Understanding the effects of treatment with potent antiviral drugs is critical for epidemic control. Currently these drugs have successfully proven effective in prolonging the lives of patients infected with HIV, but they also have the unwanted effect of inducing drug-resistant strains of HIV that can cause the return of high viral burden within treated patients and thus not only render the drug ineffective, but also promote the dissemination of drug resistance throughout populations at risk. This grant application continues to address the statistical problems that arise in studying the development of resistant HIV. Specifically, we propose methods for: (1) modeling the relationship between HIV phenotype and genotype to enable the determination of patient phenotype on the basis of the less expensive and more quickly measured genotype; (2) modeling the evolution of genotype overtime in order to better control treatment strategies; (3) optimizing pooled designs to enable the early detection of acute HIV infection in an economically feasible way in order to better control the epidemic and promoting responsible behavior; (4) developing specimen pooling strategies for the surveillance of drug resistant HIV; and (5) spatio-temporal modeling of the spread of resistance at the population level, which together with (4) provides a unique method for this important task, especially in an economically challenged situation.
These aims, if fulfilled, should help in the public health effort to control the spread of the epidemic and to better treat those already infected.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
9R01EB006195-15A1
Application #
7062604
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Peng, Grace
Project Start
2005-09-20
Project End
2009-07-31
Budget Start
2005-09-20
Budget End
2006-07-31
Support Year
15
Fiscal Year
2005
Total Cost
$399,520
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Finucane, Mariel M; Rowley, Christopher F; Paciorek, Christopher J et al. (2016) Estimating the prevalence of transmitted HIV drug resistance using pooled samples. Stat Methods Med Res 25:917-35
Jeffery, Caroline; Ozonoff, Al; Pagano, Marcello (2014) The effect of spatial aggregation on performance when mapping a risk of disease. Int J Health Geogr 13:9
Jeffery, Caroline; Ozonoff, Al; White, Laura Forsberg et al. (2013) Distance-based mapping of disease risk. Int J Biostat 9:265-90
Hedt-Gauthier, Bethany L; Tenthani, Lyson; Mitchell, Shira et al. (2012) Improving data quality and supervision of antiretroviral therapy sites in Malawi: an application of Lot Quality Assurance Sampling. BMC Health Serv Res 12:196
Vieira, VerĂ³nica M; Weinberg, Janice M; Webster, Thomas F (2012) Individual-level space-time analyses of emergency department data using generalized additive modeling. BMC Public Health 12:687
Manjourides, Justin; Lin, Hsien-Ho; Shin, Sonya et al. (2012) Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. Tuberculosis (Edinb) 92:273-9
Hedt, Bethany L; Pagano, Marcello (2011) Health indicators: eliminating bias from convenience sampling estimators. Stat Med 30:560-8
Manjourides, Justin; Pagano, Marcello (2011) Improving the power of chronic disease surveillance by incorporating residential history. Stat Med 30:2222-33
Pagano, Marcello; Valadez, Joseph J (2010) Commentary: Understanding practical lot quality assurance sampling. Int J Epidemiol 39:69-71
Cohen, Ted; Hedt, Bethany L; Pagano, Marcello (2010) Estimating the magnitude and direction of bias in tuberculosis drug resistance surveys conducted only in the public sector: a simulation study. BMC Public Health 10:355

Showing the most recent 10 out of 19 publications