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  • Research note
  • Open Access

Socio-demographic determinants of childhood immunization incompletion in Koforidua, Ghana

BMC Research Notes201811:656

https://doi.org/10.1186/s13104-018-3767-x

  • Received: 12 July 2018
  • Accepted: 5 September 2018
  • Published:

Abstract

Objective

Immunization saves more than 3 million lives worldwide each year, and it saves millions from suffering illness and lifelong disability. The study sought to assess the socio-demographic factors that influence childhood immunization incompletion. A cross-sectional descriptive design was employed for the study conducted at the Child Welfare Clinic in the Regional Hospital, Koforidua. A total of 280 caregivers/mothers who have children aged between 0 and 59 months were included in this study. Data were entered and analyzed using SPSS.

Results

The study found that being divorced (p = 0.048) and working part-time (p = 0.049) has a significant and positive association with immunization incompletion. Women who were divorced [AOR (95% CI) 3.01 (1.59–58.2)] were 3 times less likely to complete immunization than those who were cohabiting, married and widowed taken into account the effect due to all the additional confounder variables included in the analysis. Women who were working part-time were 2.28 times less likely to complete immunization schedule than those working full-time; [AOR (95% CI) 2.28 (1.031–9.11)]. This study has documented socio-demographic factors influencing childhood immunization incompletion in the Regional Hospital, Koforidua. The Ministry of Health should, therefore, put in measures like public education to encourage mothers to complete each immunization schedule.

Keywords

  • Socio-demographic factors
  • Incompletion
  • Childhood immunization
  • Mothers
  • Children under 5 years

Introduction

Childhood immunization has been established as one of the key interventions in Public Health that has resulted in child morbidity reduction [1]. It is therefore important that a child receives the complete recommended immunization dosage at the right age in order to escape childhood diseases that are vaccine-preventable [1].

The incidence of disabilities, including mental retardation as well as mobility impairment, has been greatly reduced by immunization against polio as well as rubella, meningitis, and measles [2, 3]. Vaccines have contributed to the reduction of intellectual disability caused by measles encephalitis, congenital rubella syndrome, and Haemophilus influenza type b meningitis or Hib [4].

According to the Centers for Disease Control and Prevention (CDC), brain damage may occur when a child catches pertussis (whooping cough), measles, mumps, Hib disease, or varicella (chicken pox). Encephalitis, an inflammation of the brain, is the cause of brain damage in pertussis, measles, and mumps which can be prevented through immunization [5, 6].

Immunization has brought sound health to many children in the world [7, 8] and have been effective in protecting children against vaccine-preventable diseases in low and middle-income countries [9, 10].

Presently, it is recommended that at birth all children should be vaccinated with Bacille Calmette–Guerin (BCG) as well as a total of four doses of oral polio vaccine (OPV). By the 6th, 10th and 14th week, all children should be vaccinated with OPV 0, OPV1, OPV 2 and OPV 3 respectively. A further three doses of diphtheria, pertussis, tetanus, Haemophilus influenza type B and Hepatitis B (DPT/HiB/HepB) 5-in-1 vaccine should be given on the 6th, 10th and 14th week respectively. On the 9th month, each surviving child should be given Measles and Yellow fever vaccines [11, 12]. Therefore, one can conclude that, if a child misses at least a dose out of the eight vaccines recommended when he/she is under 12 months old, that child has not completed the immunization schedule [11, 12].

This study, therefore, assesses the socio-demographic determinants of Childhood Immunization Incompletion in Koforidua, Ghana.

Main text

Methods

This study was conducted at the Child Welfare Clinic in the Regional Hospital, Koforidua in the New Juaben district of Eastern Region. The Koforidua Regional Hospital serves as a referral specialist’s center that offers specialist medical and surgical care. The hospital also trains interns and residents as well as allied health students. A clinic setting was selected because the investigators wanted to select mothers who had started the immunization already but have missed some schedules due to some socio-demographic factors.

