The reasons for and outcomes in patients lost to follow-up from African ART programs are not well understood. In a prototypical scale-up program in Mbarara, Uganda, we found that the incidence of losses to follow-up was high (39% by 3 years after ART initiation) and comparable to prior reports.3,5,18
By tracking a representative sample of patients in the community after loss, we achieved a clearer understanding of this important group. Common reasons for loss to follow-up were social or structural. These included problems with transportation, finances, and work/child care responsibilities. Among those lost to follow-up, subsequent outcomes were heterogeneous. An important fraction of patients had died, but, in fact, the majority of patients had transferred to other care facilities—a favorable outcome. Finally, we identified several clinical factors at the time of last clinic visit associated with deaths after patients become lost.
Direct interviews of lost patients provide among the most direct evidence to date as to the reasons for loss in rural Africa. Lack of transportation and distance to clinic were the most common reasons for not returning to clinic. This is not surprising because Mbarara district is mainly rural and distances between home and clinic can be great. More than 70% of sub-Saharan Africans reside in rural settings; therefore, transportation is likely to be important throughout the continent. This finding suggests that alternative models to delivering care such as more dispersed satellite clinics or home-based programs19
are needed to ensure continuous care. Lack of money was the second most common reason for absence. Although poverty likely exacerbates other barriers such as distance and transportation, 30% fewer people cited lack of money compared with transportation. We believe that this may reflect the fact that transportation is a structural barrier that cannot always be overcome by individual-level financial assistance. For example, if a road does not exist, individual financial resources cannot ensure continued attendance in clinic.20
Work and child care were also common reasons for failure to return to clinic. In resource-limited settings, accessing health care is often only one competing need in a nexus of urgent priorities, and social responsibilities to work and children may take precedent over personal health. These factors, furthermore, may affect women more than men. These reasons for failure to return to clinic argue that in Africa, socio-structural factors are more important than individual-based psychosocial factors, as put forth by prevailing Western information–motivation–behavior
Our findings confirm previous observations that a substantial fraction of lost patients had died9,22,23
and extend these observations by demonstrating that a large portion of the deaths occurred shortly after the last clinic visit. The patients who died in the first months after their last clinic visit likely died from conditions that evolved while in care
rather than from clinical deterioration after cessation of care
. In other words, not all deaths in patients lost to follow-up occurred because of loss to follow-up. Prevention of these early deaths will likely only be achieved via strengthening of the clinical infrastructure including improved point-of-care diagnostic testing and availability of effective therapies.
Our study also provides further information on the predictors of deaths, which has implications for outreach interventions. We found that several clinical characteristics present at the last available clinic visit were associated with subsequent deaths. These included older age, low blood pressure, a central nervous syndrome, and a low pre-ART CD4+ T-cell count. Whether or not these specific factors will be operative in other settings with other care paradigms requires further study, but these data provide proof-of-concept that discrete factors can be identified at the time of last clinic visit that are predictive of subsequent death. This suggests that programs may be able to efficiently target which lost patients to seek after they fail to return and how urgently to do so.
We found important heterogeneity in outcomes that challenge prevailing notions. Extrapolating the experience of the 48 lost patients we directly interviewed to all patients still alive, 66 patients (59% of the total 111 lost) had attended a different care facility in the prior 3 months. The same extrapolation would yield 56 patients (50%) having taken ART in the prior 30 days. These transfers should be considered favorable outcomes because patients are still in care and likely transferred to a more accessible clinic. The heterogeneity may mean that using losses to follow-up as an end point in epidemiologic studies or combining them together with death may be aggregating very different outcomes. Novel approaches, such as sampling, may better distinguish heterogeneous outcomes among the lost.
The validity of this sampling-based approach depends on the proportion of patients sought whose vital status is ascertained. The higher proportion found, the less potential bias. As proof that a high percentage of vital status ascertainment is possible, we determined vital status in 87% of patients sought. Interestingly, the distance a lost patient resided from clinic did not influence our ability to find him, although our analysis was admittedly underpowered to detect anything but large effects. We attribute high ascertainment of vital status to several factors. Our tracker had intimate knowledge of the community’s terrain and understanding and sensitivity to issues involving searching for persons with HIV/AIDS. Ample time (up to several days) was allowed to search for lost patients.
This study does have limitations. One is that with substantial socioeconomic, geographic, and programmatic differences across African-based treatment settings, our findings may not necessarily generalize to other clinics. Specifically, just as how our previously reported 5-fold difference in mortality after sampling-based correction for those lost to follow-up8
may not necessarily be the right correction factor for all other settings, the reasons for not returning to clinic and the diversity of outcomes after becoming lost may differ as well. For example, our finding that transportation was a major reason for loss to follow-up is in contrast with a report from urban South Africa.11
This suggests that reasons for loss likely differ across treatment delivery settings. We therefore believe that diverse treatment programs, as part of routine monitoring and evaluation, need to conduct their own analyses of losses to follow-up. Another limitation is that although our sample was unselected and consecutive, it was not a formal random sample. Hence, our findings could theoretically be biased in directions that are difficult to predict. However, our sample was objectively identified by an electronic medical record system that captures all visits and assembled by a data manager with no prior knowledge of the patients and hence no predilection for whom to track or not. Furthermore, the lack of substantial differences in characteristics between the lost patients whom we tracked in the community and all other lost patients suggests that the sample was representative. A third limitation is that although we have no reason to doubt the truthfulness of the responses given for failure to report to clinic, it is conceivable that patients may have withheld less socially desirable responses. Because only extensive in-depth interviews (which were beyond the scope of our cost-efficient approach) may have brought out these responses, our findings should not be considered to be definitive. Furthermore, a comparison group, which we did not have, would allow for a quantitative estimate of the effect of each factor reported on actual loss to follow-up. The final limitation is that working with data collected during routine clinical care in a resource-limited setting did result in our having a substantial number of missing CD4+
T-cell values for the analysis of predictors of death among the lost patients. It is theoretically possible that these missing determinations may not have been missing at random and hence could lead to bias.
Although not a threat to the validity of our findings, one potential criticism of our sampling approach is that it does not offer all lost patients the extra encouragement to return to care that might be conferred by a visit from a tracker. This underscores the fact that this sampling-based strategy is a tool for understanding outcomes and not a retention intervention per se. Clearly, if resources permitted, it would be preferable to search for all lost patients, but because this is rarely the case in the African setting, a sampling-based approach offers a resource-efficient means for individual clinics to understand the local circumstances regarding their losses to follow-up and can be used to target outreach efforts to the right patients at the right time. Interestingly, recent data from a resource-rich setting found a rate of loss to follow-up comparable to resource-limited settings,24
suggesting that a sampling-based approach to understand losses to follow-up would be appropriate there as well.
In summary, we used a sampling-based approach to understand the reasons for and outcomes of patients who become lost to follow-up after initiation in Africa. Among those deemed lost, we found a high percentage of both adverse and favorable outcomes. Deaths often occur shortly after interaction with the clinic, suggesting that they are caused by conditions that evolved while in care. Major reasons for not returning to the original clinic were transportation, money, and employment/child care responsibilities. The reasons for and outcomes of losses may not be the same across varied national, cultural, and program settings in Africa, and hence, different regions will need to study their own lost patients. To do this, we believe that a sampling-based approach is both cost efficient and feasible.