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Mul$-‐level determinants of late ART ini$a$on in sub-‐Saharan Africa
Denis Nash, PhD, MPH Associate Professor
Epidemiology and Biosta$s$cs Program CUNY School of Public Health at Hunter College
Outline • The applica$on of a mul$-‐level framework to assessing determinants in the service delivery context (an example)
Late ART Ini,a,on • Late ART ini$a$on in sub-‐Saharan Africa remains a persistent problem, even as ART coverage increases. – Interna$onal guidelines recommend star$ng ART at or above CD4 counts of 350 cells/L (200 cells/L un$l 2010)
– Most pa$ents ini$ate ART at very low CD4 counts (Median CD4: 122 cells/L; IQR: 53-‐194) and advanced clinical stages.
– Improving with $me, but leveling off; S$ll at low levels.
• Late ART ini$a$on results in: – Early on ART mortality – More complex and costly clinical management – Missed opportuni$es for HIV preven$on
Source: Keiser et al., TMIH 2008; Nash et al., AIDS 2008;
CD4 response trajectories are mostly determined by CD4 at start of ART
Nash et al. AIDS 2008
CD4 count at start of ART, 2003-‐2005 42 countries, 176 sites, 33,008 pa$ents
187
181
159
87
97
122 86
100 125
123
97
239
103 53
72
192 163
206 157
134
102
200 179
95
Egger -‐ CROI 2007 – CD4 at start – 10
164
Numbers are median CD4 counts
ART Cohort Collabora,on
Nash et al. AIDS 2008
Steps from HIV infec$on to ART ini$a$on
• Determinants of late ART ini$a$on likely operate at mul$ple levels – i.e., Individual, clinic, context
HIV infec$on Diagnosis Care
enrollment ART
ini$a$on
Pathways to Late ART Ini,a,on
A Healthcare System in Crisis
Conceptual framework for determinants of late ART ini,a,on
Program-‐ and contextual-‐level determinants of low median CD4 cell count in cohorts of persons ini,a,ng ART in 8 sub-‐Saharan African
countries
Design: • Mul$-‐level analysis of
aggregate and program-‐level service delivery data
Study Popula,on: • 267 HIV care clinics in 8 sub-‐
Saharan African countries • N= 1,690 cohorts of pa$ents
ini$a$ng ART from 2004-‐2008 • 121,504 pa$ents who ini$ated
ART
AIDS (Vol 25) 2011: advance online publica$on
ICAP-‐supported sites in Tanzania: Cohorts within clincs within regions
Methods: Mul,-‐level analysis of aggregate data
• Cohort data: routinely collected M&E PEPFAR indicators – Calendar time of ART initiation, number of persons in cohort, median
CD4+ cell count at ART initiation (outcome)
• Program-level data: routinely conducted site assessments – January 2007, July 2007, June 2008
• Time dependent covariates
– Urban/rural, type of site (primary/secondary/tertiary), presence of ancillary services (e.g., outreach, peer education), etc.
• Contextual-level data: DHS – Conducted during 2003-2005 (timing depending on country) – HIV prevalence, AIDS knowledge, testing coverage, stigma
• UNAIDS or ANC prevalence estimates used when DHS data unavailable (Mozambique and Nigeria)
Study Popula,on Sites N (%)
Cohorts N (%)
Pa,ents N (%)
Total 267 (100) 1,690 (100) 121,504 (100) Loca,on Urban 159 (60) 958 (67) 105,138 (87) Rural 108 (40) 480 (33) 16,366 (13) Type of site Primary 112 (42) 482 (34) 24,924 (21) Secondary 144 (54) 851 (59) 83,082 (68) Ter$ary 11 (4) 105 (7) 13,495 (11) Cohort size 1-‐19 pa$ents 423 (25) 4,530 (4) 20-‐42 pa$ents 425 (25) 12,479 (10) 43-‐88 pa$ents 406 (24) 26,015 (21) 89-‐928 pa$ents 436 (26) 78,480 (65)
Study Popula,on, cont’d Sites N (%)
Cohorts N (%)
Pa,ents N (%)
Total 267 (100) 1,690 (100) 121,504 (100) Year of ART ini,a,on 2004/5 101 (6) 7,164 (6) 2006 355 (21) 26,742 (22) 2007 753 (44) 54,089 (45) 2008 (through March) 481 (28) 33,309 (27) Country Ethiopia 39 (15) 336 (20) 26,514 (22) Kenya 41 (15) 210 (12) 10,191 (8) Lesotho 21 (6) 82 (5) 8,842 (7) Mozambique 38 (14) 274 (16) 29,941 (25) Nigeria 16 (6) 70 (4) 10,492 (9) Rwanda 42 (16) 323 (19) 11,854 (10) South Africa 36 (13) 234 (14) 14,764 (12) Tanzania 34 (13) 161 (10) 8,906 (7)
Distribu,on of program-‐level factors across sites (n=267) and countries (n=8)
% (Range across countries)
Secondary health care facility 58 (24-‐100) VCT as primary entry point 52 (45-‐100) PMTCT/LD as primary entry point
41 (0-‐71)
On-‐site CD4+ tes$ng 45 (14-‐81) PMTCT program on site 89 (79-‐100) VCT program on site 96 (88-‐100) TB treatment program on site 83 (71-‐100) >3 forms of adherence support* 68 (19-‐94) Peer educator program 63 (15-‐88) Outreach for missed visits 62 (9-‐95)
*including adherence support services targe$ng pre-‐ART pa$ents
Distribu,on of contextual-‐level factors across sub-‐na,onal regions (n=31) in 8 countries