For this study, a retrospective descriptive cross-sectional design was adopted using a quantitative approach. In this case, the design was adopted to describe the socio-demographic factors that influence childhood immunization incompletion. Mothers were selected for the study using a simple random sampling technique. They were asked to pick pieces of papers that were folded with ‘Yes’ and ‘No’ written in it. The mothers who picked ‘Yes’ were selected until the determined sample size was achieved. The target population considered eligible for this study were all mothers and caregivers at the Child Welfare Clinic (CWC) at the regional hospital, Koforidua. For the period of data collection, 800 mothers were found at the study site. Using the formula of Yamane [13] for determining samples, 95% confidence level and a precision of 0.05 were used to calculate for the sample using the Equation;
$$n = \frac{N}{{1 + N(e)^{2} }}$$
n represents the sample size, N represents the population size, and e represents the level of precision.
$$N = 800$$
$$1 + N (e)^{2} = { 1 } + { 8}00 \, \left( {.0 5} \right)^{ 2}$$
$$n = \frac{800}{{1 + 800 (.05)^{2} }}$$
$$n = 267$$

Since the total population was 800 and 267 was selected as a sample size, the margin of error was 4.9. Using the 95% confidence level and a level of precision of 0.5, it is claimed therefore that there is a 95% chance that the confidence interval that was calculated contains the true population mean with a margin of error of 5%. The 5% margin of error implies that even if all 800 mothers are used in this study, the results may be different if we subtract 5% or add 5% to the original score. Assuming that 5% of the sample will not be willing to be part of the study, 13 (representing 5% of the achieved sample of 267) were added to 267 to give us a sample size of 280. The number for the non-respondents rate was selected because it was expected that some respondents will opt out at a certain stage or will not be willing to be part of the study.

In collecting data from the respondents, a structured questionnaire was used as the data collection instrument. Questionnaires were administered to respondents that consented to participate in the study. The questionnaire comprised of respondents demographic data and questions to guide the researchers in describing factors that influence childhood immunization incompletion (Additional file 1). Data was sought from Hospital records and vaccination cards, as to the immunization status of the children to confirm what respondents stated. The questionnaires were directly administered to respondents by the researcher and some selected research assistants. Data were collected weekly for 4 weeks to increase the probability of having varied opinions.

The results obtained were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22. Prior to this, data was dissected to ensure all possible errors are removed by all the investigators. The research results were presented in tables as this was to help present the results of the work in a graphical and appropriate manner for easy interpretation and presentation. Permission was sought from the hospital management and the in-charge of the units under which the research was conducted. Participants were assured that the information they provided was strictly going to be confidential. Information generated was strictly used for academic purposes of which was stored appropriately at a place determined by the School.

Results

Socio-demographic characteristics of respondents

Table 1 indicates the demographic characteristics of respondents who participated in the study. From the table, the highest age group was 24–30 years representing 22.8%. The majority (67.1%) of the participants were married and had tertiary education whiles 40% had children between 3 and 4 years. Also, close to half (40%) of the respondents were employed as full-time workers. Most (28.6%) of the respondents received income between 100 and 200 GH cedis and the most common religion was Christianity (64.2%).
Table 1

Socio-demographic characteristics of respondents

Variable

Frequency (n = 280)

Percentage

Age (years)

 13–17

4

1.4

 18–23

64

22.8

 24–30

116

41.4

 Above 30

96

34.2

 

Mean = 21.3

SD = 2.12

Age of child

 Less than 1 year

86

31

 1–2 years

82

29

 3–4 years

112

40

 

Mean = 2.09

SD = 1.11

Marital status

 Single

44

15.7

 Married

188

67.1

 Divorce

12

4.3

 Widow

4

1.4

 Cohabiting

32

11.4

Level of education

 Primary

16

5.7

 JHS

32

11.4

 Secondary

76

27.1

 Tertiary

152

54.3

 No formal education

4

1.6

Employment status

 Unemployed

84

30.0

 Working part-time

84

30.0

 Working full-time

112

40.0

Level of income

 < 100

68

24.3

 100–200

80

28.6

 200–300

56

20.0

 Above 300

76

27.1

 

