Cancer care treatment attrition in adults: Measurement approaches and inequities in patient dropout rates - a rapid review.

Background

Cancer treatment attrition refers to the discontinuation of prescribed cancer therapies before completion. It can significantly impact patient outcomes and cancer survival rates making it a critical concern. There is growing evidence of inequalities in cancer incidence and care globally [ 1 ]. These inequalities often result in differences in cancer incidence, stage of disease at diagnosis, treatment access, treatment adherence, and overall survival rates. For instance, minority populations and individuals with lower socioeconomic status frequently experience delayed diagnoses, limited access to advanced treatments, and higher mortality rates compared to those from more affluent areas [ 2 ]. These widely observed disparities in access to cancer care may also have manifestations in cancer care treatment and the continuation of treatment for different groups. Cancer treatment attrition may have been overshadowed by the focus on early diagnosis and access to cancer treatment that remain critical to improving survival rates. Understanding the extent of treatment attrition, why it happens and for whom, and associated inequalities may improve cancer care delivery and patient outcomes for all. Addressing potential disparities in attrition first requires effective ways of identifying it.

Within the clinical trial environment treatment attrition measurement can ensure internal and external validity. If there is high attrition, the remaining sample may no longer be representative of the initial population impacting internal validity, it can also affect the generalisability of results if specific subgroups discontinue treatment impacting external validity. If treatment attrition is not accounted for, reported outcomes may reflect the experiences of a subset of participants who tolerated or benefited from the treatment, rather than the whole cohort. Existing literature indicates that treatment attrition can be influenced by various factors, including socioeconomic status, access to healthcare, patient-related factors (e.g., comorbidities, psychological distress), and healthcare system factors (e.g., quality of care, healthcare provider bias) [ 3 ]. Despite the recognition of these factors, there is limited comprehensive evidence outside of clinical trial environments on how treatment attrition is measured, the specific reasons for discontinuation, and whether there are inequalities associated with who stops treatment.

This rapid review aims to address these gaps by exploring the current evidence on cancer treatment attrition, focusing on measurement methods, reasons for discontinuation, and evidence of inequalities in treatment attrition.

Methods

To support future work on cancer treatment attrition a rapid review was conducted to identify existing evidence outside of a clinical trial environment on measures of cancer treatment attrition, definitions, reasons for attrition, and inequalities.

Rapid reviews follow a systematic approach, but some processes are abbreviated or omitted to facilitate evidence synthesis in a shorter timeframe [ 4 ]. For this review the number of databases that were searched were restricted to MEDLINE and Embase. MEDLINE was searched via Ovid from 1946 to 7th May 2024 and Embase was searched via Ovid from 1974 to 7th May 2024. The search strategy was designed in MEDLINE and adapted for use in Embase. The strategy included index terms and keywords for the population, phenomenon of interest, and study design. Search strategies are available in the supplementary materials.

The database searches were supplemented with reference checking of relevant published systematic reviews, additional targeted searches in PubMed and Google Scholar, and targeted searches for key grey literature from relevant organisations including Macmillan Cancer Support, and the National Cancer Institute (NCI).

Studies were included if they reported on adults with any type of cancer at any stage undergoing cancer treatment (chemotherapy, radiotherapy, and/or surgery). Any quantitative or qualitative study was included if it addressed the research question and was conducted outside of a clinical trial environment. Reports published in English were included. Studies reporting on children or adolescents were excluded. Editorials, commentaries, journal clubs, study protocols, conference abstracts, and reports published in languages other than English were also excluded.

Search results were imported into a reference manager (Zotero) and deduplicated. Study screening was conducted in Rayyan [ 5 ] by a single reviewer in two phases: (1) screening based on title and abstract; (2) full-text review. Any uncertainty was discussed with a second reviewer. The final list of studies for inclusion was verified by a second reviewer. The screening process was documented and reported in a PRISMA study flow diagram [ 6 ].

Data from each included study were extracted by a single reviewer into a data extraction table in Excel and verified by a second reviewer. The following elements were extracted: country, study objectives, study design, data source, population details, intervention details, measures and definitions of attrition, attrition rates, reasons for attrition, risk factors, inequalities, and study conclusions. Study quality was assessed with the Newcastle-Ottawa Scale (NOS) [ 7 ]. Studies were stratified by patient and study characteristics and synthesised narratively.

