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Joint modeling of correlated binary outcomes: The case of contraceptive use and HIV knowledge in Bangladesh.

Abstract Recent advances in statistical methods enable the study of correlation among outcomes through joint modeling, thereby addressing spillover effects. By joint modeling, we refer to simultaneously analyzing two or more different response variables emanating from the same individual. Using the 2011 Bangladesh Demographic and Health Survey, we jointly address spillover effects between contraceptive use (CUC) and knowledge of HIV and other sexually transmitted diseases. Jointly modeling these two outcomes is appropriate because certain types of contraceptive use contribute to the prevention of HIV and STDs and the knowledge and awareness of HIV and STDs typically lead to protection during sexual intercourse. In particular, we compared the differences as they pertained to the interpretive advantage of modeling the spillover effects of joint modeling HIV and CUC as opposed to addressing them separately. We also identified risk factors that determine contraceptive use and knowledge of HIV and STDs among women in Bangladesh. We found that by jointly modeling the correlation between HIV knowledge and contraceptive use, the importance of education decreased. The HIV prevention program had a spillover effect on CUC: what seemed to be impacted by education can be partially contributed to one's exposure to HIV knowledge. The joint model revealed a less significant impact of covariates as opposed to both separate models and standard models. Additionally, we found a spillover effect that would have otherwise been undiscovered if we did not jointly model. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed for the commonality between different responses and deflate, which is otherwise overestimated when examined separately.
PMID
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Authors

Mayor MeshTerms
Keywords
Journal Title plos one
Publication Year Start




PMID- 29351328
OWN - NLM
STAT- In-Data-Review
LR  - 20180119
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 13
IP  - 1
DP  - 2018
TI  - Joint modeling of correlated binary outcomes: The case of contraceptive use and
      HIV knowledge in Bangladesh.
PG  - e0190917
LID - 10.1371/journal.pone.0190917 [doi]
AB  - Recent advances in statistical methods enable the study of correlation among
      outcomes through joint modeling, thereby addressing spillover effects. By joint
      modeling, we refer to simultaneously analyzing two or more different response
      variables emanating from the same individual. Using the 2011 Bangladesh
      Demographic and Health Survey, we jointly address spillover effects between
      contraceptive use (CUC) and knowledge of HIV and other sexually transmitted
      diseases. Jointly modeling these two outcomes is appropriate because certain
      types of contraceptive use contribute to the prevention of HIV and STDs and the
      knowledge and awareness of HIV and STDs typically lead to protection during
      sexual intercourse. In particular, we compared the differences as they pertained 
      to the interpretive advantage of modeling the spillover effects of joint modeling
      HIV and CUC as opposed to addressing them separately. We also identified risk
      factors that determine contraceptive use and knowledge of HIV and STDs among
      women in Bangladesh. We found that by jointly modeling the correlation between
      HIV knowledge and contraceptive use, the importance of education decreased. The
      HIV prevention program had a spillover effect on CUC: what seemed to be impacted 
      by education can be partially contributed to one's exposure to HIV knowledge. The
      joint model revealed a less significant impact of covariates as opposed to both
      separate models and standard models. Additionally, we found a spillover effect
      that would have otherwise been undiscovered if we did not jointly model. These
      findings further suggested that the simultaneous impact of correlated outcomes
      can be adequately addressed for the commonality between different responses and
      deflate, which is otherwise overestimated when examined separately.
FAU - Fang, Di
AU  - Fang D
AD  - Department of Agricultural Economics and Agribusiness, University of Arkansas,
      Fayetteville, AR, United States of America.
FAU - Sun, Renyuan
AU  - Sun R
AD  - School of Mathematical and Statistical Science, Arizona State University, Tempe, 
      AZ, United States of America.
FAU - Wilson, Jeffrey R
AU  - Wilson JR
AUID- ORCID: http://orcid.org/0000-0001-9072-1714
AD  - Department of Economics, Arizona State University, Tempe, AZ, United States of
      America.
LA  - eng
PT  - Journal Article
DEP - 20180119
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
EDAT- 2018/01/20 06:00
MHDA- 2018/01/20 06:00
CRDT- 2018/01/20 06:00
PHST- 2017/02/06 00:00 [received]
PHST- 2017/12/23 00:00 [accepted]
PHST- 2018/01/20 06:00 [entrez]
PHST- 2018/01/20 06:00 [pubmed]
PHST- 2018/01/20 06:00 [medline]
AID - 10.1371/journal.pone.0190917 [doi]
AID - PONE-D-17-04874 [pii]
PST - epublish
SO  - PLoS One. 2018 Jan 19;13(1):e0190917. doi: 10.1371/journal.pone.0190917.
      eCollection 2018.