Abstract
Introduction
Breast cancer is a global health issue, contributing to a significant number of cancer-related deaths among women. Early detection through breast cancer screening is essential for reducing mortality and morbidity rates. We aimed to assess the individual and community-level factors associated with breast cancer screening among women of reproductive age in Tanzania.
Methods
An analytical cross-sectional survey was conducted using secondary data from the 2022 Tanzania demographic and health survey. Considering the complex survey design, a multilevel mixed-effects binary logistic regression was used to determine the individual and community-level factors associated with breast cancer screening. Adjusted OR with corresponding 95% CIs was used to estimate the strength of the association. Statistical significance was set at a p<0.05.
Results
The prevalence of breast cancer screening among women of reproductive age in Tanzania was 5.2% (95% CI 4.7 to 5.7). At the individual level, being aged ≥25 years, educated, working, living in wealthier households, using contraceptives, having media exposure, healthcare insurance coverage and visiting health facilities in the last 12 months were associated with higher odds of breast cancer screening. At the community level, being from communities with a high level of poverty and residing in rural settings was associated with lower odds of breast cancer screening. While residing in northern, southern and lake zones was associated with higher odds of breast cancer screening.
Conclusions
The study highlights a critical need for enhanced efforts in breast cancer screening among women of reproductive age in Tanzania, where current participation rates remain disconcertingly low. The multifactorial nature of screening behaviours, influenced by age, education, employment status and socioeconomic conditions, underscores the complexity of addressing this public health challenge. Implementing targeted educational programmes, improving healthcare access and leveraging community resources can increase awareness and utilisation of screening services.
Keywords: Public Health, Mass Screening, Preventive Medicine
WHAT IS ALREADY KNOWN ON THIS TOPIC
Prior to this study, it was known that breast cancer is a leading health issue among women in Tanzania, with a screening rate of only 5%. Existing research identified barriers to screening, including lack of awareness, limited access to healthcare and sociocultural factors that deterred women from seeking preventive services. Thus, this study was necessary to comprehensively analyse both individual and community-level factors affecting breast cancer screening uptake among women of reproductive age in Tanzania.
WHAT THIS STUDY ADDS
This study contributes new insights by revealing that factors such as age, socioeconomic status, health insurance and geographical location significantly influence breast cancer screening behaviours. Specifically, it found that older, wealthier women with health insurance and those living in urban areas are more likely to engage in screening practices. The research also highlighted the crucial barriers encountered in rural areas, underscoring the need for targeted interventions to improve access and awareness.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Enhancing access to screening services and preventive healthcare education should be prioritised, especially in rural communities where the need is most pronounced. Overall, this research lays critical groundwork for future public health strategies to improve breast cancer screening rates among women in Tanzania.
Background
Globally, breast cancer surpassed lung cancer in 2020 and became the leading cause of cancer-related cases.1 2 Data indicated that around 2.3 million new breast cancer cases were diagnosed worldwide, which represented a significant 11.7% of all cancer cases, consequently leading to approximately 685 000 fatalities.2 3 Predictions suggest a staggering rise, with the number of cases expected to escalate to 4.4 million by 2070 globally.4 5 The incidence of breast cancer has been reported to increase in developed nations due to lifestyle changes, environmental factors and an increase in early diagnosis.5 The significance of breast cancer as a public health challenge is underscored by its impact on women’s health worldwide, particularly in low-income and middle-income countries (LMICs) like Tanzania.6 In sub-Saharan Africa (SSA), the pooled prevalence from the current study was 11.35%, with an uncertainty range of 11.14% to 11.56%.7 Breast cancer ranks as the second most prevalent cancer among women in Tanzania and occupies a prominent position in the causes of cancer-related mortality within this demographic.8 Breast cancer in Tanzania is projected to increase to 82% by 20309 and 120% by 2040,6 which calls for the assessment of epidemiological changes and associated factors.