Results

Results of literature search

The electronic searches retrieved a total of 1,353 references. Duplicates were identified and 291 records removed, leaving 1,062 references for screening. During title and abstract screening 994 records were excluded, leaving 68 references to be assessed based on full text. Following full text review, 40 studies were included, 25 were excluded, and 3 were not retrievable. See Fig. 1 for the study flow diagram.

Fig. 1 PRISMA flow diagram

Description of included studies

Most of the included studies ( N = 36) had a retrospective design [ 8 – 43 ]. Two studies [ 44 , 45 ] were prospective cohorts, one study [ 46 ] was ambispective (combines retrospective and prospective data collection), and one study was population-based [ 47 ]. Studies came from Canada, ( N = 9), USA ( N = 8), Italy ( N = 4), Europe ( N = 3), China ( N = 2), India ( N = 2), Australia and UK ( N = 1), and one study each from Australia, Austria, Denmark, England, Germany, Greece, Korea, Malaysia, Slovenia, and Spain.

Studies included patients with colorectal cancer ( N = 7), lung cancer ( N = 7), multiple myeloma ( N = 6), oesophageal or gastric cancers ( N = 4), mixed populations [ 4 ], urothelial carcinoma ( N = 3), oral cancer ( N = 2), prostate cancer ( N = 2), and one study each for breast cancer, cervical cancer, hepatocellular carcinoma, mantle cell lymphoma, and vulvar cancer. Most studies ( N = 26) included patients with advanced or metastatic disease. Six studies included mixed stages, and the remaining eight studies did not report disease staging. The number of patients in each study ranged widely from 123,943 in a population-based study [ 47 ] to 20 patients in a single-centre retrospective study [ 25 ].

Treatments included a range of chemotherapies, radiotherapy, surgery, and various combinations. One study [ 44 ] also reported referral to psycho-oncology intervention. All included studies reported some measure of attrition. Twenty studies reported reasons for attrition, 19 studies reported on risk factors, and 7 studies reported information on inequalities related to treatment attrition. Included studies are summarised in Table 1 . All studies were rated as fair quality or above on the Newcastle Ottawa scale.

