Survival functions for defining a clinical management Lost To Follow-Up (LTFU) cut-off in Antiretroviral Therapy (ART) program in Zomba, Malawi

BackgroundWhile, lost to follow-up (LTFU) from antiretroviral therapy (ART) can be considered a catch-all category for patients who miss scheduled visits or medication pick-ups, operational definitions and methods for defining LTFU vary making comparisons across programs challenging. Using weekly cut-offs, we sought to determine the probability that an individual would return to clinic given that they had not yet returned in order to identify the LTFU cut-off that could be used to inform clinical management and tracing procedures.MethodsIndividuals who initiated ART with Dignitas International supported sites (n = 22) in Zomba, Malawi between January 1 2007-June 30 2010 and were ≥ 1 week late for a follow-up visit were included. Lateness was categorized using weekly cut-offs from ≥1 to ≥26 weeks late. At each weekly cut-off, the proportion of patients who returned for a subsequent follow-up visit were identified. Cumulative Distribution Functions (CDFs) were plotted to determine the probability of returning as a function of lateness. Hazard functions were plotted to demonstrate the proportion of patients who returned each weekly interval relative to those who had yet to return.ResultsIn total, n = 4484 patients with n = 7316 follow-up visits were included. The number of included follow-up visits per patient ranged from 1–10 (median: 1). Both the CDF and hazard function demonstrated that after being ≥9 weeks late, the proportion of new patients who returned relative to those who had yet to return decreased substantially.ConclusionsWe identified a LTFU definition useful for clinical management. The simple functions plotted here did not require advanced statistical expertise and were created using Microsoft Excel, making it a particularly practical method for HIV programs in resource-constrained settings.

[1]  M. Egger,et al.  Mortality and loss to follow-up in the first year of ART: Malawi national ART programme , 2012, AIDS.

[2]  K M Leung,et al.  Censoring issues in survival analysis. , 1997, Annual review of public health.

[3]  N. Ford,et al.  Generic fixed-dose combination antiretroviral treatment in resource-poor settings: multicentric observational cohort , 2006, AIDS.

[4]  B. Chi,et al.  Universal Definition of Loss to Follow-Up in HIV Treatment Programs: A Statistical Analysis of 111 Facilities in Africa, Asia, and Latin America , 2011, PLoS medicine.

[5]  Aileen Clarke,et al.  Developing a quality criteria framework for patient decision aids: online international Delphi consensus process , 2006, BMJ : British Medical Journal.

[6]  R. Singh,et al.  Survival analysis in clinical trials: Basics and must know areas , 2011, Perspectives in Clinical Research.

[7]  B. Chi,et al.  American Journal of Epidemiology Practice of Epidemiology an Empirical Approach to Defining Loss to Follow-up among Patients Enrolled in Antiretroviral Treatment Programs , 2022 .

[8]  M. Chesney,et al.  The Elusive Gold Standard: Future Perspectives for HIV Adherence Assessment and Intervention , 2006, Journal of acquired immune deficiency syndromes.

[9]  Matthias Egger,et al.  Electronic medical record systems, data quality and loss to follow-up: survey of antiretroviral therapy programmes in resource-limited settings. , 2008, Bulletin of the World Health Organization.

[10]  D. Kleinbaum,et al.  Survival Analysis: A Self-Learning Text. , 1996 .

[11]  S. Blower,et al.  Predicting the epidemiological impact of antiretroviral allocation strategies in KwaZulu-Natal: The effect of the urban–rural divide , 2006, Proceedings of the National Academy of Sciences.

[12]  Malawi Malawi Demographic and Health Survey 2015-16 , 2017 .

[13]  Sydney Rosen,et al.  Patient retention in antiretroviral therapy programs up to three years on treatment in sub-Saharan Africa, 2007–2009: systematic review , 2010, Tropical medicine & international health : TM & IH.

[14]  T. Holford The analysis of rates and of survivorship using log-linear models. , 1980, Biometrics.

[15]  P. Arora,et al.  Outcome assessment of decentralization of antiretroviral therapy provision in a rural district of Malawi using an integrated primary care model , 2010, Tropical medicine & international health : TM & IH.

[16]  Marie Lynn Miranda,et al.  GIS Modeling of Air Toxics Releases from TRI-Reporting and Non-TRI-Reporting Facilities: Impacts for Environmental Justice , 2004, Environmental health perspectives.

[17]  A. Oxman,et al.  Chemotherapy for advanced non-small-cell lung cancer: how much benefit is enough? , 1993, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[19]  A. Jahn,et al.  Vital registration in rural Africa: is there a way forward to report on health targets of the Millennium Development Goals? , 2011, Transactions of the Royal Society of Tropical Medicine and Hygiene.

