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Antiretroviral Therapy Enrollment Characteristics and Outcomes Among HIV-Infected Adolescents and Young Adults Compared with Older Adults — Seven African Countries, 2004–2013

Andrew F. Auld, MBChB1, Simon G. Agolory, MD1, Ray W. Shiraishi, PhD1, Fred Wabwire-Mangen, MD, PhD2, Gideon Kwesigabo, MD, PhD3, Modest Mulenga, MD4, Sebastian Hachizovu, MBChB4, Emeka Asadu, MD5, Moise Zanga Tuho, MD6, Virginie Ettiegne-Traore, MD6, Francisco Mbofana, MD7, Velephi Okello, MD8, Charles Azih, MD8, Julie A. Denison, PhD9, Sharon Tsui, MPH9, Olivier Koole, MD10, Harrison Kamiru, DrPH11, Harriet Nuwagaba-Biribonwoha, MBChB, PhD11, Charity Alfredo, MD12, Kebba Jobarteh, MD12, Solomon Odafe, MD13, Dennis Onotu, MD13, Kunomboa A. Ekra, MD14, Joseph S. Kouakou, MD14, Peter Ehrenkranz, MD15, George Bicego, PhD15, Kwasi Torpey, PhD16, Ya Diul Mukadi, MD17, Eric van Praag, MD18, Joris Menten, MSc10, Timothy Mastro, MD19, Carol Dukes Hamilton, MD19, Mahesh Swaminathan, MD1, E. Kainne Dokubo, MD1, Andrew L. Baughman, PhD1, Thomas Spira, MD1, Robert Colebunders, MD, PhD10, David Bangsberg, MD20, Richard Marlink, MD21, Aaron Zee, MPH1, Jonathan Kaplan, MD1, Tedd V. Ellerbrock, MD1 (Author affiliations at end of text)

Although scale-up of antiretroviral therapy (ART) since 2005 has contributed to declines of about 30% in the global annual number of human immunodeficiency (HIV)-related deaths and declines in global HIV incidence,* estimated annual HIV-related deaths among adolescents have increased by about 50% (1) and estimated adolescent HIV incidence has been relatively stable. In 2012, an estimated 2,500 (40%) of all 6,300 daily new HIV infections occurred among persons aged 15–24 years.§ Difficulty enrolling adolescents and young adults in ART and high rates of loss to follow-up (LTFU) after ART initiation might be contributing to mortality and HIV incidence in this age group, but data are limited (2). To evaluate age-related ART retention challenges, data from retrospective cohort studies conducted in seven African countries among 16,421 patients, aged ≥15 years at enrollment, who initiated ART during 2004–2012 were analyzed. ART enrollment and outcome data were compared among three groups defined by age at enrollment: adolescents and young adults (aged 15–24 years), middle-aged adults (aged 25–49 years), and older adults (aged ≥50 years). Enrollees aged 15–24 years were predominantly female (81%–92%), commonly pregnant (3%–32% of females), unmarried (54%–73%), and, in four countries with employment data, unemployed (53%–86%). In comparison, older adults were more likely to be male (p<0.001), employed (p<0.001), and married, (p<0.05 in five countries). Compared with older adults, adolescents and young adults had higher LTFU rates in all seven countries, reaching statistical significance in three countries in crude and multivariable analyses. Evidence-based interventions to reduce LTFU for adolescent and young adult ART enrollees could help reduce mortality and HIV incidence in this age group.

In each of seven countries (Côte d'Ivoire, Nigeria, Swaziland, Mozambique, Zambia, Uganda, and Tanzania), a representative sample of ART facilities was selected using either probability-proportional-to-size sampling or purposeful (nonrandom) sampling (Table 1). At each selected facility, a sample frame of study-eligible ART patients was created, and simple random sampling used to select the desired sample size. Eligibility criteria included having started ART during 2004–2012 and ≥6 months before data abstraction. Data were abstracted from ART medical records onto standard forms.

Mortality and LTFU were the primary outcomes of interest. A patient was considered LTFU if he/she had not attended the facility in the 90 days preceding data abstraction for a medication refill, a laboratory visit, or a clinician visit. Mortality ascertainment occurred largely through passive reporting to the health facility by family or friends, and to a lesser extent, through country-specific tracing activities to locate patients late for clinic appointments.