Table 1 Summary of included studies Author/Year Country Study design Total N Cancer type Treatment type Abdel-Rahman 2021 [ 8 ] Canada Retrospective 4179 Colon or rectal adenocarcinoma Chemotherapy Addeo 2021 [ 9 ] Europe, Israel Retrospective 899 Lung cancer (NSCLC) EGFR-TKI treatment (gefitinib, erlotinib, or afatinib, as monotherapy or combination therapy) Basile 2021 [ 10 ] Italy Retrospective 717 Breast cancer Chemotherapy, Endocrine therapy (ET), cyclin-dependent-kinases 4/6 inhibitors (CDK4/6i) Benda 2023 [ 11 ] Austria Retrospective 571 Multiple myeloma proteasome inhibitors, immunomodulators, antibodies, and chemotherapy. Boyne 2023 [ 12 ] Canada Retrospective 2721 Colorectal cancer Systematic therapy Carruthers 2019 [ 13 ] Australia Retrospective 807 Mixed Referral for radiation therapy Chan 2015 [ 44 ] Malaysia Prospective 467 Mixed Definitive oncology treatment (i.e. chemotherapy or radiotherapy) Dobbs 2018 [ 14 ] USA Retrospective 576 Prostate cancer Referred to urologic oncology clinic Fisch 2021 [ 15 ] Germany Retrospective 191 Lung cancer (LCNEC) Systematic therapies, palliative therapies Fonseca 2020 [ 16 ] USA Retrospective •Did not receive ASCT (non-transplant, n = 22,062) •Received ASCT (transplant, n = 2763) Multiple myeloma Chemotherapy Fu 2021 [ 17 ] China Retrospective 171 Oral cavity Neoadjuvant chemotherapy, surgery Garattini 2021 [ 18 ] Italy Retrospective 890 Colorectal cancer Chemotherapy Germani 2024 [ 19 ] Italy Retrospective 179 Colorectal cancer Encorafenib plus cetuximab +/- binimetinib Groene 2015 [ 45 ] England Prospective 2313 Oesophagogastric cancer Palliative chemotherapy Janzic 2022 [ 20 ] Bulgaria, Poland, Romania, and Slovenia Retrospective 389 Lung cancer (NSCLC) EGFR TKI therapy Jensen 2019 [ 47 ] Denmark Population-based cross‐sectional study 123,943 Mixed NR Jimenez-Zepeda 2022 [ 21 ] Canada Retrospective 1729 Multiple myeloma Chemotherapy Kakish 2023 [ 22 ] USA Retrospective Clinical trial population: 7650 Database population: 48,187 Gastric or gastroesophageal junction cancers Neoadjuvant treatment and curative intent surgery Kennecke 2019 [ 23 ] Canada Retrospective 200 Colorectal cancer Systemic treatment Kronenfeld 2021 [ 24 ] USA Retrospective 116 Gastric cancer Neoadjuvant chemotherapy Kumar 2020 [ 25 ] India Retrospective 20 Vulvar cancer Surgery, chemoradiotherapy, radiotherapy Lampaki 2022 [ 26 ] Greece Retrospective 160 Lung cancer (NSCLC) EGFR-TKI therapy Martinelli 2022 [ 28 ] Austria, Belgium, France, Germany, Italy, Spain, UK Retrospective 255 Colorectal cancer Systemic treatment Mathew 2024 [ 29 ] USA Retrospective 7260 Urothelial Carcinoma Chemotherapy McCurdy 2023 [ 30 ] Canada Retrospective 5548 Multiple myeloma anti-MM agent McLean 2016 [ 31 ] Canada Retrospective 215 Colorectal cancer Chemotherapy Minson 2024 [ 32 ] Australia, UK Retrospective 389 Mantle cell lymphoma Chmotherapy More 2023 [ 33 ] Italy Retrospective 412 Multiple myeloma Chemotherapy Morgans 2022 [ 46 ] USA Ambispective 300 Urothelial Carcinoma PD- 1/L1 inhibitor therapy following chemotherapy Nieva 2022 [ 34 ] USA Retrospective 1029 Lung cancer (NSCLC) 1 L first-generation or second-generation EGFR-TKIs O’Sullivan 2021 [ 35 ] Canada Retrospective 1941 Lung cancer (SCLC) Chemotherapy with/without radiotherapy or supportive care Paul 2010 [ 27 ] India Retrospective 784 Cervical cancer Chemotherapy, radiotherapy Rim 2018 [ 36 ] Korea (Republic of) Retrospective 353 Mixed Radiotherapy Swami 2023 [ 37 ] USA Retrospective 4758 Prostate Cancer Chemotherapy, ADTs Tang 2023 [ 38 ] China Retrospective 1255 Multiple Myeloma Chemotherapy Tapia 2024 [ 39 ] Spain Retrospective 206 Urothelial Carcinoma Systemic therapy Tsang 2020a [ 40 ] Canada Retrospective 144 Hepatocellular Carcinoma 1 L sorafenib, Tsang 2020b [ 41 ] Canada Retrospective 245 Esophagogastric cancer systemic therapy Turnsek 2022 [ 42 ] Slovenia Retrospective 120 Lung cancer (NSCLC) 1 L first-generation or second-generation EGFR-TKIs Wang 2023 [ 43 ] China Retrospective 430 Oral squamous cell carcinoma Surgery

Measures and definitions

Measures and definitions found in the included studies are summarized in Table 2 . The most common measure of attrition was the rate or proportion of patients initiating or not initiating the next line of therapy. This was reported in 25 of the included studies. Definitions varied where reported. Other identified measures included attrition rate (defined as a ratio), default rate, discontinuation from first line treatment, attendance or non-attendance, proportion of patients experiencing attrition, proportion of patients meeting the study attrition rate criteria, proportion of patients undergoing adjuvant chemo-radiotherapy/radiotherapy following surgery, surgery rates, surgery refusal, treatment completion, and unintended interruption of radiotherapy.

