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Individual-level and community-level determinants of cervical cancer screening among Kenyan women: a multilevel analysis of a Nationwide survey

BMC Women's HealthBMC series – open, inclusive and trusted201717:109

https://doi.org/10.1186/s12905-017-0469-9

Received: 7 March 2017

Accepted: 8 November 2017

Published: 15 November 2017

Abstract

Background

Studies on the determinants of cervical cancer screening in sub-Saharan Africa have focused mostly on individual-level characteristics of cervical cancer screening. Therefore, in this study, we included both individual- and community-level indicators to examine the determinants of cervical cancer screening among Kenyan women.

Methods

We analyzed data from the 2014 Kenya Demographic and Health Surveys. Our analysis focused on 9016 married women of reproductive age (15–49 years). We conducted multilevel analyses using generalized linear mixed models with the log-binomial function to simultaneously analyze the association of individual- and community-level factors with cervical cancer screening.

Results

About 72.1% of women (n = 6498) knew about cervical cancer. Of these women, only 19.4% had undergone cervical cancer screening [58.24% Papanicolaou (Pap) test and 41.76% visual inspection]. Our multivariate analysis results indicated that the prevalence of cervical cancer screening was higher among women aged 35-49 years than women aged 15-24 years. The prevalence was also higher among women residing in the Central, Nyanza, and Nairobi regions than women residing in the Coastal region. Cervical cancer screening was more prevalent among women who had media exposure, had higher household wealth index, were employed, were insured, and had visit a health facility in 12 months than did their counterparts. The prevalence of Pap test history was 19% higher among women who had sexual autonomy than women who did not have sexual autonomy. The prevalence of Pap test history was also higher among communities comprised of higher proportions of women with sexual autonomy and higher education.

Conclusions

Policies should emphasize increasing gender equality, improving education at the community level, providing employment opportunities for women, and increasing universal health insurance coverage. These focal points can ensure equity in access to health care services and further increase the prevalence of cervical cancer screening in Kenya.

Keywords

Cervical cancerPap testKenyaSub-Saharan AfricaWomen’s autonomy

Background

Cervical cancer is the fourth most common cancer in women worldwide, with an estimated 528,000 new cases and approximately 275,000 deaths reported annually [1]. Moreover, 85% of the cases and most of the deaths occur in developing countries [2]. Cervical cancer incidence rates in sub-Saharan Africa (SSA) are the highest worldwide, and the disease is the most common cause of cancer death among women in this region [3, 4]. In Kenya, cervical cancer is the most prevalent cancer among women aged 15–44 years, with an estimated 4802 women diagnosed and 2451 deaths from the disease annually [5]. These elevated incidence and mortality rates can be attributed to the absence of the HPV vaccine and the low screening coverage [6].

Nearly all cervical cancer cases are caused by infection with high-risk types of human papillomavirus (HPV). Approximately 15 HPV types are associated with an increased risk of the disease; among the oncogenic HPV types, HPV16 and HPV18 are the most dangerous [7]. The implementation of an HPV vaccine program to prevent cervical cancer is one approach. However, to effectively see reduction rates in the incidence of cervical cancer, researchers and health professionals also recommend a cervical cancer screening program. Screening for cervical cancer is essential because vaccines do not treat existing HPV infections. Currently, a large proportion of women in low and middle income countries do not benefit from the HPV vaccine program. These women were either beyond the recommended age for the vaccines and/or already exposed to HPV [8].

Screening programs can save the lives of millions of women who develop precancerous lesions. For the early detection of cervical cancer and its precursor lesions, several screening modalities are now available, such as cytology or Papanicolaou (Pap) testing, visual inspection using acetic acid (VIA) or Lugol’s iodine (VILI), and HPV-test. The Pap test is a simple, safe, noninvasive, and effective method for detecting precancerous, cancerous, and noncancerous changes in the cervix and vagina [9]. Although the Pap test is effective, sustaining high-quality cytology-based programs is difficult in low-income countries because of the complex process of collection, preparation, staining, reading, and reporting and the delay between screening and provision of test results [10]. Therefore, in low-resource areas, cost-effective strategies that are inexpensive and of reliable quality are vital for preventing and intervening cervical cancer [11]. Common alternative screening tests are VIA and VILI. Although it has a lower specificity, VIA is still advocated as a screening method alternative to the Pap test in poorly resourced locations [12]. The attractive features of VIA and VILI include their low cost, simple administration, independence from laboratory services, and provision of real-time screening results, particularly in rural areas, where people travel for hours to visit a doctor. A screening method requiring fewer visits can largely increase acceptance and participation rates [11]. Thus, in low-income areas, particularly rural areas, VIA as a visual screening test is a promising alternative to the Pap test for the early detection of cervical cancer [13]. Cervical cancer screening programs have been available in Kenya, as a part of the Ministry of Health’s National Cervical Cancer Prevention Strategic Plan from 2002 to 2006. This program was implemented with the objective of increasing the use of the Pap test, VIA, and VILI among women [14]. Approximately 86% of women in Kenya have never been screened [15].