Study design was controlled for during analysis. Age at ART initiation was divided into three age categories (3): 15–24 years, 25–49 years, and ≥50 years. Differences in demographic and clinical characteristics across age groups were assessed using chi-square tests for categorical variables and unadjusted linear regression models for continuous variables.

To estimate the association between age group and rates of death and LTFU, Cox proportional hazards regression models were used to estimate unadjusted and adjusted hazard ratios for each outcome separately. For the multivariable analysis, to best manage missing baseline demographic or clinical data, multiple imputation with chained equations was used to impute missing data included in the model (4). Twenty imputed datasets were created for each outcome: death and LTFU (4). The imputation model included the event indicator, all study variables, and the Nelson-Aalen estimate of cumulative hazard (4). The proportional hazards assumption was assessed using visual methods and the Grambsch and Therneu test.

Demographic and clinical characteristics of adults at ART initiation were compared across age groups by country (Table 2). Age distribution was relatively constant across countries, with 5%–16% aged 15–24 years, 70%–86% aged 25–49 years, and 8%–14% aged ≥50 years. In all seven countries, the youngest age group was almost exclusively female (81%–92%), and the middle-age group mostly female (60%–68%); in contrast, the oldest age group was mostly male in all countries, except Nigeria. In the six countries with data on pregnancy at ART enrollment, pregnancy prevalence was highest in the youngest age group in five countries, where it ranged from 16% to 32%. In all seven countries, being married or in a civil union was least common in the youngest age group (27%–46%), reaching statistical significance in five countries. In the four countries with data on employment status, the youngest age group was least likely to be employed at the time of ART enrollment (14%–47%) (p<0.05).

In all seven countries, median baseline weight was lowest in the youngest age group (48.2–58.0 kg), reaching statistical significance in six countries. In three countries (Nigeria, Swaziland, and Tanzania), prevalence of World Health Organization clinical stage 4 at ART initiation differed across age groups, tending to be lowest in the youngest and highest in the oldest age group (p<0.05). Median baseline CD4 count was similar across age groups in all countries, except Nigeria, where the median was highest in the youngest age group (p=0.004). Median baseline hemoglobin was significantly lower in the youngest age group in four countries (9.4–10.7 g/dL).

Compared with older adults, rates of LTFU were higher in the youngest age group in all seven countries, reaching statistical significance in unadjusted analyses in three countries (Côte d'Ivoire (p=0.005), Mozambique (p<0.001), and Tanzania (p=0.005)) (Table 3). Even after adjusting for baseline demographic and clinical characteristics, rates of LTFU were 1.66–2.45 times as high in the youngest compared with the oldest age group in these three countries (Côte d'Ivoire [p=0.001], Mozambique [p=0.002], and Tanzania [p<0.001]).

In two countries (Swaziland and Uganda), the oldest age group had significantly higher rates of documented mortality than younger age groups (Table 3), and older age remained a significant predictor of mortality even in multivariable analyses.

Discussion

The three main findings based on the experience of the seven African countries are as follows: 1) adolescents and young adults differed significantly from older adults in ART enrollment characteristics; 2) adolescents and young adults tended to have higher LTFU rates; and 3) in two countries (Uganda and Swaziland), adults ≥50 years had higher documented mortality rates.

Adolescent and young adult ART enrollees were almost exclusively female, commonly pregnant, unmarried, and unemployed. The observation that median weight was lowest among adolescents and young adults could be explained by expected weight-for-age growth, sex differences in weight, or undernutrition. Similarly, the observation that median hemoglobin tended to be lowest in the youngest age group might reflect predominantly female sex or higher prevalence of undernutrition.