Globally, numerous strategies are employed to enhance participation in breast cancer screening, addressing individual, community, collective and behaviour-oriented interventions.4 6 Breast cancer screening is a critical public health issue, particularly for women of reproductive age, as global trends indicate a rising incidence of breast cancer across various regions.6 9 Women in LMICs frequently encounter significant barriers to healthcare, stemming from inadequate healthcare systems and a profound lack of awareness around the necessity of screening procedures.7 These factors often lead to late diagnoses which diminish better treatment outcomes.10 Reports showed that a minimum number of women between the ages of 15–49 in Tanzania underwent breast cancer screening.11 This results in almost 80% of patients presenting with the late stage of the condition, which limits the options for treatment.9 12
The lack of adequate public health responses in the past has prompted the WHO to initiate the Global Breast Cancer Initiative.4 This initiative seeks to engage global partners and coordinate sustainable efforts to enhance outcomes for breast cancer patients and decrease mortality rates.6 To support these initiatives, it is crucial to have an updated understanding of the trends and variations in the burden of breast cancer and breast cancer screening.
Research conducted across various countries has shown several determinants that are significantly linked to breast cancer screening, including age, wealth index, health insurance coverage and levels of educational attainment, all of which collectively highlight the stark disparities in screening rates.5 6 9 The literature demonstrates that a combination of educational, social, economic and structural barriers hinders breast cancer screening.6 12 A notable gap persists in the existing literature regarding breast cancer screening. Previous research in this area has largely overlooked the application of multilevel modelling to account for the hierarchical nature of data from demographic and health surveys.13,15 A thorough understanding of these factors in multilevel analysis is essential for refining and enhancing strategies aimed at improving breast cancer screening within this demographic. By implementing these methodological improvements, our research not only sets itself apart but also enables a more thorough investigation of breast cancer screening and its influencing factors in the context of Tanzania. This study aimed to assess the individual and community-level factors associated with breast cancer screening among women of reproductive age in Tanzania using secondary data from the 2022 Tanzania Demographic Health Survey (TDHS).
Breast cancer screening programme in Tanzania
The Ocean Road Cancer Institute (ORCI), located in Dar es Salaam, Tanzania, serves as the national cancer centre for a country with a population of approximately 62 million,16 including about 6 million residents in Dar es Salaam.17,19 Women from all over Tanzania visit ORCI for cancer care, with a newly developed screening programme focusing on women aged 40 and older, approximately 270 000 individuals based on Tanzania’s population structure.20 The screening programme includes a clinical breast exam (CBE) conducted at ORCI’s breast cancer screening clinic, which presently accommodates up to 35 women daily, with plans to increase capacity to 100 by adding more healthcare providers. Referrals can originate from both self-referrals and the cervical screening clinic at ORCI, which has been operational since 2001, where patients might be referred via word of mouth or the healthcare system.6 16 20 Patients identified with abnormal findings during the CBE are referred for ultrasound (USS) or fine needle aspiration and cytology (FNAC) at ORCI, as there are currently no mammograms (MMG) available on-site. For patients over 40 with nonspecific symptoms, referrals for MMG are made to Muhimbili National Hospital (MNH) or other facilities, and positive results from mammography lead to further FNAC at ORCI. If FNAC suggests breast cancer, patients undergo histopathological examination and surgical review at MNH, which may involve biopsy techniques leading to a confirmed cancer diagnosis, after which treatment options at ORCI consist of chemotherapy, hormonal treatment or radiotherapy. Positive FNAC results that do not indicate breast cancer lead to additional diagnostic reviews at MNH or other facilities in Tanzania.21,23
Bugando Medical Centre in north-west Tanzania functions as the zonal consultant hospital for the entire lake zone, catering to a population exceeding 15 million individuals. This centre has focused on establishing comprehensive cancer diagnostic, treatment and radiation services, which are still underused within the broader community.9 Tanzania lacks an organised national-wide breast cancer screening programme which makes it depend on the opportunistic and symptoms-driven screening. In 2020, the policy for breast cancer treatment was introduced which emphasised the CBE.24 With the low uptake of breast cancer screening in Tanzania, studies showed improper utilisation of facilitators for self-breast cancer screening.14 These reports call for continuous assessment of the situation to inform policy makers and other stakeholders for timely policies and intervention.
Methods
Data source, design and population
This study was an analytical cross-sectional survey that used secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every 5 years. The data were extracted from the individual file record.25 The Tanzania National Bureau of Statistics conducted the survey with the Ministries of Tanzania Mainland and Zanzibar. Data for this study were obtained from the most recent TDHS conducted between 24 February and 21 July 2022, across all regions of the country. The survey targeted women of reproductive age (15–49 years), men, children and households across all regions in Tanzania. Specifically, this study focused on women of reproductive age. The detailed TDHS methodology is explained elsewhere.25 This study included a total of 15 254 women of reproductive age from the 2022 TDHS.
