Background Monitoring patient’s clinical attendance is a crucial means of improving retention in care and treatment programmes. Sickle cell patients’ outcomes are improved by participation in comprehensive care programmes, but these benefits cannot be achieved when patients are lost from clinical care. In this study, patients are defined as loss to follow-up when they did not attend clinic for more than 9 months. Precise information on the retention rate and characteristics of those who are not following their clinic appointments is needed to enable the implementation of interventions that will be effective in increasing the retention to care. Method This was a retrospective study involving sickle cell patients registered in the Muhimbili Sickle Cohort in Tanzania. Descriptive and survival analysis techniques both non-parametric methods (Kaplan-Meier estimator and Log-rank test) and semi-parametric method (Cox’s proportional hazard model), were used. A p-value of 0.05 was considered significant to make an inference from the analysis. Results A total of 5476 patients were registered in the cohort from 2004 to 2016. Of these, 3350 (58.13%) were actively participating in clinics, while 2126 (41.87%) were inactive, of which 1927 (35.19%) were loss to follow-up. We used data from 2004 to 2014 because between 2015 and 2016, patients were referred to other government hospitals. From the survival analysis results, pediatric (HR: 14.29,95% CI: 11.0071–18.5768, p <  0.001) and children between 5 and 17 years [HR:2.61,95% CI:2.2324–3.0705, p <  0.001] patients were more likely to be loss to follow-up than the adult (18 and above years) patients. It was found that patients with above averages for hematocrit (HR: 2.38, 95% CI: 1.0076–1.0404, p = 0.0039) or mean cell volume (HR: 4.28, (95% CI: 1.0260–1.0598, p < 0.001) were more likely to be loss to follow-up than their counterparts. Conclusion Loss to follow-up is evident in the cohort of patients in long term comprehensive care. It is, therefore, necessary to design interventions that improve patients’ retention. Suggested solutions include refresher training for health care workers and those responsible for patient follow-up on techniques for retaining patients and comprehensive transition programs to prepare patients who are moving from pediatric to adult clinics.

BackgroundSickle cell disease (SCD) is a severe hereditary form of anemia that contributes between 50% and 80% of under-five mortality in Africa. Eleven thousand babies are born with SCD annually in Tanzania, ranking 4th after Nigeria, the Democratic Republic of Congo and India. The absence of well-described SCD cohorts is a major barrier to health research in SCD in Africa.ObjectiveThis paper describes the Sickle Pan African Consortium (SPARCO) database in Tanzania, from the development, design of the study instruments, data collection, analysis of data and management of data quality issues.MethodsThe SPARCO registry used existing Muhimbili Sickle Cell Cohort (MSC) study case report forms (CRF) and later harmonized data elements from the SickleInAfrica consortium to develop Research Electronic Data Capture (REDCap) instruments. Patients were enrolled through various strategies, including mass screening following media sensitization and health education events during World Sickle Cell Day each June and the SCD awareness month in September. Additional patients were identified through active surveillance of previously participating patients in the MSC.ResultsThree thousand eight hundred patients were enrolled between October 2017 and May 2021. Of these, 1,946 (51.21%) were males and 1,864 (48.79%) were females. The hemoglobin phenotype distribution was 3,762 (99%) HbSS, 3 (0.08%) HbSC and 35 (0.92%) HbSβ +thalassemia. Hemoglobin levels, admission history, blood transfusion and painful events were recorded from December 2017 to May 2021.ConclusionThe Tanzania SPARCO registry will improve healthcare for SCD in Africa through the facilitation of collaborative data-driven research for SCD.

Genetic modifiers underlying various sickle cell disease phenotypic expressions are largely unexplored in Africa due to lack of databases linking biospecimens with demographic and clinical data. The problem may be compounded by a complete lack of biorepositories in these settings. This article aims to document the physical verification of biospecimens stored in the biorepository and link them to patient clinical and demographic information to facilitate plans for genomic and related clinical research studies. We reviewed and updated the existing biorepository infrastructure at Muhimbili Sickle Cell Programme in Dar es Salaam, Tanzania. The database of archived biospecimens was updated with the location information of respective biospecimens following the physical verification of biospecimens and then mapping the patient demographic and clinical data with the biospecimen data using sickle cell patients’ demographic identifiers. Three freezers maintained at -80°C store a total of 74,079 biospecimens, of which 63,345 were from 5,159 patients registered in the Muhimbili Sickle Cohort from 2004 to 2016. Out of stored biospecimens, follow-ups were 46,915 (74.06%), control 8,067 (12.74%), admission 5,517 (8.71%) and entry 2,846 (4.49%). Of these registered patients, females were 2,521 (48.87%) and males were 2,638 (51.13%). The age distribution was 1 to 59 years, with those above 18 years being 577 (11.18%) and children 4,582 (88.82%) of registered patients. The notable findings during the process include a lack of automated biospecimen checks, laboratory information management system and standardization of equipment used, biospecimens not linked to clinical and demographic data, date format inconsistencies, lack of regular updating of a database on exhausted biospecimens and updates when biospecimens are moved between positions within freezers. Well-organized biorepository plays a crucial role in answering future research questions. Enforcing strict standard operating procedures and quality control standards will ensure that laboratory scientists and other users adhere to the best biospecimen management procedures.

Sickle cell disease (SCD) is a common genetic disorder in Africa. Some ongoing work in SCD research includes the analysis and comparisons of variation in phenotypic presentations and disease outcomes with the genotypic signatures. This has contributed to the observed growth of molecular and genetic data in SCD. However, while the “omics” data continues to pile, the capacity to interpret and turn the genetic findings into clinical practice is still underdeveloped, especially in the developing region. Building bioinformatics infrastructure and capacity in the region is key to bridging the gap. This paper seeks to illustrate how the Sickle Cell Programme (SCP) at the Muhimbili University of Health and Allied Sciences (MUHAS) in Tanzania, modeled the integration of infrastructure for bioinformatics and clinical research while running day-to-day clinical care for SCD in Tanzania.