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Clients registering for HIV care between July 2008 and August ... associated with LTFU before the second visit, while distance, education status and seasonality ...
Tropical Medicine and International Health

doi:10.1111/j.1365-3156.2011.02889.x

volume 17 no 1 pp 82–93 january 2012

Early loss to follow-up of recently diagnosed HIV-infected adults from routine pre-ART care in a rural district hospital in Kenya: a cohort study Amin S. Hassan1, Katherine L. Fielding2, Nahashon M. Thuo1, Helen M. Nabwera1, Eduard J. Sanders1,3 and James A. Berkley1,3 1 KEMRI ⁄ Wellcome Trust Centre for Geographic Medicine Research (Coast), Kilifi, Kenya 2 London School of Hygiene and Tropical Medicine, London, UK 3 Centre for Clinical Vaccinology & Tropical Medicine, University of Oxford, Oxford, UK

Abstract

objective To determine the rate and predictors of early loss to follow-up (LTFU) for recently diagnosed HIV-infected, antiretroviral therapy (ART)-ineligible adults in rural Kenya. methods Prospective cohort study. Clients registering for HIV care between July 2008 and August 2009 were followed up for 6 months. Baseline data were used to assess predictors of pre-ART LTFU (not returning for care within 2 months of a scheduled appointment), LTFU before the second visit and LTFU after the second visit. Logistic regression was used to determine factors associated with LTFU before the second visit, while Cox regression was used to assess predictors of time to LTFU and LTFU after the second visit. results Of 530 eligible clients, 178 (33.6%) were LTFU from pre-ART care (11.1 ⁄ 100 personmonths). Of these, 96 (53.9%) were LTFU before the second visit. Distance (>5 km vs. 35.0 Single Married (monogamous ⁄ polygamous) Separated ⁄ Divorced ⁄ Widowed In-patient wards Out-patient ⁄ VCT centers Christian Muslim Others No schooling Primary schooling Secondary ⁄ Higher Sparsely populated (25 people ⁄ km2) Missing Mean (SD) (Min–Max) 20.0 Missing Mean (SD) (Min–Max) 5.0 Missing Dry Wet Stage I Stage II Missing Mean (SD) (Min–Max) 500.0 Missing

Age (years)* Age group (years)

Marital status

Entry point  Religion

Education status

Population density (sub-location level)

Distance from home to the hospital (km)* Group distance from home to the hospital (km)

Distance from home to the main road (km)* Group distance from home to the road (km)

Season at registration WHO staging

BMI (kg ⁄ m2)* BMI groups (kg ⁄ m2)

CD4 count (cells ⁄ ul)* CD4 groups (cells ⁄ ul)

Yes (n = 128)

No (n = 402)

17 (13.3) 111 (86.7) 32.6 (10.8) (16.8–78.2) 31 (24.2) 54 (42.2) 43 (33.6) 11 (8.6) 81 (63.3)

101 (25.1) 301 (74.9) 32.3 (10.1) (15.1–75.0) 98 (24.4) 172 (42.8) 132 (32.8) 55 (13.7) 263 (65.4)

118 (22.3) 412 (77.7) 32.4 (10.2) (15.1–78.2) 129 (24.3) 226 (42.6) 175 (33.0) 66 (12.5) 344 (64.9)

36 (28.1)

84 (20.9)

120 (22.6)

13 115 82 16 30 45 69 14 62

(10.2) (89.8) (64.1) (12.5) (23.4) (35.2) (53.9) (10.9) (48.4)

62 (48.4) 4 (3.1) 10.2 (9.2) (0.8–37.6) 49 (38.3) 50 (39.1) 25 (19.5) 4 (3.1) 3.2 (5.1) (0.0–22.3) 66 (51.6) 31 (24.2) 27 (21.1) 4 (3.1) 77 (60.2) 51 (39.8) 76 (59.4) 52 (40.6) 0 (0.0) 20.9 (3.8) (13.5–35.4) 37 (28.9) 91 (71.1) 0 (0.0) 486.8 (184.0) (206.0–1276.0) 25 (19.5) 43 (33.6) 49 (38.3) 11 (8.6)

ª 2011 The Authors. Tropical Medicine and International Health published by John Wiley & Sons Ltd.

72 330 254 82 66 113 207 82 169

(17.9) (82.1) (63.2) (20.4) (16.4) (28.1) (51.5) (20.4) (42.0)

167 (41.5) 66 (16.4) 11.8 (10.5) (0.8–44.5) 138 (34.3) 136 (33.8) 111 (27.6) 17 (4.2) 3.4 (5.5) (0.0–41.7) 194 (48.3) 87 (21.6) 104 (25.9) 17 (4.2) 227 (56.5) 175 (43.5) 182 (45.3) 165 (41.0) 55 (13.7) 21.3 (3.7) (14.5–38.7) 62 (15.4) 276 (68.7) 64 (15.9) 431.4 (205.8) (200.0–1100.0) 105 (26.1) 54 (13.4) 66 (16.4) 177 (44.0)

Total (n = 530)

85 445 336 98 96 158 276 96 231

(16.1) (83.9) (63.4) (18.5) (18.1) (29.8) (52.1) (18.1) (43.6)

229 (43.2) 70 (13.2) 11.4 (10.2) (0.8–44.5) 187 (35.3) 186 (35.1) 136 (25.7) 21 (4.0) 3.4 (5.4) (0.0–41.7) 260 (49.1) 118 (22.3) 131 (24.7) 21 (4.0) 304 (57.4) 226 (42.6) 258 (48.7) 217 (40.9) 55 (10.4) 21.2 (3.7) (13.5–38.7) 99 (18.7) 367 (69.3) 64 (12.1) 450.3 (200.1) (200.0–1276.0) 130 (24.5) 97 (18.3) 115 (1.7) 188 (35.5)

