Secondary analysis of Turkish national data investigating negative birth outcomes and stunting related to social factors and adolescent motherhood
Ceren V. Akpinar1, Asli A. Teneler2
1Department of Public Health, Giresun University Faculty of Medicine, Giresun, Turkey. 2Giresun Central Community Health Centre, Giresun, Turkey (Correspondence: A.A. Teneler:
Abstract
Background: Adolescent motherhood can cause lifelong health inequalities for mothers and children.
Am: To compare the frequency of negative birth outcomes and stunting in children aged 5 years of adolescent and nonadolescent mothers, and to determine the relationship with sociodemographic factors based on the Turkey Demographic and Health Survey.
Methods: This was a secondary analysis of the Turkey Demographic and Health Survey 2018. Logistic regression analysis was conducted on a sample of 2755 women aged 15–49 years who gave a live birth in the past 5 years.
Results: Term low birthweight and stunting were significantly higher in children of adolescent mothers. Multivariable analysis revealed that lack of education, poverty, and living in eastern Turkey increased the risk of delivering a term low birthweight infant. The risk of being stunted was 2.22 times higher in women with low socioeconomic status, and 2.86 times higher in low birthweight infants.
Conclusion: A large sample from the Turkey Demographic and Health Survey emphasized the necessity of planning of maternal and child health services by considering maternal education level, income inequality, and even regional inequality. These results support the notion that macroenvironmental factors have a marked impact on maternal and child health.
Keywords: adolescent pregnancy, low birthweight, stunting, social inequalities, Turkey
Citation: Akpinar CV, Teneler AA. Secondary analysis of Turkish national data investigating negative birth outcomes and stunting related to social factors and adolescent motherhood. East Mediterr Health J. https://doi.org/10.26719/emhj.23.074 Received: 16/8/2022; Accepted: 22/12/2022
Copyright: © Authors; licensee World Health Organization. EMHJ is an open access journal. All papers published in EMHJ are available under the Creative Commons Attribution Non-Commercial ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo).
Introduction
Adolescent pregnancy can result in lifetime health disparities for mothers and their children. Compared with nonadolescent mothers, adolescent mothers are more likely to have lower educational level, less financial independence, worse mental health, and less social support (1–4). All these factors may contribute to the high prevalence of malnutrition in adolescent mothers (5). Globally, 13% of all births are given by women aged 15–19 years in emerging countries (6). In Turkey, the adolescent pregnancy rate was 10.2% in 1993 but it decreased to 4% by 2018 (7). Although the rate of adolescent pregnancy has decreased over the years, it is still important when the resultant health and social problems are considered.
The most important indicator of chronic malnutrition in children is stunting (8). In the last 20 years, although there has been a decrease globally in stunting in children aged < 5 years, differences between regions and within countries remain (9,10). In Turkey, the rate of stunting in children aged 20 million births per year (12). In Turkey, 12% of live births are low birthweight (7). Socioeconomic factors, maternal age, maternal education, maternal body mass index, and antenatal care and nutrition are risk factors for low birthweight (3, 4, 13, 14). As expected, adolescent mothers are at higher risk of adverse birth outcomes, such as premature birth and low birthweight, because they usually have worse antenatal care and conditions (4, 15, 16).
Although there are data on the socioeconomic vulnerabilities and health risks of adolescent mothers in Turkey, it is unclear what effect adolescent motherhood has on negative birth outcomes and stunting. We used the national data from the 2018 Turkey Demographic and Health Survey to: (1) compare the frequency of negative birth outcomes (low birthweight, preterm birth, and term low birthweight) and stunting in children aged < 5 years of adolescent and nonadolescent mothers; and (2) investigate the sociodemographic factors affecting negative birth outcomes and stunting in children of adolescent and nonadolescent mothers.
Methods
Study design, participants and measurements
This study used secondary data analysis of the 2018 Turkey Demographic and Health Survey that focused on adolescent and nonadolescent mothers aged 15–49 years and their children aged 0–5 years. The survey was a nationally representative cross-section that was conducted by Hacettepe University Institute of Population Studies every 5 years since 1993 to monitor population health and maternal and child health indicators. The purpose of the survey was to gather data at the household level to formulate national indicators related to demographics, fertility, child mortality, maternal health, and nutritional status of women and children. The survey data are used by public institutions, especially the Ministry of Health, for the planning of health services (17).
For the main data of the 2018 Turkey Demographic and Health Survey, a weighted, multistage, stratified cluster sampling approach was used. Two questionnaires for households and individuals were used to collect data by face-to-face interview. Out of 9056 women aged 15–49 years in these households, 7346 (81.1%) were interviewed. The sampling design details and the results of the main data of the study were reported in the 2018 Turkey Demographic and Health Survey Analysis and Report (7).
We used the individual questionnaire dataset to analyse 2755 women aged 15–49 years who had a live birth up to 5 years before the questionnaire was administered. There were no available data for the birthweight and date of birth of 117 children, or for the height of 653 children. Identification of the secondary dataset and the inclusion and exclusion criteria for the study are shown in Figure 1.
The dependent variables were having a child in the past 5 years with negative birth outcomes (low birthweight, preterm birth, and term low birthweight) and stunting. Stunting was defined using the 2006 WHO growth standard reference point based on z score ≤ 2 standard deviations (18). Negative birth outcomes were: (1) low birthweight, < 2500 g at birth; (2) preterm, born before 37 weeks’ gestation; and (3) term low birthweight, born at ≥ 37 weeks’ gestation and < 2500 g at birth.
Maternal age was the main independent variable and was categorized as adolescent (15–19 years) and nonadolescent (20–29, 30–39, and 40–49 years). Maternal educational level was defined as no education, primary, secondary, and high school, and while evaluating the variable, the secondary and high school categories were combined. Welfare status was categorized as richest, richer, middle, poorer, and poorest according to wealth index, and evaluated by combining them into 3 groups as rich (richest and richer), middle, and poor (poorer and poorest). The country data were divided into 5 regions of west, south, central, north, and east. The residential area was considered as urban or rural. The gender of the child was categorized as male or female. For the stunting dependent variable, children’s age was evaluated monthly and categorized into 3 groups (0–11, 12–23, and 24–59 months). Antenatal visits were categorized as yes (mother attended ≥ 4 visits) or no (mother did not attend ≥ 4 visits or did not know the answer). The variables used in this study were categorized according to the Demographic and Health Surveys guidelines (19).
Ethical considerations
The 2018 Turkey Demographic and Health Survey was evaluated and approved by Hacettepe University Ethics Commission. The researchers obtained permission on 2 December 2021 from Hacettepe University Institute of Population Studies to use the data.
Data analysis
Results
In the study group of 2755 mothers, 189 (6.8%) were adolescent. Table 1 shows the sociodemographic characteristics of adolescent and nonadolescent mothers. Eighty-two (43.4%) adolescent mothers had secondary or higher education compared with 1309 (51.0%) nonadolescent mothers. One hundred and twenty-five (66.1%) adolescent mothers and 1233 (48.1%) nonadolescent mothers were poorer or poorest. Sixty-six (34.9%) adolescent and 768 (29.9%) nonadolescent mothers lived in rural areas. One hundred and six (56.0%) adolescent and 218 (28.1%) nonadolescent mothers had not received adequate antenatal care. There were significant differences between adolescent and nonadolescent mothers in terms of educational level, welfare status, and antenatal care during pregnancy (P < 0.05).
Table 2 compares the rates of negative birth outcomes and the indicators of chronic malnutrition according to maternal age (adolescent/nonadolescent category). There were 2638 negative birth outcomes: 338 (12.8%) low birthweight, 405 (15.4%) preterm birth, and 205 (7.8%) term low birthweight. In addition, 116 (5.5%) children were stunted. The rate of low birthweight was 14.8% in adolescent mothers and 12.7% in nonadolescent mothers. Preterm birth rate was similar for the 2 groups. The rate of delivering a term low birthweight infant was 11.8% in adolescent mothers and 7.5% in nonadolescent mothers, and this difference was significant (P < 0.05). Stunting, which is an indicator of chronic malnutrition in children aged < 5 years, was seen in 8.7% of children of adolescent mothers and 5.2% of children of nonadolescent mothers, and this difference was significant (P < 0.05).
Table 3 shows the factors associated with term low birthweight in a sample of 2638 women who gave birth in the past 5 years. Adolescent motherhood was evaluated as the main independent variable. Term low birthweight, which was significantly different in the children of adolescent and nonadolescent mothers, lost significance in the adjusted analysis (AOR 1.50, 95% CI: 0.90–2.49). Multivariate analysis showed that lack of maternal education (AOR 1.75, 95% CI: 1.22–2.50), poverty (AOR 2.09, 95% CI: 1.44–3.02), and living in eastern Turkey (AOR 1.39, 95% CI: 1.01–1.92) increased the risk of term low birthweight.
Multivariate analysis of the factors associated with stunting in 2102 women who gave birth in the last 5 years is shown in Table 4. Adolescent motherhood was evaluated as the main independent variable. There was a significant difference in child stunting between adolescent and nonadolescent mothers but after adjustment, there was no significance (AOR 0.77, 95% CI: 0.36–1.64). The risk of being stunted was 2.22 times higher (95% CI: 1.47–3.50) in the poorest/poorer group compared with other socioeconomic status. Children born with low birthweight were 2.86 times more likely to be stunted than children born with normal birthweight (95% CI: 1.90–4.30). The risk of stunting was increased 1.69 times (95% CI: 1.12–2.53) in children aged 12–23 months compared with 0–11 months, and 1.65 times (95% CI: 1.20–2.65) in children aged 24–59 months.
Discussion
We evaluated the sociodemographic characteristics of adolescent and nonadolescent mothers, negative birth outcomes, and frequency of stunting of their children in Turkey using data from the 2018 Turkey Demographic and Health Survey. We also investigated the risk factors associated with term low birthweight and stunting. We found that adolescent mothers had lower educational level, were poorer, and did not receive adequate antenatal care compared with nonadolescent mothers. Although term low birthweight and stunting were risk factors in children of adolescent compared with nonadolescent mothers, they were no longer significant when adjusted for socioeconomic variables. This indicates that if socioeconomic status of adolescent mothers is improved, their risk of negative birth outcomes and stunting will be reduced. Our results could be explained by women with good socioeconomic status not becoming pregnant during adolescence. However, some studies have reported that young maternal age is not the sole reason for negative birth outcomes, and other factors such as emotional response, coping skills, and social resources may be involved (20, 21). If adolescent pregnancies are well planned and there is adequate prenatal care, they will cease to be high risk (22).
