Comparative Study of Haiti and Miami Cohorts of Sickle Cell Disease (CSHSCD): Methods, Accomplishments, and Implementation
Bibliographic record
Abstract
Abstract Background: There are significant limitations in Haiti for the diagnosis and management of sickle cell disease (SCD), including the non-availability of universal newborn screening (NBS) and transcranial Doppler (TCD) ultrasound screening, and the lack of diagnostic laboratory resources, oral penicillin and hydroxyurea (HU). Methods: Beginning in September 2019, CSHSCD (R01HL149121), a 5-year NIH-sponsored observational comparative study of children with SCD from Haitian ethnicity in Miami and in Haiti compared to children of African American ethnicity with SCD, was designed to increase access to care in Haiti. The study aims are 1) to compare the incidence of SCD among newborns from Haitian and African American ethnicity in Miami, 2) to establish NBS programs for hemoglobinopathies in Haiti, and 3) to compare cohorts of children in SCD at the study sites. The participating sites are the University of Miami (UM, Miami, Florida), Hôpital Saint Damien (HSD, Tabarre, Haiti), Hôpital de l'Université d'Etat d'Haïti (HUEH, Port-au-Prince, Haiti), Hôpital Universitaire Justinien (HUJ, Cap Haitien, Haiti), and Hôpital Sacré Coeur (HSC, Milot, Haiti). HUJ and HSC use two NBS screening methods (isoelectric focusing and Sickle SCAN rapid test) and HSD and HUEH use isoelectric focusing only. CSHSCD supplies penicillin and HU and trains TCD examiners to implement stroke risk screening. Data are collected in REDCap. Results: During the first 2 years and despite the COVID-19 pandemic, we established NBS sites with a cohesive network of physicians and nurses trained in the care of children with SCD in Haiti. This capacity building will support sustainability of the program. We successfully identified at least 15 new cases of SCD via newborn screening, trained six TCD examiners, and enrolled 130 children with SCD in follow up, providing them with penicillin prophylaxis and hydroxyurea for severe cases according to local protocols . Implementation activities which have helped are close communications between the investigators, monthly Zoom meetings to coordinate efforts with enrollment updates every month, the availability of rapid tests (Sickle SCAN and Gazelle miniature cellulose acetate electrophoresis) for the diagnosis of SCD, especially when there is no laboratory equipment on site. Implementation challenges we have faced are mostly two. The first is the timely completion of DUNS and SAM registration for the two public hospitals, with one site achieving this after 9 months and the other site taking 18 months to complete. The reasons for the delay are the inability for the UM site to direct these efforts, following strict rules, and the Haitian hospital officers' lack of familiarity with website requirements. We were able to achieve these registrations with the assistance of one Haitian study staff who is very acquainted with internet navigation and became familiarized with requirements. Outsourcing materials to Haiti is another major challenge, with either gaps in the delivery of supplies because of multiple steps involved in ordering and shipping or with delays in releasing equipment once it is at the Port-au-Prince customs, resulting in gaps in NBS in one of the sites for 8 weeks. We have minimized these issues by opening a one-year ticket to order materials from the different companies involved. Also, Haiti's lack of infrastructure, available materials and medications, and political instability limit health care delivery. Conclusion: Since its inception, we have achieved major milestones, including capacity building and implementation of NBS, TCD training, and enrollment of children with SCD into the prospective cohorts despite the current COVID-19 pandemic. Material outsourcing challenges have been the major implementation problem we have faced due to systemic factors. We anticipate that these factors will be corrected or minimized as we have learned how to handle them. These problems were expected as part of conducting an international study in a low-resource setting. Acknowledgment: We acknowledge NHLBI for supporting this work. Disclosures Alvarez: Forma Therapeutics: Membership on an entity's Board of Directors or advisory committees; GBT: Membership on an entity's Board of Directors or advisory committees. Romano: Genentech: Research Funding; Vycor: Current holder of individual stocks in a privately-held company; NovaVision: Consultancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".