Mean = 180

SD = 1.43

Religion

 Christian

180

64.2

 Muslim

96

34.3

 Traditionalist

4

1.4

The relationship between socio-demographic factors and immunization completion

Table 2 shows the relationship between socio-demographic factors and immunization completion. From the table, there were significant differences in immunization schedule completion among women with different marital status (p = 0.041) as well as different employment status (p = 0.013). The immunization schedules before and after 6 months can be found in Additional file 2: Table S1 and Additional file 3: Table S2.
Table 2

Showing the relationship between socio-demographic factors and immunization completion

Variable

Completed immunization

Did not complete immunization

Total

Frequency

Percentage

Frequency

Percentage

Frequency

Percentage (%)

Mother’s age

 13–17 years

0

0.0%

4

100.0%

4

100.0

 18–23 years

28

43.8%

36

56.3%

64

100.0

 24–30 years

48

41.4%

68

58.6%

116

100.0

 Above 30 years

48

50.0%

48

50.0%

96

100.0

 Total

124

44.3%

156

55.7%

280

100.0

 

Chi Square = 1.214

df = 3

p = 0.750

  

Age of child

 Less than 1 year

29

41%

41

59%

70

100

 1–2 years

46

42%

66

58%

113

100

 3–4 years

49

50%

49

50%

98

100

 Total

124

44%

156

56%

280

100

 

Chi Square = 1.004

df = 2

p = 0.051

  

Marital status

 Single

20

45.5%

24

54.5%

44

100.0

 Married

100

53.2%

88

46.8%

188

100.0

 Divorce

0

0.0%

12

100.0%

12

100.0

 Widow

0

0.0%

4

100.0%

4

100.0

 Cohabiting

4

12.5%

28

87.5%

32

100.0

 Total

124

44.3%

156

55.7%

280

100.0

 

Chi Square = 7.981

df = 3

p = 0.041

  

Level of education

 Primary

13

75.0%

5

25.0%

18

100.0

 JHS

33

42.1%

45

57.9%

78

100.0

 Secondary

37

32.1%

77

67.9%

114

100.0

 Tertiary

37

52.9%

33

47.1%

70

100.0

 Total

120

42.6%

160

57.4%

280

100.0

 

Chi Square = 3.714

df = 2

p = 0.294

  

Employment status

 Unemployed

44

52.4%

40

47.6%

84

100.0

 Working part-time

12

14.3%

72

85.7%

84

100.0

 Working full-time

68

60.7%

44

39.3%

112

100.0

 Total

124

44.3%

156

55.7%

280

100.0

 

Chi Square = 11.281

df = 3

p = 0.013

  

Mother’s income

 < 100

9

52.9%

8

47.1%

17

100.0

 100–200

9

45.0%

11

55.0%

20

100.0

 200–300

4

28.6%

10

71.4%

14

100.0

 Above 300

8

44.4%

10

55.6%

18

100.0

 Total

30

43.5%

39

56.5%

69

100.0

 

Chi Square = 1.911

df = 3

p = 0.591

  

Religion

 Christian

19

42.2%

26

57.8%

45

100.0

 Muslim

11

45.8%

13

54.2%

24

100.0

 Traditionalist

1

100.0%

0

0.0%

1

100.0

 Total

31

44.3%

39

55.7%

70

100.0

 

Chi Square = 1.359

df = 2

p = 0.507

  
Odds ratio with 95% confidence interval was further used to determine the association between socio-demographic factors and immunization completion as shown in Table 3. From the table, women who were divorced [AOR (95% CI) 3.01 (1.59–58.2)] were 3 times less likely to complete immunization than those who were cohabiting, married and widowed taken into account the effect due to all the additional confounder variables included in the analysis. Women who were working part-time were 2.28 times less likely to complete immunization schedule than those working full-time; [AOR (95% CI) 2.28 (1.031–9.11)].
Table 3

