Outcomes after antiretroviral therapy during the expansion of HIV services in Haiti

McNairy, Margaret L.; Joseph, Patrice; Unterbrink, Michelle; Galbaud, Stanislas; Mathon, J. E.; Rivera, Vanessa; Jannat‐Khah, Deanna; Reif, Lindsey K.; Koenig, Serena P.; Domerçant, Jean Wysler; Johnson, Warren D.; Fitzgerald, Daniel W.; Pape, Jean William

doi:10.1371/journal.pone.0175521

Cited by 11 publications

(18 citation statements)

References 35 publications

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“…Worldwide, LTF rates for patients who have initiated ART treatment for at least one year range from 5 to 53% [ 1 , 3 – 10 ]. Patient characteristics associated with becoming LTF include being clinically ill, as measured by CD4 count or WHO symptom staging, low socioeconomic status, and concern for stigma, as well as structural factors such as transportation issues [ 3 , 7 – 10 , 45 – 47 ]. Several studies have reported high rates of re-engagement in care by patients who were previously labeled as LTF [ 3 , 4 , 7 , 8 , 11 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…Patient characteristics associated with becoming LTF include being clinically ill, as measured by CD4 count or WHO symptom staging, low socioeconomic status, and concern for stigma, as well as structural factors such as transportation issues [ 3 , 7 – 10 , 45 – 47 ]. Several studies have reported high rates of re-engagement in care by patients who were previously labeled as LTF [ 3 , 4 , 7 , 8 , 11 , 45 ]. A study in South Africa found that up to 50% of patients who disengaged from care will re-engage within 3 years including care received at a hospital or emergency department visit [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…A study in South Africa found that up to 50% of patients who disengaged from care will re-engage within 3 years including care received at a hospital or emergency department visit [ 7 ]. Contemporary studies that were able to determine the true status of LTF patients—which is a small number—most had transferred care to clinics closer to their home or newer clinics that provide different services; or alternatively, were alive and not engaged in care [ 3 , 4 , 7 , 11 , 45 ]. Forster et al found a strong correlation between clinics with high LTF rates also had high rates of missing data for patient characteristics [ 1 ].…”

Section: Discussionmentioning

confidence: 99%

“…HIV programs are often assessed by the proportion of patients who are alive and retained in care, which has direct consequences for funding and programmatic services offered [ 1 , 2 ]. However, among individuals who initiate antiretroviral treatment (ART), the reported rate of lost to follow up ranges from 5 to 53% [ 1 , 3 – 10 ]. Clinically, these LTF patients are at risk for adverse outcomes such as medication resistance, transmission to others, lack of care, or at best, incomplete medical records when they transfer care to another clinic [ 1 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…In resource-limited settings, tracing is difficult, costly, and often unsuccessful. In our case study, Haiti does not have a unique national identification number for its citizens, making it difficult to track patients across various health systems or to verify vital status by referencing a current national death registry [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Treating loss-to-follow-up as a missing data problem: a case study using a longitudinal cohort of HIV-infected patients in Haiti

et al. 2018

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BackgroundHIV programs are often assessed by the proportion of patients who are alive and retained in care; however some patients are categorized as lost to follow-up (LTF) and have unknown vital status. LTF is not an outcome but a mixed category of patients who have undocumented death, transfer and disengagement from care. Estimating vital status (dead versus alive) among this category is critical for survival analyses and program evaluation.MethodsWe used three methods to estimate survival in the cohort and to ascertain factors associated with death among the first cohort of HIV positive patients to receive antiretroviral therapy in Haiti: complete case (CC) (drops missing), Inverse Probability Weights (IPW) (uses tracking data) and Multiple Imputation with Chained Equations (MICE) (imputes missing data). Logistic regression was used to calculate odds ratios and 95% confidence intervals for adjusted models for death at 10 years. The logistic regression models controlled for sex, age, severe poverty (living on <$1 USD per day), Port-au-Prince residence and baseline clinical characteristics of weight, CD4, WHO stage and tuberculosis diagnosis.ResultsAge, severe poverty, baseline weight and WHO stage were statistically significant predictors of AIDS related mortality across all models. Gender was only statistically significant in the MICE model but had at least a 10% difference in odds ratios across all models.ConclusionEach of these methods had different assumptions and differed in the number of observations included due to how missing values were addressed. We found MICE to be most robust in predicting survival status as it allowed us to impute missing data so that we had the maximum number of observations to perform regression analyses. MICE also provides a complementary alternative for estimating survival among patients with unassigned vital status. Additionally, the results were easier to interpret, less likely to be biased and provided an alternative to a problem that is often commented upon in the extant literature.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-6115-0) contains supplementary material, which is available to authorized users.

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