The Impact of Expanding ART-Eligibility on Overall Treatment Initiation and Retention in Care: An Application of Regression Discontinuity in Zambia Aaloke Mody, MD Division of HIV, ID, and Global Medicine University of California, San Francisco September 28, 2018 UCSF CFAR Closing the Gap between Rigor and Relevance: Methodological Opportunities for Implementation Science

Motivation

Motivation “Treat all” is being adopted globally based on RCTs HPTN 052 – ART reduces transmission INSIGHT START and TEMPRANO – Improved patient outcomes with immediate versus deferred treatment RCTs establish the biological effects of ART  Efficacy Real world effects of adopting this policy remain unknown  Effectiveness Ulimate public health impact also depends on the behavior of individual patients and capacity of health systems to absorb new patients Benefits of expanding eligibility may be Direct  ART initiation Indirect  Improved retention Negative spillover effects could include increased congestion and crowding out of sicker patients WHO HIV Treatment Guidelines 2015; Over 2017; Abdool Karim NEJM 2015; Fox PLOS Med 2017

Quasi-Experimental Designs: Rigor and Relevance Quasi-experimental designs can approximate experimental conditions in real world data Regression Discontinuity Instrumental Variable Interrupted Time Series Difference in Differences Fixed Effect Relevance Rigor Observational RCT Quasi-Experiment Study interventions delivered through real-life systems in every-day contexts Assess effectiveness (i.e., the effect of actually expanding ART versus the biological effect of ART) Evidence with high rigor AND relevance in the right setting Rigor depends on ability to meet the underlying assumptions of the study design Relevance depends on being able to answer the right question

Motivation Zambia previously changed its HIV treatment guidelines* on April 1, 2014 to expand ART eligibility to include: Patients with a CD4 between 350 and 500 cells/L All pregnant and breastfeeding women under Option B+ We leverage this 2014 guideline change in a quasi-experimental study design to assess the effects of this expansion change and shed light onto what might be expected when expanding to “treat all” Quasi-experimental study designs offer an underused strategy to address these challenges by approximating experimental conditions in real world data An example of this is regression discontinuity which has been previously used in South Africa to assess the effects of ART eligibility in those with a CD4 below 350 Click Zambia adopted universal treatment in December 2016, but prior to this, it changed its HIV treatment guidelines on April 1, 2014 to expand ART eligibility to include patients with a CD4 between 350 and 500 as well as pregnant and breastfeeding women and their partners under Option B+. We leverage this 2014 change in Zambia’s HIV treatment guidelines in a quasi-experimental study design to shed light onto what might be expected when expanding to “treat all” *Adopted treat-all in December 2016 Zambia HIV Guidelines 2010, 2014

Study Objectives To estimate the real world effects of expanding treatment eligibility on ART initiation and retention in care in the overall clinic population using regression discontinuity design. To quantify the effect of initiating ART in routine care on retention using an instrumental variable design. Hypothesis: Expanding ART-eligibility will lead to improvements in both ART initiation and retention in care without crowding out sicker patients

Methods

Study Population Measurements ART naïve patients greater than 15 years old Newly enrolling before and after the change in Zambia’s treatment guidelines (August 1, 2013 to November 1, 2014) 64 clinics supported by the Centre for Infectious Disease Research in Zambia (CIDRZ) in Lusaka, Eastern, Western, and Southern Provinces Measurements EMR system used in routine HIV care in Zambia (SmartCare)

Regression Discontinuity Design (RDD) RDD can be used when treatment is assigned by an arbitrary threshold Compare patients just above and below that threshold (local) Since cutoff is arbitrary, assume patients are similar on measured and unmeasured characteristics, thus, exposure is as if random Bor Epidemiology 2014, Barnighausen J Clin Epi 2017

Regression Discontinuity Design (RDD) RDD to estimate the effects of implementing new ART guidelines Compare patients enrolling just before and after the guideline change Outcomes ART Initiation by 3 months Retention in Care at 6 months Visit between 3 and 9 month In care and on ART at 6 months Composite Outcome Model Estimated risk difference at the time of implementation using a modified Poisson regression with robust variances Excluded patients enrolling: 90 days prior to guideline rollout to avoid bias from “cross-over” 60 days after to account for a gradual guideline rollout Sensitivity analyses for choices in model specification (results not shown) Bor Epidemiology 2014

