Access to Cancer Medicines in Kenya: A Scoping Review of Costs, Financial Toxicity, Quality of Life, and Policy Impacts

Access to Cancer Medicines in Kenya: A Scoping Review of Costs, Financial Toxicity, Quality of Life, and Policy Impacts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Access to Cancer Medicines in Kenya: A Scoping Review of Costs, Financial Toxicity, Quality of Life, and Policy Impacts JAMES ONYURO OKETCH, DANIEL OGUNGU ONGURU, STEPHEN ASITO AMOLLO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8294040/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer, Kenya’s third leading cause of death, imposes severe health and economic burdens, driven by high costs and limited availability of cancer medicines. However, the full evidence landscape remains unclear. In this scoping review, we synthesize evidence on cancer medicine access, financial toxicity (FT), Quality of life (QoL), and policy impacts for top five cancers in Kenya, to inform cancer treatment across health systems. Methods Following PRISMA-ScR guidelines, we searched PubMed, African Journals Online, Google Scholar, and grey literature (January 2018–May 2025) for studies on Kenyan adults with breast, cervical, prostate, esophageal, or colorectal cancers. Eligible studies on medicine access, FT, QoL, or National/Social Health Authority (NHIF/SHA) outcomes were screened using Rayyan software. Data was extracted into a piloted Excel form, and synthesized descriptively and thematically. Results Of 60 included studies, cancer medicines cost 3.15–162.42 days of minimum wage per chemotherapy cycle, exceeding WHO threshold. Availability was less than 50% in public facilities, with procurement delays (4–8 months) causing stockouts. Treatment costs for stage I–III cancers ranged from USD 1,340–1,542 in public versus 10,915–11,862 in private facilities. FT affected 20–54% of households, with over half (53.8%) abandoning treatment due to costs. QoL (addressed in 9/60 studies) scores (median 41.99–53) were poor, linked to FT and late-stage diagnosis (71% stage III/IV). Insurance coverage was partial, with SHA’s KES 400,000 cap showing potential to reduce costs despite underfunding and limited adoption of expert advice. Most studies lacked pricing (47/60) and catastrophic health expenditure data (53/60). Conclusions High costs, low availability, and inadequate insurance contribute to FT and poor QoL suggesting need for price regulation, expanded SHA coverage and longitudinal economic studies to address evidence gaps. Oncology Cancer medicines financial toxicity quality of life out of pocket costs health policy Kenya Figures Figure 1 BACKGROUND Cancer presents a significant health and economic toll globally with 19.9 million new cases and 9.7 million deaths in 2022. In Kenya, it ranks as the third leading cause of death, with 44,726 new cases and 29,317 deaths in 2022, primarily from breast, cervical, prostate, colorectal, and esophageal cancers. 1 Like many Low- and middle-income countries (LMICs), Kenya faces unique challenges in delivering affordable and timely cancer treatment. Patients continue to face high out-of-pocket (OOP) costs, limited availability, and inadequate insurance coverage for cancer medicines. 2 – 4 These barriers contribute to financial toxicity (FT), both the objective burden of out of pocket (OOP) treatment costs and subjective distress. In LMICs, 13–68% of patients face catastrophic health expenditures (CHE), spending over 40% of non-food income on treatment, which significantly undermines quality of life (QoL) through financial strain and treatment delays. 5 , 6 Access to affordable and available cancer medicines is critical for treatment adherence and improved QoL particularly for breast and cervical cancers, which respond well to therapy when detected at early stages and treated early. The World Health Organization’s access to medicines framework emphasizes four pillars, availability, affordability, accessibility, and quality as essential for equitable health systems. 7 In Kenya, these pillars are compromised. Medicine availability averages less than 50% in public facilities, affordability is hindered by high costs, limited accessibility, and quality issues arise from inconsistent supply chains. 3 , 8 – 10 The World Health Organization recommends countries to finance their health systems at 6% of gross domestic product (GDP). Kenya’s health financing, at 3.5% of GDP, falls below this threshold, straining public health systems and deepening inequities in cancer care. 10 , 11 Recent shifts in health policies and frameworks, in particular, the transition from the National Hospital Insurance Fund (NHIF) to the Social Health Authority (SHA) in 2023, aim to advance universal health coverage (UHC) including reducing treatment inequities. However, structural and contextual barriers including, weak political commitment and limited adoption of expert advice disadvantage effective implementation. 10 , 12 The National Cancer Control Strategy 2023–2027, 13 recognizes these gaps but struggles with execution. This is in part, due to sparse patient level data and fragmented nature of existing evidence spanning quantitative costs, patient experiences, and policy analyses. In this review, we synthesize evidence on access to essential cancer medicines, financial toxicity, quality of life, and health policy effectiveness among Kenyan adults with breast, cervical, prostate, colorectal, or esophageal cancers. Unlike previous fragmented literature, this review uniquely consolidates cost, quality of life, and health financing data to demonstrate how policy implementation and economic inequities jointly shape patient access to cancer medicines in Kenya. The evidence aims to inform changes in policy at health system level and government levels particularly under the Social Health Authority (2023). Furthermore, this review seeks to guide future studies and decisions to enhance medicine affordability, availability while reducing financial burdens in Kenya and other LMICs. METHODS This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis; extension for Scoping Reviews (PRISMA-ScR) guidelines, adhering to the methodological framework of Arksey & O’Malley (2005), refined by Levac et al. (2010). 14,15 The protocol was registered with the Open Science Framework (contact [email protected] for draft protocol). The review followed five stages that guide how scoping reviews are done: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data, and 5) collating, summarizing, and reporting results. Search Strategy and Selection Criteria The review addressed the primary research question “What evidence exists on access to essential cancer medicines, financial toxicity, quality of life, and health policy effectiveness among adult cancer patients in Kenya?” For the purpose of this scoping review, out-of-pocket (OOP) costs refer to direct payments for care such as medicines, diagnostics, travel; Catastrophic health expenditure (CHE) is healthcare spending exceeding 40% of household non-food expenditure; and financial toxicity encompasses OOP costs, income loss, and coping strategies such as borrowing, and asset sales. The review focused on top five most prevalent cancers in Kenya, breast, cervical, prostate, colorectal, and esophageal cancers to align with Kenya’s health policy priorities. Searches were conducted on 31 May 2025 in PubMed, African Journals Online (AJOL), and Google Scholar, limited to English publications from January 2018 to May 2025. A comprehensive search strategy was used, combining Medical Subject Headings (MeSH) and keywords tailored to each database. Key terms included “cancer,” “Kenya,” “essential medicines,” “financial toxicity,” “financial burden,” “economic hardship,” “financial distress,” “catastrophic health expenditure,” “quality of life,” “NHIF,” “SHIF,” and “UHC,” combined using Boolean operators. (Table 1: supplementary shows a search strategy used in PubMed) Grey literature was sourced from WHO, Kenya Ministry of Health, NHIF/SHIF, and Kenya National Cancer Control Program websites, with credibility assessed by institutional authority and author expertise. Google Scholar searches were stopped after three consecutive pages of irrelevant results. Additional studies were identified from reference lists scanning of included studies through manual searches and institutional repositories (University of Nairobi Digital Repository). Embase, Medline, Scopus, and Web of Science were excluded due to access constraints, mitigated by AJOL (which indexes multiple journals in Africa, enhancing coverage of regionally relevant open access publications) and comprehensive gray literature searches. To assess eligibility of studies, the scoping review followed the Population, Concept, and Context (PCC) mnemonic (Table 2: Supplementary). Studies were included if published between January 2018 and May 2025, in English, and included primary research or policy evaluations. Exclusions included non-Kenyan studies, editorials, and abstracts without full text. All citations resulting from the searches were imported into web-based software platform Rayyan. After removing duplicated citations, two reviewers independently screened titles and abstracts, followed by full texts review and any disagreements on final inclusion reached by consensus. Data Analysis Data was extracted from selected articles using an Excel form, piloted on six included studies (two quantitative, two qualitative, and two mixed-methods) and refined accordingly. The data charting form captured study characteristics (author, year, design, location, publication type), population (cancer type, demographics, sample size), medicine access (prices, stockout rates, affordability), financial toxicity (OOP costs, CHE, coping strategies), quality of life (scores, qualitative themes), and policy outcomes (NHIF/SHIF coverage, procurement, barriers) and main findings (Table 3: supplementary). Authors were contacted for clarification where necessary such as cost data, with two responses received. Medicine availability was stratified by data source (national surveys, single-facility studies, supplier/agency assessments) using the Kenya Essential Medicines List (KEML) and WHO/HAI methodology, and data gaps and methodological differences documented. We used the Mixed Methods Appraisal Tool (MMAT v. 2018) to assess the quality of studies given the different types of study designs included in the scoping review. The tool included methodological quality of quantitative studies (sampling, measurement, bias), qualitative (approach, coherence), mixed-methods (integration), and policy documents (clarity, relevance). Quality ratings informed interpretation but did not affect inclusion, as the scoping review’s objective was to map all relevant evidence. Results are synthesized descriptively with numerical summaries of sample sizes and cancer types; and thematically by objectives (medicine access, financial toxicity, quality of life, policy effectiveness), to show the extent and nature of evidence. Any other emerging themes relevant to the research question are reported. Where appropriate we visualized findings using tables and graphs. Finally, the implications of findings, the broader context and recommendations for health system improvement and future studies are presented. RESULTS Following PRISMA-ScR guidelines, 393 articles were identified through searches in PubMed, African Journals Online (AJOL), Google Scholar, and grey literature (WHO, Kenya Ministry of Health, NHIF/SHIF, and Kenya National Cancer Control Programme) conducted up to May 31, 2025. After removing 157 duplicates, 236 records were screened by title/abstract, with 152 excluded due to non-Kenyan settings (n = 80), non-cancer-specific focus (n = 42), editorials (n = 20), or abstracts without full text (n = 10). Full-text review of 84 records resulted in 60 included studies (42 journal articles, 8 government reports, 7 policy briefs, 2 regulatory documents, 1 thesis). Figure 1 presents the PRISMA-ScR flow diagram. The majority of the included articles were health policy related (15, 25%), followed by those touching on multiple objectives (14, 23.3%), medicine access (12, 20%), financial toxicity (10, 16.7%), and lastly quality of life (9,15%). (Fig. 2: supplementary). The quality of studies was assessed using the Mixed Methods Appraisal Tool (MMAT version 2018). The assessment excluded 13 non-research policy/guideline documents, hence included 47 studies. All had clear research questions and data addressing the research questions. Majority were quantitative descriptive studies (n = 31) and they generally performed well across MMAT criteria. All had relevant sampling strategy (100%), appropriate measurements (100%), and appropriate statistical methods (94%). However, representativeness of the sample (68%) and low nonresponse bias (78%) were somewhat lower. Qualitative studies (n = 19) showed moderate adherence to MMAT criteria, with 74% meeting standards for appropriate qualitative approach. There were 14 mixed-methods studies, which had an adequate rationale for mixed methods (71%) and effective integration of components (86%), though adequate interpretation of integrated outputs (64%) and addressing divergences between quantitative and qualitative results (57%) were lower. Table 4 . Table 4 MMAT Table for Cancer Medicines, FT, QoL, and Health Policy Studies SCREENING QUESTIONS Yes No Can’t Tell Total S1. Are there clear research questions? 47 0 0 47 S2. Do the collected data allow to address the research questions? 47 0 0 47 1. QUALITATIVE STUDIES 1.1. Is the qualitative approach appropriate to answer the research question? 14 4 1 19 1.2. Are the qualitative data collection methods adequate to address the research question? 14 4 1 19 1.3. Are the findings adequately derived from the data? 14 4 1 19 1.4. Is the interpretation of results sufficiently substantiated by data? 15 4 0 19 1.5. Is there coherence between qualitative data sources, collection, analysis and interpretation? 14 4 1 19 4. QUANTITATIVE DESCRIPTIVE STUDIES 4.1. Is the sampling strategy relevant to address the research question? 31 0 0 31 4.2. Is the sample representative of the target population? 21 6 4 31 4.3. Are the measurements appropriate? 31 0 0 31 4.4. Is the risk of nonresponse bias low? 25 6 2 32 4.5. Is the statistical analysis appropriate to answer the research question? 30 2 0 32 5. MIXED METHODS STUDIES 5.1. Is there an adequate rationale for using a mixed methods design to address the research question? 10 2 2 14 5.2. Are the different components of the study effectively integrated to answer the research question? 12 2 0 14 5.3. Are the outputs of the integration of qualitative and quantitative components adequately interpreted? 9 5 0 14 5.4. Are divergences and inconsistencies between quantitative and qualitative results adequately addressed? 8 6 0 14 5.5. Do the different components of the study adhere to the quality criteria of each tradition of the methods involved? 11 4 0 15 Most studies were conducted in urban settings (Nairobi 28, 47%, Eldoret 10, 17%, Kisumu 8, 13%, Mombasa 6, 10%), with only 6/60 (10%) reporting rural data. Studies primarily focused on breast (38, 63%), cervical (30, 50%), prostate (16, 27%), esophageal (11, 18%), and colorectal cancers (9, 15%), with some addressing multiple cancers. Sample sizes ranged from 2 to 37,500 (median 151, IQR 77–334), with participants predominantly female (72%), aged 40–68.5 years, and of low-to-middle socioeconomic status (77% unemployed, household income < KES 5,000/month). Table 5 summarizes key study characteristics. Table 5 Study Characteristics Author(s) (Year) Location Study Design Cancer Type(s) Sample Size Outcomes Measured Summary of Key Findings 1. 16 Kisumu (JOOTRH) Cross-sectional (Mixed-methods) Cervical 334 Financial toxicity, QoL, NHIF impact Significant financial toxicity, very low insurance uptake (9% on NHIF), Fund covers inpatient services only, high OOP on medications, transport, diagnostics 2. 17 Nairobi (KNH) Cross-sectional descriptive Multiple (not specified) 389 QoL (psychological distress, depression, functioning) Increasing cancer stage correlated with higher disability (p = 0.001), depression(p = 0.038) and reduced functioning, high prevalence of mental disorders 3. 18 Nairobi, Eldoret, Mombasa Needs assessment survey + intervention Metastatic Breast Cancer 114 QoL, unmet needs, intervention uptake Psychological (63%), physical support (60.5%) and healthcare system (55.4%) needs highest unmet needs. Better QoL associated with urban residence, internet access and stable disease. Low breast cancer knowledge 4. 19 Nairobi (KNH) Cross-sectional Cervical 151 Adverse events prevalence High prevalence (100%) of adverse events, ulcerated sores (52.8%) dysuria (7.5%) thrombocytopenia (5.6%) mostly probable (80.1%) per Naranjo scale, predominantly in radiotherapy patients (80.8%) 5. 20 Nairobi (KNH) Cross-sectional Cervical 103 QoL, financial difficulties 69% poor HRQoL (mean global health score 41.99 SD = 31.4); early-stage disease patients 7.3 times more likely to have good HRQoL (AOR = 7.3 95% CI = 2.4–21.7 p = 0.000); patients with no comorbidities 3.1 times more likely to have good HRQoL (COR = 3.1 95% CI = 1.1–9.1 p = 0.037). Advanced disease stage and comorbidities predict poor HRQoL 6. 21 Nairobi (KNH) Retrospective cross-sectional Cervical 100 Treatment adverse effects (nephrotoxicity) 45% prevalence of cisplatin-induced nephrotoxicity (36% grade 1, 9% grade 2). Comorbidities (AOR = 8.4 p = 0.02) hypertension (AOR = 3.4 p = 0.02) and ≥ 3 cycles (AOR = 4.5 p = 0.027) significant risk factors 7. 22 Nairobi (KNH) Prospective cohort Breast, Prostate, Lymphoma 231 QoL, mortality, remission rates Mortality rates: 3% (breast), 4.9% (prostate), 10% (lymphoma); Most patients had partial remission (45.5% breast, 45.1% prostate, 42% lymphoma); Good overall health-related QoL (64% breast, 85% prostate, 58% lymphoma); Predictors of mortality: age > 60, comorbidities, distant metastasis, advanced stage 8. 4 Multiple regions (Nairobi, Mombasa, countrywide) Cross-sectional Breast 800 Financial toxicity, access barriers Cost and transportation major barriers for both cohorts; 53.8% with breast cancer forwent care due to cost; 91.2% reported financial impact after cancer diagnosis, 44.9% reported inadequate insurance reimbursement for medical costs 9. 23 9 counties (Kakamega, Kisumu, Nakuru, Uasin Gishu, Baringo, Nairobi, Nyeri, Machakos, Mombasa) Descriptive survey Breast, Cervical, Esophageal, Prostate, Lymphoma, Others 1,048 Access barriers, late-stage diagnosis Most prevalent cancers: breast and cervical (women), esophageal and prostate (men); 80% cases diagnosed late at advanced stages; limited screening/treatment capacity, especially in rural areas; private facilities offer more services/ mostly in Nairobi; Private facilities offer more services than public 10. 24 Eldoret (MTRH) Cross-sectional descriptive Breast 79 QoL (depression prevalence) 59.4% prevalence of depression; late-stage cancer (OR: 1.61, p = 0.319), employment (OR: 3.7, p = 0.058), and chemotherapy (neoadjuvant OR: 9.43, palliative OR: 9.5, p < 0.05) significantly associated with depression 11. 2 Kenya (nationwide) Comparative analysis (quantitative) Multiple (Breast, Cervical, Esophageal, Colorectal, Prostate, others) N/A Medicine access, affordability All WHO EML regimens unaffordable/ Generic cytotoxic affordable for governments; targeted therapies (e.g. Trastuzumab imatinib) unaffordable without 93–99% price reductions; no regimens affordable OOP 12. 