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Interactions among poverty, gender, and health systems affect women’s participation in services to prevent HIV transmission from mother to child: A causal loop analysis
Author(s) -
Jennifer Yourkavitch,
Kristen Hassmiller Lich,
Valerie L. Flax,
Elialilia S. Okello,
John Kadzandira,
Anne Katahoire,
Alister Munthali,
James C. Thomas
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0197239
Subject(s) - psychosocial , psychological intervention , affect (linguistics) , poverty , autonomy , medicine , spouse , health care , psychology , nursing , psychiatry , communication , sociology , political science , anthropology , law , economics , economic growth
Retention in care remains an important issue for prevention of mother-to-child transmission (PMTCT) programs according to WHO guidelines, formerly called the “Option B+” approach. The objective of this study was to examine how poverty, gender, and health system factors interact to influence women’s participation in PMTCT services. We used qualitative research, literature, and hypothesized variable connections to diagram causes and effects in causal loop models. We found that many factors, including antiretroviral therapy (ART) use, service design and quality, stigma, disclosure, spouse/partner influence, decision-making autonomy, and knowledge about PMTCT, influence psychosocial health, which in turn affects women’s participation in PMTCT services. Thus, interventions to improve psychosocial health need to address many factors to be successful. We also found that the design of PMTCT services, a modifiable factor, is important because it affects several other factors. We identified 66 feedback loops that may contribute to policy resistance—that is, a policy’s failure to have its intended effect. Our findings point to the need for a multipronged intervention to encourage women’s continued participation in PMTCT services and for longitudinal research to quantify and test our causal loop model.

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