RD Instrumental Variable (IV) Analysis We estimated the effect of actually initiating ART on retention in care using implementation of new guidelines as an instrumental variable In observational settings, many measured and unmeasured common causes (i.e., confounders) of ART initiation and retention in care IVs allow for unbiased estimates even with unmeasured confounding between the treatment (ART initiation) and outcome (retention) under certain assumptions: IV is associated with the treatment There are no common causes of the IV and the outcome The IV only affects the outcome through the treatment Swanson Epidemiology 2013

Patient Subgroups* Always Eligible: Eligible by 2010 Guidelines CD4 less than or equal to 350 cells/L WHO Stage 3 or 4 Active Tuberculosis Newly Eligible: Eligible by 2014 Guidelines, but not 2010 Guidelines CD4 between 350 and 500 cells/L Pregnant or Breastfeeding Women Not Yet Eligible: Not Eligible by 2010 or 2014 Guidelines CD4 greater than 500 cells/L *Defined at enrollment, but irrespective of the actual date of enrollment

Results

Patient Characteristics at Enrollment, n=34,857 Gender Male Sex, n (%) 13,635 (39.1%) Non-pregnant Female 13,998 (40.2%) Pregnant or Breastfeeding Female 7,224 (20.7%) Median Age, years (IQR) 34 (28, 41) Median CD4 count, cells/L (IQR) 268 (134, 430) WHO Stage, n (%) I 16,437 (55.5%) II 5,804 (19.6%) III 6,748 (22.8%) IV 604 (2.0%) Tuberculosis in past 6 months, n (%) 1,587 (4.6%) Eligibility Subgroup, n (%)   Always Eligible 20,819 (70.1%) Newly Eligible 5,578 (18.8%) Not Yet Eligible 3,319 (11.2%) Province, n (%) Lusaka 18,532 (53.2%) Eastern 6,201 (17.8%) Southern 4,893 (14.0%) Western 5,231 (15.0%)

PATIENT CHARACTERISTICS AT ENROLLMENT, n=34,857 Always Eligible Newly Eligible Not Yet Eligible Pre-Guidelines n=7,159 Post-Guidelines n=6,528 n=1,726 Post-Guidelines n=1,899 n=997 Post-Guidelines n=1,107 Gender, n (%) Male 3164 (44.2) 2923 (44.8) 408 (23.6) 466 (24.5) 344 (34.5) 378 (34.1) Female 2652 (37.0) 2499 (38.3) 615 (35.6) 627 (33.0) 653 (65.5) 729 (65.9) Pregnant or Breastfeeding Female 1343 (18.8) 1106 (16.9) 703 (40.7) 806 (42.4)   Median Age, years (IQR) 35 (29, 41) (30, 42) 31 (25, 38) (26, 38) 32 (27, 39) 33 (27, 40) Median CD4 count, cells/L (IQR) 183 (93, 275) 181 (92, 272) 436 (389, 486) 440 (393, 491) 628 (558, 760) 637 (560, 772) WHO Stage, n (%) 1 2775 (43.0) 2473 (42.8) 1120 (80.5) 1229 (79.1) 665 (78.5) 747 (81.2) 2 1099 (17.0) 1052 (18.2) 272 (19.5) 325 (20.9) 182 (21.5) 173 (18.8) 3 2343 (36.3) 2069 (35.8) 0 (0) 4 234 (3.6) 179 (3.1) Recent TB, n (%) 570 (8.0) 534 (8.2)

Mody PLOS Medicine 2018

All Patients: ART Initiation by 3 months Pre-Guidelines Post-Guidelines Risk Difference p-value ART Initiation, % (95% CI) 56.4 (54.5, 58.4) 70.1 (68.4, 71.8) +13.6 (11.1, 16.2) <0.001 Mody PLOS Medicine 2018

All Patients: Retention in Care at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value Retention in Care, % (95% CI) 64.9 (63.0, 66.8) 69.1 (67.3, 70.8) +4.1 (1.6, 6.7) 0.001 Mody PLOS Medicine 2018

