Abstract P4-07-46: The Out-of-Pocket Cost of Breast Cancer Care in Nigeria: A Prospective Analysis
Notice bibliographique
Résumé
Abstract Introduction A major barrier to timely breast cancer diagnosis and care in Nigeria is attributable to the out-of-pocket cost of accessing healthcare services, despite the presence of a National Health Insurance Scheme (NHIS). Excessive out-of-pocket payments are often associated with a catastrophic health care expenditure (CHE). Despite the rising incidence of breast cancer in Nigeria, there is a paucity of economic data on the cost of care and the impact healthcare expenditure may have on a household. This study provides a comprehensive, prospective analysis of out-of-pocket spending for breast cancer care at a single tertiary care institution in South West Nigeria. Methods Consecutive patients undergoing curative intent surgery for a new diagnosis of breast cancer at Obafemi Awolowo University Teaching Hospital (OAUTH) between August 2019-April 2022 were approached for enrolment. A novel, context specific questionnaire was developed for this study and administered by trained personnel. The questionnaire was delivered to patients and caregivers during hospital admission and again during six-month follow-up. Participants were asked to estimate monthly household income and expenditures. Out-of-pocket direct and indirect expenses for breast cancer diagnosis and care were elicited. Where feasible, hospital accounting records and individual receipts were used to minimize recall bias. Sequelae of the out-of-pocket costs were also elicited, such as the use of debt financing and important forgone expenditures, such as childhood education. Capacity-to-pay was calculated for each household from the provided data as the sum of annual non-food expenditures. A CHE was defined as an aggregate healthcare expenditure that exceeded 40% of a household’s capacity-to-pay. All monetary figures were collected in the local currency (Naira) and converted to USD using the Nigerian Central Bank conversion rate of 415.83N to 1USD. Research ethics board approval was obtained for this study from OAUTH. Results Data were collected from 57 eligible patients with a mean age of 49.8 years (SD 12). The median household size was five (range 1-10) and the majority (75.4%) had completed at least secondary education. Seventy four percent (73.6%) of patients had ≥ Stage III disease at presentation and 89.5% received systemic chemotherapy. Only seven percent (4/received adjuvant radiotherapy. The mean annual capacity-to-pay for the cohort was $2,840.8 ($2,913.6). The mean cost of care, including direct and indirect expenditure was $3,379.7 (SD $3032.2). Excluding indirect costs, such as the cost of travel and self-reported lost income, the mean cost of direct expenditures associated with diagnosis and treatment was $1,705.3 (SD $1,236.6). Out of the 57 patients enrolled in the study 52 (91.2%) experienced a CHE as a result of their breast cancer treatment. As a result, 56% of households had to borrow money and seven percent withdrew children from school. Sixty-three percent of patients had no form of health insurance. Conclusions Over 90% of breast cancer patients at a tertiary care facility in Nigeria experience a CHE as a result of out-of-pocket costs associated with accessing care. This limits access to costly evidence-based adjuncts (i.e. radiotherapy) and has a negative impact on the wellbeing of the broader household. There is a need for national and global initiatives to ensure financial protection from the cost of breast cancer care. Citation Format: Funmilola Wuraola, Chloe Blackman, Israel Adeyemi Owoade, Adeoluwa Oluwaseyi Adeleye, Peter Kingham, Olusegun Alatise, Gregory Knapp. The Out-of-Pocket Cost of Breast Cancer Care in Nigeria: A Prospective Analysis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-07-46.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».