HPV testing is also feasible in low-resource settings. It is cost-effective and is well suited to address some of the barriers to implementing adequate screening programs in low resource settings [16]. Even though studies suggested that sampling by a clinician (in the context of a HPV testing program) should be the recommended method, HPV testing through the self-sampling method may be an acceptable option to reach women who do not or are not able to participate in the regular screening program [17, 18]. Therefore considering the lack of human resources, poor infrastructure, cost, long hospital queues and lack of quality cytopathology as the major barriers to cervical cancer screening in most SSA countries, self-sampling for HPV DNA test is an appropriate modality that can largely increase the coverage of cervical cancer screening [19].

A few studies have investigated the possible reasons for the low participation rates of women in cervical cancer screening programs in SSA. Lack of knowledge and awareness of cervical cancer are cited as the most common barriers to cervical cancer screening programs in SSA [20]. Other barriers include lack of financial resources, long distance to health facility, and lengthy waiting times to get an appointment for a Pap test [21, 22]. By contrast, high education level and white-collar occupation are positively associated with cervical cancer screening [23, 24]. Education equips women to have better knowledge toward the disease and thus increases the acceptance of cancer screening [25]. In addition, health insurance coverage and access to information through education and media are strongly and positively associated with screening experiences [2628]. Some other researchers have investigated the influence of women’s decision-making autonomy on cervical cancer screening [29]. In most developing countries, gender norms and values continue to influence access to and utilization of sexual and reproductive health services [28, 30]. Gender norms may affect women’s mobility and decision-making power to access health care services. Women’s empowerment within the context of their household and relations with their partner can play a powerful role in their utilization of reproductive health services [31].

At the community level, low autonomy of women can influence their cervical cancer screening through cultural beliefs and practices. Most cultures in SSA consider women leaving their homes to seek health care to be unacceptable [32], particularly in rural area, where women living in communities are expected to not visit health care facilities alone; these women are less likely to use reproductive health care services [33]. By contrast, if the community norms support women’s own decision-making in health care seeking, women are more likely to use various health care services [34].

Most previous studies in SSA on the determinants of cervical cancer screening did not focus on the community-level characteristics. This study fills this gap by including both community-level indicators and individual-level characteristics. The objective of our study is to examine the determinants of cervical cancer screening among Kenyan women. We hypothesized that women who have higher levels of socioeconomic status and autonomy at both the individual and community levels, have health insurance, and feel that distance to health facilities is not a major problem are more likely to use cervical cancer screening than were their counterparts.

Methods

Data

This study used data from the most recent Kenya Demographic and Health Surveys (KDHS), conducted in 2014. The KDHS is a nationally representative dataset collected by the National Statistical Bureau in Kenya. The 2014 KDHS was designed to produce representative estimates for most of the survey indicators at the national level. A two-stage sampling design was applied that involved randomly selecting villages (clusters) in the first stage followed by randomly selecting households in the second stage. Questionnaires were pretested to ensure that the questions were clear and could be understood by respondents. Our analysis focused on 9016 married women of reproductive age (15–49 years) in 1588 clusters who were interviewed face to face about cervical cancer.

Measures

Outcome variables

This study had two outcome variables: (1) cervical cancer screening in general and (2) the Pap test. Cervical cancer screening was measured in terms of whether respondents underwent any cervical cancer examination ever; respondents were specifically asked “Have you ever been tested or examined for cervical cancer?”(No/Yes). Respondents who answered “Yes” were then asked “What type of exam did you test?” (Pap test/Visual inspection).

Individual-level variables

Individual-level variables included the women’s age (15–24, 25–34, and 35–49 years), religion (Roman Catholicism, Protestant/other Christianity, Islam, and others), region (Coast, North Eastern, Eastern, Central, Rift Valley, Western, Nyanza, and Nairobi), place of residence (urban/rural), education level (no education, primary, secondary and higher), employment (no/yes), number of living children (0,1–2, 3–4, and ≥5), amount of media exposure (exposed to 0, 1, 2, and 3 types of media), health insurance coverage (no/yes), and whether the respondent visited a health facility in the last 12 months (no/yes). The wealth index was a composite score measured by household assets such as televisions, bicycles, materials used for house construction, water access types, sanitation facilities, and other characteristics related to wealth. Factor scores of household assets were generated through a principal component analysis and were then standardized and categorized into five quintiles (poorest, poor, middleclass, rich, and richest).