Low maternal educational level was a risk factor for term low birthweight in multivariate analysis, which is consistent with previous studies (23, 24). Term low birthweight was higher in the poorest households and in eastern Turkey, which is less well developed than western Turkey. The significant association between low socioeconomic status and low birthweight that was shown in this study was also found in other studies (25, 26). Low socioeconomic and educational statuses lead to low health consciousness and low nutritional status, which can increase the risk of low birthweight. There is considerable variation in the prevalence of low birthweight across regions and within countries; however, most occurs in low- and middle-income countries and especially in the most vulnerable populations in these countries.
Multivariate analysis showed that being poorer was a risk factor for stunting. Similarly, in the National Family Health Survey of India, stunting was more common in those living in poorer rural areas (27). These findings were similar to a study conducted in the United Republic of Tanzania and indicated that children in lower socioeconomic groups had a greater risk of stunting compared with those in higher socioeconomic groups (28). Several other studies have found that household socioeconomic status is a prominent predictor of child stunting (29, 30). The availability of high-quality foods and affordability of nutrient-rich foods affect a family’s ability to provide a healthy diet and prevent child stunting. Higher household income enables more to be spent on food and child care (12). This is in line with the suggestion that households with larger income, as a proxy for household wealth, have more money to spend on child nutrition, which lowers the prevalence of stunting (31).
Our large sample data from the 2018 Turkey Demographic and Health Survey indicated that economic status was the main risk factor for negative birth outcomes and stunting. Special attention must be paid to individuals who have low educational levels, low income, and live in eastern Turkey. To improve child health, we suggest that it is more important to improve education and reduce inter-regional poverty rather than trying to reduce adolescent pregnancies. These results support the idea that macroenvironmental factors have a marked impact on maternal and child health.
In our multivariate analysis, being older than 2 years was a risk factor for stunting. Children’s age in months had a nonlinear, upward-sloping effect on the probability of stunting. Therefore, children tend to be more stunted as they age, although this effect diminishes over time (31). Stunting was more common in children older than 18 months in the National Family Health Survey of India, and children aged < 24 years had a higher risk of stunting in Rwanda (27, 32). The results are consistent with the theory that worsening intrauterine conditions, as measured by birth size and other factors, increase the likelihood of stunting (31). The fact that the risk of stunting increases with age indicates that chronic malnutrition is becoming severe.
Our study had some limitations. Firstly, there were some missing data, such as age at which the children ceased breastfeeding, accompanied by many confounding factors, maternal body mass index, and maternal birth weight. Secondly, the main data in the 2108 Turkey Demographic and Health Survey were collected 4 years ago. Finally, the lack of anthropological data for all children led to a reduction in the sample size. Regardless of the limitations, it was possible to identify the risk factors for stunting and term low birthweight in the eastern and western provinces of Turkey, after multivariate linear regression analysis or adjustment for all missing confounders.
Conclusion
This study adds to the limited research examining the association between adolescent pregnancy and adverse birth outcomes and stunting in Turkey. Our results suggest that it would be more beneficial to make changes at the macroenvironmental level to reduce low birthweight and stunting. The social role of women living in eastern Turkey is perhaps the root cause of these health inequalities. Strengthening the position of women in society will prevent adolescent pregnancies and contribute to the prevention of other health inequalities. Our results emphasize the necessity of planning maternal and child health services by considering the educational level of women, income inequalities, and even regional inequalities, while defining the risk factors related to adolescent pregnancy.
References
1. Boden JM, Fergusson DM, John Horwood L. Early motherhood and subsequent life outcomes. J Child Psychol Psychiatry. 2008 Feb;49(2):151–60. https://doi.org/10.1111/j.1469-7610.2007.01830.x PMID:18093114
2. Norris S, Norris ML, Sibbald E, Aubry T, Harrison ME, Lafontaine G et al. Demographic characteristics associated with pregnant and postpartum youth referred for mental health services in a community outreach center. J Can Acad Child Adolesc Psychiatry. 2016 Fall;25(3):152–8. PMID:27924145
3. Le Roux K, Christodoulou J, Stansert-Katzen L, Dippenaar E, Laurenzi C, M. le Roux I et al. A longitudinal cohort study of rural adolescent vs adult South African mothers and their children from birth to 24 months. BMC Pregnancy Childbirth. 2019; 19(24):1–8. https://doi.org/10.1186/s12884-018-2164-8
4. Banke-Thomas OE, Banke-Thomas AO, Ameh CA. Factors influencing utilisation of maternal health services by adolescent mothers in low-and middle-income countries: a systematic review. BMC Pregnancy Childbirth. 2017;17(65):1–14. https://doi.org/10.1186/s12884-017-1246-3
5. Olodu MD, Adeyemi AG, Olowookere SA, Esimai OA. Nutritional status of under-five children born to teenage mothers in an urban setting, south-western Nigeria. BMC Res Notes. 2019 Mar 4;12(1):116. https://doi.org/10.1186/s13104-019-4147-x PMID:30832719
6. Darroch JE, Woog V, Bankole A. Adding it up: costs and benefits of meeting the contraceptive needs of adolescents. New York: Guttmacher Institute, 2016 (https://www.guttmacher.org/report/adding-it-meeting-contraceptive-needs-of-adolescents, accessed 15 April 2023).
7. Turkey Demographic and Health Survey 2018. Ankara: Hacettepe University Institute of Population Studies, T.R. Presidency of Turkey Directorate of Strategy and Budget, and TÜBITAK; 2019 ( https://fs.hacettepe.edu.tr/hips/dosyalar/Ara%C5%9Ft%C4%B1rmalar%20-%20raporlar/2018%20TNSA/TDHS2018_mainReport_compressed.pdf, accessed 15 April 2023).
8. Stunting in a nutshell [website]. Geneva: World Health Organization; 2015 (https://www.who.int/news/item/19-11-2015-stunting-in-a-nutshell, accessed 15 April 2023).
9. Joint child malnutrition estimates [website]. Geneva: World Health Organization (https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, accessed 15 April 2023).
10. Malnutrition in children [website]. UNICEF (https://data.unicef.org/topic/nutrition/malnutrition/, accessed 15 April 2023).
11. Ndemwa M, Wanyua S, Kaneko S, Karama M, Anselimo M. Nutritional status and association of demographic characteristics with malnutrition among children less than 24 months in Kwale County, Kenya. Pan Afr Med J. 2017 Nov;28:265. https://doi.org/10.11604/pamj.2017.28.265.12703 PMID:29881508
12. Global nutrition targets 2025: low birth weight policy brief. Geneva: World Health Organization; 2014 (https://apps.who.int/iris/handle/10665/149020, accessed 15 April 2023).
13. Mumbare SS, Maindarkar G, Darade R. Maternal risk factors associated with term low birth weight neonates: a matched-pair case control study. Indian Pediatr. 2012 Jan;49(1):25-28. https://doi.org/10.1007/s13312-012-0010-z PMID:21719926
14. Nabugoomu J, Seruwagi GK, Corbett K, Kanyesigye E, Horton S, Hanning R. Needs and barriers of teen mothers in rural Eastern Uganda: stakeholders’ perceptions regarding maternal/child nutrition and health. Int J Environ Res Public Health. 2018 Dec 7;15(12):2776. https://doi.org/10.3390/ijerph15122776 PMID:30544550
15. Amjad S, MacDonald I, Chambers T, Osornio‐Vargas A, Chandra S, Voaklander D et al. Social determinants of health and adverse maternal and birth outcomes in adolescent pregnancies: a systematic review and meta-analysis. Paediatr Perinat Epidemiol. 2019 Jan;33(1):88-99. https://doi.org/10.1111/ppe.12529 PMID:30516287
16. Chen XK, Wen SW, Fleming N, Demissie K, Rhoads GG, Walker M. Teenage pregnancy and adverse birth outcomes: a large population based retrospective cohort study. Int J Epidemiol. 2007 Apr;36(2):368–73. https://doi.org/10.1093/ije/dyl284 PMID:17213208
17. Turkey Demographic and Health Survey 2018 – final report. Ankara: Hacettepe University Institute of Population Studies, T.R. Presidency of Turkey Directorate of Strategy and Budget, and TÜBITAK; 2019 (https://dhsprogram.com/publications/publication-FR372-DHS-Final-Reports.cfm, accessed 15 April 2023).
18. WHO child growth standards : training course on child growth assessment. Geneva: World Health Organization; 2008 (https://apps.who.int/iris/handle/10665/43601, accessed 15 April 2023).
19. The DHS Program Demographic and Health Surveys. Available datasets [website]. Rockville, MD, USAID (https://dhsprogram.com/data/available-datasets.cfm, accessed 15 April 2023).
20. Klein JD. American Academy of Pediatrics Committee on Adolescence. Adolescent pregnancy: current trends and issues. Pediatrics. 2005 Jul;116(1):281–6. https://doi.org/ 10.1542/peds.2005-0999 PMID:15995071
21. Aruda MM, Waddicor K, Frese L, Cole JCM, Burke P. Early pregnancy in adolescents: diagnosis, assessment, options counseling, and referral. J Pediatr Health Care. 2010 Jan–Feb;24(1):4–13. https://doi.org/10.1016/j.pedhc.2008.11.003 PMID:20122473
22. Geist RR, Beyth Y, Shashar D, Beller U, Samueloff A. Perinatal outcome of teenage pregnancies in a selected group of patients. J Adolesc Gynecol. 2006 Jun;19(3):189–93. https://doi.org/10.1016/j.jpag.2006.02.005 PMID:16731412
23. Jafari F, Eftekhar H, Pourreza A, Mousavi J. Socio-economic and medical determinants of low birth weight in Iran: 20 years after establishment of a primary healthcare network. Public Health. 2010 Mar;124(3):153–8. https://doi.org/10.1016/j.puhe.2010.02.003 PMID:20226486
24. Chen Y, Li G, Ruan Y, Zou L, Wang X, Zhang W. An epidemiological survey on low birth weight infants in China and analysis of outcomes of full-term low birth weight infants. BMC Pregnancy Childbirth. 2013 Dec 26;13:242–50. PMID:24370213
25. Viengsakhone L, Yoshida Y, Harun-Or-Rashid M, Sakamoto J. Factors affecting low birth weight at four central hospitals in vientiane, Lao PDR. Nagoya J Med Sci. 2010 Feb;72(1-2):51-58. https://doi.org/10.1186/1471-2393-13-242 PMID:20229703
26. Sharma M, Kumar D, Huria A, Gupta P. Maternal risk factors of low birth weight in Chandigarh India. Internet J Health. 2008;9(1):1–4. https://doi.org/10.5580/10f1
27. National Family Health Survey (NFHS-3) 2005–06 India. Volume 1. Mumbai: International Institute of Population Sciences; 2007 (https://dhsprogram.com/pubs/pdf/frind3/frind3-vol1andvol2.pdf, accessed 15 April 2023).
