is asymptotically unbiased, the asymptotic mean-squared error of the estimator is identical to the variance of the estimator. The estimator using only individual testers has mse equal to the sum of the variance of and the square of the bias of the prevalence estimator when the pooled testers are excluded. The ratio of the mse using the pooled strategy versus the mse using individuals only is always less than one when the pool size is less than 7 for the low, moderate, and high prevalence settings (Figure 1), indicating that the combined estimator outperforms the estimator using only individuals. Indeed, in the situations in which pooled testers have a higher prevalence than individual testers, the mse ratio ranges between 0.1 and 0.4, and the combined estimator provides substantial improvement over the estimator ignoring pooled testers. Even when the prevalence is the same in the pooled and individual testing populations, the mse ratio ranges between 0.6 and 0.85, and the combined estimator still outperforms the individuals-only estimator.

Bottom Line: : Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing.For those individuals, we suggest offering the option of being tested in a pool.We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Family and Community Medicine, University of New Mexico, 2400 Tucker NE, Albuquerque, NM 87106, USA. lhund@salud.unm.edu.

ABSTRACT
: Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing. One can ignore this problem, as is often done, but the resultant bias can be of sufficient magnitude to invalidate the results of the survey, especially if the number of non-responders is high and the reason for refusing to participate is related to the individual's HIV status. One reason for refusing to participate may be for reasons of privacy. For those individuals, we suggest offering the option of being tested in a pool. This form of testing is less certain than individual testing, but, if it convinces more people to submit to testing, it should reduce the potential for bias and give a cleaner answer to the question of prevalence. This paper explores the logistics of implementing a combined individual and pooled testing approach and evaluates the analytical advantages to such a combined testing strategy. We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach. Minimizing non-response is key for reducing bias, and, if pooled testing assuages privacy concerns, offering a pooled testing strategy has the potential to substantially improve HIV prevalence estimates.

No MeSH data available.