All Patients: In Care and on ART at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value In Care on ART, % (95% CI) 46.9 (45.0, 48.9) 57.7 (55.9, 59.6) +10.8 (8.1, 13.5) <0.001 Mody PLOS Medicine 2018

Always Eligible: ART Initiation by 3 months Pre-Guidelines Post-Guidelines Risk Difference p-value ART Initiation, % (95% CI) 72.4 (70.2, 74.6) 78.5 (76.5, 80.6) +6.2 (3.2, 9.2) <0.001 Mody PLOS Medicine 2018

Always Eligible: Retention in Care at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value Retention in Care, % (95% CI) 72.8 (70.5, 75.0) 73.2 (71.0, 75.4) +0.4 (-2.7, 3.6) 0.790 Mody PLOS Medicine 2018

Always Eligible: In Care and on ART at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value In Care on ART, % (95% CI) 61.0 (58.6, 63.4) 65.6 (63.3, 68.0) +4.6 (1.2, 8.0) 0.008 Mody PLOS Medicine 2018

Newly Eligible: ART Initiation by 3 months Pre-Guidelines Post-Guidelines Risk Difference p-value ART Initiation, % (95% CI) 31.8 (27.1, 36.5) 75.5 (71.4, 79.5) +43.7 (37.5, 49.9) <0.001 Mody PLOS Medicine 2018

Newly Eligible: Retention in Care at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value Retention in Care, % (95% CI) 62.0 (57.0, 66.9) 75.6 (71.7, 79.6) +13.6 (7.3, 20.0) <0.001 Mody PLOS Medicine 2018

Newly Eligible: In Care and on ART at 6 months Always Eligible Pre-Guidelines Post-Guidelines Risk Difference p-value In Care on ART, % (95% CI) 27.1 (22.5, 31.7) 62.6 (58.2, 67.0) +35.5 (29.2, 41.9) <0.001 Mody PLOS Medicine 2018

Not Yet Eligible: ART Initiation by 3 months Pre-Guidelines Post-Guidelines Risk Difference p-value ART Initiation, % (95% CI) 20.2 (14.5, 25.9) 32.7 (27.0, 38.5) +12.5 (4.5, 20.6) 0.0023 Mody PLOS Medicine 2018

Not Yet Eligible: Retention in Care at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value Retention in Care, % (95% CI) 52.9 (46.2, 59.5) 57.1 (51.2, 63.0) +4.3 (-4.6, 13.2) 0.349 Mody PLOS Medicine 2018

Not Yet Eligible: In Care and on ART at 6 months Pre-Guidelines Post-Guidelines Risk Difference p-value In Care on ART, % (95% CI) 16.6 (11.5, 21.6) 27.4 (22.0, 32.8) +10.8 (3.4, 18.3) 0.0042 Mody PLOS Medicine 2018

Instrumental Variable Analysis: Effect of ART Initiation on Retention in Care Actually initiating ART led to a 37.9% increase in retention in care (95% CI 28.8, 46.9%, p<0.001) 2.6 patients need to be initiated on ART to prevent one episode of LTFU (95% CI 2.1, 3.5) Mody PLOS Medicine 2018

Limitations Results only generalizable to current care setting May not apply to linkage or initiation under different circumstances Unable to assess viral suppression Retention is an imperfect proxy for virologic outcomes Excluded patients enrolling immediately before or after the guideline change Results were robust to model specification in sensitivity analyses and no evidence of short-term secular trends Limitations of primary data source Potential for misclassification of subgroup/outcome

Conclusion

Conclusions Effects of expanding ART eligibility All Patients: 13.6% increase in ART initiation, 4.1% increase in retention, and 10.8% increase in those in care and on ART at 6 months Effects primarily driven by improvements in ART initiation and retention in Newly Eligible patients Small increases in ART initiation and the percentage in care and on ART in Always Eligible and Not Yet Eligible patients. Potentially due to overall expansion of ART supply and an informal loosening of eligibility criteria for patients not yet officially eligible Initiating ART in routine care increased retention by 37.9% 70% of patients still present at a more advanced stage 25% of eligible patients are not initiated on ART by 3 months 40% are not in care and on ART at 6 months