Study variables
Dependent variable
The study’s outcome variable was ‘Screened for breast cancer’. It was derived from the question, ‘Breasts examined for cancer by healthcare provider.’ The variable was recoded into a binary variable as ‘1=Yes, and 0=Otherwise.’
Independent variables
This study examined variables at the individual and community levels based on the available data and relevant literature.4 9 12
Individual-level variables: age in years (15–24, 25–34 or 35–49), education level (no formal education, primary, secondary or higher), marital status (never married, married, cohabiting or separated/divorced), wealth index (poorest, poorer, middle, richer or richest), media exposures (yes as listening to the radio, reading the newspaper or watching television less than once a week or at least once a week or no if otherwise), parity (none, 1–4, >4), working status (working or not working), contraceptive use (yes or no), pregnancy status (yes or no/unsure), visited a health facility in the past 12 months (yes or no), breastfeeding status (yes or no) and covered by healthcare insurance (yes or no).
Community-level variables: place of residence (urban or rural), geographical zones (western, northern, central, lake, southern, eastern or Zanzibar). The level of poverty in the community was measured by the proportion of women classified in the poorer and poorest wealth quintiles, as indicated by the DHS wealth index. Communities were categorised as low poverty (where < 50% of women were in these quintiles) or high (where ≥ 50% of women were in these quintiles).
Data management and analysis
STATA V.18.5 (STATA)26 was used to clean, recode and analyse data. To account for the complexity of the survey design, we applied individual sampling weights, primary sampling units (clusters) and strata to adjust for the cluster sampling design. Descriptive statistics were summarised using means, SD, frequency and proportion for categorical variables. The prevalence of breast cancer screening was computed as the number of women screened for breast cancer divided by the total number of women in the study. The Pearson χ² test was used to compare the differences in the proportion of breast cancer screening across participants’ characteristics. Due to the hierarchical structure of the DHS data, with women nested within households and households nested within clusters, there may be greater similarity among women within the same cluster. This violates the assumption of independence of observations and equal variance across clusters. Consequently, applying a classical logistic regression model could result in underestimating the standard errors of effect sizes and produce biased estimates.
This implies considering the variability between clusters. To identify individual and community factors associated with breast cancer screening, we used multilevel mixed-effects logistic regression. Four models were applied: the null model (outcome variable only), model I (only individual-level factors), model II (only community-level factors) and model III (both individual and community-level factors). The null model, which is devoid of independent variables, was used to assess the variation in breast cancer screening rates across clusters. Model I examined the association between individual-level factors and the outcome, while model II looked at the impact of community-level factors. Finally, model III assessed the combined effects of both individual and community-level factors on the outcome (breast cancer screening).
The random effects measures include the intra-class correlation coefficient (ICC), median OR (MOR) and proportion change in variance (PCV).27 28 The ICC was calculated to assess the magnitude of the clustering effect and to evaluate the extent to which community-level factors account for the unexplained variance in the null model. The MOR, which quantifies the median difference in the likelihood of breast cancer screening between clusters. The PCV was used to assess the reduction in unexplained variance across models.
The model that suited the data the best was the one with the lowest deviation (model III). Adjusted ORs (AORs) and corresponding 95% CIs were presented to estimate the magnitude and strength of the association. A statistically significant result was considered for a p<0.05. A variance inflation factor (VIF) was used to assess for multicollinearity between independent variables before fitting a multivariable regression model. The mean VIF was <10 indicating no significant multicollinearity.
Results
Characteristics of study participants
Table 1 presents the characteristics of women of reproductive age at the individual and community levels. Nearly 4 in 10 (38.1%) were aged 15–24 years, with an overall mean age of 29.3 (SD=9.8). Four in 10 (43.5%) were married, and more than half (53.3%) had attained a primary education level. There was almost an even distribution of wealth index, with the highest observed being in the richest quantile (26.0%). More than two-thirds (69.3%) had media exposure, and more than half (53.7%) had one to four children. Nearly two-thirds (64.3%) were working, and over three-fourths were not breastfeeding (77.1%). There were significant differences in breast cancer screening with all participant’s characteristics (p<0.05) except for pregnancy status and breastfeeding status, as highlighted in (table 1).