25 Kenya (Nairobi) Cross-sectional (Mixed-methods: quantitative questionnaire and qualitative FGD) Breast and Cervical 157 (quantitative) + 10 (FGD) QoL, coping strategies High prevalence of psychological effects: anxiety (79%), negative body image (65.6%), low self-esteem (63.1%), loneliness (55.4%), sadness (51.6%). Effects aggravated by low income (< 20000 KSH/month, 73.2% of participants) and more chemotherapy sessions (r = 0.51); age (r=-0.300) and marital status (married, r=-0.389) associated with fewer effects. FGD themes include psychological stress (body image, loneliness, emotional changes, cognitive effects) and coping (prayers, finding reason to live). Impacts treatment adherence. 13. 26 Kisumu (JOOTRH) Cross-sectional descriptive Cervical 334 QoL, stage presentation Poor physical (60%) and functional (66%) well-being linked to late-stage presentation (Stage III/IV: 73%); fair overall QoL (57%); stage IV (54%) and III (19%) predominant; significant association between cancer stage and QoL (p < 0.0001) 14. 27 Nairobi, Nyeri Correlational Not specified (various cancers) 96 QoL (pain, weight loss, sleep) Low recovery outcomes: Mean 47.0 (SD 9.465); Pain: High pain (32.9%); Weight: Loss (80.5%); Sleep: Poor (57.3%); QoL: Poor (56.1%) 15. 28 Nairobi (tertiary hospital) Cross-sectional Gynecological (Cervical, Endometrial, Ovarian, Vulvar, Vaginal) 108 QoL (sexual dysfunction, body image, social support) 85% sexual dysfunction; lubrication most affected (mean 0.91) aOR = 0.05) stage 3 (aOR = 9.81) low social support (aOR = 1.29) predict dysfunction 16. 11 Nationwide (Nairobi, Eldoret, Mombasa, Nakuru, Nyeri, Kisii) Policy reviews with qualitative components All cancers (emphasis on Cervical, Breast, Prostate) 14 Policy barriers, stakeholder roles Policies established legal and implementation frameworks; Gaps in financing, human resources, decentralization; Key informant survey identified barriers: high costs, stigma, poor communication, centralized services; Stakeholder analysis highlighted roles of government, NGOs, private sector, academia, media, international groups 17. 8 Nairobi (KNH) Cross-sectional cost-of-illness Cervical, Breast, Prostate, Esophageal, others 412 Financial toxicity, treatment costs High treatment costs (avg. KES 143132); medicines and inpatient admission are major cost drivers; higher costs in private sector 18. 29 Nairobi (KNH) Cross-sectional Genitourinary, Gastrointestinal, Head and Neck, Breast, Musculoskeletal, Lung 67 Treatment adverse effects (neuropathy) 83.6% prevalence of cisplatin-induced peripheral neuropathy (CIPN); 81% mild (grades 1–2); 3.6% grade 4; overweight/obese patients nearly all developed CIPN (not statistically significant) 19. 30 Eldoret (MTRH) Cross-sectional Breast, Prostate, Kaposi Sarcoma, Lung, Colon, Esophagus, Pancreas, Rectum 100 QoL, financial difficulties Global health/QOL score 53 ± 27; functional scores 51–68; symptom scores 12–79 20. 31 Nairobi (AKUHN) Qualitative (mixed-methods pilot) Breast 40 QoL, financial toxicity High prevalence of stress, anxiety, depression; negative impacts on mental health, QoL; financial burdens, spousal relationship strain 21. 32 Nairobi, Kisumu Qualitative (key informant interviews) Not applicable (health financing focus) 13 Policy effectiveness, financial toxicity Policy effectiveness, financial toxicity Unaffordable premiums and inadequate infrastructure hinder UHC; policy implementation gaps identified 22. 33 Rift Valley (provincial hospital) Observational with historical controls Cervical 2,193 Financial toxicity, care quality Barriers to cervical screening: poor access, lack of awareness, socio-cultural influences 23. 34 Nairobi County Cross-sectional mixed-methods Breast, Cervical, Colorectal Leukemia, Nasopharyngeal) 42 Financial toxicity, QoL, stigma High financial toxicity/discrimination, reduced QoL, stigma among patients 24. 35 Eldoret (MTRH, Alexandria Equra) Longitudinal Esophageal 59 QoL, treatment outcomes Specific QoL indicators identified as prognostic factors; baseline HRQoL mean 107.1 (compromised QoL); post-treatment improvement with chemotherapy + surgery (p = 0.04), deterioration with radiotherapy alone (p = 0.0092); baseline HRQoL significantly associated with post-treatment HRQoL (p = 0.0065). 25. 36 Nairobi, Mombasa Qualitative cross-sectional Not specified Not specified Financial toxicity, access barriers Financial burden, late-stage diagnosis, cultural taboos, low referral rates 26. 37 Nairobi Qualitative longitudinal Breast, Cervical 18 QoL, side effect management Patients silently endure post-treatment symptoms (fatigue, alopecia, skin and nail changes); need for culturally relevant education 27. 38 Nationwide Quantitative cross-sectional Cancer (unspecified), Hypertension, Diabetes 37,500 Financial toxicity, catastrophic spending CHE due to cancer significantly increases household poverty; education and urban locality reduce poverty 28. 39 12 Regional Cancer Centres + National Referral Hospital Descriptive (secondary data analysis) Not specified (various cancers) 321 Access, stage presentation, policy outcomes Low service availability, urban concentration, 70–80% late diagnoses, > 50% pediatric abandonment 29. 12 Kenya (Nationwide focus on NHIF) Retrospective policy analysis (mixed-methods: interviews and document analysis) Not applicable (focus on health financing, not specific to cancer) 21 interviews Policy effectiveness, equity implications, informal sector participation, inefficiencies in purchasing/payment, Low NHIF coverage, only 17% of Kenya’s population covered by SHI (as of 2023); 27% informal sector NHIF coverage; limited stakeholder engagement and expert advice adoption; political affiliations heavily influence policies; inefficiencies in purchasing/payment (e.g., slow reimbursements, misappropriations, favoritism); group schemes and penalties exacerbate inequity in access. 30. 40 Nairobi (KNH) Mixed-methods Breast 378 Financial toxicity, diagnosis/treatment delays Significant institutional delay in access to chemotherapy and radiotherapy due to healthcare system barriers 31. 41 Kisii Descriptive cross-sectional Breast, Cervical, Prostate, Leukemia, Others 120 QoL, social support Pain relief and psychosocial counseling predominant; significant association between cancer type, treatment, and QoL scores 32. 42 Kenya (national-level model) Budget impact analysis (Markov model) Cervical N/A Cost-effectiveness, QALYs Population-based cervical cancer treatment costs $ 531,100 over 20 years, compared to $ 55,398 for ad hoc screening; systematic approach optimizes resource utilization, reduces unnecessary testing, and lowers financial burden for patients and the healthcare system. 33. 43 Mombasa County Descriptive mixed-methods (FGDs, interviews, questionnaire) Breast 72 Financial toxicity, access barriers, QoL High cost of care; barriers include transportation, stigma, poor provider communication; poor QoL due to delayed/wrong diagnoses, surgical complications, equipment failures 34. 44 Eldoret Retrospective chart review HER2-Positive Breast 95 Medicine access, treatment completion Low availability of HER2-targeted therapies (e.g., trastuzumab); high treatment abandonment due to high costs; financial burden reduces QoL; need for policies to improve access to targeted therapies 35. 45 Kenya (Nairobi, Eldoret) Cost analysis (itemization cost approach) Breast, Cervical, Prostate N/A Financial toxicity, affordability Substantial variation in patient costs between the public and private sectors. High OOP costs for diagnostics/treatment/travel; cost of care major barrier 36. 46 Nairobi, Nakuru, Kisumu, Kakamega, Kilifi, Siaya Qualitative case study (FGDs, interviews) Breast, Prostate, Cervical, Esophageal, Colon, others 32 Financial toxicity, QoL, policy effectiveness Unaffordable premiums, inadequate infrastructure, delayed payments, fraud; need to harmonize benefit packages 37. 47 Kenya (nationwide) Critical literature review Not specific (NCDs including cancer) N/A Medicine availability, affordability Low medicine availability in primary facilities; urban bias in distribution 38. 48 Machakos Cross-sectional Breast, Cervical, Esophageal, Prostate, Kaposi Sarcoma 361 QoL, distress prevalence Pain (83.3%), problem with decision making about treatment (64.9%), fatigue (59.8%). Other issues (financial constraints and eating difficulties). High distress prevalence; lower QoL in psychological domain 39. 49 Nairobi (KNH) Cross-sectional Breast, Cervical, Gastrointestinal, Prostate, Others 361 QoL, distress prevalence High distress prevalence; negative impact on QoL 40. 3 Nairobi (pharmacies) Cross-sectional Multiple (Breast, Prostate, Others) N/A Medicine availability, affordability, pricing Low availability of morphine; high OOP for medicines, availability highest in hospitals, followed by suppliers, and finally supply agencies 41. 50 Kenya (national, two counties) Mixed methods (Embedded case study) Multiple (oncology included) 41 interviews, 51 FGD participants Financial toxicity, policy effectiveness Oncology services included in new packages but limited by infrastructure gaps; inequitable access due to pro-private facility distribution. Reforms aimed to expand coverage and reduce OOP but were hindered, requiring strategic alignment for UHC progress. 42. 51 Eldoret, Kisumu (MTRH, JOOTRH) Cross-sectional Cervical 218 QoL (anxiety, depression prevalence) High anxiety (80.3%) and depression (67%) prevalence; higher in 40–49 years (anxiety: 29.8% depression: 25.2%) primary education (anxiety: 46.8% depression: 42.2%) married (anxiety: 54.1% depression: 42.7%) and those with family support (anxiety: 44.5% depression: 36.2%); no significant associations (p > 0.05) 43. 52 Kenya Cross-sectional (NHA, KHHEUS analysis) Cancer (unspecified) 37,500 Financial toxicity, catastrophic spending CHE due to cancer significantly increases household poverty; education (OR=-0.2346 p = 0.012) and urban locality (OR=-2.2645 p = 0.000) reduce poverty; household size (OR = 0.0277 p = 0.001) increases poverty; gender (male OR=-0.0372 p = 0.374) not significant 44. 53 Nationwide Policy analysis All cancers N/A Financial toxicity, policy barriers Significant financial toxicity, OOP costs a major barrier to care; inadequate NHIF coverage limits evidence-based standards. Cancer medicine access hampered by stock-outs, complex procurement processes, and poor regulation of importation/quality/pricing; KEMSA's bulk procurement leverages economies of scale for better pricing, with calls for pooled mechanisms, local production, and Public Procurement Act amendments. Policy impacts include NHIF oncology package but with gaps in equity/efficiency; overall, aims for sustainable financing, better 45. 54 Nationwide Regulatory document All cancers N/A Oncology services scope Details tariffs for oncology services 46. 55 Nationwide Cross-sectional; Policy brief, (NHA, KHHEUS analysis) Non-communicable diseases (including cancers) Not specified Financial toxicity, policy coverage OOP share of CHE decreased from 23.9% (2015/16) to 19.9% (2020/21), but absolute OOP increased 20.7% from KSh 90B to 108B; per capita health expenditure rose 35% from KSh 4,914 to 6,640; government financing at 52.3%, SHI at 12.5%; insurance coverage 25.9% but inequitable (4.1% poorest vs. 57.2% richest); CHE incidence fell from 6.6% to 4.5%, impoverishment from 1.1% to 0.7%; SHI insufficient alone for UHC 47. 10 Nationwide Mixed methods (scoping review, interviews, FGDs) Breast, Cervical, Esophageal, Prostate, Colorectal N/A Financial toxicity, policy implementation Limited availability of essential cancer medicines, with only 44% of 24 tracer medicines in facilities; KEMSA's procurement faces delays (4–8 months), High OOP costs (19.9% of CHE, KSh 108B in 2020/21) drive financial toxicity; 23% of health financing from OOP, NHIF covers only 17% of the population, with low informal sector uptake (27%), inefficient claims processing hinder UHC; Poor QoL due to advanced-stage diagnoses (70–80% late-stage), inadequate palliative care, proposed hub-and-spoke model, Cancer Fund, and NCI-K operationalization aim to improve access; policies advocate for price regulation, pooled procurement, and increased financing, but face challenges from poor coordination, 48. 56 Nationwide Policy Brief All cancers (Cervical noted) N/A Financial toxicity, policy funding Cancer-related OOP expenditure constitutes 19.9% of CHE (KSh 108B in 2020/21), with NCD spending at KSh 57.8B; NHIF covers only 17% of the population, with low informal sector uptake (27%), limiting financial protection for cancer care 49. 57 Nationwide Cross-sectional survey (KHFA) with desk review All cancers (Cervical, Breast, Prostate, Colorectal) 2,896 facilities Medicine availability, access barriers Limited availability of essential cancer medicines; only 15% of facilities have morphine for palliative care, Chemotherapy restricted to 11 public hospitals, High cost of cancer diagnosis and treatment contributes to significant financial impoverishment 50. 58 Nationwide Guideline development All cancers N/A Medicine availability, treatment protocols Standardized protocols for diagnosis/treatment/care; limited cost data 51. 59 Nationwide Cross-sectional survey /policy brief (KHFA) with desk review All cancers (Palliative care focus) 2,980 facilities Medicine availability, financial toxicity Low availability, only 44% of 24 tracer essential medicines available in primary health facilities, with none having all 24. Morphine in 10% facilities; primary facilities 33–50% vs. 70% in hospitals; 52. 60 Kenya (Kisumu) Qualitative (ethnographic fieldwork with therapeutic itineraries) Cervical, Endometrial 2 (focus on two middle-class women therapeutic itineraries, with broader data from other cancer patients) Financial toxicity, access barriers, QoL, precarity in private healthcare Middle-class cancer patients face significant financial toxicity due to high out-of-pocket (OOP) costs, reliance on private healthcare, and inadequate NHIF coverage. Prolonged treatment leads to catastrophic costs, asset depletion, and debt, exacerbating precarity. 53. 61 Kenya (National) Cross-sectional, Policy brief (NHA, KHHEUS analysis) Non-communicable diseases, including cancer) Nationwide N/A Financial toxicity, policy coverage Total health expenditure (THE) increased to USD 4.9B; OOP at 24% (KSh 108B in 2020/21); government financing at 52.3%, SHI at 12.5%; insurance coverage at 25.9% but inequitable (4.1% poorest vs. 57.2% richest); NCDs, including cancer, drive high OOP, necessitating sustainable domestic financing for UHC 54. 62 Kenya (National) Policy document / Expenditure framework General (includes cervical, colorectal, oncology services) N/A Financial toxicity, policy effectiveness, medicine access High OOP costs (24% of THE, KSh 108B in 2020/21) drive financial toxicity; cancer medicine access limited by stock-outs, inadequate KEMSA procurement, and urban bias; SHIF implementation (2.75% income-based contributions, min KES 300, max KES 5,000 monthly) reduces OOP for oncology services (e.g., chemo KES 5,000/session, radiotherapy KES 3,600/session); ECCIF supports chronic conditions; funding gaps persist, with only 17% NHIF coverage and low informal sector uptake (27%); recommends increased domestic financing and infrastructure investment to improve access and equity 55. 63 Kiambu County Case study Multiple (Prostate, Esophageal, Cervical, Cholangiocarcinoma, Others) 12 caregivers Financial toxicity, QoL, policy effectiveness High OOP costs; financial ruin to families; inadequate NHIF coverage with treatment costs draining household resources, leading to catastrophic health expenditure (CHE) and distress financing (DF) 56. 64 Kenya (Nairobi) Cross sectional, Retrospective cohort Oesophageal cancer 299 disease progression, treatment response Mortality rate 43.1%; 11.1% developed distant metastases; 20.1% disease progression despite treatment; 13.0% non-response; 1- and 5-year survival rates 86% and 25%; In advanced stages (III/IV), radiotherapy (AHR 3.3), chemotherapy (AHR 3.9), chemoradiation (AHR 5.6) significantly improved survival; Need for early detection and timely treatment to improve outcomes 57. 65 Kenya (Nairobi) Descriptive cross sectional Not specified (various cancers) 108 QoL, influencing factors Age, cancer stage, time off treatment, education, and religious affiliation significantly predicts QoL; emphasizes early detection, treatment, and spiritual support to improve QoL 58. 66 Kenya (National) Guideline development All cancers N/A Medicine availability, treatment protocols Kenya Essential Medicines List (KEML) 2023 outlines essential cancer medicines aligned with WHO EML; includes chemotherapy, hormonal therapies (e.g., tamoxifen), and supportive care (e.g., morphine); aims to standardize treatment protocols and improve access through KEMSA 59. 67 Kenya (Nationwide) Policy document/Tariff schedule All cancers (emphasis on Breast, Cervical, Prostate, Colorectal, Childhood) N/A Policy effectiveness, financial toxicity Details tariffs for oncology services under SHIF to reduce OOP; chemotherapy KES 5,000/session, 1st Line treatment – Limit of Kes. 400,000 from SHIF and KES. 250,000 from ECCIF, 2nd Line treatment – KES. 650,000 from ECCIF, aims to improve access and equity in cancer care 60. 13 Kenya (Nationwide) Policy document / Strategy All cancers N/A Policy effectiveness, financial toxicity, medicine access Aims to reduce premature mortality by a third by 2028 through prevention, early detection, diagnosis, treatment, palliative care; emphasizes UHC, equity, evidence-based interventions; discusses integration with NHIF/SHIF for financing, improving access to medicines via KEMSA procurement, addressing OOP costs, and enhancing QoL via survivorship and palliative care Notes: Sample Size : "N/A" indicates studies without direct patient recruitment (e.g., policy analyses, facility surveys, or model-based studies). Setting : Refers to the healthcare sector (public, private, faith-based, NGO) and specific facilities where applicable (e.g., KNH = Kenyatta National Hospital, MTRH = Moi Teaching and Referral Hospital, JOOTRH = Jaramogi Oginga Odinga Teaching and Referral Hospital, AKUHN = Aga Khan University Hospital Nairobi). Outcomes Measured : Aligned with the four objectives, including financial toxicity (OOP costs, catastrophic spending, coping strategies), QoL (quantitative tools, qualitative themes), medicine access/affordability, and policy effectiveness Synthesis of Results The primary assessment focused on access to essential cancer medicines (prices, availability, affordability), financial toxicity, quality of life, and health policy effectiveness. No study investigated all the four interconnected aspects in a unified manner. The focus of the studies varied from access and affordability, to financial toxicity and its coping mechanisms; from QoL assessments to health policy evaluations and their implementation barriers, with some studies addressing multiple outcomes. The findings are organized thematically into the four outcomes. About 12 studies (19%) reported medicine access using WHO/HAI methodology or Kenya Essential Medicines List (KEML) assessments. Pricing data showed high costs. A study by 3 showed minimum and maximum medicine prices across providers. Minimum prices ranged from KES 140 for Methotrexate to KES 176,000 for Pembrolizumab 100 mg. 3 45 reported specific pricing, with public sector costs for treating stages I-III of breast and cervical cancers ranging from $ 1,340– $ 1,543, while private sector costs were significantly higher at $ 7,500– $ 11,862. General chemotherapy costs ranged from KES 6,000–600,000 per course. 8 Targeted therapies like Trastuzumab required 69.67–151.74 days of minimum wage per cycle. 