28. Mtongwa RH, Festo C, Elisaria E. A comparative analysis of determinants of low birth weight and stunting among under five children of adolescent and non-adolescent mothers using 2015/16 Tanzania Demographic and Health Survey (TDHS). BMC Nutr. 2021 Nov 4;7(1):1–10. https://doi.org/10.1186/s40795-021-00468-6 PMID:34732260
29. Bukusuba J, Kaaya AN, Atukwase A. Predictors of stunting in Children Aged 6 to 59 months: a case-control study in Southwest Uganda. Food Nutr Bull. 2017 Dec;38(4):542–53. https://doi.org/10.1177/0379572117731666 PMID:28978233
30. Wamani H, Astrøm AN, Peterson S, Tumwine JK, Tylleskär T. Boys are more stunted than girls in sub-Saharan Africa: a meta-analysis of 16 demographic and health surveys. BMC Pediatr. 2007 Apr 10;7:17). https://doi.org/10.1186/1471-2431-7-17 PMID:17425787
31. Van Der Knaap I, The determinants of sex differences in child stunting in Sub Saharan Africa: a multilevel logistic regression analysis [thesis]. Nijmegen: Radboud University; 2018.
32. Habimana S, Biracyaza E. Risk factors of stunting among children under 5 years of age in the eastern and western provinces of Rwanda: analysis of Rwanda Demographic and Health Survey 2014/2015. Pediatric Health Med Ther. 2019 Oct 25;10:115–30. https://doi.org/10.2147/PHMT.S222198 PMID:31695558
Table 1. Sociodemographic characteristics of adolescent and nonadolescent mothers (N= 2755)
Sociodemographic characteristics |
Adolescent mother, n (%) |
Nonadolescent mother, n (%) |
Total n (%) |
P |
Educational level None Primary Secondary or higher |
27 (14.3) 80 (42.3) 82 (43.4) |
437 (17.0) 820 (32.0) 1309 (51.0) |
464 (16.8) 900 (32.7) 1391 (50.5) |
0.01 |
Welfare status Poorest/poorer Middle Richer/richest |
125 (66.1) 34 (18.0) 30 (15.9) |
1233 (48.1) 507 (19.8) 826 (32.1) |
1358 (49.3) 541 (19.6) 856 (31.1) |
<0.01 |
Region West South Central North East |
45 (23.8) 32 (16.9) 34 (18.0) 12 (6.3) 66 (34.9) |
646 (25.2) 348 (13.6) 437 (17.0) 233 (9.1) 902 (35.2) |
691 (25.1) 380 (13.8) 471 (17.1) 245 (8.9) 968 (35.1 ) |
0.53 |
Residential area Urban Rural |
123 (65.1) 66 (34.9) |
1798 (70.1) 768 (29.9) |
1921 (69.7) 834 (30.3) |
0.08 |
Antenatal visit (≥ 4 visits)a Yes No |
83 (43.9) 106 (56.1) |
1844 (71.9) 218 (28.1) |
1927 (85.6) 324 (14.4) |
<0.01 |
aSome missing data. |
Table 2. Number and percentage distribution of negative birth outcomes and stunting of children by maternal age
|
Adolescent mother, n (%) |
Non |
Total n (%) |
P |
Negative birth outcomes (N = 2638: 169 adolescent and 2469 nonadolescent mothers) |
||||
Low birthweight |
25 (14.8) |
313 (12.7) |
338 (12.8) |
0.24 |
Preterm birth |
28 (1 |
377 (15.3 |
405 (1 |
0.51 |
Term low birthweight |
20 (11.8) |
185 (7.5) |
205 (7.8) |
0.04 |
Chronic malnutrition (N = 2102: 138 adolescent and 1964 nonadolescent mothers) |
||||
Stunting |
12 (8.7) |
104 (5.3 |
116 (5.5) |
0.03 |
Table 3. Logistic regression results for the factors associated with term low birthweight
|
|
N |
n (%) |
Crude OR |
95% CI |
P |
AOR |
95% CI |
P |
||
Maternal age, yr |
15–19 |
169 |
20 (11.8) |
1.65 |
1.01 |
2.70 |
0.04 |
1.50 |
0.90 |
2.49 |
0.11 |
20–49 (ref) |
2469 |
185 (7.5) |
|
|
|
|
|
|
|
|
|
Educational level |
None |
407 |
64 (15.7) |
2.76 |
2.01 |
3.79 |
<0.01 |
1.75 |
1.22 |
2.50 |
0.02 |
Primary |
856 |
70 (8.2) |
1.08 |
0.80 |
1.46 |
0.5 |
|
|
|
|
|
Secondary and higher (ref) |
1375 |
71 (5.2) |
|
|
|
|
|
|
|
|
|
Welfare status
|
Poorest/poorer |
1262 |
145 (11.5) |
2.84 |
2.08 |
3.88 |
<0.01 |
2.09 |
1.44 |
3.02 |
<0.01 |
Middle |
531 |
34 (6.4) |
0.77 |
0.52 |
1.13 |
0.18 |
|
|
|
|
|
Rich/richest (ref) |
845 |
26 (3.1) |
|
|
|
|
|
|
|
|
|
Region |
West (ref) |
673 |
10 (4.5) |
|
|
|
|
|
|
|
|
South |
368 |
31 (8.4) |
1.10 |
0.74 |
1.65 |
0.61 |
|
|
|
|
|
Central |
464 |
22 (4.7) |
0.54 |
0.34 |
1.15 |
0.08 |
|
|
|
|
|
North |
241 |
11 (4.6) |
0.54 |
0.29 |
1.01 |
0.05 |
|
|
|
|
|
East |
892 |
104 (11.7) |
2.15 |
1.61 |
2.86 |
<0.01 |
1.39 |
1.01 |
1.92 |
0.04 |
|
Residential area |
Urban (ref) |
1856 |
126 (6.8) |
|
|
|
|
|
|
|
|
Rural |
782 |
79 (10.1) |
1.54 |
1.14 |
2.07 |
0.004 |
0.96 |
0.69 |
1.33 |
0.83 |
|
Antenatal visit |
Yes (ref) |
1886 |
125 (6.6) |
|
|
|
|
|
|
|
|
No |
752 |
80 (10.6) |
2.04 |
1.30 |
3.20 |
0.001 |
1.21 |
0.89 |
1.65 |
0.21 |
|
Child sex |
Male (ref) |
1335 |
92 (6.9) |
|
|
|
|
|
|
|
|
Female |
1303 |
113 (8.7) |
1.28 |
0.96 |
1.70 |
0.08 |
|
|
|
|
AOR = adjusted odds ratio; CI = confidence interval.
Table 4. Logistic regression results for the factors associated with stunting
|
|
N |
n (%) |
Crude OR |
95% CI |
P |
AOR |
95% CI |
P |
||
Maternal age, yr |
15–19 |
138 |
12 (8.7) |
1.23 |
1.02 |
2.60 |
0.03 |
0.77 |
0.36 |
1.64 |
0.50 |
20–49 (ref) |
1964 |
104 (5.2) |
|
|
|
|
|
|
|
|
|
Educational level |
None |
350 |
42 (12.0) |
1.94 |
1.33 |
2.82 |
<0.01 |
1.01 |
0.63 |
1.61 |
0.96 |
Primary |
706 |
52 (7.4) |
0.97 |
0.69 |
1.38 |
0.08 |
|
|
|
|
|
Secondary and higher (ref) |
1046 |
63 (6.0) |
|
|
|
|
|
|
|
|
|
Welfare status |
Poorest/poorer |
1070 |
114 (10.7) |
2.74 |
1.91 |
3.93 |
<0.01 |
2.22 |
1.47 |
3.50 |
<0.01 |
Middle |
405 |
21 (5.2) |
0.62 |
0.39 |
1.07 |
0.06 |
|
|
|
|
|
Rich/richest (ref) |
627 |
22 (3.5) |
|
|
|
|
|
|
|
|
|
Region |
West (ref) |
528 |
26 (4.9) |
|
|
|
|
|
|
|
|
South |
308 |
24 (7.8) |
1.05 |
0.67 |
1.65 |
0.81 |
|
|
|
|
|
Central |
343 |
20 (5.8) |
0.73 |
0.45 |
1.19 |
0.20 |
|
|
|
|
|
North |
208 |
16 (7.7) |
1.03 |
0.60 |
1.77 |
0.89 |
|
|
|
|
|
East |
715 |
71 (9.9) |
1.66 |
1.20 |
2.31 |
0.02 |
1.08 |
0.73 |
1.60 |
0.68 |
|
Residential area |
Urban (ref) |
1461 |
95 (6.5) |
|
|
|
|
|
|
|
|
Rural |
641 |
62 (9.7) |
1.54 |
1.10 |
2.15 |
0.01 |
0.99 |
0.67 |
1.48 |
0.99 |
|
Birthweight |
<2500 |
248 |
38 (15.3) |
3.01 |
2.01 |
4.48 |
<0.01 |
2.86 |
1.90 |
4.30 |
<0.01 |
≥2500 (ref) |
1781 |
101 (5.7) |
|
|
|
|
|
|
|
|
|
Child sex |
Male |
1072 |
82 (7.6) |
|
|
|
|
|
|
|
|
Female (ref) |
1030 |
75 (7.3) |
0.94 |
0.68 |
1.31 |
0.74 |
|
|
|
|
|
Child age, mo |
0–11 (ref) |
401 |
14 (3.5) |
|
|
|
|
|
|
|
|
12–23 |
380 |
39 (10.3) |
1.55 |
1.06 |
2.27 |
0.02 |
1.69 |
1.12 |
2.53 |
0.01 |
|
24–59 |
1321 |
104 (7.9) |
1.17 |
1.11 |
1.65 |
0.03 |
1.65 |
1.20 |
2.65 |
0.02 |
AOR = adjusted odds ratio; CI = confidence interval.
Figure 1. Identification of secondary dataset.
Cigarette use and exposure to second-hand smoke and advertising in Tunisian adolescents, 2001 to 2017
Yosr Ayedi,1 Chahida Hariz,1 Afef Skhiri1 and Radhouane Fakhfakh1
1Department of Epidemiology and Biostatistics, Abderrahmane Mami Hospital, Ariana, Tunisia. (Correspondence to Yosr Ayedi:
Abstract
Background: The Global Youth Tobacco Survey was conducted in Tunisia in 2001, 2007, 2010 and 2017.
Aims: To describe the trends in cigarette use among Tunisian adolescents and their exposure to second-hand smoke and tobacco advertising from 2001 to 2017.
Methods: The Global Youth Tobacco Survey is a school-based cross-sectional survey conducted by the World Health Organization. It uses a two-stage cluster sampling design to obtain a representative sample of students aged 13–15 years. A standardized questionnaire is used for data collection. The prevalence and 95% confidence intervals (CI) of ever and current cigarette use, exposure to second-hand smoke in and outside the home, and exposure to tobacco advertising were compared over the 4 years.
