Sickle cell disease (SCD) is an inherited blood disorder associated with acute illness and organ damage. In high resource settings, early screening and treatment greatly improve quality of life. In low resource settings, however, mortality rate for children is high (50-90%). Low-cost and accurate screening techniques are critical to reducing the burden of the disease, especially in remote/rural settings. The most common and severe form of SCD is sickle cell anemia (SCA), caused by the inheritance of genes causing abnormal forms of hemoglobin (called sickle hemoglobin or hemoglobin S) from both parents. The asymptomatic or carrier form of the disease, known as sickle cell trait (SCT), is caused by the inheritance of only one variant gene from one of the parents. In areas such as Nepal, β-thalassemia (another inherited blood disorder) and SCD are both prevalent, and some combinations of these diseases lead to severe symptoms.

The purpose of this study is to determine the accuracy of low-cost point-of-care techniques for screening and detecting sickle cell disease, sickle cell trait, and β-thalassaemia, which will subsequently inform on feasible solutions for detecting the disease in rural, remote, or low-resource settings. One of the goals of the study is to evaluate the feasibility of techniques, such as the sickling test with low-cost microscopy and machine learning, HbS solubility test, commercial lateral-flow assays (HemoTypeSC and Sickle SCAN), and the Gazelle Hb variant test, to supplement or replace gold standard tests (HPLC or electrophoresis), which are expensive, require highly trained personnel, and are not easily accessible in remote/rural settings.

The investigators hypothesize that:

1. an automated sickling test (standard sickling test enhanced using low-cost microscopy and machine learning) has a higher overall accuracy than conventional screening techniques (solubility and sickling tests) to detect hemoglobin S in blood samples
2. the automated sickling test can additionally classify SCD, SCT and healthy individuals with a sensitivity greater than 90%, based on morphology changes of red blood cells, unlike conventional sickling or solubility tests that do not distinguish between SCD and SCT cases
3. Gazelle diagnostic device can detect β-thalassaemia and SCD/SCT with an overall accuracy greater than 90%, compared with HPLC as the reference test

Overall, the hypothesis is that an assessment of the performance and accuracies of low-cost point-of-care techniques (automated sickling test, solubility test, lateral-flow assays, Gazelle Hb variant test) against HPLC tests will provide researchers and health workers with feasible alternative options for screening and detecting SCD, SCT and β-thalassaemia in a variety of situations based on the needs of the communities and the resources available.

Objectives

Objectives specific to the current study are to:

1. Determine accuracy (sensitivity and specificity) of automated sickling test to detect HbS, compared to gold standard HPLC, and to conventional solubility test
2. Determine whether SCD, SCT and healthy individuals can be classified using the automated sickling test that leverages machine learning on images of blood films under hypoxia
3. Validate accuracy (>95% sensitivity and specificity) of lateral- flow assays (HemoTypeSC and Sickle SCAN) to detect SCD/SCT, and of Gazelle variant test to detect SCD, SCT, and β-thalassaemia; and determine if low-cost techniques can potentially replace HPLC/electrophoresis tests in rural and remote settings

Long-term objectives of the overall project are to:

1. Implement trained machine learning algorithm to classify SCD, SCT and healthy individuals during screening tests in Nepal
2. Implement relevant low-cost point-of-care techniques in rural and remote communities of Nepal using insights and conclusions from current study

The plan of the study to screen the communities (e.g. in Nepalgunj, in Vancouver) using the following:

a. Low-cost screening i. Sickling test with low-cost microscope and automated screening with machine learning ii. Sickling test with traditional microscope (conventional manual screening used in Nepal) iii. HbS solubility test iv. Commercial point-of-care assays (HemoTypeSC and Sickle SCAN) v. Gazelle Hb variant test b. Gold standard test: HPLC, for determining the accuracies of low-cost screening techniques

De-identified data (images of blood films and associated documentation) will also be deposited in an online public repository, such as the Federated Research Data Repository (FRDR). FRDR is a service of the Digital Research Alliance of Canada (Alliance), a not-for-profit organization that supports digital research infrastructure in Canada. FRDR is hosted on national infrastructure, managed and administered by the Digital Research Alliance of Canada.

Inclusion Criteria:

Since the techniques evaluated in the study aims at detecting sickle cell disease (SCD), sickle cell trait (SCT), and β- thalassemia, the following number of participants will be included in Nepal:

• 20 individuals with SCD (HbSS)
• 20 individuals with SCT (HbAS)
• 20 individuals with sickle cell/β-thalassemia compound heterozygous form (HbS/β-thalassemia)
• 20 individuals with β-thalassemia (Hbβ/β-thalassemia)
• 20 individuals with β-thalassemia trait or carrier form (HbA/β- thalassemia)
• 20 healthy individual participants or normal participants (HbAA, participants without any known hemoglobin disorders, such as SCD, SCT or β-thalassemia)

The following number of participants will be included in Canada:

• 30 individuals with SCD (HbSS)
• 30 individuals with SCT (HbAS)
• 30 healthy individual participants or normal participants (HbAA, participants without any known hemoglobin disorders, such as SCD, SCT or β-thalassemia)

Participants older than 1 year of age at the time of drawing blood will be eligible. Signed and dated consent or assent forms will be required by the participants or their parents/guardians.

Exclusion Criteria:

The exclusion criteria for the study:

• Transfusion within the last 3 months
• Pregnancy Participants who wish to withdraw from the study will also be excluded.

Only de-identified data and test results will be shared. The test results for the low-cost tests and HPLC tests will be published in aggregate form. De-identified images of blood films will be deposited in an online public repository, such as Federated Research Data Repository (FRDR).

All the participants and study team members will be informed of the tests and devices used in the study.