Odds ratio with 95% confidence interval for association between socio-demographic factors and immunization completion

Variable

Completed immunization

Univariate

Multivariatea

Yes

No

OR (95% CI)

p-value

AOR (95% CI)

p-value

Mother’s age (years)

 13–17

0

4

1.00

 

1.00

 

 18–23

28

36

0.49 (0.41–3.02)

0.797

1.21 (0.35–4.31)

0.122

 24–30

48

68

2.66 (1.13–20.28)

0.121

1.38 (0.04–2.29)

0.211

 Above 30

48

48

3.12 (1.98–21.0)

1.432

1.99 (0.12–2.01)

0.912

Age of child

 Less than 1 year

29

41

1.00

 

1.00

 

 1–2 years

46

66

3.42 (2.91–3.91)

1.26

2.98 (1.92–3.04)

0.093

 3–4 years

49

49

4.23 (3.11–4.87)

0.09

3.12 (2.01–3.92)

0.122

Marital status

 Single

20

24

1.00

 

1.00

 

 Married

100

88

2.01 (1.32–3.33)

0.139

1.21 (0.28–3.27)

0.503

 Divorce

0

12

12.5 (3.54–45.49)

0.034

3.01 (1.59–58.2)

0.048

 Widow

0

4

1.21 (0.21–3.21)

1.09

1.34 (0.72 –1.92)

0.122

 Cohabiting

4

28

6.33 (0.08–1.56)

0.112

0.48 (0.08–2.46)

0.210

Level of education

 Primary

13

5

1.00

 

1.00

 

 JHS

33

45

1.02 (0.44–2.35)

0.838

1.62 (0.41–4.60)

0.425

 Secondary

37

77

1.14 (0.32–4.01)

0.912

2.00 (0.17–21.05)

0.291

 Tertiary

37

33

1.39 (0.81–1.91)

1.109

1.09 (0.14–1.78)

0.431

Employment status

 Unemployed

44

40

1.00

 

1.00

 

 Working part-time

12

72

8.21 (3.12–20.18)

0.02

2.28 (1.031–9.11)

0.049

 Working full-time

68

44

3.02 (2.81–2.97)

1.100

2.10 (1.082–1.93)

1.129

Mother’s income

 < 100

9

8

1.00

 

1.00

 

 100–200

9

11

3.09 (1.98–2.96)

0.762

2.41 (1.56–2.01)

0.431

 200–300

4

10

1.10 (0.48–2.48)

0.797

0.81 (0.14–3.39)

0.690

 Above 300

8

10

1.45 (0.65–0.99)

1.07

1.73 (0.76–1.01)

0.07

Religion

 Christian

19

26

1.00

 

1.00

 

 Muslim

11

13

3.11 (1.10–12.11)

0.912

0.22 (0.18–2.95)

0.218

 Traditionalist

1

0

1.89 (0.21–1.02)

1.321

2.12 (1.82–2.03)

0.982

OR odds ratio, CI confidence interval, AOR adjusted odds ratio

aMutually adjusted

Discussion

The highest age range of 24–30 years found in this study was consistent with the study conducted by Mustafi and Azad [14]. This implies that most mothers in both study locations may usually fall within the ages of 24–30 years. In terms of education, more than half of the respondents (54.3%) had a higher education which contrasted with the study conducted by Abdulraheem et al. [15]. The difference could be as a result of the study locations as this present study was conducted in an urban setting in Ghana whereas the earlier study was conducted in rural Nigeria.