Table 1. Individual-level and community-level characteristics of women of reproductive age in Tanzania, 2022 Demographic and Health Survey (N=15 254).
| Characteristics | n (%) | Screened for breast cancer, n (%) | χ² p value | |
|---|---|---|---|---|
| No | Yes | |||
| Age group (years) | <0.001 | |||
| 15–24 | 5810 (38.1) | 5707 (98.2) | 103 (1.8) | |
| 25–34 | 4609 (30.2) | 4369 (94.8) | 240 (5.2) | |
| 35–49 | 4835 (31.7) | 4390 (90.8) | 445 (9.2) | |
| Marital status | <0.001 | |||
| Single | 4047 (26.5) | 3935 (97.2) | 112 (2.8) | |
| Married | 6630 (43.5) | 6221 (93.8) | 409 (6.2) | |
| Cohabiting | 2622 (17.2) | 2503 (95.5) | 119 (4.5) | |
| Separated/widowed | 1955 (12.8) | 1806 (92.4) | 149 (7.6) | |
| Education level | <0.001 | |||
| No formal education | 2450 (16.1) | 2401 (98.0) | 49 (2.0) | |
| Primary education | 8123 (53.3) | 7706 (94.9) | 417 (5.1) | |
| Secondary/higher | 4681 (30.7) | 4358 (93.1) | 322 (6.9) | |
| Wealth Index | <0.001 | |||
| Poorest | 2466 (16.2) | 2435 (98.7) | 31 (1.3) | |
| Poorer | 2578 (16.9) | 2522 (97.8) | 56 (2.2) | |
| Middle | 2880 (18.9) | 2766 (96.0) | 114 (4.0) | |
| Richer | 3359 (22.0) | 3163 (94.2) | 196 (5.8) | |
| Richest | 3971 (26.0) | 3579 (90.1) | 392 (9.9) | |
| Media exposure | <0.001 | |||
| No | 4690 (30.7) | 4582 (97.7) | 108 (2.3) | |
| Yes | 10 564 (69.3) | 9883 (93.6) | 681 (6.4) | |
| Parity | <0.001 | |||
| None | 3874 (25.4) | 3791 (97.9) | 83 (2.1) | |
| 1–4 | 8188 (53.7) | 7655 (93.5) | 533 (6.5) | |
| >4 | 3193 (20.9) | 3020 (94.6) | 173 (5.4) | |
| Working status | <0.001 | |||
| Not working | 5452 (35.7) | 5276 (96.8) | 176 (3.2) | |
| Working | 9802 (64.3) | 9190 (93.8) | 612 (6.2) | |
| Contraceptive use | <0.001 | |||
| No | 10 536 (69.1) | 10 135 (96.2) | 401 (3.8) | |
| Yes | 4718 (30.9) | 4330 (91.8) | 388 (8.2) | |
| Pregnancy status | 0.284 | |||
| No or unsure | 14 072 (92.3) | 13 334 (94.8) | 738 (5.2) | |
| Yes | 1182 (7.7) | 1131 (95.7) | 51 (4.3) | |
| Visited a health facility last 12 months | <0.001 | |||
| No | 7167 (47.0) | 6907 (96.4) | 260 (3.6) | |
| Yes | 8087 (53.0) | 7558 (93.5) | 529 (6.5) | |
| Covered by healthcare insurance | <0.001 | |||
| No | 14 366 (94.2) | 13 728 (95.6) | 638 (4.4) | |
| Yes | 888 (5.8) | 737 (83.0) | 151 (17.0) | |
| Breastfeeding status | 0.125 | |||
| No | 11 767 (77.1) | 11 132 (94.6) | 635 (5.4) | |
| Yes | 3487 (22.9) | 3333 (95.6) | 154 (4.4) | |
| Community poverty | <0.001 | |||
| Low | 8122 (53.3) | 7486 (92.2) | 637 (7.8) | |
| High | 7132 (46.7) | 6979 (97.9) | 152 (2.1) | |
| Residence | <0.001 | |||
| Urban | 5446 (35.7) | 4971 (91.3) | 475 (8.7) | |
| Rural | 9808 (64.3) | 9494 (96.8) | 314 (3.2) | |
| Geographical zones | <0.001 | |||
| Western | 1268 (8.3) | 1247 (98.4) | 21 (1.6) | |
| Northern | 1733 (11.4) | 1618 (93.3) | 115 (6.7) | |
| Central | 1573 (10.3) | 1508 (95.9) | 65 (4.1) | |
| Southern | 3051 (20.0) | 2892 (94.8) | 159 (5.2) | |
| Lake | 4454 (29.2) | 4244 (95.3) | 210 (4.7) | |
| Eastern | 2657 (17.4) | 2464 (92.7) | 193 (7.3) | |
| Zanzibar | 518 (3.4) | 492 (90.3) | 26 (5.0) | |
Prevalence of breast cancer screening
The current study revealed an overall weighted prevalence of breast cancer screening among women of reproductive age was 5.2% (95% CI 4.7% to 5.7%).