Range across regions
HIV prevalence 0.7-‐39 % of households with electricity 2-‐59 % of households with piped water 2-‐32 % who have heard of AIDS 54-‐100 % with comprehensive knowledge about AIDS 5-‐64 % who think healthy looking person can have HIV 16-‐95 % who think HIV can be transmijed by mosquitoes 22-‐90 % with accep$ng aktudes towards PLWHA 3-‐58 % responding that condoms prevent HIV transmission 12-‐89 % responding that limi$ng to 1 sexual partner reduces HIV risk
28-‐93
% tested for HIV and received results last 12 mos. 1-‐44
Distribu,on of Cohort Median CD4 count at ART Ini,a,on (n= 1,690 cohorts; 267 clinics; 8 sub-‐Saharan African countries*)
Lowest quar$le 111 cells/μL Median
136 cells/μL
Low median CD4 at ini,a,on
Median CD4 count (cells/µL)
Countries: Ethiopia, Kenya, Lesotho, Mozambique, Nigeria, Rwanda, South Africa, Tanzania
Num
ber o
f coh
orts
Sta,s,cal Methods: Mul,-‐level analysis of aggregate data
• Three multivariate models: • Site factors only, contextual factors only, site and contextual factors
combined
• Included factors associated at p<0.20 in bivariate analysis
– Backwards, stepwise elimination
• Calendar time of ART initiation included in all models
• Hierarchical, generalized linear mixed models
– Estimate fixed effects of program and contextual-level factors on odds of low median CD4 count at ART initiation (cohorts with median CD4 <111 cells/µL)
– Random intercepts (site and region)
– Time dependent site-level covariates
Program-‐level AOR (95% CI)
Contextual-‐level AOR (95% CI)
Program & Contextual AOR (95% CI)
Loca,on
Urban vs. Rural 1.8 (1.1-‐2.9) 2.1 (1.3-‐3.3) # Adherence programs
0-‐3 vs. ≥ 6 2.0 (1.3-‐3.2) 1.6 (1.0-‐2.5) 4-‐5 vs. ≥ 6 1.0 (0.63-‐1.6) 0.94 (0.55-‐1.5)
Availability of CD4
off-‐site vs. on-‐site 0.56 (0.36-‐0.88) Availability of PMTCT
off-‐site vs. on-‐site 0.53 (0.27-‐1.0) 1.1 (0.57-‐2.2) not available vs. on-‐site 4.1 (1.1-‐15.1) 3.6 (1.0-‐12.8)
Outreach services
none vs. pre-‐ART/ART 1.1 (0.76-‐1.7) 0.88 (0.58-‐1.3) ART only vs. pre-‐ART/ART 2.0 (1.3-‐3.2) 2.4 (1.5-‐3.9)
Provider to pa,ent ra,o
<4 vs. >18 per 1,000 pa$ents 1.9 (1.1-‐3.5) 2.3 (1.3-‐4.0) 4-‐9 vs. >18 per 1,000 pa$ents 1.7 (1.0-‐2.8) 1.8 (1.1-‐3.0) 9.1-‐18 vs. >18 per 1,000 pa$ents 1.5 (0.97-‐2.4) 1.6 (1.0-‐2.5)
% Heard of AIDS 0.92 (0.86-‐0.98) 0.88 (0.83-‐0.93) % Tested with results 0.94 (0.90-‐0.99) 0.95 (0.93-‐0.98) % 1 partner limits risk 1.06 (1.03-‐1.11) 1.09 (1.06-‐1.11)
AOR: Adjusted Odds Ra$o; Models further adjusted for calendar $me of ART ini$a$on and cohort size.
Strengths • Generalizability of findings from use of rou$nely collected data – Aggregate data for > 120,000 pa$ents star$ng ART from over 250 clinics in diverse contexts within 8 sub-‐Saharan African countries
• Examined a wide array of relevant program and contextual-‐level factors – Clinic level prac$ces found to be important are already being implemented widely (i.e., they represent poten$ally feasible and sustainable interven$ons)
• Included rural, small sites, and sites without electronic data
• Able to control for confounding at each level • Robust to sensi$vity analysis
Limita,ons • Time dependent characteris$cs
– Clinic-‐level characteris$cs lacking from 2005-‐ <2007 (used data from 2007 as a proxy)
– Contextual-‐level data (DHS) from 2003-‐2005, prior to the period during which most of cohorts ini$ated ART (2007/8)
• Aggregate data prevent examina$on of pa$ent-‐level factors that may also be important
• Examined the role of observed characteris$cs rather than randomly/experimentally assigned – Observed associa$ons may be prone to unmeasured confounding
Conclusions • Determinants of late ART ini$a$on operate at mul$ple levels
• The associa$ons observed in our analyses may represent influen$al yet modifiable determinants of late ART ini$a$on among popula$ons of pa$ents ini$a$ng ART
• Efforts aimed at closer follow-‐up of pre-‐ART pa$ents and linkages to care from tes$ng points that iden$fy persons with earlier HIV disease (e.g. PMTCT) may reduce late ART ini$a$on
• Efforts aimed at increasing awareness of AIDS and tes$ng coverage may reduce the risk of low CD4 cell count at ART ini$a$on
Acknowledgements LSTART Research Team: Denis Nash, Batya Elul, Yingfeng Wu, Maria Lahuerta, Sarah Gorrell, Susie Hoffman, Robert Remien, Zenebe Melaku, Harriet Nuwagaba-‐Biribonwoha, Tsigereda Gadisa, Frances Ue, David Hoos, and Wafaa El-‐Sadr
Funding: Doris Duke ORACTA Program NIMH Grant 1R01MH089831-‐01A1 PEPFAR
ICAP Teams and MOHs: Ethiopia Lesotho Mozambique Nigeria Rwanda South Africa Tanzania