Maternal age was not a predictor of immunization incompletion in this study. The finding is inconsistent with other studies where maternal age was a predictor of vaccination incompletion [16]. However, a cross-sectional study done in Mozambique revealed that mothers’ age had no significant relationship with respect to immunization incompletion [17]. In other settings, both younger [18] and older age of mothers [19] has been reported to be associated with incomplete vaccination. The probable reason for the observed discrepancy may be due to the differences in socio-cultural characteristics of the two-study populations and exposure to immunization information during and after pregnancy.

Although other factors were key barriers to immunization incompletion, the findings of this study showed no significant association between education and immunization incompletion. This finding was consistent with findings of Ethiopia DHS data of 2005 which revealed no association between caretakers educational level and vaccination incompletion [20]. Nonetheless, previous studies by Marks et al. [21] revealed that the educational status of mothers has a strong association with a high vaccine uptake. Also, a study by Abdulraheem et al. [15] confirms this assertion from Marks et al. [21] that educational level is associated with mothers and missed opportunities for vaccination. This implies that education cannot be overlooked when assessing factors influencing childhood immunization incompletion.

The study showed that religion of respondents has no significant association with child vaccination incompletion which is in contrast with other study findings by Kulig et al. [22] among others. Some studies revealed that some religious bodies are known to discourage their members from accepting vaccination [22, 23]. Also, Religion played a role in the risk of non-immunization in a Nigerian study where a significantly higher proportion of non-immunized children were observed among mothers who were Muslim. Children with partial immunization were also found to be significantly higher among Muslim whiles full/completed immunization was observed at a significantly higher level among mothers who were Christian [24]. Nevertheless, a study in Mozambique was consistent with this study which showed no significant association between religion and vaccination incompletion. Differences in results obtained by different investigators who looked at the influence of religion on vaccination may be due to differences in socio-cultural antecedents and theological persuasions between populations involved in the studies.

In this study, significant relationships were found between marital status and immunization incompletion. A study by Falagas and Zarkadoulia [25], also agrees with this finding. Conversely, other studies have shown a no significant influence of marital status on immunization completion [16, 17]. This implies that the marital status of the mothers might influence their decision to go to the health centers to complete the immunization schedule of the child.

Furthermore, the employment status of the respondents was found to have a significant association with immunization incompletion. This is in line with a study conducted by Omole and Owodunni [26] and Abdulraheem et al. [15], which showed that income and employment status have a significant relationship with immunization completion. Antai [24], found that being employed was significantly associated with a higher likelihood of the child being fully immunized. The reason for this outcome might come from the fact that the days of immunization might impede the mother’s employment schedule.

Several studies have found a relationship between wealth status and vaccination status [27, 28]. Children from wealthy households may be more likely to have their vaccination status checked and to receive missing doses of vaccines when attending a healthcare facility than children from poor households. The findings of this study found no significant positive relationship between the income level of mothers and immunization incompletion contrary to earlier studies. This could be attributed to the fact that access to a health facility in the area is not limited by income probably due to the introduction of a National Health Insurance Scheme that covers a certain aspect of the cost incurred by mothers when they visit a healthcare facility in Ghana.

Conclusion

This study has documented socio-demographic factors influencing childhood immunization incompletion in the Regional Hospital, Koforidua. Determinants of receipt of complete and incomplete immunization are complex and interwoven. This study ascertained several socio-demographic factors influencing childhood immunization incompletion, key among them were socio-demographic factors such as being divorced and working part-time.

Limitations

The study had some limitations which included recall bias where the mother might forget the vaccination/immunization status of their children and misclassification. Also, cultural factors were not studied during this analysis; a more in-depth look at various practices of parenting might illuminate ways it influenced their behavior. However, the findings were not compromised in any way.