3 Medicine availability varied significantly by facility type across the studies. National facility surveys, such as the Kenya Health Facility Assessment 57 , 59 , reported an overall availability of 44% for 24 tracer medicines, and 15% for morphine in public facilities (n = 2,896 facilities). Single-facility studies indicated higher availability in tertiary hospitals (50.8%, n = 4 hospitals) for drugs like Carboplatin (78.9%) and Trastuzumab (73.7%). Supplier and agency assessments 3 reported lower availability at supply agencies, 33.5%(n = 5) for tier 1 suppliers, and 40.2% (n = 8) for tier 2 suppliers, with generics more available (46.9%) than originator brands (6.7%). Notably, 78.3% of studies (47/60) lacked specific availability data, particularly for private facilities, precluding direct comparison. Affordability was poor, with all medicines exceeding the WHO affordability threshold of one day’s wage, rendering them unaffordable. Treatments involving biological therapies such as Trastuzumab were significantly more expensive, requiring several months of minimum wage. One cycle of chemotherapy involving Doxorubicin/Cyclophosphamide (HER2 negative) required 3.15–9.69 days of minimum wage; Cyclophosphamide then Paclitaxel/Trastuzumab (HER2 positive) required 78.36–162.42 days; Trastuzumab, 69.67–151.74 days; Docetaxel, 9.34–10.28 days 3 . The cheapest supplier and cheapest private hospital often provided lower costs compared to Kenyatta National Hospital, the main cancer referral hospital in Kenya, particularly for expensive regimens involving Trastuzumab (Table 7: supplementary). 3 68 , highlighted tariffs in the benefit package of social health insurance Act no, 16, to reduce costs including chemotherapy KES 5,000/session, and a cap of KES 400,000 for treatment/diagnostics). Table 6 Table 6 Findings on Prices, Availability, and Affordability of Cancer Medicines in Kenya Study Prices Availability Affordability 45 Public sector: $ 1,340– $ 1,543 for breast and cervical cancer stages I–III; Private: $ 7,500– $ 11,862 Not specified High OOP costs; costs exceed average household expenditure ( $ 413/annum); large proportion lack health insurance 2 All WHO EML regimens unaffordable OOP; no specific pricing provided 93.4% alignment with WHO EML; generic cytotoxics available, targeted therapies (e.g., Trastuzumab, Imatinib) limited All regimens unaffordable; targeted therapies require 93–99% price reductions 4 Not reported Not reported; public and private sector access noted 53.8% of breast cancer patients forwent care due to cost; 91.2% reported household financial impact; 44.9% reported inadequate insurance reimbursement 8 Average treatment cost: KES 143,132; Chemotherapy: KES 6,000–600,000 per course Not reported High OOP costs; medicines and inpatient admissions are major cost drivers 33 Public sector: $ 180 for diagnostics, $ 85– $ 1,500 for treatment; Chemotherapy: $ 300 per course at KNH Cryotherapy and LEEP available on-site; chemotherapy referred to KNH High OOP costs: $ 100– $ 300 for diagnostics; cost barriers lead to forgoing care 36 Chemotherapy administration: KES 4,600, not covered by NHIF Frequent stockouts, especially for chemotherapy High costs due to drug prices and transport; limited NHIF coverage 38 Cancer treatment costs: $ 1,500– $ 4,000/year (public), $ 2,500– $ 7,500/year (private) Not reported 20.27% of households experienced catastrophic spending; CHE increases household poverty 44 Trastuzumab: KES 39,900/month (USD 399, 1.33 doses at KES 30,000/unit) Trastuzumab available but limited; only 33.4% completed 18 cycles High OOP and travel costs; high treatment abandonment due to cost 47 Not reported 44% availability for 24 tracer medicines in public sector; none in primary facilities 38% faced catastrophic health expenditure; 16% avoided care due to costs 3 Unit prices: KES 140 (Methotrexate 50 mg), KES 1,000 (Cisplatin 50 mg), KES 50,000 (Bevacizumab 400 mg), KES 86,432 (Trastuzumab 600 mg), KES 176,000 (Pembrolizumab 100 mg) Hospitals: 50.8% (e.g., Carboplatin 78.9%, Trastuzumab 73.7%); Suppliers: 37.6%; Agencies: 29.4%; generics (e.g., Capecitabine 73.7%) more available than originators (6.7%) All medicines unaffordable; chemotherapy cycles require 3.15–162.42 days of minimum wage (KES 411/day); e.g., Doxorubicin/Cyclophosphamide: 3.15–9.69 days; Paclitaxel/Trastuzumab: 78.36–162.42 days; Trastuzumab: 69.67–151.74 days; Docetaxel: 9.34–10.28 days 10 Not reported Limited availability; only 44% of 24 tracer medicines in facilities; frequent stockouts High OOP costs (19.9% of CHE, KSh 108B in 2020/21); 50% avoid care due to cost; limited NHIF coverage (17% population, 27% informal sector) 59 Not reported Morphine: 5% in rural facilities, 50% in level 4, 100% in level 5/6; overall 44% for 24 tracer medicines (n = 2,980 facilities) High OOP burden; 90% of medicine purchases OOP; forgoing care implied 23 Treatment costs described as 'astronomical'; no specific pricing Chemotherapy, surgery, radiotherapy, immunotherapy available, mostly in Nairobi High costs lead to seeking treatment abroad (e.g., India, Uganda); cost barriers limit access 55 Not reported Not specified; implied limited due to high OOP OOP at 24% of THE (KSh 108B in 2020/21); higher burden on lower wealth quintiles; insurance coverage inequitable (4.1% poorest vs. 57.2% richest) 67 Chemotherapy: KES 5,000/session; Radiotherapy: KES 3,600/session; SHIF cap: KES 400,000 (USD 3,095) for treatment/diagnostics Limited by stockouts, inadequate KEMSA procurement, urban bias High OOP costs (24% of THE, KSh 108B in 2020/21); limited NHIF coverage (17% population, 27% informal sector); SHIF aims to reduce OOP Note: KES = Kenyan Shillings; $ = US Dollars; N/S = Not Specified Financial toxicity was reported in 10/60 studies (17%), with limited available data suggesting OOP costs ranging from $1,298–$12,713/year. 46 Public sector costs for stages I–III breast and cervical cancers were $1,340–$1,543, while private sector costs were $7,500–$11,862. 45 Cervical cancer patients faced OOP costs of 87% for medication, 84% for travel, and 75% for diagnostics 16 . OOP constituted 24% of total health expenditure (KSh 108B in 2020/21), with limited NHIF uptake (17% population, 27% informal sector) exacerbating the burden. 10 CHE affected 20.27–54% of households. 38,43 Coping mechanisms included borrowing (81%), selling assets (73%), and seeking charity (13%) or family/church support (10%) 16 . Treatment abandonment was noted, with 53.8% of breast cancer patients forgoing care due to costs 4 . The sparse data and wide OOP variation suggest evidence base is insufficient for comprehensive cost-effectiveness analysis or definitive affordability assessments/ population-level projections, as 83.33% (50/60) of studies lacking specific OOP or CHE data. (Table 8 supplementary) About 9/60 studies (15%) reported Quality of life (QoL), indicating compromised QoL, with median global health status scores of 41.99–53 (EORTC QLQ-C30, FACT-Cx). Breast cancer patients reported 64% good QoL, prostate 85% 20,22 . Advanced disease (AOR = 7.3, p < 0.0001) and comorbidities (OR = 3.1, p = 0.037) predicted poor QoL (Shajahan Ahamed & Degu, 2023). High symptom burdens included fatigue (56%), pain (65%), and financial difficulties (79%). 30 Some studies reported thematic findings without quantitative tools. Qualitative themes from 10 studies highlighted psychosocial distress, stigma, social isolation, and physical limitations. In the physical domain, symptom burden and functional limitations were key, including fatigue, pain, weakness, dry mouth, insomnia/hypersomnia, and mobility/self-care difficulties. Cervical cancer patients reported poor physical (60%) and emotional QoL, with hygiene challenges and family dependence; multi-cancer studies noted reduced occupational functioning. 17 , 26 , 30 , 31 Better QoL associated with early-stage disease, urban residence, and stable remission. 20 Psychological themes included depression (59.4% in breast cancer), influenced by late-stage diagnosis (OR = 1.61, p = 0.319), employment (OR = 3.7, p = 0.058), and chemotherapy (neoadjuvant OR = 9.43, palliative OR = 9.5, p < 0.05), leading to reduced mental resilience and "constant worry" about survival/family. 24,31 Social domain themes featured isolation, stigma, and family burden, with avoidance/discrimination (awkwardness: 2.51 ± 0.75; severity: 3.22 ± 1.29). Breast/cervical patients faced rejection/over-dependence, strained relationships, reduced social life, "community stigma," and policy-level discrimination (2.99 ± 1.17), intersecting with financial strain via borrowing/charity 18 , 31 , 34 . Spiritual themes showed religion as hope/coping, but unmet needs worsened distress in metastatic cases 18 , 31 . Healthcare/systemic domain included unmet information needs (low cancer knowledge: 23.6%), system delays, and financial difficulties (79%) amplifying access issues; emotional/social functioning deficits from urban-centric services/lack of support 20 , 30 (Table 9: supplementary). About 15/60 studies (25%) evaluated health policy effectiveness, focusing on NHIF, SHA, and the National Cancer Control Strategy (2023–2027). Stratifying by policy era, 13/15 policy studies (87%) focused on pre-2023 NHIF implementation, reporting low NHIF uptake (9–17%) primarily covering inpatient services, and limited impact on treatment affordability, covering only 4 of 8 chemotherapy cycles 16 , 62 . Post-2023 SHIF data (limited to 2/60 studies) showed potential to reduce OOP costs via mandatory contributions (2.75% income-based) and the Emergency, Chronic and Critical Illness Fund (ECCIF), covering oncology services (chemotherapy KES 5,000/session, 1st line treatment limit KES 400,000 from SHIF, KES 250,000 from ECCIF), but faced infrastructure and funding gaps. 62,68 No studies directly compared pre- and pos insurance policy transition outcomes. Health financing remained limited at 3.5% of GDP (below WHO’s 6%), with OOP costs at 24% of total health expenditure (KSh 108B in 2020/21), and weak cancer registries (64% missing staging data) hindered progress 11 , 62 , 69 . Decentralization efforts aimed to improve access, but oncology services remained urban-centric, with limited screening/treatment capacity in rural areas 23 , 39 . A medium-term expenditure report, highlighted KEMSA’s role in procuring essential cancer medicines such as chemotherapy, hormonal therapies, aligned with WHO EML, but noted barriers including stockouts, inadequate procurement, and urban bias 62 . Another study reported NHIF/SHIF schemes and UHC implementation, but identified barriers such as limited NHIF uptake (17% population, 27% informal sector), and infrastructure/capacity limits. 12 (Table 10: supplementary) From the reviewed documents, 8/60 studies identified the need to expand NHIF/SHIF coverage to reduce OOP costs and FT. 3/60 studies identified the need to strengthen KEMSA. Other policy suggestions included subsidizing costs (3/60), enhancing early detection and screening programs (7/60), integrating psychological and QoL support (5/60), strengthening health infrastructure and workforce (4/60), enhancing cancer registries (2/60), monitoring and management of treatment adverse effects (3/60), and promoting digital and community support to provide education and psychological care (1/60). (Table 11: supplementary) DISCUSSION This scoping review of 60 studies (2018– May 2025) mapped evidence on access to essential cancer medicines, financial toxicity, quality of life, and health policy performance for breast, cervical, prostate, colorectal, and esophageal cancers in Kenya. The findings show that high treatment costs, limited availability of key cancer medicines, and inadequate insurance coverage remain persistent determinants of financial toxicity and diminished QoL among adult cancer patients. Consistent with patterns observed across LMICs, the review highlights fragmented systems of access, financing, and policy implementation that interact to deepen treatment inequities. This synthesis extends prior literature by concurrently mapping evidence across access, FT, QoL, and health policy domains rather than treating them as isolated components. The high cost and limited availability of cancer medicines in Kenya reflect broader regional trends, yet several gaps within Kenya are evident. While LMICs generally experience supply chain interruptions and reliance on out-of-pocket financing, Kenya’s medicine procurement delays often extending 4–8 months, contrast sharply with Uganda’s high availability, where centralized procurement systems have achieved availability levels of 85.8%. 70 Compared to a study by 71 , which mapped African medicine access pathways, this review offers detailed insights on how procurement inefficiencies, pricing structures, and reimbursement policies shape patient access and financial burden. It reveals high costs of cancer medicines in Kenya. The cost of standard chemotherapy such as Doxorubicin/Cyclophosphamide requiring 3.15–9.69 days of minimum wage, exceed World Health Organization (WHO) affordability thresholds. For targeted therapies, the gap becomes even more pronounced. Trastuzumab requires 69.67–151.74 days of minimum wage, effectively placing guideline-recommended care beyond reach for the majority of Kenyan patients. 3 , 8 , 44 Price disparities between the public and private sectors further entrench inequity. Breast cancer treatment ranges from US $ 1,340–1,542 in public hospitals but escalates to US $ 7,500–11,862 in private facilities; an eightfold increase in some cases. 39 Given that 73% of Kenyans rely on the public sector, such cost differentials heighten inequities in access, continuity of care, and survival outcomes. The review also noted scarcity of data for colorectal and esophageal cancers (4/60 studies) limiting cost-effectiveness analyses for high mortality cancers. Breast and cervical cancers dominate research agendas while gastrointestinal cancers remain underexplored, despite their rising incidence in LMICs. The paucity of data constrains policy decision-making and hinders the development of targeted financing and procurement reforms. Financial toxicity emerged as the most immediate and severe patient-level consequence across the included studies. Out-of-pocket (OOP) payments, ranging from US $ 1,298 to US $ 12,713 annually, placed substantial pressure on households. About 20.3–54% of households experienced financial toxicity and elevated risks of catastrophic health expenditure (CHE). Medication costs (87%), transport (84%), and diagnostic services (75%) were major cost drivers contributing to this burden. 16,38,46 Coping strategies, including borrowing (81%), asset sales (73%), fundraising, and treatment abandonment (53.8%), illustrate the depth of financial strain experienced by patients and families. 4,16,43,63 These patterns are consistent with a recent systematic review reporting a pooled CHE of 43.3% (95% CI 36.7–50.1) among cancer patients 72 . However, substantial variation in OOP estimates, methodological inconsistencies, and pervasive data gaps constrain comparability across studies. Notably, 78% of included studies did not report OOP data, and 88% lacked CHE estimates, similar to concerns observed in other global systematic reviews. 73,74 Underutilization of validated financial toxicity instruments, particularly the COST-FACIT and PROFFIT measures, limits the ability to generate robust, patient-centered assessments of financial hardship. A recent methodological review 75 emphasizes the importance of standardized FT measurement. Yet, these tools remain underutilized in Kenya. Quality of life findings further demonstrate that financial distress translates into significant psychological and physical hardship for cancer patients. QoL, assessed in only 9 of the 60 included studies, was consistently poor, with EORTC QLQ-C30 global health scores ranging from 41.99 to 53, indicating compromised functioning and symptom burden. These outcomes stemmed from treatment-related symptoms (fatigue 56%, pain 65%, financial difficulties 79%), psychosocial distress (depression 43.9–59.4%), and persistent financial strain. 20,30,76 Qualitative themes highlighted depression, anxiety, stigma, disrupted self-image and negative body perception. 4 , 16 , 31 , 46 Early-stage diagnosis and stable disease status were associated with better QoL outcomes, with one study reporting significantly higher odds of good QoL for early-stage cervical cancer patients (AOR = 7.3). However, heterogeneity in QoL assessment tools, including the EORTC QLQ-C30, FACT-B, HAM-D, and the Functional Evaluation of Chronic Illness Therapy limits comparability across studies and weakens the ability to generate population-level inferences. A recent LMIC-focused QoL analysis 77 demonstrated the value of standardized metrics; however, its focus on breast cancer alone disadvantages the broader gap in comprehensive QoL data for diverse cancer types in Kenya. At the policy level, this review identified persistent gaps between insurance design and the degree of financial protection actually afforded to patients. Although 15 of the 60 included studies examined cancer-related policies, progress remains mixed. NHIF coverage continues to be limited, reaching only 9–17% of the population, with particularly low enrollment among the informal sector (27%). National health financing remains at 3.5% of GDP, substantially below the WHO-recommended benchmark of 6%, constraining the ability to absorb rising cancer care costs. 11 , 16 , 62 Evidence on the post-2023 Social Health Authority (SHA) remains sparse (2/60 studies), limiting the capacity for rigorous evaluation. Nonetheless, the Social Health Authority (SHA) introduces potentially transformative mechanisms. The Emergency, Chronic, and Critical Illness Fund (ECCIF) and a proposed oncology benefit cap of KES 400,000, could meaningfully reduce financial hardship. But infrastructure gaps, including workforce shortages, low informal-sector uptake, and weak procurement governance dilute the potential impact of these reforms. 10 , 46 , 62 Procurement inefficiencies (4–8 months delays) at KEMSA cause stockouts, forcing reliance on private suppliers at higher costs. 9 While decentralization efforts have expanded chemotherapy access (69.1%) and palliative care (57.9%), radiotherapy remain domiciled in urban centers, restricting access for rural populations. 39 Weak cancer registries; 64% missing staging data undermine evidence-based policy responsiveness and resource allocation. Patient-level experiences also highlight implementation challenges. Nearly half (44.9%) of NHIF beneficiaries reported receiving lower-than-expected reimbursement, contributing to widespread distrust and underutilization of insurance benefits. 4 Overall, these findings align with WHO’s universal health coverage (UHC) framework but indicate persistent gaps in Kenya’s National Cancer Control Strategy (2023–2027), consistent with LMIC policy challenges. 