Available data suggest that this group of predominantly female adolescent and young adult ART enrollees represents a socially vulnerable population (2). Although rates of HIV-related mortality and HIV incidence have declined globally since 2005, mortality has increased and HIV incidence remained relatively stable among adolescents, with the majority of adolescent deaths and new HIV infections occurring in sub-Saharan Africa (2). In African countries with generalized epidemics, being young, female, and unemployed increases the risk for voluntary or coerced sexual contact with older, HIV-infected men (2); this might partly explain HIV infection at a young age among some of the female adolescent and young adult ART enrollees described in this report. Factors that possibly explain high LTFU rates among adolescent and young adult ART enrollees might include stigma (2), lack of money for transport (5), child care responsibilities, and migration for work (6). LTFU from ART is associated with significant increases in mortality risk (7). A recent meta-analysis suggests that 20%–60% of patients lost to follow-up die, with most of these deaths occurring after default from ART (7). Therefore, difficulties in preventing LTFU among adolescent and young adults on ART might be a contributor to HIV-related mortality in this age group. Suboptimal ART adherence among adolescents might also be contributing to adolescent mortality (1).

High rates of LTFU among adolescent and young adult ART enrollees is also concerning from a prevention perspective, because LTFU patients are at risk for transmitting HIV to seronegative partners once ART is discontinued and viral load no longer suppressed (8). High rates of LTFU among young women, among whom the prevalence of pregnancy is high, also increases the likelihood of mother-to-child HIV transmission.

Adult ART enrollees aged ≥50 years were mostly male, commonly married, and employed. In two countries, this age group had higher documented mortality, similar to findings in other studies (9). Higher mortality in this oldest age group should probably be expected because of higher background rates of mortality in the older general population. However, HIV-related reasons for higher mortality in the oldest age group might include slower ART-induced CD4 restoration among older patients (3) or incidence of HIV-associated noncommunicable diseases, especially atherosclerotic disease (10).

The findings in this report are subject to at least four limitations. First, missing data might have introduced nondifferential measurement error. Second, because of differences in cohort size, there was greater power to detect covariate effect sizes in Côte d'Ivoire, Nigeria, Swaziland, and Mozambique than in Zambia, Uganda, and Tanzania. Third, in Zambia, Uganda, and Tanzania, clinics were purposefully selected, limiting generalizability of findings. Finally, limited active tracing for defaulting patients might have resulted in overestimates of LTFU and underestimates of mortality.

The main finding of this report is that adolescent and young adult ART enrollees differ significantly from older adults in demographic and clinical characteristics and are at higher risk for LTFU. Effective interventions to reduce LTFU for adolescent and young adult ART enrollees could help reduce mortality and HIV incidence in this age group.

1Division of Global HIV/AIDS, Center for Global Health, CDC; 2Infectious Diseases Institute, Makerere University College of Health Sciences, Uganda; 3Muhimbili University of Health and Allied Sciences, Tanzania; 4Tropical Diseases Research Center, Zambia; 5Ministry of Health, Nigeria; 6Ministry of Health, Côte d'Ivoire; 7National Institute of Health, Mozambique; 8Ministry of Health, Swaziland; 9Social and Behavioral Health Sciences, FHI 360, Washington, DC; 10Institute of Tropical Medicine, Department of Clinical Sciences, Belgium; 11International Center for AIDS Care and Treatment Programs-Columbia University, New York, NY; 12Division of Global HIV/AIDS, Center for Global Health, CDC, Mozambique; 13Division of Global HIV/AIDS, Center for Global Health, CDC, Nigeria; 14Division of Global HIV/AIDS, Center for Global Health, CDC, Côte d'Ivoire; 15Division of Global HIV/AIDS, Center for Global Health, CDC, Swaziland; 16FHI 360, Zambia; 17FHI 360, Haiti; 18FHI 360, Tanzania; 19Global Health, Population and Nutrition, FHI 360, Durham, NC; 20Massachusetts General Hospital, Boston, MA; 21Harvard School of Public Health, Boston, MA (Corresponding author: Andrew F. Auld, aauld@cdc.gov, 404-639-8997)