Abbreviations

BCG: 

Bacille Calmette–Guerin

DPT: 

diphtheria, pertussis, tetanus

OPV: 

oral polio vaccine

SPSS: 

Statistical Package for Social Sciences

Declarations

Authors’ contributions

The secondary data compilation, data analysis and collection, and interpretation were done by the first author (RA). The second, third, fourth, fifth and sixth authors (EA, ABA, AKE, PO, and JD) revised the manuscript thoroughly with their individual expertise. The seventh author (AN) played a significant role in the collection of primary data. In the analysis of data, all authors played a significant part as well as in designing and preparing the manuscript. Proofreading and the final approval process were also shared accordingly among all authors and all authors have agreed to its submission for publication. All authors read and approved the final manuscript.

Acknowledgements

Our gratitude goes out to the management and staff of the selected Hospitals, as well as the regional health directorate officials in Central, Greater Accra and Ashanti region of Ghana for their support. Further thanks to all whose works on mental health helped in putting this work together.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

A complete document of this study and its results can be found at the Library of KNUST, Kumasi.

Consent to publish

Not applicable.

Ethics approval and consent to participate

Letter of introduction was sent to the Administration unit of the selected Hospital to seek permission to carry out research in their institutions. The study was approved by the committee on Human Research Publication and ethics at Kwame Nkrumah University of Science and Technology, Kumasi-Ghana.

Written and verbal permission was obtained to conduct the study in the Hospital. An introductory letter was sent to the various departments in the hospitals for approval before data collection commenced. The respondents of the study were told the objectives of the study, the possible implication and effect of the research. Verbal consent was obtained from participants before they participated in the study. Written consent was obtained from all the parents or guardians on behalf of all the participants who were under the age of 16. The data collected had no information that could directly be traced to or associated with any individual respondent by removing identifiers such as names, and contact details of respondents. Participation was purely voluntary and any participant who wanted to withdraw was allowed. Confidentiality was guaranteed before administering the questionnaires.

Funding

No external funding was received for the purpose of this study. All cost related to this research was covered by the researchers themselves.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Centre for Disability and Rehabilitation Studies, Department of Community Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
(2)
Department of Health Education and Promotion, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