We measured three aspects of women’s autonomy: decision-making power in the household, sexual autonomy, and attitudes toward wife-beating at both individual and community levels. Decision-making power in the household was measured using the answers to the following five questions: the questions as to who decides matters pertaining to (a) the woman’s health (personal decision-making authority), (b) large household purchases (economic decision-making authority), (c) visits to friends or family (mobility decision-making authority), (d) food to be cooked each day, and (e) what to do with money the husband earns. Women who made all the aforementioned decisions, either alone or jointly with her husband, were categorized as having high decision-making autonomy, whereas the other women were categorized as having low decision-making autonomy. Sexual autonomy was measured according to the respondents’ agreement with some reasons in which a woman is justified to refuse sex with her husband. Attitudes toward wife-beating or domestic violence was measured on the basis of the following five hypothetical scenarios: (1) she goes out without telling him, (2) she neglects the children, (3) she argues with him, (4) she refuses to have sexual intercourse with him, and (5) she does not cook food properly. If a respondent agreed that her husband had a right to beat her in any of these five hypothetical scenarios, she was classified as having a favorable attitude toward domestic violence against women. However, if she did not agree with all of these hypothetical scenarios, she was classified as having an opposing attitude toward domestic violence against women.

Community-level variables

We included six community-level variables in our study, obtained by aggregating individual responses for each item to the community (cluster) level. The six community-level variables were the proportions of women who had high decision-making autonomy, had sexual autonomy, had favorable attitudes toward wife-beating, received secondary and higher education, perceived the distance to a health facility as a major problem, and had a nonpoor wealth index. Each variable was categorized into low, middle, and high on the basis of tertiles.

Statistical analyses

We conducted series bivariate and multilevel analyses using SAS 9.4. We used the chi-squared test to examine the association of individual- and community-level characteristics with cervical cancer screening. We further conducted multilevel analyses using generalized linear mixed models with the log-binomial function to simultaneously analyze the relationships of individual- and community-level factors with the outcomes. Results of the multivariate relationships were expressed as Prevalence Ratio (PR) with 95% confidence intervals (95% CIs) [35, 36]. We first constructed a null model. We then included only the individual-level variables and then only community-level variables. Finally, we simultaneously included both individual- and community-level variables in the final model. We present only the final models. We also calculated intraclass correlation coefficients (ICCs), which represent the proportion of variance at the group level divided by the sum of the variances at the individual and group levels, to determine how well we adjusted the dependency of outcomes within communities. We examined the model fit measured using the Akaike information criterion (AIC). A lower AIC value represents a better model fit. We examined multicollinearity problems in the regression models by estimating the variance inflation factor and tolerance. All tolerance values were >0.1 and all variance inflation factor values were <10. Therefore, no multicollinearity problems were observed in the regression models.

Ethical considerations

All data were obtained from the 2014 KDHS. Informed consent was obtained from each respondent before the interviews [12]. We obtained approval to use the data from the DHS repository (http://dhsprogram.com/data/available-datasets.cfm).

Results

Approximately 28% of married Kenyan women had no previous knowledge of cervical cancer (Table 1). Among the women who knew about cervical cancer (72%), only 19.40% reported being tested for cervical cancer ever; among the tested women, only 58.24% had undergone the Pap test. Most women in our sample resided in rural areas, had completed primary education, and were employed. Furthermore, approximately 39% of the women had high decision-making autonomy at home. Almost half of all respondents had favorable attitudes toward wife-beating. Nearly 71.16% of all women had sexual autonomy. Only 18.27% women had health insurance.
Table 1

Characteristics of married women in the Kenya Demographic and Health Survey (KDHS)

 

Percent

Individual-level Characteristics (n = 9014)

 Heard of cervical cancer

  No

27.90

  Yes

72.10

 Any kind of cervical cancer screening (N = 6498)

  No

80.60

  Yes

19.40

 Type of cervical cancer screening (N = 1262)

  Visual inspection

41.76

  Pap smear

58.24

 Socio-economic characteristics

  Women’s Age

   15-24

20.28

   25-34

43.53

   35-49

36.19

  Religion

 

   Roman Catholic

18.83

   Protestant/other Christian

64.59

   Muslim

14.34

   Others

2.24

  Region

 

   Coast

12.49

   North Eastern

5.82

   Eastern

16.96

   Central

10.12

   Rift Valley

28.53

   Western

8.85

   Nyanza

14.38

   Nairobi

2.8

  Place of residence

   Urban

36.51

   Rural

63.49

  Women’s education level

 

