Implementing Patient-Directed Cancer Education Materials Across Nigeria

Abstract

Purpose: As access to cancer care expands in low-income countries, developing tools to educate patients is paramount. We took a picture booklet, which was initially developed by the nonprofit Global Oncology for Malawi and Rwanda, and adapted it for use in Nigeria. The primary goal was to assess acceptability and provide education. The secondary goals were (1) to describe the collaboration, (2) to assess knowledge gained from the intervention, (3) to assess patient understanding of their therapy intent, and (4) to explore patient's experiences via qualitative analysis.

Methods: We piloted the original English booklet at a single site and requested feedback from patients and providers. The booklet was updated; translated into Hausa, Yoruba, Igbo, and Pidgin English; and used at three additional sites. For the three-site cohort, we collected basic demographics, pretest and post-test assessing content in the booklet, and performed a qualitative analysis.

Results: The original booklet was widely acceptable and recommended by patients at site one (n = 31) and by providers (N = 26) representing all four sites. In the three-site cohort (n = 103), 94% of patients recommended the booklet. An immediate post-test focusing on when patients should present to care showed a statistically significant improvement in one of the seven questions. Fifty-one percent of the patients (n = 103) knew their treatment intent (curative v palliative). Qualitative analysis highlighted that the patient's thoughts on cancer are dominated by negative associations, although curability and modern therapy are also frequently cited.

Conclusion: We adapted an educational booklet to a novel context and had it delivered by local partners. The booklet was widely recommended to future patients. The booklet had an impact on patient's knowledge of cancer treatment, potentially allowing for decreased abandonment.

FIG 1

Map with participating clinical sites in Nigeria. Abuja (the capital of Nigeria) is where the pilot occurred and where the main CHAI (Clinton Health Access Initiative) office is located. The subsequent study sites are also indicated on this map, as follows: ABUTH, Ahmadu Bello University Teaching Hospital in Zaria; UNTH, University of Nigeria Teaching Hospital Enugu in Enugu; LUTH, Lagos University Teaching Hospital in Lagos.

FIG 2

Ethnic representation by clinic site. The figure shows self-reporting ethnic group representation by treatment site. ^aMultiple includes the following ethnic groups of which there were ≤ 3 representatives: Afo, Atyap, Bajju, Baruba, Bata, Bini, Boki, Dakarkari, Delta, Edo, Fulani, Fulfude, Gwadira, Idoma, Igala, Ikulu, Ikwerre, Jaba, Jinye, Jukun, Kanuri, Kare-kare, Michika, Nizun, Nupe, Tangale, Tiv, Ubibio, and Yungur. ABUTH, Ahmadu Bello University Teaching Hospital in Zaria; LUTH, Lagos University Teaching Hospital in Lagos; UNTH, University of Nigeria Teaching Hospital Enugu in Enugu.

FIG 3

Education level by site. The figure shows the available self-reported education levels of the patients at each site. It is unclear if the unknown was an attempt to record no formal education or a data entry error. The category of diploma includes the following recorded responses: diploma, Nigeria certificate in education (NCE), higher national diploma (HND), and ordinary national diploma (OND). The category of religious degrees is heterogeneous and may represent training after completion of secondary school or a religious education in lieu of another primary and secondary education. For the tertiary+ category, about half of the category had a university degree and the other half a masters. One patient at UNTH had a PhD. ABUTH, Ahmadu Bello University Teaching Hospital in Zaria; LUTH, Lagos University Teaching Hospital in Lagos; UNTH, University of Nigeria Teaching Hospital Enugu in Enugu.

FIG 4

Word cloud of the most frequent words recorded for thoughts on cancer. We took the recorded responses from a prompt “What are your thoughts on cancer?” and examined word frequency from the pooled responses. The largest words in the cloud are the frequent ones: deadly (41), body (28), curable (23), thought (19), think (15), cured (14), bad (13), and sickness (13).