Table 2 Measures and definitions of cancer treatment attrition Measure of attrition Definition (where reported) Attrition rate Ratio of patients who did not have record of a subsequent LOT because of death or loss to follow-up (.ie, no subsequent treatment in follow-up) in the database for any reason [ 16 ]. Default rate Patients who refuse, delay or fail to complete treatment or who did not attend three or more consecutive visits after their last clinic attendance [ 44 ]. Discontinuation from 1L treatment Proportion of patients who discontinued 1L treatment [ 9 ]. Follow-up attendance after surgery Number of follow-up visits within the first year after surgery [ 43 ]. Non-attendance Any unexplained non-attendance was recorded for patients in the pre-treatment (pre-RT) period, and during RT, but multiple non-attendances for a single patient were recorded only as a single non-attendance statistic within the treatment stage analyzed (either pre-RT, or during RT). Full attendance was defined as no missed appointments or missed for medical reasons [ 13 ]. Patients were categorised into “non-attenders” (no consultations) and “attenders” (one or more consultations) based on face-to-face consultations with a GP in the period up to 36 months before cancer diagnosis [ 47 ]. Note: this was therefore not specific to cancer treatment completion but used to understand potential correlation with late cancer diagnosis. Proportion of patients who missed appointments [ 14 ]. Proportion of patients experiencing attrition Attrition was defined as the inability to complete the prescribed number of NAC cycles, as determined at the inception of treatment by the medical oncologist, and as documented in the medical record [ 24 ]. Proportion of patients meeting the attrition rate criteria Attrition was defined as being either deceased, progressive without having received another LOT, or lack of follow-up for > = 5 years [ 11 ]. Proportion of patients undergoing adjuvant chemoradiotherapy/radiotherapy following surgery No further definition reported Rate or proportion of patients initiating or not initiating the next line of therapy Percentage of patients not achieving/progressing to a further line of treatment (18, 29). The attrition rate related to first-line treatment was defined as the proportion of patients who started therapy but who, at the time of disease progression were unable to receive further treatment due to disease progression, death, toxicity, or other clinical conditions [ 10 ]. Note: It is not clear how other reasons such as patient preferences were accounted for. Proportion of patients who received first-line therapy and who went on to receive subsequent lines of systemic therapy [ 12 ]. Ratio of patients who did not have a record of a subsequent LOT owing to death, loss to follow-up, or still stayed at the current LOT (i.e., no subsequent treatment during the study period) [ 38 ]. Ratio between the number of patients treated in each line to the number of patients treated in the previous one [ 19 ]. Failure to receive a subsequent line of therapy due to (1) death or (2) despite progression patients alive at the time of last follow-up [ 30 ]. Line of treatment discontinuation was defined as the last day supply before a gap of > 90 days without treatment or the day before initiation of the next LOT [ 37 ]. Surgery rates No further definition reported Surgery refusal No further definition reported Treatment completion The study captured treatment completion by adopting the data item from the National Cancer Dataset. The item captured whether treatment was delivered as planned or the reason for a change from this. This information was recorded by local clinicians and was calculated for first-line treatment only [ 45 ]. Completed the primary treatment [ 27 ]. Unintended interruption of radiotherapy Three levels of intolerance were used: The first and second levels “early-phase incompletion” and “mid-phase incompletion” were defined as completion of < = 50 and < = 80% of a planned radiotherapy, respectively. The third level, “total interruption,” was defined as completion of < = 90% of a planned radiotherapy or temporary discontinuation of ≥ 5 consecutive practical days or ≥ 10 discontinued practical days. In addition, treatment related mortality within 2 months was included in total interruption (meaning a death without evidence of disease progression as judged by an experienced oncologist under the circumstances) [ 36 ]. Abbreviations: 1L, first line; HRQOL, health-related quality of life; LOT, line of treatment; RT, radiotherapy

Treatment attrition rates

Rates appeared to differ with cancer types, although attrition from chemotherapy was variously reported with regards to line of therapy (LOT) which makes comparison difficult. The attrition rate from first line (1 L) to second line (2 L) was reported as 9.67% in breast cancer patients [ 10 ]. In a study of cervical cancer patients, 12% did not complete their treatment [ 27 ]. Attrition rate from 1 L in colorectal cancers ranged from 27 to 53% [ 18 , 19 ]. In oesophagogastric cancer, 24% of patients experienced attrition [ 41 ], and in non-small cell lung cancer, 33–43% of patients did not receive a 2 L treatment [ 9 , 26 ]. In multiple myeloma, attrition from 1 L ranged from 7 to 45.7% [ 30 , 38 ] while attrition across all lines of therapy ranged from 43 to 57% in non-transplanted patients, and 21–37% in transplanted patients [ 16 ].

Attrition rates tended to increase with increasing lines of therapy [ 11 , 19 , 20 , 30 , 33 , 38 ] regardless of cancer type. Where attrition was reported as a proportion of patients receiving a subsequent line of therapy, the inverse was true, i.e. the proportion of people receiving a subsequent line decreased with increasing lines of therapy [ 8 , 12 , 21 , 23 , 28 , 31 – 35 , 37 , 39 , 41 , 42 , 46 ].