   No Education

16.56

   Primary

52.65

   Secondary and higher

30.78

  Women’s employment

   No

35.24

   Yes

64.76

  Number of living children

   0

4.95

   1-2

36.30

   3-4

32.86

   5+

25.89

  Amount of media exposure

   0

19.18

   1

33.66

   2

28.44

   3

18.81

  Wealth Index

   Poorest

24.67

   Poor

19.22

   Middle class

18.67

   Rich

18.03

   Richest

18.41

 Women’s autonomy

  Women’s decision-making power at home

   Low

60.94

   High

39.06

  Sexual autonomy

   No

26.63

   yes

71.16

  Attitudes toward wife beating

   Opposing

50.13

   Favorable

49.87

 Health care access

  Covered by health insurance

   No

81.73

   Yes

18.27

  Visiting health facility in the last 12

   No

24.57

   Yes

75.43

  Community-level Characteristics (n = 1588)

  % of women with high decision-making autonomy

   Low

36.37

   Medium

38.71

   High

24.92

  % of women with high sexual autonomy

   Low

30.07

   Medium

30.86

   High

39.08

  % of women with favorable attitudes toward wife beating

   Low

48.21

   Medium

22.35

   High

29.41

  % women with secondary and higher education

   Low

42.00

   Medium

20.34

   High

37.66

  %of women who perceived the distance to a health facility as a major problem

   Low

46.91

   Medium

23.56

   High

29.53

  % of women with non-poor wealth index

   Low

35.78

   Medium

24.23

   High

39.99

Table 2 presents the results of the bivariate analysis of the individual- and community-level characteristics and cervical cancer screening. All individual- and community-level variables were significantly associated with any type of cervical cancer screening in the bivariate analysis, except for the numbers of living children, whether the woman visit a health facility in the last 12 months, and the proportion of women in communities with high decision-making autonomy. In addition, the outcome of having undergone the Pap test ever was significantly associated with most individual-level and all community-level variables. Among individual-level variables, women’s religion, employment status, decision-making power at home, attitudes toward wife-beating, and whether the woman visited a health facility in the last 12 months were not significantly associated with a history of the Pap test.
Table 2

Bivariate associations of individual and community-level characteristics with cervical cancer screening history

 

Any kind of cervical cancer screening history

(N = 6498) %

Pap smear history

(N = 1262) %

Individual-level characteristics

No

Yes

No

Yes

Socio-demographic characteristics

 Women’s Age

  15-24

87.34

12.66*

53.10

46.90*

  25-34

80.78

19.24

43.28

56.72

  35-49

77.20

22.80

36.90

63.10

 Religion

  Roman Catholic

78.44

21.56*

38.24

61.76

  Protestant/other Christian

79.79

20.21

42.81

57.19

  Muslim

93.70

6.30

32.14

67.86

  Others

86.05

13.95

50.00

50.00

 Region

  Coast

88.03

11.97*

41.57

58.43*

  North Eastern

84.00

16.00

66.44

33.56

  Eastern

81.55

18.45

41.94

58.06

  Central

66.25

33.75

48.71

51.29

  Rift Valley

82.99

17.01

43.25

56.75

  Western

84.41

15.59

50.57

49.43

  Nyanza

81.74

18.26

36.14

63.86

  Nairobi

68.86

31.14

14.08

85.92

 Place of residence

  Urban

76.60

23.40*

35.53

64.47*

  Rural

83.30

16.70

47.26

52.74

 Women’s education level

  No Education

94.29

5.71*

54.55

45.45*

  Primary

83.33

16.67

48.03

51.97

  Secondary and higher

74.43

25.57

35.15

64.85

 Women’s employment

  No

85.60

14.40*

45.95

54.05

  Yes

78.65

21.35

40.47

59.53

 Number of living children

  0

80.87

19.13

45.61

54.39*

  1-2

79.01

20.99

41.81

58.19

  3-4

78.59

21.41

37.45

62.55

  5+

86.41

13.59

50.00

50.00

 Amount of media exposure

  0

90.62

9.38*

58.73

41.27*

  1

85.87

14.13

44.26

55.74

  2

79.93

20.07

40.96

59.04

  3

69.58

30.42

38.16

61.84

 Wealth Index

  Poorest

91.86

8.14*

37.84

62.16*

  Poor

86.08

13.92

51.40

48.60

  Middle class

82.80

17.20

55.56

44.44

  Rich

78.55

21.45

42.14

57.86

  Richest

68.30

31.70

31.13

68.87

 Women’s autonomy

  Women’s decision-making power at home

   Low

82.24

17.76*

42.38

57.62

   High

78.11

21.89

40.69

59.31

  Sexual autonomy

   No

82.87

17.13*

50.00

50.00*

   Yes

79.80

20.20

39.30

60.70

  Attitudes toward wife beating

   Opposing

78.91

21.09*

39.95

60.05

   Favorable

82.67

17.33

44.09

55.91

 Health care access

  Covered by health insurance

   No

84.98

15.02*

47.06

52.94*

   Yes

66.05

33.95

33.73

66.27

  Visit health facility in the last 12

   No

82.05

17.95

87.83

12.17

   Yes

80.00

20.00

88.95

11.05

 Community-level characteristics(n = 1588)

