Article type: Original article
Title: Prevalence and factors associated with anemia in senior high school female students in Khoklor subdistrict, Muang district, Trang province, Thailand
Author: Waritsara Piyanonpong, MD
Department of Family Medicine, Trang Hospital, 69 Khokkhan Rd, Tubtiang, Muang, Trang 92000 Thailand
Tel: +66 81 5355639 Email: [email protected]
Abstract
Background: Khoklor is a semi-urban, semi-rural subdistrict with popular schools in Trang province located. The annual anemia screening project has never been conducted in secondary school students. Since anemia is a major global health problem, especially in Southeast Asia, which the prevalence of anemia is the highest, screening for anemia in the age group with high prevalence should be done, together with finding associated factors, to identify the causes and prevent this problem.
Objectives: To find prevalence and factors associated with anemia and the prevalence of anemia in senior high school female students in our population.
Methods: A school-based, analytic cross-sectional study was conducted. 240 students age of 16 years and over were chosen by cluster random sampling. Data was collected by Google Forms, then assessed by R. Capillary blood samples were taken for hematocrit test, which under 36% was defined for anemia. Letters were sent to the students diagnosed with anemia for further investigation and treatment.
Outcomes: The prevalence of anemia was 7.5%. Number of family members were
significantly associated with anemia (p = 0.046). Anemia was 3 times higher among students who lived in family with members of 5 and above (AOR: 3.04, 95% CI: 1.05-8.84).
Conclusion: The prevalence of anemia in this study is lower than global and Southeast Asia region prevalence, and significantly associated with number of family members. Those living in family with members from 5 persons and above were about three times more likely to be anemic.
Keywords: Anemia, High school, Female students
Running title
Prevalence and factors associated with anemia in senior high school female students in Khoklor subdistrict, Muang, Trang, Thailand
Introduction
Anemia is a major global health problem. According to WHO, the global prevalence of anemia is highest in preschool-age children (47.4%), following by pregnant women (41.8%), non-pregnant women (30.2%), school-age children (25.4%), elderly (23.9), and lowest in men (12.7%). In Southeast Asia region, the prevalence of anemia in preschool-age children is 65.5%, 48.2% in pregnant women, and 45.7% in non-pregnant women. By region, prevalence of anemia is the highest in Southeast Asia, and reflected major health problem in the area.(1) The effects of anemia could be shown in symptoms such as fatigue, lightheadedness, dizziness, palpitation, and could cause poor school performance, or heart failure if severe.(2,3) If the asymptomatic persons never take any blood test, anemia might not be diagnosed, and might have effects on family planning if those persons have inherited blood diseases, such as Thalassemia, or result in the symptoms mentioned above. On the contrary, diagnosis of anemia could lead to identifying causes, and receive treatment if treatable.
Khoklor is a semi-urban, semi-rural subdistrict in Muang district of Trang province with popular schools located, which the students studying in those schools come from all over the province. Regularly, there would be annual projects from Khoklor health promoting hospital, screening for anemia in children age 6 months to 1 year, and primary school children in the subdistrict, but there is no previous screening project in secondary school students in the subdistrict. Since anemia is a major global health problem, especially in Southeast Asia, which prevalence of anemia is the highest, and non-pregnant women ranked third in highest prevalence of anemia, screening for anemia in this age group should be done, together with finding
associated factors, to identify the causes and help preventing this problem.
Materials and Methods
A school-based, analytic cross-sectional study was conducted. A total of 240 healthy non- pregnant senior high school female students in Khoklor subdistrict, Muang district, Trang
province, Thailand, age of 16 years and above were included. The sample size was calculated using Taro Yamane’s formula (N= 603, e = 0.05), and error of 15% was added to the sample size, which made the final sample size 276. A total of 307 students were chosen by cluster random sampling, and 271 students participated the study (response rate 88.27%).
Data was collected by Google Forms. Missing participant data was 31 and did not include in further assessment.