Measures of variation and model fitness
The null model indicated a variance of 0.67 with a p<0.001, demonstrating significant variation in breast cancer screening across localities. According to model I’s ICC value, 6.1% of the variation in breast cancer screening results from individual differences. The likelihood of breast cancer screening varied by 1.14 times between low and high breast cancer screenings in the model (I). Also, model II was created using community-level variables and the null model. Based on the ICC value from model II, 7.8% of the variability in breast cancer screening was attributed to cluster variations (table 2).
Table 2. Measure of variation and model fitness for determinants of breast cancer screening among women of reproductive age in Tanzania.
| Parameters | Model 0 | Model I | Model II | Model III |
|---|---|---|---|---|
| Variance | 0.67 | 0.19 | 0.27 | 0.19 |
| PCV (%) | Ref | 68.7% | 58.2% | 71.6% |
| ICC (%) | 16.9% | 5.5% | 7.7% | 5.4% |
| AIC | 5889.21 | 5321.68 | 5730.14 | 5339.67 |
| BIC | 5904.48 | 5489.60 | 5806.47 | 5553.38 |
| MOR | 1.36 | 1.14 | 1.15 | 1.14 |
| Model fitness | ||||
| LLR | −2942.60 | −2638.84 | −2856.07 | −2641.84 |
| Deviance | 5885.21 | 5277.68 | 5710.14 | 5283.67 |
AIC, Akaike information criterion; BIC, Bayesian information criterion; ICC, intra cluster correlation; LLR, log-likelihood ratio; MOR, median OR; PCV, proportion change in variance.
Multilevel analysis of factors associated with breast cancer screening
In the final fitted model of multivariable multilevel logistic regression analysis (model III), the odds of breast cancer screening were 1.86 and 4.08 times higher among women aged 25–34 years and ≥35 years compared with those aged 15–24 years, respectively (AOR=1.86, 95% CI 1.41 to 2.46) and (AOR=4.08, 95% CI 3.02 to 5.51). Women with primary education (AOR=1.48, 95% CI 1.09 to 2.01) and secondary or higher education (AOR=2.01, 95% CI 1.42 to 2.83) had a higher odd of breast cancer screening than those without formal education. Women who were working had a higher odd of breast cancer screening compared with their counterparts (AOR=1.31, 95% CI 1.08 to 1.59). Women who used contraceptives (AOR=1.42, 95% CI 1.20 to 1.69) had a higher likelihood of breast cancer screening compared with their counterparts. The odds of breast cancer screening were 1.29 times higher among women who visited health facilities in the last 12 months (AOR=1.28, 95% CI 1.08 to 1.52) compared with their counterparts. Women in the middle wealth quintile (AOR=1.57, 95% CI 1.03 to 2.39), richer quintile (AOR=1.63, 95% CI 1.04 to 2.55) and richest quintile (AOR=2.11, 95% CI 1.32 to 3.89) had significantly higher odds of breast cancer screening compared with women in the poorest quintile. Women who were covered by healthcare insurance had 2.14 times higher odds of breast cancer screening compared with their counterparts (AOR=2.14, 95% CI 1.69 to 2.71). Women from high levels of community poverty were 37% less likely to practise breast cancer screening compared with their counterparts (AOR=0.63, 95% CI 0.47 to 0.84). Women in Northern (AOR=2.13, 95% CI 1.24 to 3.65), southern (AOR=1.84, 95% CI 1.11 to 3.06) and Lake zones (AOR=1.78, 95% CI 1.06 to 2.98) had a higher odd of breast cancer screening compared with women in the western zone (table 3).