  High decision-making autonomy a

   Low

83.27

16.73

82.28

17.72*

   Medium

80.54

19.46

45.36

54.64

   High

79.09

20.91

41.39

58.61

  % of women with high sexual autonomy

   Low

83.38

16.62*

90.96

9.04*

   Medium

82.30

17.70

40.43

59.57

   High

80.24

19.76

39.92

60.08

  % of women with favorable attitudes toward wife beating

   Low

85.07

14.93*

46.35

53.65*

   Medium

81.13

18.87

48.44

51.65

   High

77.25

22.25

80.37

19.63

  % women with secondary and higher education

   Low

87.87

12.13*

87.04

12.96*

   Medium

80.25

19.75

33.33

66.67

   High

76.67

23.33

38.50

61.50

  %of women who perceived the distance to a health facility as a major problem

   Low

80.26

19.74*

76.46

23.54*

   Medium

86.27

13.73

45.65

54.35

   High

86.23

13.77

43.55

56.45

  % of women with non-poor wealth index

   Low

89.74

10.26*

92.07

7.93*

   Medium

85.71

14.29

52.24

47.76

   High

73.31

26.69

40.82

59.18

* P < 0.05

The results of the multilevel analyses are presented in Table 3. The results of the null model for any type of cervical cancer screening outcome, (result not shown) demonstrated a significant variance in cervical cancer screening behavior between communities. About 30 % of the total variance in the cervical cancer screening behavior was at the community level (ICC = 0.31, p < 0.001).
Table 3

Multilevel analyses of factors associated with any type of cervical cancer screening (pap smear or visual inspection) and pap smear test to detect cervical cancer, among married women in Kenya

 

Any type of cervical cancer screening (n = 6498).

Pap smear test (n = 1262).

Individual Characteristics

Adjusted PR (95% CI)

Adjusted PR (95% CI)

Socio-Demographic and Economic Factors

 Age

  15-24

1.00

1.00

  25-34

1.18(0.97-1.44)

1.18 (0.89-1.58)

  35-49

1.29 (1.09-2.97)*

1.38(1.04-1.85)*

 Religion

  Roman Catholic

1.00

1.00

  Protestant/other Christian

0.93(0.79-1.10)

0.95 (0.77-1.16)

  Muslim

0.42(0.28-1.05)

1.13 (0.62-2.03)

  Others

1.20(0.64-2.24)

0.52 (0.21-1.30)

 Region

  Coast

1.00

1.00

  North Eastern

1.18(0.34-4.13)

3.07 (0.69-5.68)

  Eastern

1.32(0.91-1.92)

1.14(0.77-1.67)

  Central

1.84(1.47-2.31)*

1.02(0.69-1.51)

  Rift Valley

1.17 (0.93-1.48)

1.07(0.75-1.54)

  Western

1.29 (0.96-1.74)

1.05(0.69-1.60)

  Nyanza

1.40(1.10-2.76)*

1.17(0.80-1.69)

  Nairobi

1.65(1.26-2.17)*

2.23 (1.49-4.09)*

 Place of residence

  Urban

1.00

1.00

  Rural

0.86(0.69-1.07)

0.97 (0.78-1.20)

 Women’s education level

  No Education

1.00

1.00

  Primary

1.47(0.98-2.23)

1.29 (0.66-2.54)

  Secondary and higher

1.43(0.92-2.21)

1.22 (0.59-2.51)

 Women’s employment

  No

1.00

1.00

  Yes

1.21(1.08-1.39)*

1.35 (1.13-1.61)*

 Number of living children

  0

1.00

1.00

  1-2

0.95 (0.77-1.16)

1.09(0.77-1.54)

  3-4

1.03 (0.83-1.28)

1.16(0.84-1.65)

  5+

0.80 (0.62-1.03)

0.92(0.60-1.39)

 Amount of media exposure

  0

1.00

1.00

  1

1.05(0.81-1.36)

1.26 (0.83-1.91)