78 Limited SHA data (2/60 studies) and short evaluation window (1–2 years) preclude definitive conclusions regarding the early impact of recent reforms. This review adhered to PRISMA-ScR guidelines, was prospectively registered on OSF, employed the MMAT for methodological appraisal, and incorporated grey literature to enhance transparency and contextual relevance. However, this study has some limitations. Most studies available were conducted in urban regions, reducing representativeness for rural Kenya, where access barriers may be more pronounced. However, this pattern reflects concentration of oncology centers in urban areas in Kenya. Evidence for colorectal and esophageal cancers was notably limited, restricting comprehensive assessment across all major cancer types. We also noted methodological inconsistencies, particularly in cost reporting and definitions of financial toxicity and catastrophic health expenditure. Exclusion of databases (Embase, Medline, Scopus) due to access limitations may have introduced selection bias, potentially missing international studies with Kenyan sub-analyses. However, the identification of 60 eligible studies suggests that the data is sufficiently broad to capture prevailing trends in access to cancer medicines, financial toxicity, quality of life, and policy impacts in Kenya. Evidence Before This Study Globally, financial toxicity is recognized as a major barrier to cancer care even in high-income countries with universal health coverage. Studies report catastrophic health expenditure in 13–68% of households, while public sector medicine availability of cancer medicine in East Africa is below 50%. Existing LMIC reviews on cancer care have laid a thoughtful foundation. The body of evidence though varied, highlights systemic weaknesses; low insurance penetration, procurement inefficiencies as issues of significance in cancer treatment access. In addition, the current body of evidence shows diverse methodologies in cost reporting for cancer medicines, suggesting the need for country specific analyses to illuminate national treatment access landscape. Added Value of This Study This is the first scoping review to consolidate evidence on cancer medicine access, financial toxicity, quality of life, and policy effectiveness in Kenya. While individual studies provide valuable insights, the current evidence base is fragmented, methodologically heterogeneous, and limited in scope particularly for colorectal and esophageal cancers. Nevertheless, this review demonstrates that Kenya’s cancer treatment access stem not from medical scarcity but policy misalignment where high costs, fragmented coverage, and inefficient procurement systems perpetuate inequity. Importantly, no studies directly compared outcomes before and after the transition from NHIF to the Social Health Insurance Act (SHA), highlighting a major policy evaluation gap. The review also finds that validated tools for assessing financial toxicity and QoL are underutilized in Kenya, demonstrating an urgent need for standardized methodologies, longitudinal data, and policy-oriented research to inform the implementation of the National Cancer Control Strategy (2023–2027). Implications of All the Available Evidence High treatment costs, procurement inefficiencies, and inadequate insurance coverage significantly undermine cancer treatment access in Kenya. Corrective strategies must integrate price regulation, pooled regional procurement, and expanded SHA coverage to reduce financial toxicity and improve quality of life (QoL). Strengthening cancer registries is essential for improving surveillance, resource planning, and policy evaluation. Adoption of validated financial toxicity and QoL instruments (e.g., COST, EORTC QLQ-C30) is critical for capturing the true burden of disease and guiding patient-centered interventions. Longitudinal and multi-stakeholder research involving patients, families, providers, and policymakers is needed to understand trajectories of financial hardship and QoL over time, particularly in the context of rising cancer incidence in Kenya. Declarations Authors Contributions JOO, DOO, and SAA conceived and designed the scoping review. JOO and SAA conducted study selection and data extraction. All authors contributed to data interpretation and analysis. JOO drafted the manuscript, with revisions and supervisory support from SAA and DOO. All authors read and approved the final manuscript. Declaration of Interest I/we declare no conflicts of interest. Data Sharing The data used is available upon reasonable request Acknowledgments We thank the Kenya Ministry of Health and the University of Nairobi Digital Repository for providing access to gray literature and institutional reports. Funding Information The scoping review is a preliminary investigation and part of a larger study funded by National Cancer Research Fund (NRF), (NCI-NRF001/2024), Kenya. However, the funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. 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PLOS Global Public Health. 2023 Sep 1;3(9 September). Daniel O, Ashrafi A, Muthoni MA, Njoki N, Eric H, Marilynn O, et al. Delayed breast cancer presentation, diagnosis, and treatment in Kenya. Breast Cancer Res Treat. 2023 Dec 1;202(3):515–27. Orindi T, Dickens O Aduda, Fred A Amimo. Socio-Demographic Characteristics Associated with Quality of Life-Scores among Palliative Care Cancer Patients in Kenya. J Community Med Public Health. 2021 Aug 30; Olwanda E, Owiti E, David N. Budget Impact Analysis of Cervical Cancer Treatment in Kenya. Public Health Research [Internet]. 2024;2024(1):12–21. Available from: http://journal.sapub.org/phr Sherman S, Okungu V. Access to Breast Cancer Treatment Services in Mombasa County, Kenya: A Quality of Care Analysis of Patient and Survivor Experiences. Am J Public Health Res. 2018 Jun 26;6(4):189–94. Wabende LN, Bhatia M, Kiptoo S, Kisilu N, Kiboss C, Kibiwot S, et al. The Cost Implication of Trastuzumab in Patients With HER2-Positive Breast Cancer at Moi Teaching and Referal Hospital, Eldoret, Kenya. JCO Glob Oncol. 2022 May;8(Supplement_1):48–48. Subramanian S, Gakunga R, Kibachio J, Gathecha G, Edwards P, Ogola E, et al. Cost and affordability of non-communicable disease screening, diagnosis and treatment in Kenya: Patient payments in the private and public sectors. PLoS One. 2018 Jan 5;13(1):e0190113. Lehmann J, Machira YW, Schneidman M, Chuma J. ECONOMIC AND SOCIAL CONSEQUENCES OF CANCER IN KENYA CASE STUDIES OF SELECTED HOUSEHOLDS D I S C U S S I O N P A P E R M A Y 2 0 2 0. 2020. Toroitich AM, Dunford L, Armitage R, Tanna S. Patients Access to Medicines – A Critical Review of the Healthcare System in Kenya. Vol. 15, Risk Management and Healthcare Policy. Dove Medical Press Ltd; 2022. p. 361–74. Kinoti FJ, Oluchina S, Mbithi BW. Psychosocial distress among patients with cancer at a county referral hospital in Kenya. International Journal of Advanced Nursing Studies. 2020; Ong’ondi M, Njuguna I, Obulumire R, Munyoro E, Okech V, Ann N, et al. Psychological distress in patients with cancer at the Kenyatta National Hospital in Nairobi, Kenya, during the COVID-19 pandemic. J Psychosoc Oncol Res Pract. 2023 Apr;5(2). Mbau R, Kabia E, Honda A, Hanson K, Barasa E. Examining purchasing reforms towards universal health coverage by the National Hospital Insurance Fund in Kenya. Int J Equity Health. 2020 Dec 3;19(1):19. Angachi M, Lucy Njiru, Wachira Muturi. Prevalence of Anxiety and Depression among Cervical Cancer Patients in Referral Hospitals in Western Kenya . African Journal of Clinical Psychology . 2022; Mwarania M. Catastrophic Healthcare Expenditures and Household Poverty in Kenya: The Case of Cancer. Journal of Medicine, Nursing & Public Health. 2022 Jan 10;5(4):52–61. MOH. KENYA CANCER POLICY 2019-2030. 2019. Republic of Kenya 2023, Social Health Insurance Act (GENERAL) REGULATIONS ,2023. Njuguna D, Wangia E, Pepela W, Yoshida K. Is social health insurance the magic bullet to achieving Universal Health Coverage? A case to reduce direct out of pocket payments. 2022 Apr. Gathecha Gladwell, Watiri T. Policy Brief: Rising Burden of NCDs to Household: A call to balanced investment. 2022 Apr. Kagiri H, Kiarie H, Karagu A. Policy Brief on Assessment of Demand Versus Supply of Cancer Services on Kenya Insights from the Kenya Health Facility Assessment (KHFA) 2018/2019. 2020. MOH. Kenya National Cancer Treatment Protocols 2019. 2019. Karumbi Jamlick, Njuguna David, Leonard Cosmas, Hellen Kiarie. Policy Brief: Essential medicines availability in primary health care facilities Insights from the KHFA 2018. 2020 Dec. Prince RJ. Private health care, cancer, and the vulnerable middle class in Kenya. Am Ethnol. 2023 Nov 24;50(4):595–608. Njuguna D, Mwai D, Kwesiga B, Yoshida K. Policy Brief: A Call to more Sustainable Domestic Financing For Health in Kenya. 2022 Apr. Republic of Kenya. Health Sector Report, Medium Term Expenditure Framework (MTEF) For The Period 2025/26-2027/28. 2024. Too W, Faith Lelei. Financial Toxicity Related to Advanced Cancer Patients’ Care: Case study of Selected Households of Kiambu County, Kenya. Journal of Health, Medicine and Nursing. 2022 Jul; Degu A, Karimi PN, Opanga SA, Nyamu DG. Determinants of survival outcomes among esophageal cancer patients at a national referral hospital in Kenya. Chronic Dis Transl Med. 2023 Mar 20;9(1):20–8. Kung’u M, Onsongo L, O Ogutu J. Factors influencing quality of life among cancer survivors in Kenya. Afr Health Sci. 2022 Dec 22;22(4):87–95. MOH. Kenya Essential Medicines List 2023. 2023. MOH. Tariffs To The Benefit Package Under The Social Health Insurance Act No. 16 of 2023. 2023. MOH. TARIFFS TO THE BENEFIT PACKAGE UNDER THE SOCIAL HEALTH INSURANCE ACT NO. 16 OF 2023. 2023. Njuguna D, Mwai D, Kwesiga B, Yoshida K. Policy Brief: A Call to More Sustainable Domestic Financing for Health in Kenya. 2022. Osinde G, Niyonzima N, Mulema V, Kyambadde D, Mulumba Y, Obayo S, et al. Increasing Access to Quality Anticancer Medicines in Low- and Middle-Income Countries: The Experience of Uganda. Future Oncology. 2021 Jul 15;17(21):2735–45. Lane J, Nakambale H, Kadakia A, Dambisya Y, Stergachis A, Odoch WD. A systematic scoping review of medicine availability and affordability in Africa. BMC Health Serv Res. 2024 Jan 17;24(1):91. Doshmangir L, Hasanpoor E, Abou Jaoude GJ, Eshtiagh B, Haghparast-Bidgoli H. Incidence of Catastrophic Health Expenditure and Its Determinants in Cancer Patients: A Systematic Review and Meta-analysis. Appl Health Econ Health Policy. 2021 Nov 28;19(6):839–55. Jiang H, Lyu J, Mou W, Jiang Q, Du J. Association between financial toxicity and health‐related quality of life in cancer survivors: A systematic review. Asia Pac J Clin Oncol. 2023 Aug;19(4):439–57. Witte J, Mehlis K, Surmann B, Lingnau R, Damm O, Greiner W, et al. Methods for measuring financial toxicity after cancer diagnosis and treatment: a systematic review and its implications. Annals of Oncology. 2019 Jul;30(7):1061–70. Thomy LB, Crichton M, Jones L, Yates PM, Hart NH, Collins LG, et al. Measures of financial toxicity in cancer survivors: a systematic review. Supportive Care in Cancer. 2024 Jul 4;32(7):403. Degu A, Karimi PN, Opanga SA, Nyamu DG. Health‐related quality of life among patients with esophageal, gastric, and colorectal cancer at Kenyatta National Hospital. Cancer Rep. 2024 Mar 20;7(3). Ngo NTN, Nguyen HT, Nguyen PTL, Vo TTT, Phung TL, Pham AG, et al. Health-related quality of life in breast cancer patients in low-and-middle-income countries in Asia: a systematic review. Front Glob Womens Health. 2023 Jun 14;4. Bamodu OA, Chung CC. Cancer Care Disparities: Overcoming Barriers to Cancer Control in Low- and Middle-Income Countries. JCO Glob Oncol. 2024 Aug;(10). 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1","display":"","copyAsset":false,"role":"figure","size":41078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA-ScR Flow Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8294040/v1/0ca417bff43b2fb24d18c1c7.png"},{"id":98621958,"identity":"4fd45e43-a38e-456f-92ae-c4b17e04a986","added_by":"auto","created_at":"2025-12-19 16:38:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1686065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8294040/v1/b715b402-6f04-4a65-affa-3c243e903336.pdf"},{"id":97897029,"identity":"0c4e0e32-f2c7-41a2-b35b-dfbe24a3d636","added_by":"auto","created_at":"2025-12-10 15:37:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":567040,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Data\u003c/p\u003e","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-8294040/v1/6cde601f6457cccd8bcfb7c6.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAccess to Cancer Medicines in Kenya: A Scoping Review of Costs, Financial Toxicity, Quality of Life, and Policy Impacts\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCancer presents a significant health and economic toll globally with 19.9\u0026nbsp;million new cases and 9.7\u0026nbsp;million deaths in 2022. In Kenya, it ranks as the third leading cause of death, with 44,726 new cases and 29,317 deaths in 2022, primarily from breast, cervical, prostate, colorectal, and esophageal cancers.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Like many Low- and middle-income countries (LMICs), Kenya faces unique challenges in delivering affordable and timely cancer treatment. Patients continue to face high out-of-pocket (OOP) costs, limited availability, and inadequate insurance coverage for cancer medicines.\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR80\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e These barriers contribute to financial toxicity (FT), both the objective burden of out of pocket (OOP) treatment costs and subjective distress. In LMICs, 13\u0026ndash;68% of patients face catastrophic health expenditures (CHE), spending over 40% of non-food income on treatment, which significantly undermines quality of life (QoL) through financial strain and treatment delays.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAccess to affordable and available cancer medicines is critical for treatment adherence and improved QoL particularly for breast and cervical cancers, which respond well to therapy when detected at early stages and treated early. The World Health Organization\u0026rsquo;s access to medicines framework emphasizes four pillars, availability, affordability, accessibility, and quality as essential for equitable health systems.\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In Kenya, these pillars are compromised. Medicine availability averages less than 50% in public facilities, affordability is hindered by high costs, limited accessibility, and quality issues arise from inconsistent supply chains.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The World Health Organization recommends countries to finance their health systems at 6% of gross domestic product (GDP). Kenya\u0026rsquo;s health financing, at 3.5% of GDP, falls below this threshold, straining public health systems and deepening inequities in cancer care.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eRecent shifts in health policies and frameworks, in particular, the transition from the National Hospital Insurance Fund (NHIF) to the Social Health Authority (SHA) in 2023, aim to advance universal health coverage (UHC) including reducing treatment inequities. However, structural and contextual barriers including, weak political commitment and limited adoption of expert advice disadvantage effective implementation.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The National Cancer Control Strategy 2023\u0026ndash;2027, \u003csup\u003e13\u003c/sup\u003e recognizes these gaps but struggles with execution. This is in part, due to sparse patient level data and fragmented nature of existing evidence spanning quantitative costs, patient experiences, and policy analyses.\u003c/p\u003e\u003cp\u003eIn this review, we synthesize evidence on access to essential cancer medicines, financial toxicity, quality of life, and health policy effectiveness among Kenyan adults with breast, cervical, prostate, colorectal, or esophageal cancers. Unlike previous fragmented literature, this review uniquely consolidates cost, quality of life, and health financing data to demonstrate how policy implementation and economic inequities jointly shape patient access to cancer medicines in Kenya. The evidence aims to inform changes in policy at health system level and government levels particularly under the Social Health Authority (2023). Furthermore, this review seeks to guide future studies and decisions to enhance medicine affordability, availability while reducing financial burdens in Kenya and other LMICs.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis; extension for Scoping Reviews (PRISMA-ScR) guidelines, adhering to the methodological framework of Arksey \u0026amp; O\u0026rsquo;Malley (2005), refined by Levac et al. (2010). \u003csup\u003e14,15\u003c/sup\u003e The protocol was registered with the Open Science Framework (contact [email protected] for draft protocol). The review followed five stages that guide how scoping reviews are done: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data, and 5) collating, summarizing, and reporting results.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSearch Strategy and Selection Criteria\u003c/h2\u003e\u003cp\u003eThe review addressed the primary research question \u0026ldquo;What evidence exists on access to essential cancer medicines, financial toxicity, quality of life, and health policy effectiveness among adult cancer patients in Kenya?\u0026rdquo; For the purpose of this scoping review, out-of-pocket (OOP) costs refer to direct payments for care such as medicines, diagnostics, travel; Catastrophic health expenditure (CHE) is healthcare spending exceeding 40% of household non-food expenditure; and financial toxicity encompasses OOP costs, income loss, and coping strategies such as borrowing, and asset sales. The review focused on top five most prevalent cancers in Kenya, breast, cervical, prostate, colorectal, and esophageal cancers to align with Kenya\u0026rsquo;s health policy priorities.\u003c/p\u003e\u003cp\u003eSearches were conducted on 31 May 2025 in PubMed, African Journals Online (AJOL), and Google Scholar, limited to English publications from January 2018 to May 2025. A comprehensive search strategy was used, combining Medical Subject Headings (MeSH) and keywords tailored to each database. Key terms included \u0026ldquo;cancer,\u0026rdquo; \u0026ldquo;Kenya,\u0026rdquo; \u0026ldquo;essential medicines,\u0026rdquo; \u0026ldquo;financial toxicity,\u0026rdquo; \u0026ldquo;financial burden,\u0026rdquo; \u0026ldquo;economic hardship,\u0026rdquo; \u0026ldquo;financial distress,\u0026rdquo; \u0026ldquo;catastrophic health expenditure,\u0026rdquo; \u0026ldquo;quality of life,\u0026rdquo; \u0026ldquo;NHIF,\u0026rdquo; \u0026ldquo;SHIF,\u0026rdquo; and \u0026ldquo;UHC,\u0026rdquo; combined using Boolean operators. (Table\u0026nbsp;1: supplementary shows a search strategy used in PubMed)\u003c/p\u003e\u003cp\u003eGrey literature was sourced from WHO, Kenya Ministry of Health, NHIF/SHIF, and Kenya National Cancer Control Program websites, with credibility assessed by institutional authority and author expertise. Google Scholar searches were stopped after three consecutive pages of irrelevant results. Additional studies were identified from reference lists scanning of included studies through manual searches and institutional repositories (University of Nairobi Digital Repository). Embase, Medline, Scopus, and Web of Science were excluded due to access constraints, mitigated by AJOL (which indexes multiple journals in Africa, enhancing coverage of regionally relevant open access publications) and comprehensive gray literature searches.\u003c/p\u003e\u003cp\u003eTo assess eligibility of studies, the scoping review followed the Population, Concept, and Context (PCC) mnemonic (Table\u0026nbsp;2: Supplementary). Studies were included if published between January 2018 and May 2025, in English, and included primary research or policy evaluations. Exclusions included non-Kenyan studies, editorials, and abstracts without full text. All citations resulting from the searches were imported into web-based software platform Rayyan. After removing duplicated citations, two reviewers independently screened titles and abstracts, followed by full texts review and any disagreements on final inclusion reached by consensus.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData was extracted from selected articles using an Excel form, piloted on six included studies (two quantitative, two qualitative, and two mixed-methods) and refined accordingly. The data charting form captured study characteristics (author, year, design, location, publication type), population (cancer type, demographics, sample size), medicine access (prices, stockout rates, affordability), financial toxicity (OOP costs, CHE, coping strategies), quality of life (scores, qualitative themes), and policy outcomes (NHIF/SHIF coverage, procurement, barriers) and main findings (Table\u0026nbsp;3: supplementary). Authors were contacted for clarification where necessary such as cost data, with two responses received. Medicine availability was stratified by data source (national surveys, single-facility studies, supplier/agency assessments) using the Kenya Essential Medicines List (KEML) and WHO/HAI methodology, and data gaps and methodological differences documented.