Attrition from surgery was found to be marginally lower in a group of patients who received surgery before neoadjuvant chemotherapy for oesophagogastric cancer when compared with a group who received neoadjuvant chemotherapy before surgery [ 22 ]. The rate of surgery in the surgery-first arms was 90.1%, compared to 84.4% in the neoadjuvant- firstarm. Non-attendance at follow up visits following surgery for oral cancer was high, with only 1.6% of patients adhering to a monthly follow-up and 12.6% failing to attend any follow-up visits [ 43 ].

In a study of radiotherapy for various types of cancer, interrupted treatment was reported in 19.3% of patients [ 36 ].

A summary of treatment attrition stratified by cancer type can be found in Table 3 .

Table 3 Rates of attrition from cancer treatment Author/year Cancer type Treatment Attrition rates - summary Basile 2021 [ 10 ] Breast cancer Chemotherapy, Endocrine therapy, cyclin-dependent-kinases 4/6 inhibitors · Attrition rate 1–2 L: 9.67% Paul 2010 [ 27 ] Cervical cancer Chemotherapy, radiotherapy · Did not complete planned treatment: 94 (12%) Abdel 2021 [ 8 ] Colorectal cancer Chemotherapy · Received only 1 L: 42.5% · Received 2 L: 30.5% · Received 3 L: 17.1% · Received 4 L: 7.4% · Received 4 L+: 2.6% Boyne 2023 [ 12 ] Systematic therapy · Initiated 2 L: 207 (65%) · Initiated 3 L: 125 (39%) Garattini 2021 [ 18 ] Chemotherapy · Attrition rate 1–2 L: 27.62% Germani 2004 [ 19 ] Encorafenib plus cetuximab +/- binimetinib · Attrition rate 1 L: 53% · Attrition rate 2 L: 62% · Attrition rate 3 L: 78% Kennecke 2019 [ 23 ] Systemic treatment · Initiated 2 L: 139 (70%) · Initiated 3 L: 60 (30%) · Initiated 4 L: 29 (15%) Martinelli 2022 [ 28 ] Systemic treatment · Received 2 L: 52.5% · Received 3 L: 30.2% McLean 2016 [ 31 ] Chemotherapy · Received 2 L: 160 (74.4%) · Received 3 L: 79 (36%) · Received 4 L: 35 (16.3%) · Received 5 L: 12 (5.6%) · Received 6 L: 2 (0.9%) Tsang 2020b [ 41 ] Esophagogastric cancer Systemic therapy · Received only 1 L: 122 (50%) · Received 2 L: 83 (34%) · Received 3 L: 34 (14%) · Received 4 L: 6 (2%) Kronenfeld 2021 [ 24 ] Neoadjuvant chemotherapy · Experienced attrition: 28 (24%) Kakish 2023 [ 22 ] Neoadjuvant treatment and curative intent surgery · Rate of surgery, neoadjuvant therapy arms: 5876 (84.6%) · Rate of surgery, surgery-first arms: 1774 (90.1%) Groene 2015 [ 45 ] Palliative chemotherapy · Completed chemotherapy: 917 (52.7%) · Died before all treatment received: 244 (14%) Tsang 2020a [ 40 ] Hepatocellular Carcinoma 1 L sorafenib, · Received 2 L: 12 (8%) Fisch 2021 [ 15 ] Lung cancer (LCNEC) Systematic therapies, palliative therapies · Died without systemic treatment: 23 (12%) · Received 2 L: 46 (41%) · Attrition 2–3 L: 25 (54%) Addeo 2021 [ 9 ] Lung cancer (NSCLC) EGFR-TKI treatment (gefitinib, erlotinib, or afatinib, as monotherapy or combination therapy) · Discontinued 1 L EGFRTKI treatment: 765 (85%) · Did not receive 2 L therapy: 250 (33%) Janzic 2022 [ 20 ] EGFR TKI therapy · Discontinued 1 L EGFR TKI therapy: 320 (82%) · No further treatment: 84 (30%) · No further treatment after 2 L: 54 (47%) · No further treatment after 3 L: 28 (67%) · Received 4 L: 14 (27%) · Received 5 L: n = 5 · Discontinued 5 L: n = 5 Lampaki 2022 [ 26 ] EGFR-TKI therapy · Did not receive 2 L: 59 (43%) Nieva 2022 1 L first-generation or second-generation EGFR-TKIs · Initiated 2 L: 539 (65%) · Initiated 3 L: 258 (25%) Turnsek 2022 [ 42 ] 1 L first-generation or second-generation EGFR-TKIs · Initiated 2 L treatment: 66 (74%) · Received 3 L: 19 (40%) · Received 4 L: 5 (28%) · Received 5 L: n = 4 O’Sullivan 2021 [ 35 ] Lung cancer (SCLC) Chemotherapy with/without radiotherapy or supportive care · Initiated 1 L: 903 (46.5%) · Initiated 2 L: 169 (8.7%) · Initiated 3 L: 28 (1.4%) Minson 2024 [ 32 ] Mantle cell lymphoma Chemotherapy · Received 2 L: n = 150 · Received 3 L: n = 55 · Received 4 L+: n = 26 Carruthers 2019 [ 13 ] Mixed Referral for radiation therapy · Did not complete RT for non-medical reasons, n = 19 · Non-medical break, n = 65 · Fully compliant, n = 499 Chan 2015 [ 44 ] Definitive oncology treatment (i.e. chemotherapy or radiotherapy) · Rate of treatment and/or visit default: 159 (34.0%) Jensen 2019 [ 47 ] NR · Non-attenders: 11,567 (9.3%) Rim 2018 [ 36 ] Radiotherapy · Early-phase incompletion: 15 (4.2%) · Mid-phase incompletion: 33 (9.3%) · Total interruption: 68 (19.3%) · Mid-phase incompletion without early-phase incompletion: N = 18 Benda 2023 [ 11 ] Multiple myeloma Proteasome inhibitors, immunomodulators, antibodies, and chemotherapy. · Met attrition criteria at 1 L: 63 (21.9%) · Met attrition criteria at 2 L: 31 (19.5%) · Met attrition criteria at 3 L: 20 (23.5%) · Met attrition criteria at 4 L: 10 (27%) · Met attrition criteria at 5 L: 3 (16.7%) · AR across 1 L to 4 L: 22% Fonseca 2020 [ 16 ] Chemotherapy Attrition rates across all LOTs · Non-transplanted patients : range, 43–57% · Transplanted patients : range, 21–37% Jimenez 2022 [ 21 ] Chemotherapy Non-ASCT patients · Received 2 L: 52.7% · Received 3 L: 22.7% · Received 4 L+: 14.7% ASCT cohort · Received 2 L: 57.6% · Received 3 L: 31.4% · Received 4 L+: 29.6% McCurdy 2023 [ 30 ] Anti-MM agent ASCT cohort · IL attrition rate: 7% · 2 L attrition rate: 12% · 3 L attrition rate: 23% Non-ASCT cohort · 1 L attrition rate: 19% · 2 L attrition rate: 26% · 3 L attrition rate: 40% More 2023 [ 33 ] Chemotherapy · Received 2 L: 200 (73%) · Received 3 L: 92 (61%) · Attrition rate 2 L: 27% · Attrition rate 3 L: 39% · Attrition rate 4 L: 39% · Attrition rate 5 L+: 50% Tang 2023 [ 38 ] Chemotherapy · Attrition rate, 1–2 L: 45.7%, · Attrition rate, 2–3 L: 48.7%, · Attrition rate, 3–4 L: 58.9% · Attrition rate, 4–5 L: 62.5% Fu 2021 [ 17 ] Oral cancer Neoadjuvant chemotherapy, surgery · No surgery: n = 23/171 Wang 2023 [ 43 ] Surgery · Did not attend any follow-up visits: 54 (12.6%) · Attended ≤ 5 follow-up visits: 255 (59.3%) · Adhered to the monthly follow-up: 7 (1.6%) Dobbs 2018 [ 14 ] Prostate cancer Referred to urologic oncology clinic · Missed 1 appointment: 28.1%, n = 162 · Missed 2 appointments: 2.1%, n = 12 · Missed more than 2 appointments: 0 Swami 2023 [ 37 ] Chemotherapy, ADT · Received 2 L: 57.4% · Received 3 L: 49.3% Germani 2004 [ 19 ] Urothelial Carcinoma Chemotherapy · Received 2 L: 2714 (37.4%) · Received 3 L: 857 (11.8%) Morgans 2022 [ 46 ] PD- 1/L1 inhibitor therapy following chemotherapy Following PD-1/L1 inhibitor as 1 L · Received 2 L: 34.3% ( n = 68) Following PD-1/L1 inhibitor as 2 L · Received 3 L: 29.4% ( n = 30) Following PD-1/L1 inhibitor as 2–3 L · Received second PD-1/L1 inhibitor: 20% (approx.) Tapia 2024 [ 39 ] Urothelial Carcinoma Systemic therapy · Received 2 L: 98 (48%) · Received 3 L: 54 (26%) Kumar 2020 [ 25 ] Vulvar cancer Surgery, chemoradiotherapy, radiotherapy · Defaulted in postoperative period: n = 3 · Not interested in further treatment other than surgery: n = 3 Abbreviations: 1L: first line, 2L: second line, 3L: third line, 4L: fourth line, 5L: fifth line, ADTs: androgen deprivation therapy, ASCT: autologous stem cell transplant, CAF: cytoxan, adriamycin and 5-flurouricil, EGFR: Epidermal growth factor receptor, EGFR-TKI : EGFR tyrosine kinase inhibitor, LCNEC: large-cell neuroendocrine carcinoma, MM: multiple myeloma, NSCLC: non-small cell lung cancer, PD- 1/L1 : Programmed death-ligand 1, RT: radiotherapy, SCLC: small cell lung cancer