Table 3. Multivariable multilevel logistic regression analysis of individual-level and community-level factors associated with breast screening among reproductive age group women in Tanzania.
| Characteristics | Model I | Model II | Model III |
|---|---|---|---|
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Age group (years) | |||
| 15–24 | Ref | Ref | |
| 25–34 | 1.88 (1.42 to 2.47)* | 1.86 (1.41 to 2.46)* | |
| 35–49 | 4.21 (3.12 to 5.67)* | 4.08 (3.02 to 5.51)* | |
| Marital status | |||
| Single | Ref | Ref | |
| Married | 0.92 (0.68 to 1.24) | 1.03 (0.77 to 1.41) | |
| Cohabiting | 1.07 (0.76 to 1.51) | 1.09 (0.77 to 1.53) | |
| Separated/widowed | 1.18 (0.84 to 1.66) | 1.20 (0.86 to 1.68) | |
| Education level | |||
| No formal education | Ref | Ref | |
| Primary | 1.52 (1.12 to 2.06)* | 1.48 (1.09 to 2.01)* | |
| Secondary/higher | 1.97 (1.40 to 2.76)* | 2.01 (1.42 to 2.83)* | |
| Wealth Index | |||
| Poorest | Ref | Ref | |
| Poorer | 1.23 (0.79 to 1.91) | 1.16 (0.74 to 1.80) | |
| Middle | 2.01 (1.34 to 3.01)* | 1.57 (1.03 to 2.39)* | |
| Richer | 2.50 (1.67 to 3.76)* | 1.63 (1.04 to 2.55)* | |
| Richest | 3.64 (2.40 to 5.50)* | 2.11 (1.32 to 3.89)* | |
| Media exposure | |||
| No | Ref | Ref | |
| Yes | 1.26 (0.99 to 1.59) | 1.27 (1.01 to 1.62)* | |
| Parity | |||
| None | Ref | Ref | |
| 1–4 | 1.45 (1.03 to 2.03)* | 1.39 (0.99 to 1.95) | |
| >4 | 1.25 (0.84 to 1.87) | 1.28 (0.86 to 1.91) | |
| Working status | |||
| Not working | Ref | Ref | |
| Working | 1.30 (1.08 to 1.58)* | 1.31 (1.08 to 1.59)* | |
| Contraceptive use | |||
| No | Ref | Ref | |
| Yes | 1.48 (1.25 to 1.75)* | 1.42 (1.20 to 1.69)* | |
| Pregnancy status | |||
| No or unsure | Ref | Ref | |
| Yes | 1.28 (0.92 to 1.78) | 1.27 (0.91 to 1.77) | |
| Visited a health facility in last 12 months | |||
| No | Ref | Ref | |
| Yes | 1.27 (1.07 to 1.50)* | 1.28 (1.08 to 1.52)* | |
| Covered by healthcare insurance | |||
| No | Ref | Ref | |
| Yes | 2.16 (1.72 to 2.72)* | 2.14 (1.69 to 2.71)* | |
| Breastfeeding status | |||
| No | Ref | Ref | |
| Yes | 0.98 (0.79 to 1.21) | 0.99 (0.79 to 1.22) | |
| Community poverty | |||
| Low | Ref | Ref | |
| High | 0.39 (0.30 to 0.51)* | 0.63 (0.47 to 0.84)* | |
| Residence | |||
| Urban | Ref | Ref | |
| Rural | 0.67 (0.53 to 0.83)* | 0.80 (0.64 to 1.01) | |
| Geographical zones | |||
| Western | Ref | Ref | |
| Northern | 3.21 (1.85 to 5.67)* | 2.13 (1.24 to 3.65)* | |
| Central | 1.98 (1.09 to 3.56)* | 1.48 (0.83 to 2.63) | |
| Southern | 2.48 (1.48 to 4.16)* | 1.84 (1.11 to 3.06)* | |
| Lake | 2.03 (1.19 to 3.45)* | 1.78 (1.06 to 2.98)* | |
| Eastern | 2.22 (1.29 to 3.82)* | 1.62 (0.95 to 2.76) | |
| Zanzibar | 1.61 (0.93 to 2.78) | 1.31 (0.76 to 2.25) |
p<0.05.
AOR, adjusted OR.