\u003c/p\u003e\u003cp\u003eWe used the Mixed Methods Appraisal Tool (MMAT v. 2018) to assess the quality of studies given the different types of study designs included in the scoping review. The tool included methodological quality of quantitative studies (sampling, measurement, bias), qualitative (approach, coherence), mixed-methods (integration), and policy documents (clarity, relevance). Quality ratings informed interpretation but did not affect inclusion, as the scoping review\u0026rsquo;s objective was to map all relevant evidence.\u003c/p\u003e\u003cp\u003eResults are synthesized descriptively with numerical summaries of sample sizes and cancer types; and thematically by objectives (medicine access, financial toxicity, quality of life, policy effectiveness), to show the extent and nature of evidence. Any other emerging themes relevant to the research question are reported. Where appropriate we visualized findings using tables and graphs. Finally, the implications of findings, the broader context and recommendations for health system improvement and future studies are presented.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFollowing PRISMA-ScR guidelines, 393 articles were identified through searches in PubMed, African Journals Online (AJOL), Google Scholar, and grey literature (WHO, Kenya Ministry of Health, NHIF/SHIF, and Kenya National Cancer Control Programme) conducted up to May 31, 2025. After removing 157 duplicates, 236 records were screened by title/abstract, with 152 excluded due to non-Kenyan settings (n\u0026thinsp;=\u0026thinsp;80), non-cancer-specific focus (n\u0026thinsp;=\u0026thinsp;42), editorials (n\u0026thinsp;=\u0026thinsp;20), or abstracts without full text (n\u0026thinsp;=\u0026thinsp;10). Full-text review of 84 records resulted in 60 included studies (42 journal articles, 8 government reports, 7 policy briefs, 2 regulatory documents, 1 thesis). Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the PRISMA-ScR flow diagram.\u003c/p\u003e\n\u003cp\u003eThe majority of the included articles were health policy related (15, 25%), followed by those touching on multiple objectives (14, 23.3%), medicine access (12, 20%), financial toxicity (10, 16.7%), and lastly quality of life (9,15%). (Fig.\u0026nbsp;2: supplementary).\u003c/p\u003e\n\u003cp\u003eThe quality of studies was assessed using the Mixed Methods Appraisal Tool (MMAT version 2018). The assessment excluded 13 non-research policy/guideline documents, hence included 47 studies. All had clear research questions and data addressing the research questions. Majority were quantitative descriptive studies (n\u0026thinsp;=\u0026thinsp;31) and they generally performed well across MMAT criteria. All had relevant sampling strategy (100%), appropriate measurements (100%), and appropriate statistical methods (94%). However, representativeness of the sample (68%) and low nonresponse bias (78%) were somewhat lower. Qualitative studies (n\u0026thinsp;=\u0026thinsp;19) showed moderate adherence to MMAT criteria, with 74% meeting standards for appropriate qualitative approach. There were 14 mixed-methods studies, which had an adequate rationale for mixed methods (71%) and effective integration of components (86%), though adequate interpretation of integrated outputs (64%) and addressing divergences between quantitative and qualitative results (57%) were lower. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMMAT Table for Cancer Medicines, FT, QoL, and Health Policy Studies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCREENING QUESTIONS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCan\u0026rsquo;t Tell\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS1. Are there clear research questions?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS2. Do the collected data allow to address the research questions?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. QUALITATIVE STUDIES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1. Is the qualitative approach appropriate to answer the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2. Are the qualitative data collection methods adequate to address the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3. Are the findings adequately derived from the data?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4. Is the interpretation of results sufficiently substantiated by data?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5. Is there coherence between qualitative data sources, collection, analysis and interpretation?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. QUANTITATIVE DESCRIPTIVE STUDIES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1. Is the sampling strategy relevant to address the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2. Is the sample representative of the target population?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3. Are the measurements appropriate?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4. Is the risk of nonresponse bias low?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5. Is the statistical analysis appropriate to answer the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5. MIXED METHODS STUDIES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1. Is there an adequate rationale for using a mixed methods design to address the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2. Are the different components of the study effectively integrated to answer the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3. Are the outputs of the integration of qualitative and quantitative components adequately interpreted?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4. Are divergences and inconsistencies between quantitative and qualitative results adequately addressed?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5. Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMost studies were conducted in urban settings (Nairobi 28, 47%, Eldoret 10, 17%, Kisumu 8, 13%, Mombasa 6, 10%), with only 6/60 (10%) reporting rural data. Studies primarily focused on breast (38, 63%), cervical (30, 50%), prostate (16, 27%), esophageal (11, 18%), and colorectal cancers (9, 15%), with some addressing multiple cancers. Sample sizes ranged from 2 to 37,500 (median 151, IQR 77\u0026ndash;334), with participants predominantly female (72%), aged 40\u0026ndash;68.5 years, and of low-to-middle socioeconomic status (77% unemployed, household income\u0026thinsp;\u0026lt;\u0026thinsp;KES 5,000/month). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes key study characteristics.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStudy Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor(s) (Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy Design\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCancer Type(s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample Size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcomes Measured\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummary of Key Findings\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKisumu (JOOTRH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional (Mixed-methods)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, QoL, NHIF impact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant financial toxicity, very low insurance uptake (9% on NHIF), Fund covers inpatient services only, high OOP on medications, transport, diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. \u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional descriptive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple (not specified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL (psychological distress, depression, functioning)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreasing cancer stage correlated with higher disability (p\u0026thinsp;=\u0026thinsp;0.001), depression(p\u0026thinsp;=\u0026thinsp;0.038) and reduced functioning, high prevalence of mental disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. \u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi, Eldoret, Mombasa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeeds assessment survey\u0026thinsp;+\u0026thinsp;intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetastatic Breast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, unmet needs, intervention uptake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychological (63%), physical support (60.5%) and healthcare system (55.4%) needs highest unmet needs. Better QoL associated with urban residence, internet access and stable disease. Low breast cancer knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdverse events prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh prevalence (100%) of adverse events, ulcerated sores (52.8%) dysuria (7.5%) thrombocytopenia (5.6%) mostly probable (80.1%) per Naranjo scale, predominantly in radiotherapy patients (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, financial difficulties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69% poor HRQoL (mean global health score 41.99 SD\u0026thinsp;=\u0026thinsp;31.4); early-stage disease patients 7.3 times more likely to have good HRQoL (AOR\u0026thinsp;=\u0026thinsp;7.3 95% CI\u0026thinsp;=\u0026thinsp;2.4\u0026ndash;21.7 p\u0026thinsp;=\u0026thinsp;0.000); patients with no comorbidities 3.1 times more likely to have good HRQoL (COR\u0026thinsp;=\u0026thinsp;3.1 95% CI\u0026thinsp;=\u0026thinsp;1.1\u0026ndash;9.1 p\u0026thinsp;=\u0026thinsp;0.037). Advanced disease stage and comorbidities predict poor HRQoL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective cross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment adverse effects (nephrotoxicity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45% prevalence of cisplatin-induced nephrotoxicity (36% grade 1, 9% grade 2). Comorbidities (AOR\u0026thinsp;=\u0026thinsp;8.4 p\u0026thinsp;=\u0026thinsp;0.02) hypertension (AOR\u0026thinsp;=\u0026thinsp;3.4 p\u0026thinsp;=\u0026thinsp;0.02) and \u0026ge;\u0026thinsp;3 cycles (AOR\u0026thinsp;=\u0026thinsp;4.5 p\u0026thinsp;=\u0026thinsp;0.027) significant risk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. \u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Prostate, Lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, mortality, remission rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMortality rates: 3% (breast), 4.9% (prostate), 10% (lymphoma); Most patients had partial remission (45.5% breast, 45.1% prostate, 42% lymphoma); Good overall health-related QoL (64% breast, 85% prostate, 58% lymphoma); Predictors of mortality: age\u0026thinsp;\u0026gt;\u0026thinsp;60, comorbidities, distant metastasis, advanced stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple regions (Nairobi, Mombasa, countrywide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, access barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost and transportation major barriers for both cohorts; 53.8% with breast cancer forwent care due to cost; 91.2% reported financial impact after cancer diagnosis, 44.9% reported inadequate insurance reimbursement for medical costs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 counties (Kakamega, Kisumu, Nakuru, Uasin Gishu, Baringo, Nairobi, Nyeri, Machakos, Mombasa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptive survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Esophageal, Prostate, Lymphoma, Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess barriers, late-stage diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMost prevalent cancers: breast and cervical (women), esophageal and prostate (men); 80% cases diagnosed late at advanced stages; limited screening/treatment capacity, especially in rural areas; private facilities offer more services/ mostly in Nairobi; Private facilities offer more services than public\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEldoret (MTRH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional descriptive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL (depression prevalence)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.4% prevalence of depression; late-stage cancer (OR: 1.61, p\u0026thinsp;=\u0026thinsp;0.319), employment (OR: 3.7, p\u0026thinsp;=\u0026thinsp;0.058), and chemotherapy (neoadjuvant OR: 9.43, palliative OR: 9.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) significantly associated with depression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (nationwide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparative analysis (quantitative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple (Breast, Cervical, Esophageal, Colorectal, Prostate, others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine access, affordability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll WHO EML regimens unaffordable/ Generic cytotoxic affordable for governments; targeted therapies (e.g. Trastuzumab imatinib) unaffordable without 93\u0026ndash;99% price reductions; no regimens affordable OOP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12. \u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nairobi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional (Mixed-methods: quantitative questionnaire and qualitative FGD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast and Cervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (quantitative)\u0026thinsp;+\u0026thinsp;10 (FGD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, coping strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh prevalence of psychological effects: anxiety (79%), negative body image (65.6%), low self-esteem (63.1%), loneliness (55.4%), sadness (51.6%). Effects aggravated by low income (\u0026lt;\u0026thinsp;20000 KSH/month, 73.2% of participants) and more chemotherapy sessions (r\u0026thinsp;=\u0026thinsp;0.51); age (r=-0.300) and marital status (married, r=-0.389) associated with fewer effects. FGD themes include psychological stress (body image, loneliness, emotional changes, cognitive effects) and coping (prayers, finding reason to live). Impacts treatment adherence.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKisumu (JOOTRH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional descriptive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, stage presentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor physical (60%) and functional (66%) well-being linked to late-stage presentation (Stage III/IV: 73%); fair overall QoL (57%); stage IV (54%) and III (19%) predominant; significant association between cancer stage and QoL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi, Nyeri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified (various cancers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL (pain, weight loss, sleep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow recovery outcomes: Mean 47.0 (SD 9.465); Pain: High pain (32.9%); Weight: Loss (80.5%); Sleep: Poor (57.3%); QoL: Poor (56.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (tertiary hospital)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGynecological (Cervical, Endometrial, Ovarian, Vulvar, Vaginal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL (sexual dysfunction, body image, social support)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85% sexual dysfunction; lubrication most affected (mean 0.91) aOR\u0026thinsp;=\u0026thinsp;0.05) stage 3 (aOR\u0026thinsp;=\u0026thinsp;9.81) low social support (aOR\u0026thinsp;=\u0026thinsp;1.29) predict dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide (Nairobi, Eldoret, Mombasa, Nakuru, Nyeri, Kisii)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy reviews with qualitative components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers (emphasis on Cervical, Breast, Prostate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy barriers, stakeholder roles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicies established legal and implementation frameworks; Gaps in financing, human resources, decentralization; Key informant survey identified barriers: high costs, stigma, poor communication, centralized services; Stakeholder analysis highlighted roles of government, NGOs, private sector, academia, media, international groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional cost-of-illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical, Breast, Prostate, Esophageal, others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, treatment costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh treatment costs (avg. KES 143132); medicines and inpatient admission are major cost drivers; higher costs in private sector\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenitourinary, Gastrointestinal, Head and Neck, Breast, Musculoskeletal, Lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment adverse effects (neuropathy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.6% prevalence of cisplatin-induced peripheral neuropathy (CIPN); 81% mild (grades 1\u0026ndash;2); 3.6% grade 4; overweight/obese patients nearly all developed CIPN (not statistically significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEldoret (MTRH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Prostate, Kaposi Sarcoma, Lung, Colon, Esophagus, Pancreas, Rectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, financial difficulties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal health/QOL score 53\u0026thinsp;\u0026plusmn;\u0026thinsp;27; functional scores 51\u0026ndash;68; symptom scores 12\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (AKUHN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative (mixed-methods pilot)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, financial toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh prevalence of stress, anxiety, depression; negative impacts on mental health, QoL; financial burdens, spousal relationship strain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi, Kisumu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative (key informant interviews)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable (health financing focus)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy effectiveness, financial toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy effectiveness, financial toxicity\u003c/p\u003e\n \u003cp\u003eUnaffordable premiums and inadequate infrastructure hinder UHC; policy implementation gaps identified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRift Valley (provincial hospital)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservational with historical controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, care quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarriers to cervical screening: poor access, lack of awareness, socio-cultural influences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi County\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional mixed-methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Colorectal Leukemia, Nasopharyngeal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, QoL, stigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh financial toxicity/discrimination, reduced QoL, stigma among patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEldoret (MTRH, Alexandria Equra)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLongitudinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEsophageal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, treatment outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific QoL indicators identified as prognostic factors; baseline HRQoL mean 107.1 (compromised QoL); post-treatment improvement with chemotherapy\u0026thinsp;+\u0026thinsp;surgery (p\u0026thinsp;=\u0026thinsp;0.04), deterioration with radiotherapy alone (p\u0026thinsp;=\u0026thinsp;0.0092); baseline HRQoL significantly associated with post-treatment HRQoL (p\u0026thinsp;=\u0026thinsp;0.0065).