  2

1.14(0.87-1.49)

1.06 (0.73-1.62)

  3

1.36(1.02-1.81)*

0.84(0.58-1.29)

 Wealth Index

  Poorest

1.00

1.00

  Poor

1.37(1.03-2.17)*

1.08 (0.78-1.48)

  Middle class

1.52(1.12-2.05)*

1.13 (0.81-1.58)

  Rich

1.66(1.22-2.24)*

1.52 (1.25-2.45)*

  Richest

2.02(1.48-2.77)*

2.60(1.78-3.79)*

Women’s autonomy

 Women’s decision-making power at home

  Low

1.00

1.00

  High

1.08(0.94-1.24)

1.07 (0.82-1.40)

 Sexual autonomy

  No

1.00

 

  Yes

0.91(0.77-1.07)

1.19 (1.01-1.45)*

 Justifying wife beating

  Opposing

1.00

1.00

  Favorable

1.03(0.88-1.19)

1.04 (0.78-1.39)

Health care access

 Covered by health insurance

  No

1.00

1.00

  Yes

1.62(1.45-1.83)*

2.05 (1.70-2.47)*

 Visit health facility in the last 12

  No

1.00

1.00

  Yes

1.20(1.03-1.40)*

0.90 (0.76-1.07)

Community- level characteristics

 High decision-making autonomy at home a

  Low

1.00

1.00

  Medium

1.01(0.79-1.29)

1.08 (0.79-1.47)

  High

1.07(0.84-1.35)

0.90 (0.66-1.23)

 % of women with high sexual autonomy

  Low

1.00

1.00

  Medium

1.10(0.85-1.43)

0.98 (0.78-1.24)

  High

0.92(0.70-1.121)

1.90 (1.35-2.67)*

 % of women with favorable attitudes toward wife beating

  Low

1.00

1.00

  Medium

1.04(0.82-1.32)

1.36 (0.97-1.90)

  High

1.03(0.79-1.34)

0.99(0.71-1.40)

 % of women with secondary and above education

  Low

1.00

1.00

  Medium

0.88(0.68-1.15)

1.37 (0.99-1.89)

  High

1.01(0.78-1.32)

1.32(1.12-1.79)

 % of women who perceived the distance to a health facility as a major problem

  Low

1.00

1.00

  Medium

1.04(0.86-1.42)

1.32 (0.90-1.92)

  High

1.03(0.99-1.90)

1.14 (0.87-1.48)

 % of women with non-poor wealth index

  Low

1.00

1.00

  Medium

1.03(0.77-1.37)

0.86 (0.58-1.26)

  High

1.06(0.77-1.47)

0.74 (0.55-1.01)

  ICC

0.23*

0.10*

  AIC

8852.95

5484.54

PR prevalence ratio, CI confidence interval, ICC intraclass correlation coefficient, AIC Akaike information criterion. * Significant at P-value <0.05,

aPercentage of women with high decision-making autonomy at home

Italic*P < 0.05

The analysis of only individual-level variables, revealed that women’s age, religion, region, education level, employment, amount of media exposure, household wealth index, visiting a health facility, and health insurance were significantly associated with use of any type of cervical cancer screening; the ICC indicated that 29% of the variation in cervical cancer screening was attributable to among community differences (ICC = 0.29, p < 0.001) (results not shown).

In the final model (Table 3), included both the individual- and community-level characteristics. The results showed that the prevalence of any type of cervical cancer screening was 1.29 times higher among older women than among younger women (APR = 1.29; 95% CI = 1.09–2.97). The prevalence was also higher among women residing in the Central, Nyanza, and Nairobi regions than among women residing in the Coastal region (APR = 1.84, 95% CI 1.47-2.31; APR = 1.40, 95% CI = 1.10–2.76; and APR = 1.65, 95% CI = 1.26–2/17, respectively). Regarding employment, any type of cervical cancer screening was 1.21 times more prevalent among employed women when compared to unemployed women (APR = 1.21; 95% CI = 1.08-1.39). Media exposure was also positively associated with any type of cervical cancer screening. The prevalence ratio for women who had exposure to three types of media was 1.36 compared to women who had no exposure to any media (APR = 1.36; 95% CI = 1.02-1.81). The prevalence ratio for women from the richest households was 2.02 compared with women from the poorest households (APR = 2.02; 95% CI = 1.48-2.77). Even after the inclusion of both individual- and community-level variables, the variation in cervical cancer screening behavior between communities remained significant; as shown by the estimated ICC, 23% of the variability in any type of cervical cancer screening was attributable to community differences (ICC = 0.23, P < 0.001).