Reasons for treatment attrition

Information on reasons for cancer treatment discontinuation were reported in eighteen of the included studies [ 9 – 11 , 13 , 15 , 17 , 20 , 22 , 25 , 31 – 34 , 36 , 40 , 42 , 45 , 46 ]. The most frequently reported reason was disease progression ( N = 9), followed by death ( N = 8), clinical deterioration or progression ( N = 5), toxicity ( N = 3), adverse event ( N = 2), concern about lack of family/social support ( N = 2), radiological progression ( N = 2), and ‘unknown’ reasons ( N = 2). The following reasons were reported in one study each: complications due to previous therapy, fear of consequences of surgery, loss to follow up, medical reasons, missing data, non-medical reasons, patient refusal, poor overall status/life expectancy, severe concomitant illness, symptoms relieved, technical problems, unwilling to have further treatment, personal preference, worsening health condition, and ‘other’.

Nineteen of the included papers included information on the risk factors associated with treatment attrition. There was a diverse array of influences contributing to treatment discontinuation across the different cancer types. Significant risk factors included older age ( N = 11), advanced disease stage ( N = 3), and the presence of multiple comorbidities ( N = 5), which are often associated with increased treatment side effects and reduced tolerability. Socioeconomic factors also play a role, with lower income ( N = 3) and lack of insurance coverage ( N = 3) linked to higher attrition rates. Ethnicity, living in a rural location or with long travel times to treatment, and receiving care in a non-academic health provider were additional factors considered, but were not shown to be significant once adjusting for other factors. These findings underscore the complex interplay of medical, socioeconomic, and psychological elements that need to be addressed to reduce treatment attrition and improve cancer care outcomes.

Discussion

The findings of this rapid review underscore the complexity of cancer treatment attrition and highlight significant inequalities. The variation in definitions and measures of attrition complicates the comparison of rates across studies. However, the identified reasons for attrition and associated inequalities align with existing literature [ 1 , 3 ], reinforcing the need for targeted interventions to support vulnerable populations.

Measures and definitions of attrition

The review identified considerable variability in the measures and definitions of treatment attrition used across studies. Most commonly, attrition was quantified by the rate or proportion of patients who did not proceed to the next line of therapy (LOT). This measure varied in specificity, with some studies providing detailed definitions that included criteria such as death, loss to follow-up, or patient refusal, while others used broader terms like “discontinuation from treatment” or “non-attendance.” The lack of a standardized definition complicates comparisons across studies and highlights the need for a unified approach to measuring treatment attrition. Standardized measures would not only improve the comparability of studies but also enhance the clarity of communication between healthcare providers and patients regarding the implications of treatment discontinuation.

Rates of attrition

The rates of attrition varied significantly across different cancer types and treatments. For example, attrition from first-line to second-line therapy was as low as 9.67% in breast cancer patients receiving chemotherapy, while it ranged from 27 to 53% in colorectal cancer. Multiple myeloma patients exhibited a wide range of attrition rates depending on whether they underwent autologous stem cell transplantation, with non-transplanted patients experiencing higher rates of attrition (43–57%) compared to those who received transplants (21–37%). Attrition tended to increase with each successive line of therapy, indicating that patients often discontinue treatment as the disease progresses or as they experience increasing treatment-related burdens. These differences underscore the influence of both cancer type and treatment regimen on patient adherence.