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi, Mombasa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative cross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, access barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial burden, late-stage diagnosis, cultural taboos, low referral rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative longitudinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, side effect management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients silently endure post-treatment symptoms (fatigue, alopecia, skin and nail changes); need for culturally relevant education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuantitative cross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer (unspecified), Hypertension, Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, catastrophic spending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHE due to cancer significantly increases household poverty; education and urban locality reduce poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.\u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 Regional Cancer Centres\u0026thinsp;+\u0026thinsp;National Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptive (secondary data analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified (various cancers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess, stage presentation, policy outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow service availability, urban concentration, 70\u0026ndash;80% late diagnoses, \u0026gt;\u0026thinsp;50% pediatric abandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nationwide focus on NHIF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective policy analysis (mixed-methods: interviews and document analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable (focus on health financing, not specific to cancer)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 interviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy effectiveness, equity implications, informal sector participation, inefficiencies in purchasing/payment,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow NHIF coverage, only 17% of Kenya\u0026rsquo;s population covered by SHI (as of 2023); 27% informal sector NHIF coverage; limited stakeholder engagement and expert advice adoption; political affiliations heavily influence policies; inefficiencies in purchasing/payment (e.g., slow reimbursements, misappropriations, favoritism); group schemes and penalties exacerbate inequity in access.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed-methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, diagnosis/treatment delays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant institutional delay in access to chemotherapy and radiotherapy due to healthcare system barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.\u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKisii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptive cross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Prostate, Leukemia, Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, social support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain relief and psychosocial counseling predominant; significant association between cancer type, treatment, and QoL scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32. \u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (national-level model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBudget impact analysis (Markov model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost-effectiveness, QALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation-based cervical cancer treatment costs \u003cspan\u003e$\u003c/span\u003e531,100 over 20 years, compared to \u003cspan\u003e$\u003c/span\u003e55,398 for ad hoc screening; systematic approach optimizes resource utilization, reduces unnecessary testing, and lowers financial burden for patients and the healthcare system.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33. \u003csup\u003e43\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMombasa County\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptive mixed-methods (FGDs, interviews, questionnaire)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, access barriers, QoL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh cost of care; barriers include transportation, stigma, poor provider communication; poor QoL due to delayed/wrong diagnoses, surgical complications, equipment failures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.\u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEldoret\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective chart review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2-Positive Breast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine access, treatment completion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow availability of HER2-targeted therapies (e.g., trastuzumab); high treatment abandonment due to high costs; financial burden reduces QoL; need for policies to improve access to targeted therapies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nairobi, Eldoret)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost analysis (itemization cost approach)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Prostate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, affordability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstantial variation in patient costs between the public and private sectors. High OOP costs for diagnostics/treatment/travel; cost of care major barrier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36. \u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi, Nakuru, Kisumu, Kakamega, Kilifi, Siaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative case study (FGDs, interviews)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Prostate, Cervical, Esophageal, Colon, others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, QoL, policy effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnaffordable premiums, inadequate infrastructure, delayed payments, fraud; need to harmonize benefit packages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37. \u003csup\u003e47\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (nationwide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical literature review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specific (NCDs including cancer)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine availability, affordability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow medicine availability in primary facilities; urban bias in distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38. \u003csup\u003e48\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachakos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Esophageal, Prostate, Kaposi Sarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, distress prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain (83.3%), problem with decision making about treatment (64.9%), fatigue (59.8%). Other issues (financial constraints and eating difficulties).\u003c/p\u003e\n \u003cp\u003eHigh distress prevalence; lower QoL in psychological domain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39. \u003csup\u003e49\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (KNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Gastrointestinal, Prostate, Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, distress prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh distress prevalence; negative impact on QoL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40. \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNairobi (pharmacies)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple (Breast, Prostate, Others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine availability, affordability, pricing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow availability of morphine; high OOP for medicines, availability highest in hospitals, followed by suppliers, and finally supply agencies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41. \u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (national, two counties)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed methods (Embedded case study)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple (oncology included)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 interviews, 51 FGD participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOncology services included in new packages but limited by infrastructure gaps; inequitable access due to pro-private facility distribution. Reforms aimed to expand coverage and reduce OOP but were hindered, requiring strategic alignment for UHC progress.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEldoret, Kisumu (MTRH, JOOTRH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL (anxiety, depression prevalence)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh anxiety (80.3%) and depression (67%) prevalence; higher in 40\u0026ndash;49 years (anxiety: 29.8% depression: 25.2%) primary education (anxiety: 46.8% depression: 42.2%) married (anxiety: 54.1% depression: 42.7%) and those with family support (anxiety: 44.5% depression: 36.2%); no significant associations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43. \u003csup\u003e52\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional (NHA, KHHEUS analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer (unspecified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, catastrophic spending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHE due to cancer significantly increases household poverty; education (OR=-0.2346 p\u0026thinsp;=\u0026thinsp;0.012) and urban locality (OR=-2.2645 p\u0026thinsp;=\u0026thinsp;0.000) reduce poverty; household size (OR\u0026thinsp;=\u0026thinsp;0.0277 p\u0026thinsp;=\u0026thinsp;0.001) increases poverty; gender (male OR=-0.0372 p\u0026thinsp;=\u0026thinsp;0.374) not significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant financial toxicity, OOP costs a major barrier to care; inadequate NHIF coverage limits evidence-based standards. Cancer medicine access hampered by stock-outs, complex procurement processes, and poor regulation of importation/quality/pricing; KEMSA\u0026apos;s bulk procurement leverages economies of scale for better pricing, with calls for pooled mechanisms, local production, and Public Procurement Act amendments. Policy impacts include NHIF oncology package but with gaps in equity/efficiency; overall, aims for sustainable financing, better\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.\u003csup\u003e54\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulatory document\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOncology services scope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetails tariffs for oncology services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.\u003csup\u003e55\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional; Policy brief, (NHA, KHHEUS analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-communicable diseases (including cancers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOOP share of CHE decreased from 23.9% (2015/16) to 19.9% (2020/21), but absolute OOP increased 20.7% from KSh 90B to 108B; per capita health expenditure rose 35% from KSh 4,914 to 6,640; government financing at 52.3%, SHI at 12.5%; insurance coverage 25.9% but inequitable (4.1% poorest vs. 57.2% richest); CHE incidence fell from 6.6% to 4.5%, impoverishment from 1.1% to 0.7%; SHI insufficient alone for UHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed methods (scoping review, interviews, FGDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast, Cervical, Esophageal, Prostate, Colorectal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited availability of essential cancer medicines, with only 44% of 24 tracer medicines in facilities; KEMSA\u0026apos;s procurement faces delays (4\u0026ndash;8 months),\u003c/p\u003e\n \u003cp\u003eHigh OOP costs (19.9% of CHE, KSh 108B in 2020/21) drive financial toxicity; 23% of health financing from OOP, NHIF covers only 17% of the population, with low informal sector uptake (27%), inefficient claims processing hinder UHC; Poor QoL due to advanced-stage diagnoses (70\u0026ndash;80% late-stage), inadequate palliative care, proposed hub-and-spoke model, Cancer Fund, and NCI-K operationalization aim to improve access; policies advocate for price regulation, pooled procurement, and increased financing, but face challenges from poor coordination,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.\u003csup\u003e56\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy Brief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers (Cervical noted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy funding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer-related OOP expenditure constitutes 19.9% of CHE (KSh 108B in 2020/21), with NCD spending at KSh 57.8B; NHIF covers only 17% of the population, with low informal sector uptake (27%), limiting financial protection for cancer care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.\u003csup\u003e57\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional survey (KHFA) with desk review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers (Cervical, Breast, Prostate, Colorectal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,896 facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine availability, access barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited availability of essential cancer medicines; only 15% of facilities have morphine for palliative care, Chemotherapy restricted to 11 public hospitals, High cost of cancer diagnosis and treatment contributes to significant financial impoverishment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuideline development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine availability, treatment protocols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandardized protocols for diagnosis/treatment/care; limited cost data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.\u003csup\u003e59\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional survey /policy brief (KHFA) with desk review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers (Palliative care focus)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,980 facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine availability, financial toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow availability, only 44% of 24 tracer essential medicines available in primary health facilities, with none having all 24. Morphine in 10% facilities; primary facilities 33\u0026ndash;50% vs. 70% in hospitals;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52. \u003csup\u003e60\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Kisumu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative (ethnographic fieldwork with therapeutic itineraries)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical, Endometrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (focus on two middle-class women therapeutic itineraries, with broader data from other cancer patients)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, access barriers, QoL, precarity in private healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle-class cancer patients face significant financial toxicity due to high out-of-pocket (OOP) costs, reliance on private healthcare, and inadequate NHIF coverage. Prolonged treatment leads to catastrophic costs, asset depletion, and debt, exacerbating precarity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53. \u003csup\u003e61\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003cp\u003e(National)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional, Policy brief (NHA, KHHEUS analysis)\u003c/p\u003e\n \u003cp\u003eNon-communicable diseases, including cancer)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal health expenditure (THE) increased to USD 4.9B; OOP at 24% (KSh 108B in 2020/21); government financing at 52.3%, SHI at 12.5%; insurance coverage at 25.9% but inequitable (4.1% poorest vs. 57.2% richest); NCDs, including cancer, drive high OOP, necessitating sustainable domestic financing for UHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.\u003csup\u003e62\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (National)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy document / Expenditure framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral (includes cervical, colorectal, oncology services)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, policy effectiveness, medicine access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs (24% of THE, KSh 108B in 2020/21) drive financial toxicity; cancer medicine access limited by stock-outs, inadequate KEMSA procurement, and urban bias; SHIF implementation (2.75% income-based contributions, min KES 300, max KES 5,000 monthly) reduces OOP for oncology services (e.g., chemo KES 5,000/session, radiotherapy KES 3,600/session); ECCIF supports chronic conditions; funding gaps persist, with only 17% NHIF coverage and low informal sector uptake (27%); recommends increased domestic financing and infrastructure investment to improve access and equity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.\u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKiambu County\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCase study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple (Prostate, Esophageal, Cervical, Cholangiocarcinoma, Others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 caregivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial toxicity, QoL, policy effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs; financial ruin to families; inadequate NHIF coverage with treatment costs draining household resources, leading to catastrophic health expenditure (CHE) and distress financing (DF)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56. \u003csup\u003e64\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nairobi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross sectional, Retrospective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOesophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edisease progression, treatment response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMortality rate 43.1%; 11.1% developed distant metastases; 20.1% disease progression despite treatment; 13.0% non-response; 1- and 5-year survival rates 86% and 25%; In advanced stages (III/IV), radiotherapy (AHR 3.3), chemotherapy (AHR 3.9), chemoradiation (AHR 5.6) significantly improved survival; Need for early detection and timely treatment to improve outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57. \u003csup\u003e65\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nairobi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptive cross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified (various cancers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQoL, influencing factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, cancer stage, time off treatment, education, and religious affiliation significantly predicts QoL; emphasizes early detection, treatment, and spiritual support to improve QoL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.