Discussion
This study aimed to find the individual-level and community-level factors associated with breast cancer screening among women of reproductive age in Tanzania by using a multilevel analysis of the 2022 national survey. Breast cancer poses a significant health challenge, especially in Tanzania, where the prevalence of screening among women of reproductive age stands at an unacceptable range, highlighting a concerning issue in early detection efforts. Comparatively, Namibia reports a much higher screening prevalence of 24.5%, while Ivory Coast matches Tanzania’s low figures of 5.2%.29 These variations can be attributed to differences in healthcare infrastructure, societal perceptions of women’s health and the availability of screening programmes. This low prevalence could be influenced by factors such as the prolonged average time between initial screening and diagnostic results, which is 42 days for mammography, 20 days for USS and 18 days for fine-needle aspiration cytology in Tanzania.9 12 In Ghana, it is only 18.4%,30 while in South Africa, previous data indicate that only 13.4% of women aged 30 and above participated in mammography screening, emphasising a possible correlation between awareness and screening uptake.31 Thus, the low screening prevalence in Tanzania is a stark indicator of a pressing public health crisis, necessitating significant improvements in access to healthcare and awareness initiatives.
The analysis of variation in breast cancer screening rates across communities reveals significant insights into the distribution of screening practices and the factors influencing them. One study focused on geographic disparities in breast cancer screening and highlighted community-level variables as critical factors.31 In contrast, the current analysis showed that the greatest variance in screening was due to differences within clusters, suggesting a strong relationship driven by localised factors that could include healthcare access and socioeconomic conditions, as seen in previous literature. Moreover, the ICC values from models I and II point to a smaller contribution of individual differences compared with the comprehensive findings of another study that indicated a considerable individual effect,7 29 31 emphasising the importance of accounting for community-level factors in understanding screening disparities. The models reflected the nuanced impacts of individual and community characteristics, similar to the findings presented, where multilevel analyses showed modest increases in screening likelihood when community factors were considered.7 31 Overall, these findings underscore the necessity for further research into both individual and community-level drivers of breast cancer screening to develop targeted interventions that enhance screening rates effectively across diverse populations.
The findings indicate that the chances of breast cancer screening increase significantly with age, with advanced age having higher odds of screening compared with younger women. This finding aligns with other studies conducted in SSA, which show that older women are more likely to engage in breast cancer screening due to increased awareness and perceived risk of developing the disease.32 As women age, they often accumulate more knowledge about health issues, including breast cancer, through various sources such as healthcare encounters, public health campaigns and discussions within their social networks. A study done in Kenya indicated similar trends, with older women exhibiting higher screening rates as they perceive themselves at greater risk of cancer.33 Additionally, older women may have fewer barriers to accessing healthcare, such as greater financial independence, more established relationships with healthcare providers and increased comfort with medical procedures.32 It is essential to target educational programmes specifically for younger women to raise awareness about breast cancer and the importance of regular screening. Healthcare systems could implement school-based or community programmes that emphasise self-examination and early detection strategies for breast cancer among younger demographics.
Educational attainment significantly influences breast cancer screening behaviours. Women with primary education had a higher likelihood of screening compared with those with no formal education, and this likelihood increased even further for women with secondary or higher education. Different studies support these findings, noting the role of education in improving health-seeking behaviours and knowledge about breast cancer screening.34 35 Individuals with more education are generally more exposed to health information, possess enhanced critical thinking skills and have a greater capacity to understand complex health messages regarding breast cancer risks and the benefits of early detection. Studies also reported women in Malaysia36 and Uganda with higher educational levels consistently reported better understanding and utilisation of breast cancer screening services.37 Strategies should be adopted to enhance educational initiatives, promoting health literacy regarding breast cancer and screening programmes in both formal and informal educational settings. This can be achieved through collaboration with local educational institutions to develop tailored health education curricula. Community education and sensitisation were reported to be highly effective in Ghana.38
The multivariable analysis confirmed that working women had a higher likelihood of breast cancer screening compared with those who were not working. Employment often provides access to health insurance and promotes awareness through work-based health programmes. This aligns with findings in various studies indicating that employed women engage more with health services, as supported by several studies.15 33 The consistent exposure to health information and the structured environment of a workplace can foster a greater sense of health responsibility and proactive engagement with health services among employed women. Employers should implement workplace health initiatives that provide information on breast cancer, facilitate access to screening programmes, and possibly offer leave days dedicated to health checks, thus supporting women in their health-seeking behaviours.