The multilevel analyses of factors associated with the utilization of Pap test; the results for the null model indicated that 40% of the total variance in Pap test was accounted by between-community variations (ICC = 0.40, P < 0.001) (results not shown). The analysis of only individual-level variables revealed that the prevalence of Pap test was higher among the women who were older, resided in Central and Nairobi regions, were employed, had rich and the richest wealth index scores, had sexual autonomy, and had health insurance coverage than were their counterparts. The ICC indicated that 17% of the variation in the utilization of Pap test was attributable to community differences (ICC = 0.17, P < 0.001) (results not shown).

The final model was presented in Table 3. After included both individual and community-level variables in the final model, age, residence in the Nairobi region, employment, wealth index, sexual autonomy, and health insurance coverage were significantly associated with the prevalence of Pap test history (APR = 1.38, 95% CI = 1.04-1.85; APR = 2.23, 95% CI = 1.49-4.09; APR = 1.35, 95% CI = 1.13-1.61; APR = 2.60, 95% CI = 1.78-3.79; APR = 1.19, 95% CI = 1.01-1.45; and APR = 2.05, 95% CI = 1.70-2.47, respectively). The prevalence of Pap test history was higher among communities comprised of higher proportions of women with sexual autonomy and higher education (APR = 1.90, 95% CI = 1.35-2.67 and APR = 1.32, 95% CI = 1.12-1.79, respectively) than the counterparts. The estimated ICC indicated that 10% of the variability in Pap test history was attributable to community differences (ICC = 0.10, P < 0.001).

Discussion

Our study contributes to the understanding of factors associated with cervical cancer screening in Kenya, where the prevalence of this screening remains low [24]. To our knowledge, this is the first study to assess both the individual- and community-level factors associated with cervical cancer screening in Africa. Our findings reveal that a significant number of women (80.6%) who had knowledge of cervical cancer did not use screening services; in addition, approximately 28% of women in Kenya had no prior knowledge of cervical cancer. Lack of knowledge, younger age, lack of income, fear of the Pap test, and lack of access to screening services were significantly associated with low cervical cancer screening rates [37, 38]. This suggests that cervical cancer screening programs can incorporate self-sampling HPV DNA tests. Recent studies have recommended that cervical cancer screening programs that incorporate self-sampling and HPV DNA tests are feasible, and may significantly improve uptake of cervical cancer screening in SSA [19]. Our study also showed that the prevalence of any type of cervical cancer screening was higher among those residing in the Central and Nyanza regions, while the prevalence of using a Pap test was higher in the Nairobi region. A possible explanation for the regional variation observed is that the Central, Nyanza, and Nairobi regions characteristically have higher socioeconomic status, less cultural conservatism, and easier access to health care services [39, 40].

Corroborating the results of similar studies, our results demonstrate that women’s employment and household wealth were positively associated with cervical cancer screening [27, 41, 42]. The lower prevalence of cervical cancer screening among unemployed and poorer women may indicate financial burden, which is a barrier to accessing cervical cancer screening services. Employed women were more likely to undergo cervical cancer screening because this group of women is most likely to own private health insurance [27].

Our study determined that both individual- and community-level women’s sexual autonomy had a positive influence on Pap testing behavior, indicating that gender inequality, as assessed through low sexual autonomy, can affect cervical cancer screening. Studies have indicated that women’s sexuality is much more controlled than men’s in most developing countries, where women are perceived as passive and powerless and societies describe sex as primarily a male domain [43, 44]. Furthermore, several myths and misconceptions related to women’s sexual and reproductive health after undergoing screening (e.g., cervical cancer screening reduces sexual satisfaction for men and women) have been widely accepted [45, 46]; consequently, this can cause various health problems. Our results indicate that empowering women to control their own lives and make their own decisions regarding their sexual and reproductive health is necessary [47].

Women who resided in communities comprising a higher proportion of women with secondary and higher education were likely to have a history of the Pap test. This finding is consistent with those of previous study results, suggesting that a community with a high concentration of educated women can increase the utilization of health care services including cervical cancer screening [48, 49]. Education is frequently associated with increased access to health care services and more knowledge regarding health behavior. Increasing the proportion of educated women may facilitate the dissemination of knowledge to those with lower education, aiding them in accessing health services through informal social networks and contacts.

Contradicting with our hypothesis and previous study findings [27, 50], we found a marginal positive association between the proportion of women that perceived distance to a health facility as a major problem and cervical cancer screening. The possible explanation for this unexpected result is that women who live in rural and/or remote areas have low expectations of health services, and thus judge the distance to a health facility as not a major problem. Other logistical barriers such as lack of transportation and lack of finance to access screening services are influential factors that can further influence screening behavior [21]. However, these variables are not available in our data set (KDHS). Future research should further include these potential logistical factors associated with cervical cancer screening in the study design and analysis.