\u003csup\u003e66\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (National)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuideline development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine availability, treatment protocols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya Essential Medicines List (KEML) 2023 outlines essential cancer medicines aligned with WHO EML; includes chemotherapy, hormonal therapies (e.g., tamoxifen), and supportive care (e.g., morphine); aims to standardize treatment protocols and improve access through KEMSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.\u003csup\u003e67\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nationwide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy document/Tariff schedule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers (emphasis on Breast, Cervical, Prostate, Colorectal, Childhood)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy effectiveness, financial toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetails tariffs for oncology services under SHIF to reduce OOP; chemotherapy KES 5,000/session, 1st Line treatment \u0026ndash; Limit of Kes. 400,000 from SHIF and KES. 250,000 from ECCIF, 2nd Line treatment \u0026ndash; KES. 650,000 from ECCIF, aims to improve access and equity in cancer care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60. \u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya (Nationwide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy document / Strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy effectiveness, financial toxicity, medicine access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAims to reduce premature mortality by a third by 2028 through prevention, early detection, diagnosis, treatment, palliative care; emphasizes UHC, equity, evidence-based interventions; discusses integration with NHIF/SHIF for financing, improving access to medicines via KEMSA procurement, addressing OOP costs, and enhancing QoL via survivorship and palliative care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eNotes: Sample Size\u003c/strong\u003e: \u0026quot;N/A\u0026quot; indicates studies without direct patient recruitment (e.g., policy analyses, facility surveys, or model-based studies). \u003cstrong\u003eSetting\u003c/strong\u003e: Refers to the healthcare sector (public, private, faith-based, NGO) and specific facilities where applicable (e.g., KNH\u0026thinsp;=\u0026thinsp;Kenyatta National Hospital, MTRH\u0026thinsp;=\u0026thinsp;Moi Teaching and Referral Hospital, JOOTRH\u0026thinsp;=\u0026thinsp;Jaramogi Oginga Odinga Teaching and Referral Hospital, AKUHN\u0026thinsp;=\u0026thinsp;Aga Khan University Hospital Nairobi). \u003cstrong\u003eOutcomes Measured\u003c/strong\u003e: Aligned with the four objectives, including financial toxicity (OOP costs, catastrophic spending, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ecoping strategies), QoL (quantitative tools, qualitative themes), medicine access/affordability, and policy effectiveness\u003c/span\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eSynthesis of Results\u003c/h3\u003e\n\u003cp\u003eThe primary assessment focused on access to essential cancer medicines (prices, availability, affordability), financial toxicity, quality of life, and health policy effectiveness. No study investigated all the four interconnected aspects in a unified manner. The focus of the studies varied from access and affordability, to financial toxicity and its coping mechanisms; from QoL assessments to health policy evaluations and their implementation barriers, with some studies addressing multiple outcomes. The findings are organized thematically into the four outcomes.\u003c/p\u003e\n\u003cp\u003eAbout 12 studies (19%) reported medicine access using WHO/HAI methodology or Kenya Essential Medicines List (KEML) assessments. Pricing data showed high costs. A study by \u003csup\u003e3\u003c/sup\u003e showed minimum and maximum medicine prices across providers. Minimum prices ranged from KES 140 for Methotrexate to KES 176,000 for Pembrolizumab 100 mg. \u003csup\u003e3 45\u003c/sup\u003e reported specific pricing, with public sector costs for treating stages I-III of breast and cervical cancers ranging from \u003cspan\u003e$\u003c/span\u003e1,340\u0026ndash;\u003cspan\u003e$\u003c/span\u003e1,543, while private sector costs were significantly higher at \u003cspan\u003e$\u003c/span\u003e7,500\u0026ndash;\u003cspan\u003e$\u003c/span\u003e11,862. General chemotherapy costs ranged from KES 6,000\u0026ndash;600,000 per course. \u003csup\u003e8\u003c/sup\u003e Targeted therapies like Trastuzumab required 69.67\u0026ndash;151.74 days of minimum wage per cycle. \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMedicine availability varied significantly by facility type across the studies. National facility surveys, such as the Kenya Health Facility Assessment \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, reported an overall availability of 44% for 24 tracer medicines, and 15% for morphine in public facilities (n\u0026thinsp;=\u0026thinsp;2,896 facilities). Single-facility studies indicated higher availability in tertiary hospitals (50.8%, n\u0026thinsp;=\u0026thinsp;4 hospitals) for drugs like Carboplatin (78.9%) and Trastuzumab (73.7%). Supplier and agency assessments\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e reported lower availability at supply agencies, 33.5%(n\u0026thinsp;=\u0026thinsp;5) for tier 1 suppliers, and 40.2% (n\u0026thinsp;=\u0026thinsp;8) for tier 2 suppliers, with generics more available (46.9%) than originator brands (6.7%). Notably, 78.3% of studies (47/60) lacked specific availability data, particularly for private facilities, precluding direct comparison.\u003c/p\u003e\n\u003cp\u003eAffordability was poor, with all medicines exceeding the WHO affordability threshold of one day\u0026rsquo;s wage, rendering them unaffordable. Treatments involving biological therapies such as Trastuzumab were significantly more expensive, requiring several months of minimum wage. One cycle of chemotherapy involving Doxorubicin/Cyclophosphamide (HER2 negative) required 3.15\u0026ndash;9.69 days of minimum wage; Cyclophosphamide then Paclitaxel/Trastuzumab (HER2 positive) required 78.36\u0026ndash;162.42 days; Trastuzumab, 69.67\u0026ndash;151.74 days; Docetaxel, 9.34\u0026ndash;10.28 days \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The cheapest supplier and cheapest private hospital often provided lower costs compared to Kenyatta National Hospital, the main cancer referral hospital in Kenya, particularly for expensive regimens involving Trastuzumab (Table\u0026nbsp;7: supplementary).\u003csup\u003e3 68\u003c/sup\u003e, highlighted tariffs in the benefit package of social health insurance Act no, 16, to reduce costs including chemotherapy KES 5,000/session, and a cap of KES 400,000 for treatment/diagnostics). Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFindings on Prices, Availability, and Affordability of Cancer Medicines in Kenya\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAvailability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAffordability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic sector: \u003cspan\u003e$\u003c/span\u003e1,340\u0026ndash;\u003cspan\u003e$\u003c/span\u003e1,543 for breast and cervical cancer stages I\u0026ndash;III; Private: \u003cspan\u003e$\u003c/span\u003e7,500\u0026ndash;\u003cspan\u003e$\u003c/span\u003e11,862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs; costs exceed average household expenditure (\u003cspan\u003e$\u003c/span\u003e413/annum); large proportion lack health insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll WHO EML regimens unaffordable OOP; no specific pricing provided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.4% alignment with WHO EML; generic cytotoxics available, targeted therapies (e.g., Trastuzumab, Imatinib) limited\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll regimens unaffordable; targeted therapies require 93\u0026ndash;99% price reductions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported; public and private sector access noted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.8% of breast cancer patients forwent care due to cost; 91.2% reported household financial impact; 44.9% reported inadequate insurance reimbursement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage treatment cost: KES 143,132; Chemotherapy: KES 6,000\u0026ndash;600,000 per course\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs; medicines and inpatient admissions are major cost drivers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic sector: \u003cspan\u003e$\u003c/span\u003e180 for diagnostics, \u003cspan\u003e$\u003c/span\u003e85\u0026ndash;\u003cspan\u003e$\u003c/span\u003e1,500 for treatment; Chemotherapy: \u003cspan\u003e$\u003c/span\u003e300 per course at KNH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCryotherapy and LEEP available on-site; chemotherapy referred to KNH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs: \u003cspan\u003e$\u003c/span\u003e100\u0026ndash;\u003cspan\u003e$\u003c/span\u003e300 for diagnostics; cost barriers lead to forgoing care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy administration: KES 4,600, not covered by NHIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequent stockouts, especially for chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh costs due to drug prices and transport; limited NHIF coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer treatment costs: \u003cspan\u003e$\u003c/span\u003e1,500\u0026ndash;\u003cspan\u003e$\u003c/span\u003e4,000/year (public), \u003cspan\u003e$\u003c/span\u003e2,500\u0026ndash;\u003cspan\u003e$\u003c/span\u003e7,500/year (private)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.27% of households experienced catastrophic spending; CHE increases household poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrastuzumab: KES 39,900/month (USD 399, 1.33 doses at KES 30,000/unit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrastuzumab available but limited; only 33.4% completed 18 cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP and travel costs; high treatment abandonment due to cost\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e47\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44% availability for 24 tracer medicines in public sector; none in primary facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38% faced catastrophic health expenditure; 16% avoided care due to costs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnit prices: KES 140 (Methotrexate 50 mg), KES 1,000 (Cisplatin 50 mg), KES 50,000 (Bevacizumab 400 mg), KES 86,432 (Trastuzumab 600 mg), KES 176,000 (Pembrolizumab 100 mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitals: 50.8% (e.g., Carboplatin 78.9%, Trastuzumab 73.7%); Suppliers: 37.6%; Agencies: 29.4%; generics (e.g., Capecitabine 73.7%) more available than originators (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll medicines unaffordable; chemotherapy cycles require 3.15\u0026ndash;162.42 days of minimum wage (KES 411/day); e.g., Doxorubicin/Cyclophosphamide: 3.15\u0026ndash;9.69 days; Paclitaxel/Trastuzumab: 78.36\u0026ndash;162.42 days; Trastuzumab: 69.67\u0026ndash;151.74 days; Docetaxel: 9.34\u0026ndash;10.28 days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited availability; only 44% of 24 tracer medicines in facilities; frequent stockouts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs (19.9% of CHE, KSh 108B in 2020/21); 50% avoid care due to cost; limited NHIF coverage (17% population, 27% informal sector)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e59\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMorphine: 5% in rural facilities, 50% in level 4, 100% in level 5/6; overall 44% for 24 tracer medicines (n\u0026thinsp;=\u0026thinsp;2,980 facilities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP burden; 90% of medicine purchases OOP; forgoing care implied\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment costs described as \u0026apos;astronomical\u0026apos;; no specific pricing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy, surgery, radiotherapy, immunotherapy available, mostly in Nairobi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh costs lead to seeking treatment abroad (e.g., India, Uganda); cost barriers limit access\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e55\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified; implied limited due to high OOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOOP at 24% of THE (KSh 108B in 2020/21); higher burden on lower wealth quintiles; insurance coverage inequitable (4.1% poorest vs. 57.2% richest)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e67\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy: KES 5,000/session; Radiotherapy: KES 3,600/session; SHIF cap: KES 400,000 (USD 3,095) for treatment/diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited by stockouts, inadequate KEMSA procurement, urban bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh OOP costs (24% of THE, KSh 108B in 2020/21); limited NHIF coverage (17% population, 27% informal sector); SHIF aims to reduce OOP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: KES\u0026thinsp;=\u0026thinsp;Kenyan Shillings; $ = US Dollars; N/S\u0026thinsp;=\u0026thinsp;Not Specified\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFinancial toxicity was reported in 10/60 studies (17%), with limited available data suggesting OOP costs ranging from $1,298\u0026ndash;$12,713/year. \u003csup\u003e46\u003c/sup\u003ePublic sector costs for stages I\u0026ndash;III breast and cervical cancers were $1,340\u0026ndash;$1,543, while private sector costs were $7,500\u0026ndash;$11,862. \u003csup\u003e45\u003c/sup\u003e Cervical cancer patients faced OOP costs of 87% for medication, 84% for travel, and 75% for diagnostics \u003csup\u003e16\u003c/sup\u003e. OOP constituted 24% of total health expenditure (KSh 108B in 2020/21), with limited NHIF uptake (17% population, 27% informal sector) exacerbating the burden. \u003csup\u003e10\u003c/sup\u003e CHE affected 20.27\u0026ndash;54% of households. \u003csup\u003e38,43\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCoping mechanisms included borrowing (81%), selling assets (73%), and seeking charity (13%) or family/church support (10%) \u003csup\u003e16\u003c/sup\u003e. Treatment abandonment was noted, with 53.8% of breast cancer patients forgoing care due to costs \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The sparse data and wide OOP variation suggest evidence base is insufficient for comprehensive cost-effectiveness analysis or definitive affordability assessments/ population-level projections, as 83.33% (50/60) of studies lacking specific OOP or CHE data. (Table\u0026nbsp;8 supplementary)\u003c/p\u003e\n\u003cp\u003eAbout 9/60 studies (15%) reported Quality of life (QoL), indicating compromised QoL, with median global health status scores of 41.99\u0026ndash;53 (EORTC QLQ-C30, FACT-Cx). Breast cancer patients reported 64% good QoL, prostate 85% \u003csup\u003e20,22\u003c/sup\u003e. Advanced disease (AOR\u0026thinsp;=\u0026thinsp;7.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and comorbidities (OR\u0026thinsp;=\u0026thinsp;3.1, p\u0026thinsp;=\u0026thinsp;0.037) predicted poor QoL (Shajahan Ahamed \u0026amp; Degu, 2023). High symptom burdens included fatigue (56%), pain (65%), and financial difficulties (79%). \u003csup\u003e30\u003c/sup\u003e Some studies reported thematic findings without quantitative tools.\u003c/p\u003e\n\u003cp\u003eQualitative themes from 10 studies highlighted psychosocial distress, stigma, social isolation, and physical limitations. In the physical domain, symptom burden and functional limitations were key, including fatigue, pain, weakness, dry mouth, insomnia/hypersomnia, and mobility/self-care difficulties. Cervical cancer patients reported poor physical (60%) and emotional QoL, with hygiene challenges and family dependence; multi-cancer studies noted reduced occupational functioning.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Better QoL associated with early-stage disease, urban residence, and stable remission. \u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePsychological themes included depression (59.4% in breast cancer), influenced by late-stage diagnosis (OR\u0026thinsp;=\u0026thinsp;1.61, p\u0026thinsp;=\u0026thinsp;0.319), employment (OR\u0026thinsp;=\u0026thinsp;3.7, p\u0026thinsp;=\u0026thinsp;0.058), and chemotherapy (neoadjuvant OR\u0026thinsp;=\u0026thinsp;9.43, palliative OR\u0026thinsp;=\u0026thinsp;9.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), leading to reduced mental resilience and \u0026quot;constant worry\u0026quot; about survival/family. \u003csup\u003e24,31\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSocial domain themes featured isolation, stigma, and family burden, with avoidance/discrimination (awkwardness: 2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75; severity: 3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29). Breast/cervical patients faced rejection/over-dependence, strained relationships, reduced social life, \u0026quot;community stigma,\u0026quot; and policy-level discrimination (2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17), intersecting with financial strain via borrowing/charity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Spiritual themes showed religion as hope/coping, but unmet needs worsened distress in metastatic cases \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Healthcare/systemic domain included unmet information needs (low cancer knowledge: 23.6%), system delays, and financial difficulties (79%) amplifying access issues; emotional/social functioning deficits from urban-centric services/lack of support\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;9: supplementary).\u003c/p\u003e\n\u003cp\u003eAbout 15/60 studies (25%) evaluated health policy effectiveness, focusing on NHIF, SHA, and the National Cancer Control Strategy (2023\u0026ndash;2027). Stratifying by policy era, 13/15 policy studies (87%) focused on pre-2023 NHIF implementation, reporting low NHIF uptake (9\u0026ndash;17%) primarily covering inpatient services, and limited impact on treatment affordability, covering only 4 of 8 chemotherapy cycles \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Post-2023 SHIF data (limited to 2/60 studies) showed potential to reduce OOP costs via mandatory contributions (2.75% income-based) and the Emergency, Chronic and Critical Illness Fund (ECCIF), covering oncology services (chemotherapy KES 5,000/session, 1st line treatment limit KES 400,000 from SHIF, KES 250,000 from ECCIF), but faced infrastructure and funding gaps. \u003csup\u003e62,68\u003c/sup\u003e No studies directly compared pre- and pos insurance policy transition outcomes. Health financing remained limited at 3.5% of GDP (below WHO\u0026rsquo;s 6%), with OOP costs at 24% of total health expenditure (KSh 108B in 2020/21), and weak cancer registries (64% missing staging data) hindered progress\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Decentralization efforts aimed to improve access, but oncology services remained urban-centric, with limited screening/treatment capacity in rural areas \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. A medium-term expenditure report, highlighted KEMSA\u0026rsquo;s role in procuring essential cancer medicines such as chemotherapy, hormonal therapies, aligned with WHO EML, but noted barriers including stockouts, inadequate procurement, and urban bias\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Another study reported NHIF/SHIF schemes and UHC implementation, but identified barriers such as limited NHIF uptake (17% population, 27% informal sector), and infrastructure/capacity limits. \u003csup\u003e12\u003c/sup\u003e (Table\u0026nbsp;10: supplementary)\u003c/p\u003e\n\u003cp\u003eFrom the reviewed documents, 8/60 studies identified the need to expand NHIF/SHIF coverage to reduce OOP costs and FT. 3/60 studies identified the need to strengthen KEMSA. Other policy suggestions included subsidizing costs (3/60), enhancing early detection and screening programs (7/60), integrating psychological and QoL support (5/60), strengthening health infrastructure and workforce (4/60), enhancing cancer registries (2/60), monitoring and management of treatment adverse effects (3/60), and promoting digital and community support to provide education and psychological care (1/60). (Table\u0026nbsp;11: supplementary)\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis scoping review of 60 studies (2018\u0026ndash; May 2025) mapped evidence on access to essential cancer medicines, financial toxicity, quality of life, and health policy performance for breast, cervical, prostate, colorectal, and esophageal cancers in Kenya. The findings show that high treatment costs, limited availability of key cancer medicines, and inadequate insurance coverage remain persistent determinants of financial toxicity and diminished QoL among adult cancer patients. Consistent with patterns observed across LMICs, the review highlights fragmented systems of access, financing, and policy implementation that interact to deepen treatment inequities. This synthesis extends prior literature by concurrently mapping evidence across access, FT, QoL, and health policy domains rather than treating them as isolated components.