References

  1. Integrated management of childhood illness. USAID, GH, HIDN: Child Survival and Health Grants Program. 2007:30.Google Scholar
  2. Maurice JM, Davey S. State of the World’s vaccines and immunization. Geneva: World Health Organization; 2009.Google Scholar
  3. Pywell S. Particular issues of public health: vaccination. 2001; pp. 299–327.Google Scholar
  4. Alexander GR, Slay M. Prematurity at birth: trends, racial disparities, and epidemiology. Dev Disabil Res Rev. 2002;8(4):215–20.Google Scholar
  5. Centers for Disease Control and Prevention (CDC). Ten great public health achievements—the United States, 2001–2010. MMWR Morb Mortal Wkly Rep. 2011;60(19):619.Google Scholar
  6. Gentile A, Bhutta Z, Bravo L, Samy AG, Garcia RD, Hoosen A, Islam T, Karimi A, Salem M, Simasathien S, Sohail A. Pediatric disease burden and vaccination recommendations: understanding local differences. Int J Infect Dis. 2010;14(8):e649–58.View ArticlePubMedGoogle Scholar
  7. Ogunlana S. The importance of immunization. 2005. http://ezinearticles.com/. Accessed 03 May 2017.
  8. Vallbona C. Importance of immunization in child care and prevention. Child Adolesc Soc Work J. 1993;10(5):365–77.View ArticleGoogle Scholar
  9. Danielsson N, Fakakovikaetau T, Szegedi E. Improved immunization practices reduce childhood hepatitis B infection in Tonga. Vaccine. 2009;27(33):4462–7.View ArticlePubMedGoogle Scholar
  10. Lee EH, Lewis RF, Makumbi I, Kekitiinwa A, Ediamu TD, Bazibu M, Braka F, Flannery B, Zuber PL, Feikin DR. Haemophilus influenza type b conjugate vaccine is highly effective in the Ugandan routine immunization program: a case–control study. Trop Med Int Health. 2008;13(4):495–502.View ArticlePubMedGoogle Scholar
  11. Etana B, Deressa W. Factors associated with complete immunization coverage in children aged 12–23 months in Ambo Woreda, Central Ethiopia. BMC Public Health. 2012;12(1):566.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Ghana Health Service 2012 annual report. Accra: PPME/GHS. 2013.Google Scholar
  13. Yamane T. Elementary sampling theory. Prentice-Hall: Englewood Cliffs, N.J.; 1967.Google Scholar
  14. Mustafi M, Azad M. Factor influencing of child immunization in Bangladesh. Int J Math Stat Stud. 2013;1(3):55–65.Google Scholar
  15. Abdulraheem IS, Onajole AT, Jimoh AA, Oladipo AR. Reasons for incomplete vaccination and factors for missed opportunities among rural Nigerian children. J Public Health Epidemiol. 2011;3(4):194–203.Google Scholar
  16. Shamsul Azhar S, Nirmal K, Nazarudin S, Rohaizat H, Azimatun Noor A. Factors influencing childhood immunization defaulters in Sabah, Malaysia. Int Med J Malaysia. 2012;11:17–22.Google Scholar
  17. Jani JV, De Schacht C, Jani IV, Bjune G. Risk factors for incomplete vaccination and missed opportunity for immunization in rural Mozambique. BMC Public Health. 2008;8(1):161.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Glenda LL, Brynley CM, Peter BM. Reasons for incomplete immunization among Australian children. Aust Fam Physician. 2004;33(7):13–9.Google Scholar
  19. Akesode FA. Factors affecting the use of primary health care clinics for children. J Epidemiol Community Health. 1982;36(4):310–4.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Central Statistical Agency, Addis Ababa, Ethiopia and ORC Macro. Ethiopia, demographic and health survey 2005. Calverton, Maryland, USA. 2006. http://www.measuredhs.com/pubs/pdf/FR179/FR179.pdf. Accessed 7 May 2017.
  21. Marks JS, Halpin TJ, Irvin JJ, Johnson DA, Keller JR. Risk factors associated with failure to receive vaccinations. Pediatrics. 1979;64(3):304–9.PubMedGoogle Scholar
  22. Kulig JC, Meyer CJ, Hill SA, Handley CE, Lichtenberger SM, Myck SL. Refusals and delay of immunization within Southwest Alberta: understanding alternative beliefs and religious perspectives. Can J Public Health. 2002;1:109–12.Google Scholar
  23. Henderson L, Millett C, Thorogood N. Perceptions of childhood immunization in a minority community: qualitative study. J R Soc Med. 2008;101(5):244–51.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Antai D. Faith and child survival: the role of religion in childhood immunization in Nigeria. J Biosoc Sci. 2009;41(1):57–76.View ArticlePubMedGoogle Scholar
  25. Falagas ME, Zarkadoulia E. Factors associated with suboptimal compliance to vaccinations in children in developed countries: a systematic review. Curr Med Res Opin. 2008;24(6):1719–41.View ArticlePubMedGoogle Scholar
  26. Omole MK, Owodunni KO. Mothers’ knowledge of immunization programme and factors influencing their compliance at a children hospital in South West Nigeria. J Pharm Biomed Sci. 2012;21:1–4.Google Scholar
  27. Babalola S, Fatusi A. Determinants of use of maternal health services in Nigeria; looking beyond individual and household factors. BMC Pregnancy Childbirth. 2009;15(9):43.View ArticleGoogle Scholar
  28. Ndiritu M, Cowgill KD, Ismail A, Chiphatsi S, Kamau T, Fegan G, Feikin DR, Newton CRJC, Scott J. Immunization coverage and risk factors for failure to immunize within the Expanded Programme on Immunization in Kenya after introduction of new Haemophilus influenzae type b and hepatitis b virus antigens. BMC Public Health. 2006;17(6):132.View ArticleGoogle Scholar

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