References

  1. Idele P, Gillespie A, Porth T, et al. Epidemiology of HIV and AIDS among adolescents: current status, inequities, and data gaps. J Acquir Immune Defic Syndr 2014;66(Suppl 2):S144–53.
  2. Kasedde S, Luo C, McClure C, Chandan U. Reducing HIV and AIDS in adolescents: opportunities and challenges. Curr HIV/AIDS Rep 2013;10:159–68.
  3. Grabar S, Kousignian I, Sobel A, et al. Immunologic and clinical responses to highly active antiretroviral therapy over 50 years of age. Results from the French Hospital Database on HIV. AIDS 2004;18:2029–38.
  4. White IR, Royston P. Imputing missing covariate values for the Cox model. Stat Med 2009;28:1982–98.
  5. Geng EH, Bangsberg DR, Musinguzi N, et al. Understanding reasons for and outcomes of patients lost to follow-up in antiretroviral therapy programs in Africa through a sampling-based approach. J Acquir Immune Defic Syndr 2009;53:405–11.
  6. CDC. Differences between HIV-infected men and women in antiretroviral therapy outcomes—six African countries, 2004–2012. MMWR Morb Mortal Wkly Rep 2013;62:946–52.
  7. Brinkhof MW, Pujades-Rodriguez M, Egger M. Mortality of patients lost to follow-up in antiretroviral treatment programmes in resource-limited settings: systematic review and meta-analysis. PLoS One 2009;4:e5790.
  8. Cohen MS, Chen YQ, McCauley M, et al. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med 2011;365:493–505.
  9. May M, Sterne JA, Sabin C, et al. Prognosis of HIV-1-infected patients up to 5 years after initiation of HAART: collaborative analysis of prospective studies. AIDS 2007;21:1185–97.
  10. Bloomfield GS, Khazanie P, Morris A, et al. HIV and noncommunicable cardiovascular and pulmonary diseases in low- and middle-income countries in the ART era: what we know and best directions for future research. J Acquir Immune Defic Syndr 2014;67(Suppl 1):S40–53.

* Information available at http://www.unaids.org/en/media/unaids/contentassets/documents/epidemiology/2013/gr2013/UNAIDS_Global_Report_2013_en.pdf.

Sources: Kasedde S, Luo C, McClure C, Chandan U. Reducing HIV and AIDS in adolescents: opportunities and challenges. Curr HIV/AIDS Rep 2013;10:159–68; and UNAIDS. Report on the Global AIDS Epidemic, 2012, unpublished estimates; Spectrum 2012.

§ Information available at http://www.unaids.org/sites/default/files/en/media/unaids/contentassets/documents/epidemiology/2012/gr2012/JC2434_WorldAIDSday_results_en.pdf.


What is already known on this topic?

Although scale-up of antiretroviral therapy (ART) since 2005 has contributed to a decline of about 30% in the global annual number of human immunodeficiency (HIV)–related deaths and declines in global HIV incidence, estimated annual HIV-related deaths among adolescents have increased by about 50%, and estimated adolescent HIV incidence has been relatively stable. In 2012, an estimated 2,500 (40%) of all 6,300 daily new HIV infections occurred among persons aged 15–24 years. Difficulty enrolling adolescents and young adults in ART and high rates of loss to follow-up (LTFU) after ART initiation might be contributing to mortality and HIV incidence in this age group, but data are limited.

What is added by this report?

Age-related differences in enrollment characteristics and outcomes were analyzed among 16,421 patients aged ≥15 years starting ART in seven African countries (Côte d'Ivoire, Nigeria, Swaziland, Mozambique, Zambia, Uganda, and Tanzania) during 2004–2012. Patient characteristics and outcomes were compared across three age groups: adolescents and young adults (15–24 years), middle-aged adults (25–49 years), and older adults (≥50 years). Compared with older adults, adolescents and young adults had higher LTFU rates in all seven countries, reaching statistical significance in three countries (Côte d'Ivoire, Mozambique, and Tanzania) in both crude and multivariable analyses.

What are the implications for public health practice?

The higher risk for LTFU among adolescent and young adult ART enrollees, compared with older adults, increases their risk for death and increases the risk they will transmit HIV to seronegative sex partners. Effective interventions to reduce LTFU for adolescent and young adult ART enrollees could help reduce mortality and lower HIV incidence in this age group.


