Transcript Social Economic Determinants of Cervical Cancer among Women
Social Economic Determinants of Cervical Cancer among Women Attending Referral Hospitals in Dar Es Salaam, Tanzania 2012-13
Karugira Rweyemamu 1,3 , Janneth Mghamba 2,3 , Peter Mmbuji 2, 3 , Ahmed Abade 3 Sembuche 2,3 , Loveness Zubeda Ngware Urio 3 , Rogath 2,3 , Senga Kishimba 3 , C.Moshiro
1
Background (1/3)
• Cervical cancer: 3 rd most common cancer; 4 th 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 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
Cases n (%)
3 (1.8) 88 (53.3) 74 (44.8) 56 (33.9) 97 (58.8) 12 (7.3) 37 (22.4) 30 (18.2) 98 (59.4) 17 (10.8) 22 (15.9) 39 (24.7) 33 (20.9) 47 (29.7)
Controls n (%)
16 (9.7) 135 (81.8) 14 (8.5) 16 (9.7) 85 (51.5) 64 (38.8) 91 (60.7) 45 (30) 14 (9.3) 45 (28.3) 42 (26.4) 25 (15.7) 29 (18.2) 18 (11.3)
P value
<0.0001
<0.0001
<0.0001
<0.0001
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) 1.0
3.48 (0.98 – 12.28) 28.19 (7.24 – 109.71) 18.67 (8.14 – 42.81) 6.09 (3.08 – 12.04) 1.0
1.0
1.64 (0.9 – 2.99) 17.22 (8.74 – 33.91) 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)
AOR (95% CI)
1.11 (1.06 – 1.15) 1.0
0.70 (0.13 – 3.82) 2.25 (0.35 – 14.32) 0.51 (0.17 – 1.52) 0.30 (0.07 – 1.25) 1.0
1.0
1.58 (0.61 – 4.14)
6.20 (2.12 – 18.13)
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
• AFENET • TFELTP • CDC • MUHAS • ORCI