Results: Current cigarette use decreased from 11.1% (95% CI: 10.0–12.3%) in 2001 to 7.7% (95% CI: 6.5–9.0%) in 2017, P < 0.001. Exposure to second-hand smoke at home decreased from 62.5% (95% CI: 60.7–64.2%) to 46.7% (95% CI: 44.5–49.0%) over the same period, P < 0.001, but exposure outside the home increased from 65.4% (95% CI: 63.7–67.1%) in 2001 to 73.3% (95% CI: 71.2–75.3%) in 2017, P < 0.001. Exposure to anti-tobacco messages in the media fell from 87.8% (95% CI: 86.3–89.1%) in 2001 to 64.4% (95% CI: 62.2–66.5%) in 2017, P < 0.001.
Conclusion: While the prevalence of cigarette use and second-hand smoke exposure at home fell, exposure outside the home increased. Efforts are needed to ensure compliance with smoke-free laws to decrease the prevalence of second-hand smoke.
Keywords: tobacco use, tobacco smoke pollution, adolescent, prevalence, Tunisia.
Citation: Ayedi Y; Hariz C; Skhiri A; Fakhfakh R. Cigarette use and exposure to second-hand smoke and advertising in Tunisian adolescents, 2001 to 2017. East Mediterr Health J. https://doi.org/10.26719/emhj.23.075 Received:09/05/2022; accepted: 04/01/2023
Copyright © World Health Organization (WHO) 2023. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license https://creativecommons.org/licenses/by-nc-sa/3.0/igo
Introduction
Tobacco was the main cause of death in males in 2019 worldwide, responsible for 20% of deaths in males. For women, tobacco was the sixth leading cause of death worldwide, responsible 15.4% of all deaths in women (1). Worldwide, in 2019, about 1.14 billion people aged 15 years and older smoked cigarettes (2). Generally, regular adult smokers began smoking during adolescence and one third started at 14 years (3). People who smoke their first cigarette before the age of 18 years are more likely to become heavy smokers and nicotine dependent in the future, and are less likely to quit, which puts them at higher risk of lung cancer or other tobacco-induced diseases (4).Considerable effort has been made globally to control tobacco use by helping smokers to quit and preventing smoking initiation.
In 2004, 603 000 deaths were estimated to be related to second-hand smoke; 28% of these death occurred in children (6). The increase in people’s knowledge of the effects of tobacco use and second-hand smoke as a result of the media and anti-tobacco messages has helped tobacco control efforts (7). The World Health Organization (WHO) launched the Framework Convention on Tobacco Control (FCTC) in 2003, which was the first international treaty on tobacco control (8). In line with the FCTC, WHO introduced the WHO MPOWER measures: M for Monitoring tobacco use and prevention policies, P for Protecting people from tobacco smoke; O for Offering help to quit tobacco use; W for Warning about the dangers of tobacco; E for Enforcing bans on tobacco advertising, promotion and sponsorship; and R for Raising taxes on tobacco.
Tunisia started a national strategic plan to curb the epidemic of tobacco use in adults and young people in 1998. The strategy was further enforced by Tunisia’s ratification of the FCTC in 2010 (10). In Tunisia, the prevalence of smoking among adult males was reported to be 48.3% (95% confidence interval (CI): 46.3–50.3%) according to the Tunisian Health Examination Survey in 2016 (9). The Global Youth Tobacco Survey (GYTS) is a main component of the MPOWER plan of action. It is a multinational survey conducted by WHO (11) in more than 185 countries to monitor tobacco use among young people aged 13–15 years (12). In Tunisia, this survey has been conducted four times: in 2001, 2007, 2010 and 2017. To our knowledge, the GYTS is the only national survey that examined exposure to second-hand smoke and to the media and advertising in young people.
The aim of our study was to identify the trends in cigarette use in Tunisian adolescents from 2001 to 2017 and to describe their exposure to second-hand smoke and the media and advertising related to tobacco.
Methods
Study design
The GYTS is a cross-sectional, descriptive and school-based survey conducted by WHO. It uses a two-stage cluster sample design to obtain representative samples of students aged 13–15 years. In Tunisia, the age 13–15 years old matches students in the seventh, eighth and ninth school grades. In the GYTS, the complete list of all public schools is sent to the tobacco centre at the United States Centers for Disease Control and Prevention (CDC) where schools are chosen randomly in proportion to the number of students enrolled in the specified grade. Then, classes are randomly chosen according to the city population and size (one or two classes per school).
The GYTS in Tunisia are carried out in April and May of each survey year. Physicians and nurses of medical schools are responsible for data collection, under the direction of the entity Medicine School and University, which takes care of the health of students in schools and universities. The surveys are funded by WHO. Each student in the age range 13–15 years range (seventh, eighth and ninth grades) in the selected classes who is present in the class on the day of survey is eligible to participate in the study.
Questionnaire
The GYTS survey uses a standard methodology and the questionnaire was validated by CDC and WHO experts (13). It contains core questions about the main tobacco concerns focusing on:
• prevalence of all smoked tobacco products and conventional cigarettes
• smokers’ access to tobacco products
• smokers’ behaviours related to stopping smoking
• exposure to the media and advertising
• exposure to second-hand smoke.
The questionnaire has been translated into Arabic and then re-translated into English and sent back to CDC for further checks to ensure accuracy and reliability. It was first pretested with a focus group of adolescents to endure the translation was pertinent and precise. The questionnaire contained 69 questions in 2001, 63 questions in 2007, 70 questions in 2010 and 63 in 2017: 27 questions are common to all four surveys.
We focused on trends in the prevalence of conventional cigarette smoking and exposure to second-hand smoke and to the media and advertising.
Measures
Ever cigarette smoker was defined as someone who had ever smoked cigarettes, even if they had only taken one or two puffs in their lives. Current cigarette user was defined as someone who had smoked cigarettes anytime during the past 30 days, that is, had given any answer other than 0 days to the question, “In the past 30 days, how many days did you smoke cigarettes?”
Participants were considered to have been exposed to second-hand smoke inside the home if they gave any answer other than 0 days to the question, “In the past 7 days, how many days have people smoked in your home, in your presence?” Similarly, they were considered exposed outside the home if they gave any answer other than 0 days to the question and “In the past 7 days, how many days have people smoked in your presence in places other than in your home?”
Consent
Oral consent of the parents of the students is taken the day before the survey.
Data analysis
Anonymized data were available at the official CDC site (https://nccd.cdc.gov/GTSS/rdPage.aspx?rdReport=OSH_GTSS.ExploreByLocation&rdRequestForwarding=Form). We analysed the data using R version 4.2.0 and R studio version 2022.07.01 software. In each survey, adjusted and weighting factors were applied to each student record to adjust for the probability of selection and non-response (by school, class and student).
The weighting factor was: W = W1 × W2 × F1 × F2 × F3 × F4, where: W1 = the reverse of probability of selection of the school; W2 = the reverse of probability of selection of the class within the school; F1 = adjustment factor of non-response of schools according to size (large, medium, small); F2 = adjustment factor of class calculated by school; F3 = adjustment factor of student non-response calculated within this class; and F4 = adjustment factor post-stratification calculated by sex and grade.
The weighting factor was applied through the survey package of R Studio. Unweighted numbers of students were inserted in tables. Indicators were described using weighted percentages reflecting the population estimates. We calculated the 95% confidence intervals (CI) for each proportion. The association between two qualitative variables was assessed with the chi-squared test. Trends were assessed using the Cochrane Armitage trend test. A two-sided 5% significance level was used for all calculations.
Results
From 2001 to 2017, the number of schools included in the survey increased from 50 to 67. The overall response rate varied from 94.1% (2942/3127) in 2001 to 92.9% (1863/2005) in 2017 (Table 1).
Conventional cigarettes
The male to female ratio was about the same in the 4 years: 0.97 in 2001 and 0.93 in 2017. In 2001, about 23.0% (95% CI: 21.5–24.5%) of the respondents had tried to smoke a cigarette, even if only one or two puffs: 35.4% (95% CI: 32.9–37.9%) of boys and 11.4% (95% CI: 9.9–13.1%) of girls. This proportion increased to 25.0% (95% CI: 23.1–27.1%) in 2017, with the increase greater in boys: 38.8% (95% CI: 35.6–42.0%) in boys and 11.6% (95% CI: 9.6–13.8%) in girls. However, these increases were not significant (P > 0.05).
As for current cigarette use, the prevalence decreased significantly from 11.1% (95% CI: 10.0–12.3%) in 2001 to 7.7% (95% CI: 6.5–9.0%) in 2017 (P < 0.001). In boys over the same period, the prevalence of smoking decreased from 19.1% (95% CI: 17.1–21.2%) to 14.2% (95% CI: 12.1–16.7%; P < 0.001). In girls, the prevalence decreased from 3.6% (95% CI: 2.8–4.7%) to 1.4% (95% CI: 0.8–2.4%; P < 0.001) (Table 2).
Exposure to second-hand smoke
Between 2001 and 2017, exposure to second-hand smoke at home in the 7 days before the survey decreased significantly from 62.5% (95% CI: 60.7–64.2%) to 46.7% (95% CI: 44.5–49.0%; P < 0.001). This reduction was significant for both boys and girls (P < 0.001) (Table 3).
Exposure to second-hand smoke outside the home increased significantly between 2001 and 2017, from 65.4% (95% CI: 63.7–67.1%) to 73.3% (95% CI: 71.2–75.3%; P < 0.001). This exposure increased significantly for both boys and girls (P < 0.001) (Table 3).
Most respondents were in favour of implementing smoke-free places by law, although this support fell significantly from 87.0% (95% CI: 85.7–88.2%) in 2001 to 81.8% (95% CI: 80.0–83.5%) in 2017 (P < 0.001), and decreased for both boys and girls (P < 0.001) (Table 3)
Exposure to the media and advertising
Exposure to anti-tobacco messages in the media deceased from 87.8% (95% CI: 86.3–89.1%) in 2001 to 64.4% (95% CI: 62.2–66.5%) in 2017 (P < 0.001). This exposure decreased significantly for both boys and girls (P < 0.001) (Table 4). However, exposure to anti-tobacco messages at sports and cultural events increased significantly, from 34.2% (95% CI: 32.5–35.9%) in 2001 to 72.2% (95% CI: 70.1–74.2%) in 2017 (P < 0.001). This exposure increased significantly for both boys and girls (P < 0.001) (Table 4). Two thirds of students (67.3%; 95% CI: 64.7–69.8%) had seen advertising for tobacco use in 2010. This proportion fell significantly to 43.7% (95% CI: 41.2–46.2%) in 2017 (P < 0.001). This exposure decreased significantly for both boys and girls (P < 0.001) (Table 4). The proportion of respondents who had had received free promotional cigarettes was small and did not change significantly over the years (Table 4).