TABLE 1. Summary of sampling strategies to select cohorts of enrollees for antiretroviral therapy (ART) — seven African countries, 2004–2013

Region and country

Stage 1: Selection of study facilities

Stage 2: Selection of study patients

No. of ART clinics

No. of
ART enrollees at
ART clinics

Clinic eligibility criteria
for
study

No. of study-eligible clinics

Estimated no. of study-eligible adult ART enrollees at study-eligible clinics

Site sampling technique

No. of clinics selected

Age-eligibility criteria (age at
ART initiation)

ART enrollment years

Patient sampling technique at
selected study clinics

Planned sample size

No. of eligible patient charts abstracted

Date of data collection

West Africa

Côte d'Ivoire

124 by Dec 2007

36,943

Enrolled ≥50 adults by Dec 2007

78

36,110

PPS

34

Adults aged
≥15 yrs

2004–2007

SRS

4,000

3,682

Nov 2009–March 2010

Nigeria

178 by Dec 2009

168,335

Enrolled ≥50 adults by Dec 2009

139

167,438

PPS

35

Adults aged
≥15 yrs

2004–2012

SRS

3,500

3,496

Dec 2012–Aug 2013

Southern Africa

Swaziland

31 by Dec 2009

50,767

All ART initiation sites eligible

31

50,767

PPS

16

Adults aged
≥15 yrs

2004–2010

SRS

2,500

2,510

Nov 2011– Feb 2012

Mozambique

152 by Dec 2006

43,295

Enrolled ≥50 adults by Dec 2006

94

42,234

PPS

30

Adults aged
≥15 yrs

2004–2007

SRS

2,600

2,596

Sept–Nov 2008

Zambia

322 by Dec 2007

65,383

Enrolled ≥300 adults by Dec 2007

129*

58,845*

Purposeful

6

Adults aged
≥15 yrs

2004–2009

SRS

1,500

1,214

April–July 2010

East Africa

Uganda

286 by Dec 2007

45,946

Enrolled ≥300 adults by Dec 2007

114*

41,351*

Purposeful

6

Adults aged
≥15 yrs

2004–2009

SRS

1,500

1,466§

April–July 2010

Tanzania

210 by Dec 2007

41,920

Enrolled ≥300 adults by Dec 2007

85

37,728*

Purposeful

6

Adults aged
≥18 yrs

2004–2009

SRS

1,500

1,457

April–July 2010

Total

 

452,589 

670

434,473

133

17,100

16,421

Abbreviations: PPS = probability-proportional-to-size; SRS = simple random sampling.

* Estimates based on available published data.

In Zambia, from 1,457 records sampled, 243 were excluded because of noncompliance with simple random sampling procedures at one site.

§ In Uganda, from 1,472 records samples, six patients were excluded because of absence of age data at ART initiation.

In Tanzania, from 1,458 records samples, one patient was excluded because of absence of age data at ART initiation.


TABLE 2. Demographic and clinical characteristics of patients at initiation of antiretroviral therapy (ART) — seven African countries, 2004–2012*

Characteristic and
age group (yrs) 

Côte d'Ivoire

(N = 3,682)

Nigeria

(N = 3,496)

Swaziland

(N = 2,510)

Mozambique

(N = 2,596)

Zambia

(N = 1,214)

Tanzania

(N = 1,457 )

Uganda

(N = 1,466)

Age at ART initiation (No. and %)

15–24

188

5%

399

11%

398

16%

284

12%

95

8%

83

6%

95

6%

25–49

3,087

83%

2,805

81%

1,759

70%

2,069

79%

1,000

82%

1,198

82%

1,261

86%

≥50

407

12%

292

9%

353

14%

243

10%

119

10%

176

12%

110

8%

Female (No. and %)

15–24

166

87%

366

92%

326

82%

45

86%

82

86%

73

88%

77

81%

25–49

2,077

68%

1,808

64%

1,120

64%

838

60%

599

60%

813

68%

837

66%

≥50

179

46%

146

51%

175

49%

137

48%

45

38%

87

49%

50

45%

p–value

<0.001§

<0.001

<0.001

<0.001

<0.001

<0.001

<0.001

Among females, pregnant (No. and %)

15–24

4

3%

56

16%

82

26%

61

30%

15

32%

25

18%

25–49

64

4%

188

10%

117

11%

138

14%

56

12%

102

9%

≥50

0

0%

0

0%

2

1%

0

0%

0

0%

0

0%

p-value

0.567

<0.001

<0.001

0.002

0.003

<0.001

Married/Civil union (No. and %)

