Social Economic Determinants of Cervical Cancer
among Women Attending Referral Hospitals in
Dar Es Salaam, Tanzania 2012-13
Karugira Rweyemamu1,3, Janneth Mghamba2,3, Peter
Mmbuji2, 3, Ahmed Abade3 Zubeda Ngware2,3, Senga
Sembuche2,3, Loveness Urio3, Rogath Kishimba3,
C.Moshiro 1
Background (1/3)
• Cervical cancer: 3rd most common cancer; 4th
cause of cancer death in females world wide
• SSA: > 85% of the global burden
• East Africa: cervical cancer mortality rate - 34
deaths per 100,000/year (4 times global
mortality rate – 9/100,000)
Background (2/3)
Cervical Cancer in Tanzania
• WHO estimates > 7000 new cases/year are diagnosed
• 4th country with many cases of cervical cancers
• Leading among East Africa countries
• Account for 35.3% of cancer diagnosis at Ocean Road Cancer
Institute in TZ
• Estimates are projected to rise to more than 12000 new cases
and 9900 deaths per year
Background (3/3)
Little is known about social and economic factors that
influence cervical cancer in Tanzania
Our findings will generate new knowledge to:
– feed into strategies of the National Cervical Cancer
Prevention and Control (NCCPC)
– create awareness to both health specialists and policy
makers for effective primary cervical cancer prevention
policies and guidelines
Broad Objective
To determine social economic factors associated
with cervical cancer among women attending
referral hospitals in Dar es Salaam
Methodology (1/2)
• Study design: Unmatched 1:1 case-control study
• Study setting: 2 national referral hospitals (ORCI and
Muhimbili National hospital (MNH)
• Case definition: a woman attending ORCI
diagnosed with cervical cancer in preceding 6 months
by histopathology
• A control : a woman attending Gynaecology
department at MNH with non-cancer related
diagnosis
Methodology (2/2)
• Sample size: 330
• All incident cases and control during the study period
were recruited
• Research instrument: Standardised questionnaire
• Data analysis:
– STATA (11.2)
– α=0.05
Results (1/2)
• Mean age (sd): Cases 51(12), Controls 33(11) years
• Occupation: Cases 59.4% were subsistence farmers,
Controls 60.7% were employed
• Wealth: 29.7% of cases ranked in the Lowest wealth
quintile while 28.3% of controls ranked in the
Highest wealth quintile
Demographic characteristics of cases and controls
Characteristic
P value
Cases n (%)
Controls n (%)
3 (1.8)
88 (53.3)
16 (9.7)
135 (81.8)
74 (44.8)
14 (8.5)
56 (33.9)
97 (58.8)
12 (7.3)
16 (9.7)
85 (51.5)
64 (38.8)
<0.0001
37 (22.4)
30 (18.2)
98 (59.4)
91 (60.7)
45 (30)
14 (9.3)
<0.0001
17 (10.8)
22 (15.9)
39 (24.7)
33 (20.9)
47 (29.7)
45 (28.3)
42 (26.4)
25 (15.7)
29 (18.2)
18 (11.3)
<0.0001
Marital status
Single
Married /cohabiting
Divorced /separated/
widowed
<0.0001
Education level
None
Primary
Secondary and above
Occupation
Employed
Housewife
Subsistence farmers
Wealth quintile
Highest
Fourth
Third
Second
Lowest
Crude and Adjusted odds ratios for social economic factors
associated with cervical cancer
Factor
Age (per year)
Marital Status
Single
Married /Cohabiting
Divorced /separated/ Widowed
Education level
None
Primary
Secondary and above
Occupation
Employed
Housewife
Subsistence farmers*
Wealth quintile
Highest
Fourth
Third
Second
Lowest*
COR (95% CI)
1.14 (1.11 – 1.18)
AOR (95% CI)
1.11 (1.06 – 1.15)
1.0
3.48 (0.98 – 12.28)
28.19 (7.24 – 109.71)
1.0
0.70 (0.13 – 3.82)
2.25 (0.35 – 14.32)
18.67 (8.14 – 42.81)
6.09 (3.08 – 12.04)
1.0
0.51 (0.17 – 1.52)
0.30 (0.07 – 1.25)
1.0
1.0
1.64 (0.9 – 2.99)
17.22 (8.74 – 33.91)
1.0
1.58 (0.61 – 4.14)
6.20 (2.12 – 18.13)
1.0
1.39 (0.65 – 2.96)
4.13 (1.95 – 8.75)
3.01 (1.43 – 6.37)
6.92 (3.17 – 15.06)
1.0
0.43 (0.12 – 1.57)
2.91 (0.92 – 9.22)
1.69 (0.46 – 6.2)
6.29 (1.58 – 25.0)
Discussion (1/3)
• Findings consistent with other studies (Hammoud et
al 2005 in Algeria, Chaouki et al 1998 in Morocco)
• Women in low socioeconomic strata:
– Marginalized from accessing health program
(screening, health education)
– Medical access to early infection and treatment
(STI)
Discussion (2/3)
Strengths
• Participant from National Referral Hospital –wide
geographical area
• Cases diagnosed by Histopathology result minimize misclassification bias
Discussion (3/3)
Limitations
• Residual confounding
• Representativeness of cases (Berkson’s bias)
• Misclassifications of controls as cervical cancer
screening was not done
Conclusion and Recommendation
• Socio-economic factors may increase susceptibility to
cervical cancer in Tanzania
• Efforts to include women subsistence farmers of low
social economical status in the current cervical cancer
control programmes should be made
Acknowledgment
• AFENET
• TFELTP
• CDC
• MUHAS
• ORCI