Discussion
The GYTS is one of the most important tobacco monitoring tools and helps countries implement the MPOWER package. The questions are in line with the MPOWER package and focus on important aspects of tobacco use and tobacco control. Monitoring the prevalence of tobacco use over time is essential to identify changes and link the national tobacco control strategy to the current situation.
Conventional cigarettes
One in four students had ever tried to smoke a cigarette: one boy out of three and one girl out of 10. In the United States, data from the National Youth Tobacco Survey from 2014 to 2016 showed that 21% of adolescents had ever tried to smoke a cigarette (14). The GYTS in the United Arab of Emirates in 2013 focused on expatriate adolescents only and reported that 32% of boys had tried to smoke a cigarette, at a mean age of 12–13 years (15). A previous Tunisian national survey, which included 4172 adolescents aged 12–20 years from public and private schools, reported that among students aged 12–14 years, 26.9% had tried to smoke a cigarette in their lives (16). In the Sfax region in the south of Tunisia, ever cigarette smoking was reported in 16.7% of school students (32.6% of boys and 5.9% of girls) (17). Our findings are similar to these studies and indicate a high prevalence of cigarette experimentation among boys and girls in Tunisia.
Our findings show that the prevalence of current cigarette use in adolescent Tunisians has decreased over time, overall and for boys and girls. According to the last Youth Risk Behavior Survey in the United States conducted in 2019, a significant decrease in current cigarette use had occurred among students in the ninth grade (14–15 years), from 13.5% in 2009 (18) to 3.8% in 2019 (19). In a 45-country analysis of GYTS data in 2013 and 2014, the median global prevalence of current cigarette use across all countries was 6.8% (9.7% in boys and 3.5% in girls), which is lower than the prevalence in our four surveys overall and for boys, but higher than current cigarette use we found for girls. Given the findings of the 2017 GYTS in Tunisia, the country has the fourth highest prevalence of adolescent cigarette use in the Middle East and North African region, after Jordan (2014 GYTS), Lebanon (2013 GYTS) and Qatar (2013 GYTS) (20). Other studies of North African countries showed that a greater proportion of Tunisian boys smoked than Egyptian, Libyan, Moroccan and Sudanese boys (21). In Malaysia, the prevalence of current cigarette use decreased from 19.9% in 2003 to 14.8% in 2016, which is almost double of the prevalence in our study (22). In Morocco, Tunisia’s neighbour, the current cigarette use among 13–15-year-old schoolchildren increased from 3.0% in 2006 to 5.2% in 2010, but both are lower than the prevalence in Tunisian schoolchildren (23). In the city of Sousse in Tunisia, the results of a cross-sectional survey in 2013–2014 in 16 public schools found that 4.5% of participants were cigarette users, which is lower than the national prevalence in the GYTS surveys (24). Even though cigarette consumption in Tunisian schoolchildren fell from 2001 to 2017, it is nonetheless still high and needs to be tackled to reduce the its prevalence further.
Exposure to second-hand smoke
Second-hand smoke outside the home in adolescents increased from 2001 to 2017. A study in 131 countries found that exposure to second-hand smoke outside the home was 57.6% in 2018 and it had not decreased from 1999 – it remained the same in 46 of 131 countries (35.1%) and increased in 40 (30.5%). This increase was found in almost all WHO regions (exposure was 59.4% for exposure at least one day a week in the Middle East and North Africa region) and in countries that did not ratify the FCTC (25). The overall exposure to second-hand smoke in public places among non-smoking adolescents was 44.2% across 168 countries from 1999 to 2008. The exposure was higher in boys than girls. Exposure ranged from 39.8% in the Middle East and North Africa region to 73.7% in the European region (26). In Africa, from 2006 to 2011, exposure to second-hand smoke among adolescents was 39.0%; it ranged from 24.9% in Cape Verde to 80.4% in Mali, with no differences between the sexes (27). These results show that exposure to second-hand smoke in Tunisia is among the highest in the world.
The proportion of students in favour of laws that establish smoke-free places decreased for both sexes. Tunisia established its first tobacco law (no. 98-17) in February 1998 which aimed to protect people from tobacco harm. Article 10 of this law prohibits smoking in public places (28). This law was enforced by the decree of November 1998 (29), decree of September 2009 (30) and ratification of the FCTC in 2010. Article 8 of the FCTC calls for countries to adopt and implement effective national legislations to protect people from exposure to tobacco in indoor and outdoor public places (8). However, the compliance of Tunisians and respect of these laws seem to be weak given the high rates of exposure to second-hand smoke outside the home (31). In the most recent report of MPOWER in the Middle East and North Africa region, Tunisia had a score of 1 out of 3 for smoke-free places, which means only up to two public places were completely smoke free (32). A longitudinal study found evidence that, in addition to positive impact on exposure to second-hand smoke, laws on smoke-free places led to a possible decrease in smoking prevalence (33).
Exposure to the media and advertising
In the 2001 Tunisian GYTS, a greater proportion of respondents were exposed to anti-tobacco messages in the media (internet, magazines, television) than respondents in the 2017 GYTS. This result is similar to findings in Greece (34), Italy (35) and Myanmar (36). A longitudinal study in the United States found a positive effect of anti-tobacco messages on teenagers’ susceptibility to smoke. In fact, this exposure decreased the susceptibility to smoke by 2 or 3 years (37). From 2010 to 2017, the proportions of students exposed to cigarette advertising at points of sale decreased. A systematic review in 2009 concluded that exposure to promotion of cigarette use at points of sale increased the odds of ever smoking, frequent smoking or occasional smoking (38). This explains why the tobacco industry spends around 80% of their advertising budget on promotions at points of sale (39). Article 13 of the FCTC calls for countries to ban every kind of tobacco promotions, advertisings and sponsorships. In Tunisia, law no. 98-17 forbids all types of promotion of tobacco products in public places, but it does not include a ban on promotion at points of sale (28).
Strengths and limitations
A strength of our study is that the GYTS is the only standardized worldwide survey on tobacco use and attitudes in adolescents aged 13–15 years. In addition, the GYTS is a national survey conducted in all governorates and cities in the country. Furthermore, the sample size of students who answered the questionnaire was large and the response rates were always more than 92%.
Our study has some limitations. Smoking behaviour and exposure to second-hand smoke were self-reported and no quantitative method was used to confirm the students’ responses, which may introduce biases. In addition, only students in public schools were included, thus students in private schools or adolescents who were not in school were not represented. Students in private schools and teenagers who don’t go to schools represent about 10% of Tunisian adolescents according to a 2015 report (40).
Conclusions
WHO recommends that countries implement a monitoring survey every 5 years. It has been 5 years since the last GYTS in Tunisia and a new GYTS survey is needed. In addition, efforts to ensure complete compliance with smoke-free laws are needed to decrease the prevalence of second-hand smoke. Finally, a complete ban of point of sales promotions is strongly recommended to decrease the exposure of vulnerable young people to this tobacco advertising.
Acknowledgements
This study was a collaborative project of WHO, CDC and the Ministry of Health of Tunisia. We thank the study participants and research assistants.
Funding: None.
Competing interests: None declared.
References
1. Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. https://doi.org/10.1016/S0140-6736(20)30752-2
2. Reitsma MB, Kendrick PJ, Ababneh E, Abbafati C, Abbasi-Kangevari M, Abdoli A, et al. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet. 2021;397(10292):2337–60. https://doi.org/10.1016/S0140-6736(21)01169-7
3. The epidemiology of tobacco use among young people in the United States and worldwide. In: Preventing tobacco use among youth and young adults: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2012.
4. Cantrell J, Bennett M, Mowery P, Xiao H, Rath J, Hair E, et al. Patterns in first and daily cigarette initiation among youth and young adults from 2002 to 2015. PLoS One. 2018;13(8):1–20. https://doi.org/10.1371/journal.pone.0200827
5. Hughes J, Kabir Z, Bennett K, Hotchkiss JW, Kee F, Leyland AH, et al. Modelling future coronary heart disease mortality to 2030 in the British Isles. PLoS One. 2015;10(9):1–12. https://doi.org/ 10.1371/journal.pone.0138044
6. Öberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. Lancet. 2011;377(9760):139–46. https://doi.org/10.1016/S0140-6736(10)61388-8
7. Blake KD, Viswanath K, Blendon RJ, Vallone D. The role of tobacco-specific media exposure, knowledge, and smoking status on selected attitudes toward tobacco control. Nicotine Tob Res. 2010;12(2):117–26. https://doi.org/10.1093/ntr/ntp184
8. WHO Framework Convention on Tobacco Control [Internet]. Geneva: World Health Organization; 2005 (https://fctc.who.int/, accessed 20 January 2022).
9. Tunisian Health Examination Survey – 2016. Tunis: République Tunisienne, Ministère de la Santé, Institut National de la Santé; 2019 (http://www.santetunisie.rns.tn/images/rapport-final-enquete2020.pdf, accessed 20 January 2022).
10. Harizi C, El-Awa F, Ghedira H, Audera-Lopez C, Fakhfakh R. Implementation of the WHO Framework Convention on Tobacco Control in Tunisia: Progress and challenges. Tob Prev Cessat. 2020;6:1–8. https://doi.org/10.18332/tpc/130476
11. Global Youth Tobacco Survey. Tunisia [Internet]. Geneva: World Health Organization (https://extranet.who.int/ncdsmicrodata/index.php/catalog/GYTS#_r=&collection=&country=217&dtype=&from=1999&page=1&ps=&sid=&sk=&sort_by=nation&sort_order=&to=2019&topic=&view=s&vk=, accessed 9 September 2020).
12. Global youth tobacco survey [Internet]. Geneva: World Health Organization (https://www.who.int/tobacco/surveillance/gyts/en/, accessed 31 August 2020).
13. Global Youth Tobacco Survey Collaborative Group. Global youth tobacco survey (GYTS): core questionnaire with optional questions. Version 1.2. Atlanta, GA: Centers for Disease Control and Prevention; 2014.
14. Sharapova S, Reyes-Guzman C, Singh T, Phillips E, Marynak KL, Agaku I. Age of tobacco use initiation and association with current use and nicotine dependence among US middle and high school students, 2014–2016. Tob Control. 2020;29(1):49–54. https://doi.org/10.1136/tobaccocontrol-2018-054593
15. Asfour LW, Stanley ZD, Weitzman M, Sherman SE. Uncovering risky behaviors of expatriate teenagers in the United Arab Emirates: a survey of tobacco use, nutrition and physical activity habits. BMC Public Health. 2015;15(1):944. https://doi.org/10.1186/s12889-015-2261-9
16. Fakhfakh R, Jaidane I, Hsairi M, Ben Hamida AM. Les facteurs de risque et de protection de l’initiation à la cigarette chez les adolescents tunisiens [Cigarette smoking initiation among Tunisian adolescents: Risk and protective factors]. Rev Epidemiol Sante Publique. 2015;63(6):369–79. http://dx.doi.org/10.1016/j.respe.2015.09.005
17. Ben Ayed H, Yaich S, Ben Hmida M, Ben Jemaa M, Trigui M, Karray R, et al. Prevalence and factors associated with smoking among Tunisian secondary school-adolescents. Int J Adolesc Med Health. 2021;33(6):379–87. https://doi.org/10.1515/ijamh-2019-0088
18. Centers for Disease Control and Prevention. Youth risk behavior surveillance—United States, 2009. Surveillance summaries, June 2010. MMWR Surveill Summ. 2010;59(5):1–142.