15–24

41

27%

177

43%

85

28%

99

41%

38

46%

28

41%

21

34%

25–49

1,393

50%

1,795

64%

725

47%

999

55%

520

60%

505

53%

431

43%

≥50

202

54%

200

67%

190

65%

113

55%

67

64%

71

49%

40

43%

Missing

414

11%

86

2%

384

15%

233

9%

166

14%

299

21%

313

21%

p-value

<0.001

<0.001

<0.001

0.001

0.022

0.115

0.354

Employed (No. and %)

15–24

59

47%

91

30%

68

31%

28

14%

25–49

1,394

63%

1,541

66%

551

48%

860

49%

≥50

148

53%

165

70%

73

32%

104

56%

Missing

1,081

29%

420

12%

925

37%

328

13%

 

 

 

 

 

 

p-value

<0.001

<0.001

<0.001

<0.001

 

 

 

Baseline weight (No. and median [kg])

15–24

162

49.0

371

52.0

356

58.0

223

50.0

83

49.0

80

48.2

86

52.7

25–49

2,743

53.0

2589

57.0

1575

60.0

1,658

54.5

882

53.0

1,163

51.1

1,145

55.0

≥50

351

54.0

274

57.0

301

59.9

180

52.5

108

55.0

172

50.2

101

56.0

Missing

426

12%

262

7%

278

11%

535

21%

141

12%

42

3%

134

9%

p-value

0.005

<0.001

0.024

0.015

0.001

0.296

0.001

WHO clinical stage 4 (No. and %)

15–24

25

18%

25

5%

22

6%

32

20%

11

13%

20

29%

12

14%

25–49

462

22%

197

8%

218

13%

205

15%

96

11%

257

27%

137

12%

≥50

67

25%

24

11%

53

16%

22

15%

5

5%

48

35%

11

12%

Missing

1,101

30%

232

7%

290

12%

979

38%

157

13%

293

20%

164

11%

p-value

0.468

0.012

<0.001

0.066

0.100

<0.001

0.551

Baseline CD4 count (No. and median [cells/µL])

15–24

165

122

320

192

359

158

249

175

69

147

50

175

76

161

25–49

2,811

136

2321

157

1618

141

1,794

157

701

128

933

126

1,011

133

≥50

367

132

244

142

319

160

211

133

79

158

137

160

79

147

Missing

339

9%

611

17%

214

9%

342

13%

365

30%

337

23%

300

20%

p-value

0.216

0.004

0.139

0.077

0.704

0.243

0.501

Baseline hemoglobin (No. and median [g/dL])

15–24

156

10.0

190

10.3

229

10.7

211

9.4

52

10.1

37

9.6

55

11.5

25–49

2,646

9.9

1,365

10.3

1165

11.2

1,515

10.2

582

10.6

648

10.2

748

11.9

≥50

347

9.9

145

10.8

218

11.6

173

10.6

70

11.6

90

10.9

62

12.1

Missing

533

14%

1,796

51%

898

36%

697

27%

510

42%

682

47%

601

41%

p-value

0.524

0.690

<0.001

<0.001

0.002

0.028

0.306

Abbreviation: WHO = World Health Organization.

* Although the study captured patient follow-up time through 2013, all patients started ART during the period 2004–2012.

Proportions from Côte d'Ivoire, Nigeria, Swaziland, and Mozambique are weighted to account for sampling design.

Bold-typed p-values are statistically significant (p<0.05).


TABLE 3. Association between age group at initiation of antiretroviral therapy and rates of loss to follow-up and death — seven African countries, 2004–2013

Country 

 Age group (yrs) 

No. 