Capillary blood samples were taken for hematocrit test by portable hematocrit centrifuge, and determine hematocrit value by hematocrit reader, which was read by the same person for every sample. Hematocrit level under 36% was defined for anemia. (4)
The data was assessed by R. Descriptive statistics including frequency and percentage were used to identify participant’s characteristics, and inferential statistics such as Chi-squared test and logistic regression were used to determine factors associated with anemia. P-value ≤ 0.05 was considered statistically significant.
Letters were sent to the students diagnosed with anemia for further investigation and treatment.
This study has been approved by research ethics committee of Trang Hospital. It has been reviewed and approved by Committee on Human Rights Related to Researches Involving Human Subjects, based on the Declaration of Helsinki.
Results
Data assessed was obtained from 240 healthy non-pregnant senior high school female students in Khoklor subdistrict, Muang district, Trang province, Thailand. They were aged between 16-19 years old. About half of them had normal BMI (57.08%), and lived in family with 1-4 members (55.83%). Mostly their parents’ total incomes were 10,001-20,000 Thai Baht per month (41.25%), with meat consumption more than 3 days per week (91.67%), and had period of 1-7 days per month (93.75%).
In this study, 18 of 240 students were diagnosed with anemia, which the prevalence of anemia was 7.5%.
Factors significantly associated with anemia in the population was number of family members (p = 0.046), while BMI, parents’ total income per month, days of meat intake per week, days of menstruation per month were not associated with anemia (Table 1). Students in family with members from 5 and above were 3 times more likely to develop anemia (AOR: 3.04, 95%
CI: 1.05-8.84) (Table 2).
Discussion
This study assessed the prevalence and associated factors of anemia in senior high school female students in Khoklor subdistrict, Muang district, Trang province, Thailand. The prevalence of anemia in this study was 7.5%, which was lower than global and Southeast Asia region prevalence, of 30.2 and 45.7%, respectively. In this study, prevalence of anemia in the
population was classified as mild public health problem. (1) Likewise, the prevalence of anemia in this study was very close to a study in high school girls in Nakhon Si Thammarat, a nearby province in the same region, the southern part of Thailand. The prevalence of anemia in the study mentioned was 8.3% (3). On the other hand, the study among female university students in Khon Kaen Province, a province located in the different region, Northeast Thailand, found that the prevalence of anemia in their population was 36.8%, and iron deficiency along with iron deficiency anemia were consequences from iron imbalance resulting from menstruation (5).
Factor associated with anemia in this study was number of family members (p = 0.046).
Students in family with members from 5 and above were 3 times more likely to develop anemia, comparing to family with 1-4 members (AOR: 3.04, 95% CI: 1.05-8.84), which was in the same way as the study in adolescents in South Ethiopia, finding that participants living in family with members of 5-8 were almost 10 times more prone to develop anemia (AOR: 9.82, 95% CI: 2.42–
39.88), but anemia was not significantly different in family with members from 8 and above comparing to family with 1-4 members (6). An increase of family members led to more sharing in basic needs and money. Oppositely, greater number of family members with more breadwinners might lead to better wealth index and better basic needs support.
There were no associations between BMI, parents’ total income per month, days of meat intake per week, days of menstruation per month and anemia. These findings were in line with
several studies. A study in South Ethiopia showed no association between BMI, wealth index and anemia (6). In Khon Kaen, the study revealed no association between salary, frequency of meat consumption per week, menstruation period per month and anemia (5), also in Nakhon Si Thammarat, period time of menstruation was not associated with iron deficiency anemia (3). According to meat consumption or length of menstruation period, further elaborately specified details might be needed in future studies, such as diet recall, amount of each food group, nutrition calculations, number and size of sanitary pads used each day.
The limitation of this study was the laboratory investigation. Laboratory tests and
radiological imaging were not available at Khoklor health promoting hospital, so this study could not identify infectious causes that could lead to anemia, such as HIV, hookworm, or tuberculosis
(7), and because of Trang hospital’s policy, each health promoting hospital had its limitation in number of blood specimens submitted to Trang hospital’s laboratory per month, and cost of submission depended on health insurance of each individuals, which they had various backgrounds, and the expenditure could be problematic.