19. Creamer MLR, Everett Jones S, Gentzke AS, Jamal A, King BA. Tobacco product use among high school students – youth risk behavior survey, United States, 2019. MMWR Suppl. 2020;69(1):56–63. https://doi.org/10.15585/mmwr.su6901a7
20. D’Angelo D, Ahluwalia IB, Pun E, Yin S, Palipudi K, Mbulo L. Current cigarette smoking, access, and purchases from retail outlets among students aged 13–15 years—Global Youth Tobacco Survey, 45 countries, 2013 and 2014. MMWR Morb Mortal Wkly Rep. 2016;65(34):898–901. https://doi.org/10.15585/mmwr.mm6534a3
21. Madkour AS, Ledford EC, Andersen L, Johnson CC. Tobacco advertising/promotions and adolescents’ smoking risk in Northern Africa. Tob Control. 2014;23(3):244–52. https://doi.org/10.1136/tobaccocontrol-2012-050593
22. Lim K, Ghazali S, Lim H, Kee C, Cheah Y, Pradhaman Singh B, et al. Tobacco use and other aspects related to smoking among school-going adolescents aged 13–15 years in Malaysia: analysis of three cross-sectional nationally representative surveys in 2003, 2009 and 2016. Tob Induc Dis. 2020;18(September):1–10. https://doi.org/10.18332/tid/127231
23. Shaikh MA. Prevalence, correlates, and changes in tobacco use between 2006 and 2010 among 13–15 year Moroccan school attending adolescents. J Pak Med Assoc. 2014;64(11):1306–9.
24. Nawel Z, Jihen M, Rim G, Sana B, Hassen G. Tobacco use: the main predictor of illicit substances use among young adolescents in Sousse, Tunisia. Int J Adolesc Med Health. 2018;32(5). https://doi.org/10.1515/ijamh-2017-0213
25. Ma C, Heiland EG, Li Z, Zhao M, Liang Y, Xi B. Global trends in the prevalence of secondhand smoke exposure among adolescents aged 12–16 years from 1999 to 2018: an analysis of repeated cross-sectional surveys. Lancet Glob Health. 2021;9(12):e1667–78. https://doi.org/10.1016/S2214-109X(21)00365-X
26. Ma C, Heiland EG, Li Z, Zhao M, Liang Y, et al. Secondhand smoke exposure among never-smoking youth in 168 countries. J Adolesc Health. 2015;56(2):167–73. https://doi.org/10.1016/S2214-109X(21)00365-X
27. Owusu D, Mamudu HM, John RM, Ibrahim A, Ouma AEO, Veeranki SP. Never-smoking adolescents’ exposure to secondhand smoke in Africa. Am J Prev Med. 2016;51(6):983–98. https://doi.org/10.1016/j.amepre.2016.08.040
28. [Law no. 98-17 of February 23 1998, relative to prevention of the harmful effects of smoking]. Journal Officiel du la République Tunisienne;1998 27 février:399–400.
29. Decree no. 98–2248, November, 16 1998. Fixant les lieux affectés à l'usage collectif dans lesquels il est interdit de fumer [Identifying smoke-free public places]. Tunis: Government of Tunisia; 1998.
30. Décret no. 2009-2611 du 14 septembre 2009, complétant le décret n° 98-2248 du 16 novembre 1998 fixant les lieux affectés à l’usage collectif dans lesquels il est interdit de fumer [Completing decree no. 98-2248 of November, 16 1998]. Tunis: Government of Tunisia; 2009.
31. Ben Amar W, Chakroun A, Zribi M, Khemekhem Z, Ben Jemaa F, Maatoug S. Dispositif législatif de lutte anti-tabagique en Tunisie : entre insuffisances et défaut d’application [Anti tobacco legislation and regulation in Tunisia: between shortcomings and lack of application]. JIM Sfax. 2017;17:21–6.
32. Heydari G, Zaatari G, Al-Lawati JA, El-Awa F, Fouad H. MPOWER, needs and challenges: trends in the implementation of the WHO FCTC in the Eastern Mediterranean Region. East Mediterr Health J. 2018;24(01):63–71. https://doi.org/10.26719/2018.24.1.63
33. Becker CM, Lee JGL, Hudson S, Hoover J, Civils D. A 14-year longitudinal study of the impact of clean indoor air legislation on state smoking prevalence, USA, 1997–2010. Prev Med (Baltim). 2017;99:63–6. https://doi.org/10.1016/j.ypmed.2017.01.016
34. Kyrlesi A, Soteriades ES, Warren CW, Kremastinou J, Papastergiou P, Jones NR, et al. Tobacco use among students aged 13–15 years in Greece: the GYTS project. BMC Public Health. 2007;7(1):3. https://doi.org/10.1186/1471-2458-7-3
35. Gorini G, Gallus S, Carreras G, Cortini B, Vannacci V, Charrier L, et al. A long way to go: 20-year trends from multiple surveillance systems show a still huge use of tobacco in minors in Italy. Eur J Public Health. 2019;29(1):164–9. https://doi.org/10.1093/EURPUB/CKY132
36. Tun N, Chittin T, Agarwal N, New M, Thaung Y, Phyo P. Tobacco use among young adolescents in Myanmar: findings from global youth tobacco survey. Indian J Public Health. 2017;61(5):54. https://doi.org/10.4103/ijph.IJPH_236_17
37. Weiss JW, Cen S, Schuster D, Unger J, Johnson CA, Mouttapa M, et al. Longitudinal effects of pro‐tobacco and anti‐tobacco messages on adolescent smoking susceptibility. Nicotine Tob Res. 2006;8(3):455–65. https://doi.org/10.1080/14622200600670454
38. Paynter J, Edwards R. The impact of tobacco promotion at the point of sale: a systematic review. Nicotine Tob Res. 2009;11(1):25–35. https://doi.org/10.1093/ntr/ntn002
39. Ma H, Reimold AE, Ribisl KM. Trends in cigarette marketing expenditures, 1975–2019: an analysis of federal trade commission cigarette reports. Nicotine Tob Res. 2022;24(6):919–23. https://doi.org/10.1093/ntr/ntab272
40. Middle East and North Africa out-of-school children initiative. Summary, Tunisia: country report on out-of-school children. Amman: UNICEF MENA Regional Office; 2015 (https://www.unicef.org/mena/media/6661/file/Tunisia%20Country%20Report%20on%20OOSC%20Summary_EN.pdf%20.pdf, accessed 20 January 2022).
Table 1. Schools, classes and students in included in the Global
Table 2. Ever and current cigarette smoking, by sex and year, Tunisia Global Youth Tobacco Survey
Table 3. Exposure to second-hand smoke and perceptions of mandated smoke-free places, by year, Tunisia Global Youth Tobacco Survey
Table 4. Prevalence of exposure to the media and advertising, by year, Tunisia, Global Youth Tobacco Survey
Response to cholera outbreaks, Somalia, 2017–2019: challenges, interventions and lessons learnt
Mutaawe Lubogo,1 Buliva Evans,2 Abubakar Abdinasir,2 Elnossery Sherein,2 Tayyab Muhammad,2 Ahmed M. Mohamed,3 Aden Hussein,3 Fayez Abdulrazeq2 and Malik Sk Md Mamunur1
1World Health Organization, Somalia Country Office, Mogadishu, Somalia.
2Emergency Programme, World Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
3Federal Ministry of Health, Mogadishu, Somalia. (Correspondence to Mutaawe Lubogo:
Abstract
Background: Somalia reported repeated cholera outbreaks between 2017 and 2019. These outbreaks were attributed to the presence and occurrence of multiple risk factors for cholera, which made response challenging.
Aims: To describe the challenges faced by Somalia in responding to cholera outbreaks between 2017 and 2019 and provide lessons for Somalia and other countries with a similar context on how to better prepare for future outbreaks.
Methods: We reviewed outbreak response reports, surveillance records and preparedness plans for cholera outbreaks in Somalia from January 2017 to December 2019 and other relevant literature. We present data on cholera-related response indicators including cholera cases and deaths and case fatality rates in the 3 years. Qualitative data were collected from five focus group discussions and 10 key informant interviews to identify challenges, interventions and lessons learnt from the Somali experience.
Results: In 2017, 78 701 cholera cases and 1163 related deaths were reported (case fatality rate 1.48%), in 2018, 6448 cholera cases and 45 deaths were reported (case fatality rate 0.70%), while in 2019, 3089 cases and four deaths were reported in Somalia (case fatality rate 0.13%). The protracted conflict, limited access to primary health care, and limited access to safe water and proper sanitation among displaced populations were the main drivers of repeated cholera outbreaks.
Conclusions: Periodic assessment of response to and preparedness for potential epidemics is essential to identify and rectify gaps within current systems. Somalia’s experience offers important lessons for countries experiencing complex humanitarian emergencies that may help prevent and control future cholera outbreaks.
Keywords: cholera, disease outbreaks, emergencies, Somalia.
Citation: Lubogo M; Evans B; Abdinasir A; Sherein E; Muhammad T; Mohamed AM; Hussein A, et al. Response to cholera outbreaks, Somalia, 2017–2019: challenges, interventions and lessons learnt. East Mediterr Health J. https://doi.org/10.26719/emhj.23.078 Received: 24/05/2022; accepted: 05/01/2023
Copyright © World Health Organization (WHO) 2023. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license https://creativecommons.org/licenses/by-nc-sa/3.0/igo
Introduction
Cholera is a disease of inequity, affecting the world’s most vulnerable and marginalized communities. It is an epidemic-prone disease of global importance (1). The global burden of cholera is unknown because of underreporting (2), but an estimated 3–5 million cholera cases every year cause 100 000–120 000 deaths (3). In the Eastern Mediterranean Region of the World Health Organization (WHO), cholera remains a public health concern, especially in countries facing complex emergencies. In the past decade, 14 out of the 22 countries in the Eastern Mediterranean Region have reported cholera cases; in Afghanistan, Djibouti, Iraq, Pakistan, Somalia, Sudan and Yemen, the numbers have reached epidemic proportions. The regional burden of cholera is also difficult to capture because of weak surveillance systems and underreporting of cases (4). Nevertheless, the number of cases is estimated to be around 188 000 a year (3). In Somalia and other counties in the Horn of Africa, cholera outbreaks are spreading at an alarming rate and affecting communities already suffering from conflicts and droughts (5). Controlling cholera is crucial to achieve the Sustainable Development Goals which call for the good health and well-being of all and a reduction in inequity (6).
