Currently, the most promising approach to control the HIV epidemic is to prevent new infections. To this end, early detection of new infections and monitoring the growth of the epidemic are critical. We propose statistical measurement techniques to identify acutely infected individuals;to simultaneously measure comorbidities;to improve the efficacy of treatment programs by monitoring the emergence of drug resistance;and to evaluate prevention program performance and monitor the epidemic. In summary, we present extensions of matrix pooling to identify acutely infected individuals, to identify individ- uals with drug resistant mutations and to estimate incidence of disease. We propose cluster detection methods to identify outbreaks of new infections, individuals impacted by comorbidities and relationships between HIV and comorbidities. We investigate several methods to estimate parameters associated with HIV epidemic monitoring, including mathematical modeling to estimate the reproductive number, viral load averaging, and annealing esti- mators. We introduce methods for investigating the order of appearance of TB and HIV resistance mutations with a goal of developing optimal sequences of anti-TB therapies. We also present extensions of lot quality assurance sampling (LQAS), including Large Country-LQAS and combining pooling with LQAS to classify areas with high burden of comorbidities.

Public Health Relevance

Currently, the most promising approach to control the HIV epidemic is to prevent new infections. To this end, early detection of new infections and monitoring the growth of the epidemic are critical. We propose statistical measurement techniques to identify acutely infected individuals;to simultaneously measure comorbidities;to improve the efficacy of treatment programs by monitoring the emergence of drug resistance;and to evaluate prevention program performance and monitor the epidemic.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
High Priority, Short Term Project Award (R56)
Project #
2R56EB006195-19A1
Application #
7901847
Study Section
Special Emphasis Panel (ZRG1-AARR-J (40))
Program Officer
Peng, Grace
Project Start
2005-09-20
Project End
2012-01-31
Budget Start
2009-08-10
Budget End
2012-01-31
Support Year
19
Fiscal Year
2009
Total Cost
$424,136
Indirect Cost
Name
Harvard University
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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Mitchell, Shira; Pagano, Marcello (2012) Pooled testing for effective estimation of the prevalence of Schistosoma mansoni. Am J Trop Med Hyg 87:850-861
Hedt, Bethany Lynn; van Leth, Frank; Zignol, Matteo et al. (2012) Multidrug resistance among new tuberculosis cases: detecting local variation through lot quality-assurance sampling. Epidemiology 23:293-300
Mitchell, Shira A; Pagano, Marcello (2012) Effective classification of the prevalence of Schistosoma mansoni. Trop Med Int Health 17:1470-7
Hedt, Bethany L; Pagano, Marcello (2011) Health indicators: eliminating bias from convenience sampling estimators. Stat Med 30:560-8