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Tropical Medicine and International Health

volume 17 no 1 pp 82–93 january 2012

A. S. Hassan et al. Pre-ART loss to follow-up in Kenya

Table 1 (Continued) On AHT ⁄ FBP Risk factor

Categories

Yes (n = 128)

No (n = 402)

Total (n = 530)

Hemoglobin (g ⁄ dl)*

Mean (SD) (Min–Max) 12.0 Missing

10.2 (2.0) (5.3–15.4) 18 (14.1) 25 (19.5) 36 (28.1) 18 (14.1) 31 (24.2)

10.2 (2.3) (4.8–18.0) 29 (7.2) 48 (11.9) 51 (12.7) 26 (6.5) 248 (61.7)

10.2 (2.2) (4.8–18.0) 47 (8.9) 73 (13.8) 87 (16.4) 44 (8.3) 279 (52.6)

Hemoglobin groups (g ⁄ dl)

AHT ⁄ FBP, Anti Helminthic Trial ⁄ Food By Prescription. *Mean [standard deviation (SD)] and [Minimum ⁄ Maximum (Min–Max)] included for continuous variables.  Site where clients have been referred from, Body Mass Index (BMI), Voluntary Counseling and Testing (VCT), World Health Organization (WHO).

Survival probability

1.00 0.75 0.50 0.25 0.00 0

1

2 3 4 Follow up in months

5

6

Figure 3 Kaplan–Meier curve showing loss to follow-up of recently diagnosed HIV infected adults from routine preantiretroviral therapy care, followed up for 6 months in a district hospital in Kenya (N = 530).

Ooko et al. 2010). However, age was not found to be an independent predictor of pre-ART LTFU in our setting. Interestingly, level of education at registration into HIV care had a weak association with LTFU after the second visit, suggesting that better-educated clients were likely to return after registration for follow-up visits but dropout thereafter. A plausible explanation is that educated clients have better-paying jobs, and may opt to acquire the main pre-ART intervention, the cheap and readily available cotrimoxazole, over the counter to avoid the HIV-related stigma of being seen in the clinic. We also found weak evidence of an association between dry seasons and LTFU after the second visit. Given that the community is mainly agrarian, some clients may be forced to seek alternative socio-economic activities to sustain their livelihoods during the dry seasons. This may necessitate working long hours or out-migration to other districts in search of jobs.

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Importantly, HIV disease severity as determined by lower CD4 count, lower haemoglobin levels, lower BMI and late clinical staging did not predict pre-ART LTFU in this setting. Most previous studies on loss to HIV care in clients on ART have identified these factors as independently associated with LTFU. Our findings, together with recent data from South Africa (Losina et al. 2010), suggest that the dynamics and risk factors for pre-ART retention differ considerably from those found among clients who have started ART. In view of the fact that literature suggests high rates of early mortality after ART initiation in Africa (Lawn et al. 2008; Brinkhof et al. 2008; Bassett et al. 2010), it is plausible that recently diagnosed HIV-infected clients register for care and dropout while they are still healthy, only to present later with advanced HIV disease necessitating immediate ART initiation. If this is the case, then we argue that focusing and redirecting resources towards provision of an enhanced standard package of pre-ART care may improve timely initiation of ART and influence early adverse outcomes. The pre-ART package of care may include a structured framework of counselling and support at both testing and registration into HIV care. This approach has been applied in ART programmes to enhance retention and ART adherence in different settings with relative success (Etienne et al. 2010). Evidently, the same approach is equally important in pre-ART clients registering for HIV care. Other pre-ART care services may include provision of prophylactic anthelmintics, isoniazid preventive therapy (IPT), multivitamins and nutritional support in form of food programmes. These interventions may serve as an incentive for follow-up and counter the indirect costs incurred.

ª 2011 The Authors. Tropical Medicine and International Health published by John Wiley & Sons Ltd.

Tropical Medicine and International Health

volume 17 no 1 pp 82–93 january 2012

A. S. Hassan et al. Pre-ART loss to follow-up in Kenya

Table 2 Cox univariable and multivariable analysis for predictors of pre-antiretroviral therapy ‘LTFU’ in newly diagnosed HIV infected adult clients registered for routine HIV care in a district hospital in Kenya (N = 530)

Risk factors

Categories

Gender

Male Female 15.0–25.0 25.1–35.0 >35.0 Single Married (monogamous ⁄ polygamous) Separated ⁄ Divorced ⁄ Widowed In-patient wards Out-patient ⁄ VCT centers Christian Muslim Others No schooling Primary schooling Secondary ⁄ Higher Sparsely populated (25 people ⁄ km2) 5.0 Dry Wet I II 500.0 12.0 Dry Wet

Age group (years) Marital status

Entry point

Religion

Education status

Population density (sublocation level) Distance from home to the road (km) Season at registration WHO staging§ BMI groups (kg ⁄ m2)§ CD4 groups (cells ⁄ ul)§ Hemoglobin groups (g ⁄ dl)§

Time updated season–

Cox univariable analysis

Cox multivariable analysis (n = 509) Adjusted HRà

95% CI

0.52





0.19

– 1.0 0.5

– – 0.3–0.6



0.01

0.5

0.3–0.8