Cholera is a multifactorial disease occurring and re-emerging frequently as a result of interaction between different risk factors (7). Identified risk factors fall into four main categories: (i) factors related to water and sanitation such as lack of rainfall and decreased vegetation cover (8), flooding leading to contamination of water sources (9), lack of adequate clean water and proper sanitation facilities because of inadequate infrastructure (10), and poor sanitary practices by communities at risk of cholera infections (11); (ii) sociodemographic factors such as poverty, overcrowding and living in a camp for refugees or internally displaced persons (12); (iii) behavioural factors such as open defecation and funeral practices such as washing the bodies of those who have died from cholera (13); and (iv) gaps in knowledge and false beliefs about cholera infection and transmission, oral cholera vaccines, and cholera case management (14).
In Somalia, the protracted conflict, drought and flooding have resulted in over 2.6 million people living in camps for internally displaced persons where access to safe water and proper sanitation is limited (15). Cholera is endemic in the country and outbreaks occur during both the drought and rainy seasons.
Most of the country, particularly the regions of Shabelle and Juba, is prone to flooding about twice a year leading to contamination of water sources. Moreover, in 2017, Somalia experienced severe drought that affected over 60% of the country and led to severe water and food shortages (16). It was estimated that more than 3 million people were at risk of starvation and malnutrition (17). The limited access to safe water and poor sanitation conditions contributed to Somalia’s worst cholera outbreak in a decade (2,18–20). In January 2017, the Federal Ministry of Health confirmed a cholera outbreak in the Hiran region following the isolation of Vibrio cholera, serotype Ogawa from stool samples of suspected cases. The epidemic spread rapidly to most districts and regions of Somalia and reached its peak during May and June 2017 (19).
This paper aimed to describe the main challenges faced by Somalia during the 2017–2019 cholera outbreaks, highlight Somalia’s response to those challenges with the support of WHO and other partners, and identify lessons learnt. The paper also provides guidelines on how to better prepare for future outbreaks in Somalia and other countries with complex humanitarian emergencies and poor operating environments.
Methods
We undertook a literature review specific to Somalia of available information on the cholera outbreaks of 2017–2019 with particular reference to: preparedness and response; focus group discussions and key informant interviews; and interpretation of the cholera-related indicators in the 3 years of the outbreaks.
Literature review and cholera indicators
The qualitative and quantitative review was undertaken to analyse decision-making, policy, and actions taken during the 2017–2019 cholera emergency in Somalia. The literature search included cholera preparedness and response plans, surveillance records, monitoring and evaluations reports, needs assessments reports, meeting notes, presentations, internal reports, peer-reviewed articles, and relevant grey literature. The data on key indicators including cases, deaths and case fatality rates (CFRs) were collected from the surveillance records of Somalia’s Early Warning and Response Network Surveillance System (EWARN) (21), WHO’s Global Health Observatory and published records (2,18), and cholera situation reports by the WHO Regional Office for the Eastern Mediterranean (19,22). The data were summarized and changes in the indicators over time are presented. A cholera case was defined as a suspected case with V. cholerae 01 and O139 confirmed by stool culture. The cholera CFR was defined as the proportion of cholera-related deaths among total cholera cases during 2017–2019 and was expressed as a percentage.
Focus group discussions and key informant interviews
Participants for 10 key informant interviews and five focus group discussions were selected purposively based on their prominent roles in the health services, and/or their acknowledged understanding and custodianship of the health care system. The key informant interviews and focus group discussions helped provide in-depth information/perspectives for the qualitative review.
Nineteen participants from eight regions in Somalia were included in the focus group discussions, which were held in Mogadishu in 2017 by a trained interviewer who guided the discussion based on pre-identified themes. The interviewer encouraged participants to express their thoughts and ideas freely without interruptions. Pre-identified themes for the focus group discussions included: outbreak detection/confirmation; organization of response; use of reactive oral cholera vaccines; information management; case management; mortality reduction; hygiene measures at the health facility level; involvement of the community to reduce the effect of the disease; surveillance; funeral practices; and three themes related to control of the environment – safe water, safe food and sanitation.
The key informant interviews were done in 2019 mainly with senior staff at the Federal Ministry of Health and with department heads at relevant international agencies who had extensive knowledge of their organization’s involvement in the cholera response.
Both the focus group discussions and key informant interviews were audio-recorded and transcribed separately by two researchers. The transcribed data were analysed thematically by a researcher who was blind to the aims of the focus group discussions and key informant interviews.
Results
Cholera outbreaks in Somalia
In East Africa, particularly the Horn of Africa, and the Middle East, large cholera outbreaks with high mortality are frequently reported (18). The historic trend of cholera in Somalia has not been much studied (23). Although Somalia has long faced cholera outbreaks, the earliest record of a cholera outbreak appears in the WHO’s Global Health Observatory for 1970 (18). For the past 3 decades, almost all small-to-large cholera outbreaks in Somalia coincided with outbreaks globally and in the Eastern Mediterranean Region. However, except for the small-scale outbreaks in 2008–2010 and 2014–2015, the cholera cases, deaths and CFRs in Somalia were higher than the corresponding global and regional averages.
On average in the decade 2010–2019, 22 505 cholera cases and 379 deaths (CFR 1.68%) occurred in Somalia, which is higher than the regional average of 16 918 cases and 133 deaths (CFR 0.79%) and the global average of 9765 cases and 95 deaths (CFR 0.97%). The higher values of the indicators can be attributed to two large cholera epidemics of almost similar scale in Somalia, in 2011 and 2017. Except for in 2002, the cholera CFR in Somalia has always been higher than the global and regional CFR averages (2,18,19,22).
The 2017 cholera outbreak was the largest since 1970, with 78 701 cases and 1163 deaths, mainly among children younger than 5 years. The outbreak was more widespread and severe, encompassing 85 districts in 20 regions within the country, nine of which were classified as partially accessible (urban areas were accessible but not villages) because of political conflict (2,19,22). The highest incidence was in Bay region with 14 964 reported cases, while the lowest was in Sahil with only three cases reported (Figure 1 and Table 1). The overall CFR in 2017 was 1.48%. Of the 78 701 cases and 1163 deaths, 42 987 (56.42%) cases and 582 (51.86%) deaths were reported from the partially accessible regions (Table 1).
The highest peak of the 2017 outbreak was in epidemiological week 22 (29 May–4 June) when more than 5000 cases of cholera were reported. Thereafter, the number of reported cases declined gradually and reached its minimum of 144 cases in epidemiological week 47 (20 November–26 November). After that time, sporadic cases of cholera were reported during December 2017 (22) (Figure 2). This 2017 peak in cases was attributed to a series of unfavourable events that began with heavy rains which caused flash floods that led to contamination of water sources and displacement of communities to camps where access to safe water and proper sanitation was limited. After the flash floods, drought occurred in parts of Somalia, which led to food insecurity and malnutrition among children and resulted in lowered immunity to waterborne infections. Communities did not have enough time to fully recover from each of these hazards, and this situation, together with the weak health system, contributed to the repeated cholera outbreaks with varying degrees of severity.
Cholera cases continued to be reported in 2018 and 2019 (19,22), but the total number of cases was much lower than in 2017 (Table 1 and Figure 2). In 2018, the cumulative total of cholera cases was 6448 with 45 associated deaths (CFR 0.70%) in 23 districts from accessible regions (Banadir and Lower Shabelle) and partially accessible regions (Hiran, Lower Juba, Lower Shabelle, Middle Shabelle and Lower Shabelle) (19,22). In 2019, the cumulative total of cholera cases was 3089 with four associated deaths (CFR 0.13%) from 19 districts in Banadir region and the partially accessible regions of Gedo and Lower Juba (19,22). Overall, the ongoing cholera outbreak that started in December 2017 up to December 2019 has resulted in 13 818 cases and 72 deaths (CFR 0.52%), reported from three states of Somalia (Hirshabelle, Jubaland and South West) and Banadir region (19,24).
Discussion
According to WHO, preparedness, response and post-endemic activities collectively comprise three phases of effective cholera control (25). However, the level of preparedness is the most crucial phase of cholera control and essentially determines the success of an outbreak response (26). Therefore, we identified lessons learnt from the 2017–2019 cholera response in Somalia to provide recommendations on how to better prepare for and respond to future potential outbreaks in the country and other countries with poor operating environments during humanitarian emergencies.
A combination of factors led to Somalia’s severe 2017 cholera outbreak. Protracted conflict contributed to the weakening of Somalia’s health system. Conflict led to displacement of people to camps where access to safe water and sanitation is limited. Severe drought in 2016 and 2017 led to water shortages, displacement, food shortages and malnutrition in children younger than 5 years which in turn led to low immunity.
Our analysis of the trends in the number of cholera cases and deaths and the CFR shows that, despite many years of public health interventions, cholera is still a recurring and important risk to vulnerable communities in Somalia. In 2017, the country experienced the worst cholera outbreak in 5 years, with 78 701 cases and 1163 deaths, mostly in children younger than 5 years (18,19,22).
The Global Task Force for Cholera Control was established in 1991 and revitalized in 2011 as a result of the World Health Assembly resolution WHA64.15, which requested the WHO Director-General to strengthen WHO’s work in this area (27,28). Later in 2017, this task force lunched “Ending cholera: a global roadmap to 2030” and formulated a framework to achieve that target (6). In October 2017, a call to action to fight cholera through implementation of the global roadmap was made by 35 task force partners (27). Despite global efforts to end cholera in Somalia, cholera outbreaks are still reported since conflicts are still ongoing. Therefore, reassessment of cholera preparedness and response plans is important to achieve the goal to end cholera by 2030.
Several countries with complex emergencies have experienced repeated cholera outbreaks and successfully implemented response activities and interventions with the support of WHO and other partners. However these efforts faced challenges and obstacles (29–31). Although Somalia faces a similar situation (19), the humanitarian crisis in Somalia is characterized by multiple hazards occurring in quick succession without any time for full recovery from preceding hazards. Recognizing these challenges and exploring Somalia’s experiences is important to identify and bridge gaps within the current surveillance and response systems at both national and subnational levels. EWARN was launched in Somalia in 2010 but collapsed during ongoing conflict. Until 2017, no reliable surveillance system existed for timely detection of alerts of cholera and other epidemic-prone diseases. With the support of WHO, EWARN was re-activated in 2017 to provide timely detection and response alerts for cholera (32).