[20]  Richard D Moore,et al.  Comparing different measures of retention in outpatient HIV care , 2012, AIDS.

[21]  Nan M. Laird,et al.  Covariance Analysis of Censored Survival Data Using Log-Linear Analysis Techniques , 1981 .

[22]  B. Rachlis Losses to Follow-up from an Antiretroviral Therapy (ART) Program in the Zomba District of Malawi , 2013 .

[23]  J. Farrar,et al.  Use of the cumulative proportion of responders analysis graph to present pain data over a range of cut-off points: making clinical trial data more understandable. , 2006, Journal of pain and symptom management.

[24]  L. Myer,et al.  Impact of definitions of loss to follow-up (LTFU) in antiretroviral therapy program evaluation: variation in the definition can have an appreciable impact on estimated proportions of LTFU. , 2013, Journal of clinical epidemiology.

[25]  S. Rosen,et al.  Retention in HIV Care between Testing and Treatment in Sub-Saharan Africa: A Systematic Review , 2011, PLoS medicine.

[26]  C. Fraser,et al.  Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong , 2003, The Lancet.

[27]  R. Kay The Analysis of Survival Data , 2012 .

[28]  Jayajit Chakraborty,et al.  International Journal of Health Geographics Improving Environmental Exposure Analysis Using Cumulative Distribution Functions and Individual Geocoding , 2022 .

[29]  Joseph Cavanaugh,et al.  Probability and Random Variables , 1999, Mathematical Modelling.

[30]  J. Rehm,et al.  BMC Medical Research Methodology , 2008 .

[31]  W. El-Sadr,et al.  Association of Adherence Support and Outreach Services with Total Attrition, Loss to Follow-Up, and Death among ART Patients in Sub-Saharan Africa , 2012, PloS one.

[32]  K A Schulman,et al.  Mathematical Models in Decision Analysis , 1997, Infection Control & Hospital Epidemiology.

[33]  D.,et al.  Regression Models and Life-Tables , 2022 .

[34]  P. Fayers,et al.  The Visual Display of Quantitative Information , 1990 .

[35]  N. Ford,et al.  Scaling up of highly active antiretroviral therapy in a rural district of Malawi: an effectiveness assessment , 2006, The Lancet.

[36]  B. Chi,et al.  Adherence to first-line antiretroviral therapy affects non-virologic outcomes among patients on treatment for more than 12 months in Lusaka, Zambia , 2009, International journal of epidemiology.

[37]  S. Vermund,et al.  Impact of definitions of loss to follow-up on estimates of retention, disease progression, and mortality: application to an HIV program in Mozambique. , 2013, American journal of epidemiology.

[38]  Sydney Rosen,et al.  Patient Retention in Antiretroviral Therapy Programs in Sub-Saharan Africa: A Systematic Review , 2007, PLoS medicine.

[39]  B. Chi,et al.  Estimating Loss to Follow-Up in HIV-Infected Patients on Antiretroviral Therapy: The Effect of the Competing Risk of Death in Zambia and Switzerland , 2011, PloS one.

[40]  G. Maartens,et al.  Outcomes after two years of providing antiretroviral treatment in Khayelitsha, South Africa , 2004, AIDS.

[41]  D. Ross-Degnan,et al.  Monitoring Adherence and Defaulting for Antiretroviral Therapy in 5 East African Countries: An Urgent Need for Standards , 2008, Journal of the International Association of Physicians in AIDS Care.

[42]  Hadi Dowlatabadi,et al.  Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: an HIV Model, as an Example , 1994 .

[43]  M. Szklo,et al.  Epidemiology: Beyond the Basics , 1999 .

[44]  S. Biadgilign,et al.  Predictors of mortality among HIV infected patients taking antiretroviral treatment in Ethiopia: a retrospective cohort study , 2012, AIDS Research and Therapy.

[45]  D. Cole,et al.  Follow-Up Visit Patterns in an Antiretroviral Therapy (ART) Programme in Zomba, Malawi , 2014, PloS one.

[46]  F. Noubary,et al.  Not All Are Lost: Interrupted Laboratory Monitoring, Early Death, and Loss to Follow-Up (LTFU) in a Large South African Treatment Program , 2012, PloS one.

[47]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[48]  J. Ware,et al.  Applied Longitudinal Analysis , 2004 .

[49]  J. Cerhan,et al.  Epidemiologic evaluation of measurement data in the presence of detection limits. , 2005, Environmental health perspectives.

[50]  David W. Hosmer,et al.  Applied Survival Analysis: Regression Modeling of Time-to-Event Data , 2008 .

[51]  Christa R. Nevin,et al.  From access to engagement: measuring retention in outpatient HIV clinical care. , 2010, AIDS patient care and STDs.