\u003c/p\u003e\u003cp\u003eThe high cost and limited availability of cancer medicines in Kenya reflect broader regional trends, yet several gaps within Kenya are evident. While LMICs generally experience supply chain interruptions and reliance on out-of-pocket financing, Kenya\u0026rsquo;s medicine procurement delays often extending 4\u0026ndash;8 months, contrast sharply with Uganda\u0026rsquo;s high availability, where centralized procurement systems have achieved availability levels of 85.8%.\u003csup\u003e70\u003c/sup\u003e Compared to a study by \u003csup\u003e71\u003c/sup\u003e, which mapped African medicine access pathways, this review offers detailed insights on how procurement inefficiencies, pricing structures, and reimbursement policies shape patient access and financial burden. It reveals high costs of cancer medicines in Kenya. The cost of standard chemotherapy such as Doxorubicin/Cyclophosphamide requiring 3.15\u0026ndash;9.69 days of minimum wage, exceed World Health Organization (WHO) affordability thresholds. For targeted therapies, the gap becomes even more pronounced. Trastuzumab requires 69.67\u0026ndash;151.74 days of minimum wage, effectively placing guideline-recommended care beyond reach for the majority of Kenyan patients.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePrice disparities between the public and private sectors further entrench inequity. Breast cancer treatment ranges from US\u003cspan\u003e$\u003c/span\u003e1,340\u0026ndash;1,542 in public hospitals but escalates to US\u003cspan\u003e$\u003c/span\u003e7,500\u0026ndash;11,862 in private facilities; an eightfold increase in some cases. \u003csup\u003e39\u003c/sup\u003e Given that 73% of Kenyans rely on the public sector, such cost differentials heighten inequities in access, continuity of care, and survival outcomes. The review also noted scarcity of data for colorectal and esophageal cancers (4/60 studies) limiting cost-effectiveness analyses for high mortality cancers. Breast and cervical cancers dominate research agendas while gastrointestinal cancers remain underexplored, despite their rising incidence in LMICs. The paucity of data constrains policy decision-making and hinders the development of targeted financing and procurement reforms.\u003c/p\u003e\u003cp\u003eFinancial toxicity emerged as the most immediate and severe patient-level consequence across the included studies. Out-of-pocket (OOP) payments, ranging from US\u003cspan\u003e$\u003c/span\u003e1,298 to US\u003cspan\u003e$\u003c/span\u003e12,713 annually, placed substantial pressure on households. About 20.3\u0026ndash;54% of households experienced financial toxicity and elevated risks of catastrophic health expenditure (CHE). Medication costs (87%), transport (84%), and diagnostic services (75%) were major cost drivers contributing to this burden. \u003csup\u003e16,38,46\u003c/sup\u003e Coping strategies, including borrowing (81%), asset sales (73%), fundraising, and treatment abandonment (53.8%), illustrate the depth of financial strain experienced by patients and families. \u003csup\u003e4,16,43,63\u003c/sup\u003e These patterns are consistent with a recent systematic review reporting a pooled CHE of 43.3% (95% CI 36.7\u0026ndash;50.1) among cancer patients \u003csup\u003e\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. However, substantial variation in OOP estimates, methodological inconsistencies, and pervasive data gaps constrain comparability across studies. Notably, 78% of included studies did not report OOP data, and 88% lacked CHE estimates, similar to concerns observed in other global systematic reviews. \u003csup\u003e73,74\u003c/sup\u003e Underutilization of validated financial toxicity instruments, particularly the COST-FACIT and PROFFIT measures, limits the ability to generate robust, patient-centered assessments of financial hardship. A recent methodological review \u003csup\u003e\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e emphasizes the importance of standardized FT measurement. Yet, these tools remain underutilized in Kenya.\u003c/p\u003e\u003cp\u003eQuality of life findings further demonstrate that financial distress translates into significant psychological and physical hardship for cancer patients. QoL, assessed in only 9 of the 60 included studies, was consistently poor, with EORTC QLQ-C30 global health scores ranging from 41.99 to 53, indicating compromised functioning and symptom burden. These outcomes stemmed from treatment-related symptoms (fatigue 56%, pain 65%, financial difficulties 79%), psychosocial distress (depression 43.9\u0026ndash;59.4%), and persistent financial strain. \u003csup\u003e20,30,76\u003c/sup\u003e Qualitative themes highlighted depression, anxiety, stigma, disrupted self-image and negative body perception.\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eEarly-stage diagnosis and stable disease status were associated with better QoL outcomes, with one study reporting significantly higher odds of good QoL for early-stage cervical cancer patients (AOR\u0026thinsp;=\u0026thinsp;7.3). However, heterogeneity in QoL assessment tools, including the EORTC QLQ-C30, FACT-B, HAM-D, and the Functional Evaluation of Chronic Illness Therapy limits comparability across studies and weakens the ability to generate population-level inferences. A recent LMIC-focused QoL analysis \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e demonstrated the value of standardized metrics; however, its focus on breast cancer alone disadvantages the broader gap in comprehensive QoL data for diverse cancer types in Kenya.\u003c/p\u003e\u003cp\u003eAt the policy level, this review identified persistent gaps between insurance design and the degree of financial protection actually afforded to patients. Although 15 of the 60 included studies examined cancer-related policies, progress remains mixed. NHIF coverage continues to be limited, reaching only 9\u0026ndash;17% of the population, with particularly low enrollment among the informal sector (27%). National health financing remains at 3.5% of GDP, substantially below the WHO-recommended benchmark of 6%, constraining the ability to absorb rising cancer care costs.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e Evidence on the post-2023 Social Health Authority (SHA) remains sparse (2/60 studies), limiting the capacity for rigorous evaluation. Nonetheless, the Social Health Authority (SHA) introduces potentially transformative mechanisms. The Emergency, Chronic, and Critical Illness Fund (ECCIF) and a proposed oncology benefit cap of KES 400,000, could meaningfully reduce financial hardship. But infrastructure gaps, including workforce shortages, low informal-sector uptake, and weak procurement governance dilute the potential impact of these reforms.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eProcurement inefficiencies (4\u0026ndash;8 months delays) at KEMSA cause stockouts, forcing reliance on private suppliers at higher costs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e While decentralization efforts have expanded chemotherapy access (69.1%) and palliative care (57.9%), radiotherapy remain domiciled in urban centers, restricting access for rural populations.\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Weak cancer registries; 64% missing staging data undermine evidence-based policy responsiveness and resource allocation. Patient-level experiences also highlight implementation challenges. Nearly half (44.9%) of NHIF beneficiaries reported receiving lower-than-expected reimbursement, contributing to widespread distrust and underutilization of insurance benefits.\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Overall, these findings align with WHO\u0026rsquo;s universal health coverage (UHC) framework but indicate persistent gaps in Kenya\u0026rsquo;s National Cancer Control Strategy (2023\u0026ndash;2027), consistent with LMIC policy challenges.\u003csup\u003e\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e Limited SHA data (2/60 studies) and short evaluation window (1\u0026ndash;2 years) preclude definitive conclusions regarding the early impact of recent reforms.\u003c/p\u003e\u003cp\u003eThis review adhered to PRISMA-ScR guidelines, was prospectively registered on OSF, employed the MMAT for methodological appraisal, and incorporated grey literature to enhance transparency and contextual relevance. However, this study has some limitations. Most studies available were conducted in urban regions, reducing representativeness for rural Kenya, where access barriers may be more pronounced. However, this pattern reflects concentration of oncology centers in urban areas in Kenya. Evidence for colorectal and esophageal cancers was notably limited, restricting comprehensive assessment across all major cancer types. We also noted methodological inconsistencies, particularly in cost reporting and definitions of financial toxicity and catastrophic health expenditure. Exclusion of databases (Embase, Medline, Scopus) due to access limitations may have introduced selection bias, potentially missing international studies with Kenyan sub-analyses. However, the identification of 60 eligible studies suggests that the data is sufficiently broad to capture prevailing trends in access to cancer medicines, financial toxicity, quality of life, and policy impacts in Kenya.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEvidence Before This Study\u003c/h2\u003e\u003cp\u003eGlobally, financial toxicity is recognized as a major barrier to cancer care even in high-income countries with universal health coverage. Studies report catastrophic health expenditure in 13\u0026ndash;68% of households, while public sector medicine availability of cancer medicine in East Africa is below 50%. Existing LMIC reviews on cancer care have laid a thoughtful foundation. The body of evidence though varied, highlights systemic weaknesses; low insurance penetration, procurement inefficiencies as issues of significance in cancer treatment access. In addition, the current body of evidence shows diverse methodologies in cost reporting for cancer medicines, suggesting the need for country specific analyses to illuminate national treatment access landscape.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAdded Value of This Study\u003c/h3\u003e\n\u003cp\u003eThis is the first scoping review to consolidate evidence on cancer medicine access, financial toxicity, quality of life, and policy effectiveness in Kenya. While individual studies provide valuable insights, the current evidence base is fragmented, methodologically heterogeneous, and limited in scope particularly for colorectal and esophageal cancers. Nevertheless, this review demonstrates that Kenya\u0026rsquo;s cancer treatment access stem not from medical scarcity but policy misalignment where high costs, fragmented coverage, and inefficient procurement systems perpetuate inequity. Importantly, no studies directly compared outcomes before and after the transition from NHIF to the Social Health Insurance Act (SHA), highlighting a major policy evaluation gap. The review also finds that validated tools for assessing financial toxicity and QoL are underutilized in Kenya, demonstrating an urgent need for standardized methodologies, longitudinal data, and policy-oriented research to inform the implementation of the National Cancer Control Strategy (2023\u0026ndash;2027).\u003c/p\u003e\n\u003ch3\u003eImplications of All the Available Evidence\u003c/h3\u003e\n\u003cp\u003eHigh treatment costs, procurement inefficiencies, and inadequate insurance coverage significantly undermine cancer treatment access in Kenya. Corrective strategies must integrate price regulation, pooled regional procurement, and expanded SHA coverage to reduce financial toxicity and improve quality of life (QoL). Strengthening cancer registries is essential for improving surveillance, resource planning, and policy evaluation. Adoption of validated financial toxicity and QoL instruments (e.g., COST, EORTC QLQ-C30) is critical for capturing the true burden of disease and guiding patient-centered interventions. Longitudinal and multi-stakeholder research involving patients, families, providers, and policymakers is needed to understand trajectories of financial hardship and QoL over time, particularly in the context of rising cancer incidence in Kenya.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJOO, DOO, and SAA conceived and designed the scoping review. JOO and SAA conducted study selection and data extraction. All authors contributed to data interpretation and analysis. JOO drafted the manuscript, with revisions and supervisory support from SAA and DOO. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI/we declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used is available upon reasonable request\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Kenya Ministry of Health and the University of Nairobi Digital Repository for providing access to gray literature and institutional reports.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scoping review is a preliminary investigation and part of a larger study funded by National Cancer Research Fund (NRF), (NCI-NRF001/2024), Kenya. However, the funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data are available, including full search strategy for PubMed, detailed inclusion d exclusion criteria (based on PCC framework), and data charting template (Excel format)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobocan. The Global Cancer Observatory | Globocan 2022 (version 1.1) - 08.02.2024. 2022. \u003c/li\u003e\n\u003cli\u003eKizub DA, Naik S, Abogan AA, Pain D, Sammut S, Shulman LN, et al. Access to and Affordability of World Health Organization Essential Medicines for Cancer in Sub-Saharan Africa: Examples from Kenya, Rwanda, and Uganda. Oncologist. 2022 Nov 1;27(11):958\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eMutugi L, Okalebo FA, Guantai A. N, Opanga S. A. 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American Journal of Nursing Science [Internet]. 2018;7(6):325\u0026ndash;32. Available from: http://www.sciencepublishinggroup.com/j/ajns\u003c/li\u003e\n\u003cli\u003eWang\u0026rsquo;ombe JM, Kathungu B. Palliative Care Recovery Outcomes: Nature of Recovery Outcomes Among Cancer Patients In Nairobi And Nyeri Counties, Kenya. Journal of Health, Medicine and Nursing. 2021 Dec;6(2). \u003c/li\u003e\n\u003cli\u003eObora M, Onsongo L, Ogutu JO. Determinants of sexual function among survivors of gynaecological cancers in a tertiary hospital: a cross-sectional study. Ecancermedicalscience. 2022;16. \u003c/li\u003e\n\u003cli\u003eEzzi MS, Othieno-Abinya NA, Amayo E, Oyiro ; Peter, Mcligeyo A, Robert ;, et al. Prevalence and Predictors of Cisplatin-Induced Peripheral Neuropathy at the Kenyatta National Hospital. J Global Oncol [Internet]. 2019; Available from: https://doi.\u003c/li\u003e\n\u003cli\u003eDavda J, Kibet H, Achieng E, Atundo L, Komen T. 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JCO Glob Oncol. 2024 Aug;(10). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Jaramogi Oginga Odinga University of Science and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer medicines, financial toxicity, quality of life, out of pocket costs, health policy, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-8294040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8294040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCancer, Kenya\u0026rsquo;s third leading cause of death, imposes severe health and economic burdens, driven by high costs and limited availability of cancer medicines. However, the full evidence landscape remains unclear. In this scoping review, we synthesize evidence on cancer medicine access, financial toxicity (FT), Quality of life (QoL), and policy impacts for top five cancers in Kenya, to inform cancer treatment across health systems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFollowing PRISMA-ScR guidelines, we searched PubMed, African Journals Online, Google Scholar, and grey literature (January 2018\u0026ndash;May 2025) for studies on Kenyan adults with breast, cervical, prostate, esophageal, or colorectal cancers. Eligible studies on medicine access, FT, QoL, or National/Social Health Authority (NHIF/SHA) outcomes were screened using Rayyan software. Data was extracted into a piloted Excel form, and synthesized descriptively and thematically.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf 60 included studies, cancer medicines cost 3.15\u0026ndash;162.42 days of minimum wage per chemotherapy cycle, exceeding WHO threshold. Availability was less than 50% in public facilities, with procurement delays (4\u0026ndash;8 months) causing stockouts. Treatment costs for stage I\u0026ndash;III cancers ranged from USD 1,340\u0026ndash;1,542 in public versus 10,915\u0026ndash;11,862 in private facilities. FT affected 20\u0026ndash;54% of households, with over half (53.8%) abandoning treatment due to costs. QoL (addressed in 9/60 studies) scores (median 41.99\u0026ndash;53) were poor, linked to FT and late-stage diagnosis (71% stage III/IV). Insurance coverage was partial, with SHA\u0026rsquo;s KES 400,000 cap showing potential to reduce costs despite underfunding and limited adoption of expert advice. Most studies lacked pricing (47/60) and catastrophic health expenditure data (53/60).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHigh costs, low availability, and inadequate insurance contribute to FT and poor QoL suggesting need for price regulation, expanded SHA coverage and longitudinal economic studies to address evidence gaps.\u003c/p\u003e","manuscriptTitle":"Access to Cancer Medicines in Kenya: A Scoping Review of Costs, Financial Toxicity, Quality of Life, and Policy Impacts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 05:13:15","doi":"10.21203/rs.3.rs-8294040/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7075a82c-74b3-4b44-9f24-d6e2c01274e3","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59200950,"name":"Oncology"}],"tags":[],"updatedAt":"2025-12-09T05:13:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 05:13:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8294040","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8294040","identity":"rs-8294040","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}