As anticipated, health insurance coverage was strongly associated with both primary and secondary outcomes. Our finding regarding the association between health insurance and screening use is consistent with the results of previous studies [51, 52]. Our study results prove that the adoption of a universal health insurance scheme ensuring equity in access to health care can largely enhance the possibility of cervical cancer screening use [27]. Cost is one of the main barriers to obtaining a cervical cancer screening among women in resource-constrained countries [21, 50]. In a region where the poverty is high, emergency needs are given greater priority to out-of-pocket payments than preventive services [53]. Consequently, health insurance coverage may potentially reduce the financial burden for women to access preventive health care services, including cervical cancer screening.

Consistent with other studies, our study suggested that visiting a health facility in the last 12 months is positively associated with cervical cancer screening behavior. Having a usual source of care is important for women’s access to screening services [53, 54]. Particularly in resource-poor settings, contact with health professional workers while visiting a health facility can increase women’s exposure to related health knowledge and encourage women to undertake preventive services [55]. Prior studies also demonstrated that a health provider’s recommendation was consistently found to be a strong predictor of completion of cervical cancer screening [56].

Our study has some potential limitations. First, the cross-sectional study design limited our ability to draw causal inferences for the association of individual- and community-level factors with cervical cancer screening. Second, because of the limited number of variables collected by the KDHS, we could not examine a full array of factors related to cervical cancer screening, particularly cultural and supply-side factors including health service quality and other factors related accessibility to the services. Third, our community measures were based on aggregating individual responses to the community level, which may increase the likelihood of misclassifying individuals into inappropriate administratively defined boundaries (clusters).

Conclusion

Our study determined that both individual- and community-level factors influence cervical cancer screening behavior. Specifically, geographical distribution of medical resources, exposure to health information through media, employment opportunities, health insurance coverage, and women’s own sexual autonomy, as well as women’s sexual autonomy and education at the community level, all contribute to their screening behavior. Our findings provide suggestions for future studies to address these factors associated with cervical cancer screening rates in Kenya.

Implications for practice and/or policy

Our results suggest that the adoption of policies promoting access to information about the benefit of cervical cancer screening through media and improve gender equality can empower women to use screening services. Employment programs should aim to provide employment opportunities among women. Establishing income-generating programs for women may increase their intention toward and actual use of cervical cancer screening. Moreover, the influence of extending insurance overage could be substantial. Health insurance coverage can potentially reduce out-of-pocket health expenses for women and empower them financially to demand and be able to use health services. Health policymakers should also address the issue of geographical inequalities in screening behavior; this can be achieved through approaches such as increasing health facility and medical personnel in rural areas to minimize geographical inequality. Public health programs must target young adults to inform them about the benefits of early detection of cervical cancer and motivate them to initiate preventive behavioral changes. In addition, to improve cervical cancer screening coverage and achieve optimal protection, HPV testing by using self-collected samples is a plausible modality for cervical cancer screening in future policy development.

Abbreviations

AIC: 

Akaike information criterion

APR: 

Adjusted prevalence ratio

CI: 

Confidence interval

DNA: 

Deoxyribon ncleic acid

HPV: 

Human papilloma virus

ICC: 

Intraclass correlation coefficient

KDHS: 

Kenya demographic health survey

Pap: 

Papanicolaou

SAS: 

Statistical analysis software

SSA: 

Sub-Saharan Africa

VIA: 

Visual inspection using acetic acid

VILI: 

Visual inspection using Lugol’s iodine

Declarations

Acknowledgements

We give thanks to the MEASURE DHS for providing us with the population-based dataset through their archives which can be downloaded from http://dhsprogram.com/data/available-datasets.cfm.

Funding

None.

Availability of data and materials

Data are from the Demographic and Health Survey. The dataset is open to qualified researchers free of charge. To request access to the dataset, please apply at http://dhsprogram.com/data/Access-Instructions.cfm.

Authors’ contributions

FNT and Y-CC designed the study. FNT analyzed the data and wrote the manuscript. Y-CC, K-YC, and PAMN revised the draft. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All data were obtained from the 2014 KDHS. Informed consent was obtained from each respondent before the interviews. Procedures and questionnaires for standard DHS surveys have been reviewed and approved by the ICF International Institutional Review Board (IRB). We obtained approval to use the data from the DHS repository (http://dhsprogram.com/data/available-datasets.cfm).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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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)
School of Public Health, Taipei Medical University, Taipei City, Taiwan

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Copyright

© The Author(s). 2017

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