1. To study the profile of
blood donors in a tertiary care centre in south India
Study design: Retrospective study
Study settings: Department of Immuno-Haematology and Blood Transfusion, Kasturba
Hospital, Manipal
Study period: Date of obtaining ethical clearance to next 3 months.
Inclusion criteria: Data of all the
adults age > 18 years who have donated blood at the department of Immuno-Haematology
and Blood Transfusion, Kasturba Hospital, Manipal in the last 3 years will be
considered for the study. DGHS guidelines will be followed [7].
Sample size:There are approximately
15,000 blood donations every year at Kasturba Hospital, Manipal. Hence over a
period of 3 years data of approximately 45,000 blood donors will be collected.
Sampling technique: Complete enumeration
of the profile of all the blood donors will be done.
Methodology: Data will be collected
using a pre-designed pre-tested data abstraction form. The data abstraction
form will contain-1. socio-demographic information of study participants like
name, age, gender. 2. Anthropometric details- height, weight 3. Blood pressure
4. Temperature 5. Lab investigations- blood group, CBC.
Outcome measures: Profile of repeat
blood donors and one-time donors will be assessed.
Statistical analysis: Data will be
entered in Microsoft excel and analysed using Jamovi software. Categorical data
will be expressed as percentages and proportions. Continuous variables will be
summarised as mean and standard deviations. Chi square test will be conducted
to study the determinants significantly associated with repeat blood donors and
one-time donors.
2. Scoping review
A scoping review will be conducted based on
a scoping review framework (Arksey & O’Malley, 2005) and the Methodology
for Joanna Briggs Institute (JBI) Scoping Reviews (The Joanna Briggs Institute,
2015). Identified studies will be selected for the review using the Preferred
Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) of health
care interventions. Data extraction and a thematic analysis will be conducted.
The research question used will be- What are the motivators and barriers for
voluntary blood donation among donors in Low- and middle-income countries. Relevant
literature will be identified in the following database- PubMed, EMBASE,
Scopus, Web of Science. All the relevant articles in the last 10 years,
starting from January 2014 till date will be considered. Appropriate search
criteria will be developed using MeSH terms.
The selected literature will be evaluated
for its significance and relevance based on the content and the publication
type.
Inclusion
criteria: It can include original articles, review
articles, systematic reviews, case report and series. Only articles published
in English will be included.
Exclusion
criteria: Editorials, unpublished manuscripts,
conference abstracts.
The eligibility criteria will be used to
perform a preliminary screening of the articles based on their titles and
abstracts. After identifying the articles in the aforementioned databases, they
will be imported to Rayyan Software, where duplicated will be removed. Two
reviewers will select the titles and abstracts of potentially eligible studies, which will then be reviewed at full text by
two reviewers. These procedures will be
carried out independently and disagreements will be resolved by discussion until
a consensus is reached. If consensus is not reached between the two reviewers,
the opinion of a third reviewer will be considered.
Charting
the data
A data extraction form will be used to
study characteristics like Authors, Year of Publication, Country of origin of
study, study design, Population, intervention, context, outcome.
3. Development and validation
of AI prediction model to predict repeat blood donation among donors visiting
Kasturba Hospital Manipal& To measure the effect of incorporation of
motivators and barriers in the AI predictive model
Development
of AI prediction model-1
An AI predictive model to predict repeat
blood donation by the donors will be built with the help of the Department of
Computer Science, Manipal Institute of Technology, Manipal. The data obtained
from the department of Immuno-Haematology and Blood Transfusion, Kasturba
Hospital, Manipal of voluntary blood donors who have donated blood in Kasturba
Hospital in the last 3 years will be used to build the AI predictive model-1.
The predictive model will be built based on the following information that is
recorded from the donors- 1. Socio-demographic information of study
participants like age, occupation, education etc,2. Anthropometric measurements
namely height and weight, 3. Blood pressure 4. Lab investigations- CBC, blood
group 5. Temperature.
Development
of AI prediction model-2
The investigator will telephonically
contact the list of repeated blood donors as well as one-time blood donors from
the data set available in the department of Immuno-Haematology
and Blood Transfusion, Kasturba Hospital, Manipal. All the adults age >
18 years whose data is available, satisfying the inclusion criteria of the 1st
objective will be considered eligible for the study.