We identified several important challenges including: a weak health system; fragile water, sanitation and hygiene (WASH) infrastructure; difficulty in obtaining real-time information; poor resources; and limited funding. However, the most important challenge was conflict, which was responsible for all the other challenges. With support of WHO and other partners, Somalia was able to overcome these challenges and successfully responded to the cholera outbreaks in 2017–2019. However, these outbreaks will not be the last; therefore, ongoing support is vital for the prevention and early detection of, rapid response to and containment of future outbreaks.
Successful interventions that were implemented and contributed to the effective management of the cholera outbreak included: efficient leadership and coordination of epidemic preparedness and response plans at all levels; timely detection and response to alerts; timely dissemination of epidemiological information that was useful for public health action; comprehensive risk assessment; proper case management; enhancement of surveillance and laboratory capacities; strengthening of WASH preparedness; campaigns for community engagement, risk communication; and implementation of campaigns for preventive oral cholera vaccination. The impact of these interventions was evident by the reduction in the total number of cholera cases reported from 78 701 cases in 2017 to 6448 cases in 2018 and 3089 cases in 2019. Similarly, the CFR declined from 1.48% in 2017 to 0.70% in 2018 and 0.13% in 2019.
Based on the forecasting exercise conducted in Somalia in 2018, the total number of reported cases in 2018 was 37.08% less than the best case scenario in which 17 389 individuals were suspected to have cholera the same year. Similar successful experiences were reported from other countries such as Haiti which succeeded in controlling the cholera outbreak following the 2010 earthquake by prioritizing investment in safe water supplies and improved sanitation (33).
Recommendations
Based on our assessment of the experience of responding to the cholera outbreaks in Somalia, the following recommendations are proposed for the country and other countries with similar contexts.
1. Strengthen coordination and leadership to review and update preparedness and response plans for cholera.
2. Integrate diseases surveillance and response systems that include an early warning alert and response network to support timely detection of and response to any alerts of cholera and other epidemic-prone diseases.
3. Increase the number of people with access to safe water and proper sanitation through the establishment of sustainable water systems.
4. Raise awareness of communities in high-risk areas for cholera of the importance of adopting hygienic behaviour; and
5. Perform continuous risk assessments to identify hotspots for cholera and have plans to implement preventive cholera vaccination campaigns.
Acknowledgements
The authors thank the Federal Ministry of Health the Somalia for providing the cholera-related data and permission to submit this manuscript for publication in a peer reviewed journal.
Funding: None.
Competing interests: None declared.
References
1. Cowman G, Otipo S, Njeru I, Achia T, Thirumurthy H, Bartram J, et al. Factors associated with cholera in Kenya, 2008–2013. Pan Afr Med J. 2017;28:101. https://doi.org/10.11604/pamj.2017.28.101.12806
2. Cholera, 2017. Wkly Epidemiol Rec. 2018 93(38):489–500.
3. Risk of cholera in the Eastern Mediterranean Region. Cairo: World Health Organization Regional Office for the Eastern Mediterranean; 2019 (http://www.emro.who.int/health-topics/cholera/index.html, accessed 4 January 2022).
4. Ganesan D, Gupta SS, Legros D. Cholera surveillance and estimation of burden of cholera. Vaccine. 2020; 38 Suppl 1:A13–A17. https://doi.org/10.1016/j.vaccine.2019.07.03
5. Green A. Cholera outbreak in the Horn of Africa. Lancet. 2017;389(10085):2179. https://doi.org/10.1016/S0140-6736(17)31541-6
6. Ending cholera: a global roadmap to 2030 control. Geneva: Global Task Force on Cholera Control; 2017 (https://www.who.int/cholera/publications/global-roadmap/en/, accessed 4 December 2019).
7. Ripoll S. Contextual factors shaping cholera transmission and treatment-seeking in Somalia and the Somali region of Ethiopia. Brighton: Social Science in Humanitarian Action; 2017.
8. Rebaudet S, Sudre B, Faucher B, Piarroux R. Environmental determinants of cholera outbreaks in inland Africa: a systematic review of main transmission foci and propagation routes. J Infect Dis. 2013;208 Suppl 1:S46–54. https://doi.org/10.1093/infdis/jit195
9. Rieckmann A, Tamason CC, Gurley ES, Rod NH, Jensen PKM. Exploring droughts and floods and their association with cholera outbreaks in sub-Saharan Africa: a register-based ecological study from 1990 to 2010. Am J Trop Med Hyg. 2018;98(5):1269–74. https://doi.org/10.4269/ajtmh.17-0778
10. Mengel MA, Delrieu I, Heyerdahl L, Gessner BD. Cholera outbreaks in Africa. Curr Top Microbiol Immunol. 2014;379:117–44. https://doi.org/10.1007/82_2014_369
11. Kwesiga B, Pande G, Ario AR, Tumwesigye NM, Matovu JKB, Zhu BP. A prolonged, community-wide cholera outbreak associated with drinking water contaminated by sewage in Kasese District, western Uganda. BMC Public Health. 2017;18(1):30. https://doi.org/10.1186/s12889-017-4589-9.
12. Swaddiwudhipong W, Ngamsaithong C, Peanumlom P, Hannarong S. An outbreak of cholera among migrants living in a Thai-Myanmar border area. J Med Assoc Thai. 2008;91(9):1433–40.
13. Ringane A, Milovanovic M, Maphakula D, Makete F, Omar T, Martinson N, et al. An observational study of safe and risky practices in funeral homes in South Africa. S Afr Med J. 2019;109(8):587–91. https://doi.org/10.7196/SAMJ.2019.v109i8.13523
14. Taylor DL, Kahawita TM, Cairncross S, Ensink JH. The impact of water, sanitation and hygiene interventions to control cholera: a systematic review. PLoS One. 2015;10(8):e0135676. https://doi.org/10.1371/journal.pone.0135676
15. Camp Coordination and Camp Management (CCCM) Cluster. Somalia dashboard. Internally displaced persons (IDP). Geneva: United Nations High Commissioner for Refugees; 2018 (https://data2.unhcr.org/en/documents/download/62286, accessed 4 January 2022).
16. Somalia drought impact and needs assessment. Volume 1. Synthesis report. New York: United Nations Development Programme; 2017 (https://www.undp.org/publications/somalia-drought-impact-and-needs-assessment, accessed 4 January 2022).
17. Horn of Africa: humanitarian impacts of drought–issue 6. New York: United Nations Office for the Coordination of Humanitarian Affairs; 2017 (https://reliefweb.int/sites/reliefweb.int/files/resources/HOA_drought_update_16June2017.pdf accessed 4 January 2022).
18. Global Health Observatory data repository: explore a world of health data. Geneva: World Health Organization; 2022 (https://www.who.int/data/gho, accessed 4 January 2022).
19. Cholera situation in Somalia. Cairo: World Health Organization Regional Office for the Eastern Mediterranean; 2020 (http://www.emro.who.int/pandemic-epidemic-diseases/outbreaks/outbreaks-archive.html, accessed 4 January 2022).
20. Legros D. Global cholera epidemiology: opportunities to reduce the burden of cholera by 2030. J Infect Dis. 2018;218(Suppl_3):S137–40. https://doi.org/10.1093/infdis/jiy486
21. Somalia – EWARN May 2020: COVID-19 information note 2. Cairo: World Health Organization Regional Office for the Eastern Mediterranean; 2020 (https://reliefweb.int/report/somalia/somalia-ewarn-may-2020-covid-19-information-note-2, accessed 4 January 2022).
22. Cholera situation in Somalia, December 2017. Cairo: World Health Organization Regional Office for the Eastern Mediterranean; 2017 (https://reliefweb.int/sites/reliefweb.int/files/resources/cholera_situation_update_somalia_december_2017.pdf, accessed 4 January 2022).
23. Somalia integrated cholera response plan for central-south zones. April to June 2017. New York: United Nations Children’s Fund; 2017 (https://reliefweb.int/report/somalia/unicef-somalia-integrated-cholera-response-plan-central-south-zones-april-june-2017, accessed 4 January 2022).
24. Weekly AWD/cholera situation report – Somalia. Epidemiological week 22 (21–31 May 2020). Cairo: World Health Organization Regional Office for the Eastern Mediterranean; 2020 (https://reliefweb.int/report/somalia/weekly-awdcholera-situation-report-somalia-epidemiological-week-22-21-31-may-2020, accessed 4 January 2022).
25. Prevention and control of cholera outbreaks: WHO policy and recommendations. Geneva: World Health Organization; 2010.
26. Ateudjieu J, Yakum MN, Goura AP, Nafack SS, Chebe AN, Azakoh JN, et al. Health facility preparedness for cholera outbreak response in four cholera-prone districts in Cameroon: a cross sectional study. BMC Health Serv Res. 2019;19(1):458. https://doi.org/10.1186/s12913-019-4315-7
27. The Global Task Force on Cholera Control. Cholera unveiled. Geneva: World Health Organization; 2020 (http://whqlibdoc.who.int/hq/2003/WHO_CDS_CPE_ZFK_2003.3.pdf, accessed 4 January 2022).
28. Report of first meeting of the global task force for cholera control, 26–27 June 2014, Chavannes-de-Bogis, Switzerland. Geneva: World Health Organization; 2014 (https://apps.who.int/iris/handle/10665/205122, accessed 4 January 2022).
29. Ayenigbara IO, Ayenigbara GO, Adeleke RO. Contemporary Nigerian public health problem: prevention and surveillance are key to combating cholera. GMS Hyg Infect Control. 2019;14:Doc16. https://doi.org/10.3205/dgkh000331
30. Lucien MAB, Adrien P, Hadid H, Hsia T, Canarie MF, Kaljee LM, et al. Cholera outbreak in Haiti: epidemiology, control, and prevention. Infect Dis Clin Pract. 2019;27(1):3–11.
31. Ngwa MC, Liang S, Mbam LM, Mouhaman A, Teboh A, Brekmo K, et al. Cholera public health surveillance in the Republic of Cameroon – opportunities and challenges. Pan Afr Med J. 2016;24:222. https://doi.org/10.11604/pamj.2016.24.222.8045
32. Health workers trained on electronic disease early warning surveillance systems. Cairo: World Health Organization Regional Office for the Eastern Mediterranean: 2018 (/pandemic-epidemic-diseases/news/health-workers-trained-on-electronic-disease-early-warning-surveillance-systems.html, accessed 4 January 2022).
33. Robbins A. Lessons from cholera in Haiti. J Public Health Policy. 2014;35(2):135–6. https://doi.org/10.1057/jphp.2014.5
Table 1. Cases of and deaths from cholera and CFR, by region, during cholera outbreaks, Somalia, 2017–2019
Figure 1. Regions most affected by cholera, Somalia, 2017
Figure 2. Trend in cholera cases and case fatality rate during cholera outbreaks, Somalia, 2017–2019