Implications Adopting “treat all” will likely lead to similar improvements in the number of patients on ART and retained in care There was no evidence of increased clinic congestion or crowding out of sicker patients Efforts still needed to diagnose and link patients to care earlier and then keep them retained in care Overall effects of expanding ART eligibility alone may be modest Further evidence still needed on real world effects of “treat all” Overall impact on viral suppression Better understanding of those that never start ART or quickly drop out Targeted interventions for those that don’t benefit from eligibility alone Quasi-experimental designs are a powerful tool for understanding the effects of real world implementation of evidence-based interventions

Thank you! UCSF Elvin Geng Diane Havlir Monica Gandhi Monika Roy Nancy Czaicki (in memorium) UC Berkeley Nancy Padian Georgetown University Charles Holmes CIDRZ Izukanji Sikazwe Carolyn Bolton-Moore Kombatende Sikombe Thea Savory Mwanza wa Mwanza Jake Pry Anjali Sharma Arianna Zanolini Paul Somwe Zambian Ministry of Health Bill and Melinda Gates Foundation

Results of Regression Discontinuity Sensitivity Analyses Final Model 100 Day Bandwidth 50 day Bandwidth 30 Day Transition Period Adjusted Weighted   Risk Difference 95% CI All Patients ART Initiation 13.6 11.1 – 16.2 13.5 10.4 – 16.7 16.1 11.4 – 20.7 11.1 8.6 – 13.5 14.6 12.0 – 17.3 10.8 – 16.4 Retention in Care 4.1 1.6 – 6.7 5.4 2.2 – 8.5 4.9 0.3 – 9.6 3.4 0.9 – 5.8 3.8 1.1 – 6.5 4.5 1.7 – 7.3 In Care on ART 10.8 8.1 – 13.5 11.2 7.9 – 14.5 12.3 7.5 – 17.1 8.9 6.3 – 11.4 12.0 9.1 – 14.9 10.6 7.7 – 13.6 IV Estimate 37.9 28.8 – 46.9 31.3 20.0 – 42.7 38.3 21.4 – 55.2 38.6 29.5 – 47.7 35.5 26.4 – 44.7 35.1 24.7 – 45.5 Always Eligible 6.2 3.2 – 9.2 6.4 2.7 – 10.1 8.3 2.9 – 13.7 2.0 – 7.7 5.9 2.9 – 8.9 6.0 2.7 – 9.3 0.4 -2.7 – 3.6 1.1 -2.7 – 5.0 0.3 -5.4 – 5.9 -0.3 -3.3 – 2.7 -2.7 – 3.5 0.1 -3.3 – 3.5 4.6 1.2 – 8.0 4.7 0.5 – 8.9 6.5 0.4 – 12.7 3.7 0.5 – 6.9 1.1 – 7.9 0.4 – 7.8 Newly Eligible 43.7 37.5 – 49.9 39.4 31.7 – 47.0 40.0 28.5 – 51.6 34.1 – 46.0 46.4 40.2 – 52.6 40.9 34.1 – 47.7 7.3 – 20.0 14.5 6.7 – 22.2 11.0 -0.4 – 22.3 12.8 6.7 – 18.8 14.4 8.1 – 20.7 13.8 6.9 – 20.7 29.2 – 41.9 33.3 25.5 – 41.1 31.0 19.9 – 42.1 32.1 26.1 – 38.1 38.4 32.1 – 44.7 26.4 – 40.2 Not Yet Eligible 12.5 4.5 – 20.6 15.9 6.5 – 25.4 16.6 3.2 – 30.0 7.0 -0.3 – 14.2 9.2 1.4 – 17.0 5.9 – 23.0 4.3 -4.6 – 13.2 10.4 -0.3 – 21.1 -1.8 – 29.0 -8.4 – 8.5 2.5 -6.4 – 11.4 7.7 -1.9 – 17.3 3.4 – 18.3 15.0 6.6 – 23.5 14.3 2.6 – 26.0 5.6 -1.0 – 12.2 1.0 – 15.7 12.7 5.0 – 20.4