Lost to follow-up

Died

Rate
(per 100) 

Crude

Adjusted 

Rate
(per 100) 

Crude

Adjusted

HR

(95% CI)

p-value

AHR*

(95% CI)

p-value

HR

(95% CI)

p-value

AHR*

(95% CI)

p-value

Côte d'Ivoire 

 

≥50

407

14.5

1.00

1.00

4.2

1.00

1.00

 

25–49

3,087

17.5

1.21

(0.92–1.59)

0.171

1.33

(1.00–1.77)

0.052

2.9

0.68

(0.45–1.05)

0.077

0.76

(0.51–1.12)

0.155

 

15–24

188

23.0

1.54

(1.15–2.04)

0.005

1.66

(1.24–2.22)

0.001

3.8

0.87

(0.37–2.03)

0.732

0.97

(0.43–2.18)

0.935

Nigeria 

 

≥50

399

15.3

1.00

1.00

1.5

1.00

1.00

 

25–49

2,805

13.7

0.91

(0.70–1.18)

0.446

0.94

(0.73–1.22)

0.640

1.1

0.79

(0.43–1.46)

0.441

0.89

(0.47–1.68)

0.714

 

15–24

292

16.5

1.09

(0.79–1.50)

0.604

1.04

(0.75–1.44)

0.818

0.8

0.51

(0.20–1.34)

0.166

0.74

(0.30–1.86)

0.514

Swaziland§

≥50

353

11.0

1.00

1.00

3.0

1.00

1.00

 

25–49

1,759

11.4

1.06

(0.91–1.23)

0.452

0.99

(0.81–1.20)

0.887

1.9

0.66

(0.46–0.93)

0.021

0.56

(0.39–0.81)

0.006

 

15–24

398

13.2

1.26

(0.94–1.70)

0.113

1.22

(0.89–1.68)

0.198

1.9

0.65

(0.46–0.92)

0.018

0.58

(0.38–0.90)

0.019

Mozambique  

 

≥50

243

16.4

1.00

1.00

3.8

1.00

1.00

 

25–49

2,069

14.4

0.96

(0.78–1.18)

0.686

1.02

(0.79–1.32)

0.872

3.2

0.94

(0.55–1.59)

0.805

1.10

(0.62–1.96)

0.733

 

15–24

284

28.4

1.80

(1.46–2.21)

<0.001

1.76

(1.27–2.43)

0.002

5.0

1.40

(0.72–2.71)

0.296

1.33

(0.72–2.45)

0.339

Zambia 

 

≥50

95

21.4

1.00

1.00

3.6

1.00

1.00

 

25–49

1,000

21.7

1.01

(0.75–1.37)

0.928

0.94

(0.69–1.29)

0.722

2.3

0.63

(0.29–1.33)

0.223

0.66

(0.30–1.47)

0.312

 

15–24

119

25.6

1.14

(0.75–1.74)

0.539

1.21

(0.78–1.89)

0.393

5.1

1.32

(0.49–3.51)

0.582

1.26

(0.43–3.71)

0.679

Tanzania

 

≥50

83

13.0

1.00

1.00

8.0

1.00

1.00

 

25–49

1,198

17.8

1.36

(0.98–1.90)

0.067

1.47

(1.05–2.06)

0.024

6.4

0.80

(0.52–1.23)

0.309

0.90

(0.58–1.42)

0.661

 

15–24

176

30.1

2.01

(1.24–3.25)

0.005

2.45

(1.50–4.01)

<0.001

13.5

1.37

(0.70–2.70)

0.358

1.40

(0.69–2.82)

0.354

Uganda

 

≥50

95

6.0

1.00

1.00

2.8

1.00

1.00

 

25–49

1,261

7.6

1.29

(0.76–2.17)

0.346

1.37

(0.81–2.34)

0.240

1.0

0.35

(0.15–0.80)

0.013

0.31

(0.13–0.76)

0.010

 

15–24

110

7.1

1.18

(0.57–2.44)

0.664

1.19

(0.56–2.51)

0.647

1.0

0.34

(0.07–1.66)

0.184

0.25

(0.05–1.29)

0.098

Abbreviations: HR = hazard ratio; CI = confidence interval; AHR = adjusted hazard ratio.

* All variables presented in the table were included in the multivariable model for each country.

Bold-typed p-values are statistically significant (p<0.05) or borderline significant (p=0.05–0.10).

§ In Swaziland, the study was designed to assess the effect of interfacility transfer of stable patients (down-referral) on risk for loss to follow-up, and this time-varying covariate was included in the multivariable model in addition to variables presented in the table.



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