Out‐of‐pocket payments by end‐stage kidney disease patients on regular hemodialysis: Cost of illness analysis, experience from Sudan

Abstract

Introduction In Sudan, the number of end‐stage kidney disease (ESKD) patients receiving hemodialysis (HD) is growing. Patients and their families incur a high out‐of‐pocket expenditure (OOPE), given that HD treatment is expensive. There are limited data about OOP spending on HD in the country. This study aims to explore patients' OOP expense on direct medical and nonmedical goods and services and to which extent they can be predicted from sociodemographic characteristics, health insurance status, comorbidity, and accommodation change. Methods This is descriptive a cross‐sectional study conducted in Ibn Sina Hospital. One hundred and thirty patients undergo regular HD were randomly selected. Findings Among the study participants (130), the median of the overall total OOP (direct medical and direct nonmedical) spending per patients per year was found to be US$ 3859.1 (interquartile range [IQR]: 2298.1–6261.1). As for the medians OOP expenditure on direct medical and nonmedical costs, they were found to be US$ 2327.6 (IQR: 1421.5–3804.8) and US$ 1096 (IQR: 715.2–2345.2), respectively. The direct medical expenditure (355,586 US$) accounted for 60% of the overall total expenses. Discussion Medications and investigations were the primary drivers of direct medical spending. Higher OOPE rates were found among those with one or more of these factors; uninsured patients, patient with comorbidity, female gender, and over 40 years aged. The multivariate analysis showed that the significant predictors of direct medical expenditure were health insurance and comorbid conditions, where as the predictors for direct nonmedical expenditure were accommodation change and gender. This study results in a better understanding of OOP spending on direct medical and nonmedical services and its associated predictors among HD patients within the context of Sudan. Further research is needed in this area.

Gastos do próprio bolso de usuários em hemodiálise no sul do Rio Grande do Sul

“…7,[9][10] Aponta-se ainda que com o avançar da idade aumentam as despesas, pois mais comorbidades surgem. 11 Os entrevistados eram 59% masculino e 41% feminino. No Brasil, a prevalência de 58% 6 e 57% 16 de pacientes do sexo masculino em TRS, corroborando com os dados deste estudo.…”

“…No Brasil, a prevalência de 58% 6 e 57% 16 de pacientes do sexo masculino em TRS, corroborando com os dados deste estudo. Em outros países estudos [7][8][9][10][11] também demonstraram mais usuários em TRS do sexo masculino. O sexo não interfere na progressão da doença, entretanto pode ser afetado pela tendência das mulheres buscarem atendimento com mais frequência.…”

“…Esse gasto elevado influência na adesão ou no abandono ao tratamento. [7][8][9][10][11] Sendo assim, a motivação de escolha desse tema é ampliar a visibilidade dos pacientes em DRC em HD e suas necessidades de acesso aos serviços de terapia renal substitutiva (STRS). 12 De forma que o objetivo deste estudo é relatar os gastos do próprio bolso das pessoas com doença renal crônica em hemodiálise em seis serviços de terapia de substituição renal da metade sul do Rio Grande do Sul, Brasil.…”

Drug costs in context: assessing drug costs in cost-of-illness analyses

“…Yousif et al used this approach to assess out-of-pocket payments by Sudanese patients with end-stage kidney disease on regular hemodialysis. 24 Adane et al employed a cross-sectional design with a semi-structured questionnaire given to 442 adult patients with hypertension from a clinic in Ethiopia. 25 …”

“…Yousif et al used this approach to assess out-of-pocket payments by Sudanese patients with endstage kidney disease on regular hemodialysis. 24 Adane et al employed a cross-sectional design with a semi-structured questionnaire given to 442 adult patients with hypertension from a clinic in Ethiopia. 25 A challenge with describing a COI study using epidemiological terms in the methods may create confusion for reviewers expecting to see exposed versus unexposed groups within a cohort compared when many of these studies are analysed as a descriptive analysis of the included individuals with no comparator.…”