Conclusion
Anemia is a worldwide major problem. Its prevalence could classify category of public health significance. The prevalence of anemia is up to age group and region, which in this study is lower than global and Southeast Asia region prevalence, and significantly associated with number of family members. Those living in family with members from 5 persons and above were about three times more likely to be anemic.
Acknowledgements
I would like to acknowledge the support from my advisor, Dr Kornkanok
Kowuttikulrangsee, for suggestions and support, and I would also like to show my gratitude to Mr Kittisak Choomalee, statistician, for the advices in the methodology and statistical analysis.
Finally, this research could not be done without greatest support from staffs of Khoklor health promoting hospital.
References
1. De Benoist B. Worldwide prevalence of anaemia 1993-2005. Geneva: WHO press; 2008.
2. Sinlapamongkolkul P. Diagnostic anemia in children. TMJ. 2013;13(2):253-61.
3. Sarakul O, Kotepui M, Marasa R, Thepwarin W. Anemia and Iron Deficiency Anemia in High School Girls in Nakhon Si Thammarat, Thailand. J Health Sci Med Res. 2018;36(3):197- 204.
4. Billett HH. Hemoglobin and Hematocrit. In: walker H, Hall W, Hurst J, editors. Clinical Methods: The History, Physical, and Laboratory Examinations 3rd edition. Boston:
Butterworths; 1990.
5. Tangsuwansopin P, Sanchaisuriya K, Chaitripop C, Sanchaisuriya P, Yamsri S,
Fucharoen G, et al. Anemia, iron deficiency and iron deficiency anemia among female university students in Khon Kaen Province: relationship to menstruation. J Med Tech Phy Ther.
2014;26(3):245-54.
6. Shaka MF, Wondimagegne YA. Anemia, a moderate public health concern among adolescents in South Ethiopia. PLoS ONE. 2018;13(7).
7. Viprakasit V. Approach to childhood anemia. J Hematol Transfus Med. 2014;24(4):395- 405.
Tables and Figures
Table 1 Chi-squared test determining association of between each characteristic with anemia
Characteristics Chi-square Significance
1. BMI
2. Parents’ total income per month (Thai Baht) 3. Days of meat intake per week
4. Days of menstruation per month 5. Number of family members
0.773 1.756 0.197 0.785 3.995
0.679 0.624 0.658 0.376 0.046
Table 2 Descriptive statistics and logistic regression analysis showing the impact of selected variables on Anemia
Characteristics Frequency (percent)
Frequency of anemia
(percent)
Crude OR (95%CI)
Adjusted OR
(95% CI) p-value BMI (kg/m2)
< 18.5 64 (26.67) 4 (6.25) Reference
18.5 – 24.9 137 (57.08) 12 (8.76) 1.44 (0.45 – 4.65) 1.11 (0.32 – 3.88) 0.865
≥ 25 39 (16.25) 2 (5.13) 0.81 (0.14 – 4.65) 0.61 (0.1 – 3.77) 0.594
Parents’ total income per month (Thai Baht)
≤ 10,000 52 (21.67) 3 (5.77) Reference
10,001 – 20,000 99 (41.25) 10 (10.10) 1.84 (0.48 – 6.98) 1.63 (0.4 – 6.64) 0.492 20,001 – 30,000 43 (17.92) 2 (4.65) 0.8 (0.13 – 5) 0.56 (0.08 – 3.77) 0.551
≥ 30,001 46 (19.17) 3 (6.52) 1.14 (0.22 – 5.94) 0.89 (0.16 – 5.03) 0.897
Days of meat intake per week
≤ 3 20 (8.33) 1 (5.00) Reference
> 3 220 (91.67) 17 (7.73) 1.59 (0.2 – 12.62) 1.45 (0.17 – 12.5) 0.737
Days of menstruation per month
1-7 225 (93.75) 16 (7.11) Reference
> 7 15 (6.25) 2 (13.33) 2.01 (0.42 – 9.69) 1.54 (0.27 – 8.7) 0.622
Number of family members
1-4 134 (55.83) 6 (4.48) Reference
≥ 5 106 (44.17) 12 (11.32) 2.72 (0.99 – 7.52) 3.04 (1.05 – 8.84) 0.041