Similarly, women exposed to media have higher odds of breast cancer screening compared with those without such exposure. This linkage underscores the power of media campaigns in raising awareness, which was supported by various studies.39,41 This suggests that sustained, broad-reaching public health messaging through television and radio, potentially featuring community leaders or local celebrities, can be highly effective in encouraging screening participation by informing women and potentially mitigating cultural stigmas or misconceptions. Stakeholders also should increase public health messaging via television and radio, leveraging community leaders and local celebrities to promote breast cancer awareness and screening. Ensure that promotional materials address cultural myths and misconceptions that may prevent women from seeking screening.
The analysis revealed that women living in high-poverty communities were less likely to engage in breast cancer screening compared with those in wealthier settings. This corresponds to findings in various contexts, including studies showing that socioeconomic inequalities directly affect healthcare access and breast cancer screening uptake.33 42 This reduced engagement can be attributed to a confluence of factors, including financial barriers, limited health literacy and a lack of access to health insurance, which collectively impede access to necessary screening services.
Similarly, women in urban settings demonstrated significantly higher breast cancer screening rates compared with those in rural areas, who faced barriers like lack of facilities and transport issues.43 The geographical dispersion of rural populations can lead to longer travel times and distances to screening centres, further exacerbating the issue of access. To enhance screening uptake, efforts must focus on improving healthcare infrastructure in rural areas and addressing socioeconomic disparities. As seen in India, providing mobile screening units and community-based health workers in rural areas can help bridge this gap, ensuring that women from low-resource settings have greater access to screening opportunities.41 The observed lower breast cancer screening rates in the Western zone, in contrast to the Northern, Southern and Lake zones, are associated with geographic and socioeconomic disparities likely to play a significant role in access to healthcare facilities and reduced travel times to mammography centres.43 The Western zone is facing a shortage of specialised hospitals for breast cancer diagnostic procedures.9 These findings show the need for a comprehensive strategy which aims to overcome the current observed geographic barrier.
Strengths and limitations
This study has several strengths. First, it uses a large sample size based on nationally representative, weighted data. Second, a multilevel approach was applied to account for the hierarchical structure of the DHS data, ensuring more reliable SEs and estimates. Third, using national survey data provides valuable insights for policymakers in designing and implementing effective breast cancer screening interventions. However, there are some limitations. Since the DHS survey relied on women’s self-reports, recall and social desirability biases may have influenced the results. Additionally, the cross-sectional design of the survey prevents establishing temporal relationships between breast cancer screening and the independent variables. Finally, because the data were secondary, variables such as women’s knowledge and attitudes towards breast cancer and screening could not be included in the analysis.
Recommendations for clinical practice
To improve breast cancer screening rates among women of reproductive age in Tanzania, it is essential to implement targeted interventions that address the various factors identified in the study. Programmes should prioritise educational outreach specifically aimed at younger women to raise awareness about breast cancer and the importance of regular screening. This could be achieved through school-based initiatives or community health programmes that emphasise self-examination and early detection strategies. Additionally, collaboration with local educational institutions is vital for developing health literacy curricula that promote an understanding of screening services and their significance. Furthermore, enhancing access to screening through workplace health initiatives for employed women, such as providing information on breast cancer and facilitating screening opportunities, should be a priority. Public health campaigns using media to raise awareness, dispel cultural myths and highlight the importance of screening can significantly enhance community engagement. Lastly, increasing healthcare infrastructure in rural areas and addressing socioeconomic disparities by providing mobile screening units and using community health workers are crucial steps to ensure equitable access to breast cancer screening services across all demographics.
Conclusions
The study highlights a critical need for enhanced efforts in breast cancer screening among women of reproductive age in Tanzania, where current participation rates remain disconcertingly low. The multifactorial nature of screening behaviours, influenced by age, education, employment status and socioeconomic conditions, underscores the complexity of addressing this public health challenge. Implementing targeted educational programmes, improving healthcare access and leveraging community resources can increase awareness and utilisation of screening services. As demonstrated by the evidence from various studies and contexts, the commitment to dismantling barriers to screening is essential for ensuring early detection and better health outcomes for women at risk of breast cancer in Tanzania. Comprehensive and collaborative approaches will be vital in achieving substantial improvements in screening rates and ultimately combating the rising breast cancer burden.
Acknowledgements
We thank the DHS programme for making the data available for this study.
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Data availability free text: The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com.
Patient consent for publication: Not applicable.
Ethics approval: Not applicable.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
Data are available in a public, open access repository.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available in a public, open access repository.