Sample
size: According to a previous study conducted by
Narayanan D et al [8] in Khozikode, Kerala the most important motivational
factor for blood donation was desire to help others which was seen in 58.5 %
blood donors. Considering this prevalence and using the formula for prevalence
to calculate sample size, for a precision of 10%
Sample size= 4PQ/D2, where
P=Prevalence, Q=1-P, D= level of precision
The desired sample size is 97. Considering
10% non-response the sample size is 107. Hence, we will be recruiting 107
one-time donors and 107 repeat blood donors.
Sampling
technique: Random sampling will be employed to
select the required number of samples using random number table.
Methods: Questions regarding motivators and barriers for blood donation will
be asked to the participants after obtaining their informed consent. The
scoping review conducted will be used as aid to formulate questions regarding
motivators and barriers for voluntary blood donation among donors. These
questions will be asked to 107 repeated blood donors and 107 participants who
are not repeated blood donors from the list of last 3 years voluntary blood
donors available at the department of Immuno-Haematology and Blood Transfusion,
Kasturba Hospital, Manipal through telephonic interview using a pre-designed
semi-structured questionnaire which comprises questions on motivators and
barriers for blood donation. Help of the counsellor in the department of
Haematology and blood transfusion will be obtained for the telephonic interview
of participants. The most common motivators and barriers will be listed. These
parameters regarding the most common motivators and barriers will be fed into
the already developed AI predictive model (AI Predictive model-1) to obtain a
modified AI predictive model (AI Predictive model-2).
Validation
of the AI predictive models
Study
design: Prospective longitudinal study
Study
settings: The study will be conducted at the
department of Immuno-Haematology and Blood Transfusion, Kasturba Hospital,
Manipal
Study
period: Date of obtaining Ethical clearance to July
2026
Inclusion
criteria:All the adults age
> 18 years attending the department of Immuno-Haematology and Blood
Transfusion, Kasturba Hospital, Manipal will be considered eligible for the
study. DGHS guidelines will be followed [7].
Exclusion
Criteria: Patients with HIV, HbsAg, Anemia etc
(conditions which are contra-indicated for blood donation) will be excluded.
Sample
size: According to a previous study conducted by
Uma S et al [7] in Chennai the prevalence of repeated blood donation among
blood donors was 53.7%. Considering this prevalence and using the formula for
prevalence to calculate sample size, for a precision of 5%.
Sample size= 4PQ/D2, where
P=Prevalence, Q=1-P, D= level of precision
The desired sample size is 398. Considering
10% non-response rate the sample size is 438.
Sampling
technique: Convenient sampling will be employed to
select 400 blood donors visiting the department of haematology and blood
transfusion, Kasturba Hospital, Manipal for blood donation.
Methods:
All the participants meeting the inclusion
criteria will be administered a pre-designed questionnaire after obtaining
their informed consent (interview personally) .The data collection tool will
consist of 1. Socio-demographic information of study participants like age,
occupation, education etc, 2. Anthropometric measurements namely height and
weight, 3. Blood pressure 4. Lab investigations- CBC, blood group 5.
Temperature.
Weight: will be measured to the nearest 100
gms, in light clothing, using a standard weighing machine after correcting the
zero error.
Height: will be measured to the nearest 0.5
cm with the person standing upright against the wall with heels together and
touching the wall, and the head held in upright position.
Blood pressure: Blood pressure will be
recorded using a calibrated mercury sphygmomanometer. Blood pressure will be
taken in sitting position and in the right arm. Appearance of first sound of
Korotkoff (phase 1) will be taken as systolic and total disappearance (phase 5)
of Korotkoff sound will be taken as diastolic BP. Blood pressure will be taken
once the person was relaxed. Two readings will be taken atleast 10 mins apart
and average of 2 readings will be taken.
All the above data will be fed into the AI
prediction model-1 and the prediction as to whether the
person is a potential repeated blood donor or not will be given by the model.
The patients will be contacted after 1 year telephonically and will be asked
whether they have donated blood again or not. Following analysis will be
conducted using the AI prediction model-1 i.e;
Sensitivity, specificity, positive predictive value and negative predictive
value.
Simultaneously the data regarding
motivators and barriers will be fed into the AI predictive model-2 and the
prediction as to whether the person is a potential repeated blood donor or not
given by the model will be noted. Information obtained from the aforementioned
telephonic follow up of the participants will be used to calculate Sensitivity,
specificity, positive predictive value and negative predictive value of the AI
predictive model-2. |