Reasons for attrition

Several studies provided insights into the reasons for treatment attrition, which include medical complications, treatment toxicity, progression of disease, and patient choice influenced by quality of life considerations. Psychological factors, such as distress and the burden of treatment, also contributed to patients’ decisions to discontinue therapy. Additionally, logistical barriers like travel difficulties and financial constraints were noted as significant contributors to attrition, particularly among socioeconomically disadvantaged populations. There was not a standardised mechanism for collecting data on the reasons for treatment attrition, or the timing for when to gather that information and how.

Inequalities in treatment attrition

The review identified potential inequalities in treatment attrition, with some studies showing people with lower income and those who did not have health insurance (which can act as another marker for low income) were more likely to experience early discontinuation of treatment. While other factors were commented on, there was no comprehensive assessment of the inequalities associated with cancer treatment attrition. Wider disparities such as differences in access to healthcare services or cultural differences in attitudes towards healthcare were not reported on. These findings suggest that whilst systemic factors may play a critical role in treatment adherence, at present data collection on the wider characteristics of patients who drop out of treatment is inconsistent and does not appear to be collected as part of ongoing local or national datasets.

Implications for policy and practice

The findings of this review have important implications for clinical practice and health policy. Clinicians should be aware of the factors contributing to treatment attrition and proactively address them through patient education, close monitoring of treatment-related side effects, and early intervention and signposting for appropriate support for wider social and cultural factors. Health systems and policymakers must focus on reducing barriers to care, particularly for vulnerable populations, to ensure equitable access to treatment and adherence throughout treatment regimes.

At present, treatment attrition is a poorly measured construct. Without more widespread data collection and consistent measurement and analysis of attrition, it is not possible to conclude why people stop treatment and whether there is scope for improvement. More comprehensive data collection would inform whether interventions are needed and where to target them. This requires development of a consistent treatment attrition measurement which, in the UK context, could be embedded into national cancer surveillance data as well as available to service teams and provider organisations. There also needs to be development of a mechanism for collecting data on the reasons for treatment attrition. The timing and format of that data collection needs to be sensitive to the patient’s context and realistic to implement, but again would benefit from consistency and comprehensively covering the range of risk factors highlighted above. Gathering data on the scale of treatment attrition, when it happens, to whom, and why, will provide critical insights with regards to where interventions are needed.

Limitations

The review followed a rapid methodology. Searches were restricted to two databases, and studies were screened and extracted by single reviewer therefore it is possible that relevant evidence was missed. The rapid review methodology may also limit the depth of analysis and exclude relevant studies published outside the search period or in languages other than English. The terminology used to describe treatment attrition may vary and despite a comprehensive search strategy which included a range of index terms and text words it is possible that the search may have missed relevant publications that used different descriptors. The variability in the definitions and measures of treatment attrition used across the included studies, may also affect the comparability of findings and lead to inconsistencies in reported attrition rates. The review does not account for all possible confounding factors, such as differences in healthcare systems, availability of treatments, and cultural attitudes towards healthcare, which may influence attrition rates. Decisions to discontinue treatment may be in line with patients’ values and preferences rather than an indication of systemic issues in care delivery. However, without data on reasons for treatment attrition, it is not possible to conclude whether interventions are required or not.

Conclusions

This rapid review identified significant variability in how treatment attrition is measured and defined, as well as notable inequalities in who discontinues treatment. To improve cancer care outcomes and equity, it is essential to develop standardized measures of attrition, understand the underlying reasons for discontinuation, and implement targeted interventions to support at-risk populations. Measurement of cancer attrition and the reasons for it has been more widely incorporated into clinical trials, but as yet is not part of standard data collection within service delivery. Future research and policy efforts should focus on developing standardized attrition metrics, and measurement of reasons for cancer attrition (including the breadth of social, cultural and personal considerations as well as clinical) and exploring interventions that specifically target the identified disparities to support cancer patients to stay in treatment. Understanding and addressing the root causes of treatment discontinuation may support more equitable and effective cancer care.

Electronic supplementary material

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Supplementary material 1

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Acknowledgements

The authors would like to thank colleagues at Macmillan, UCLPartners and UCL who are supporting the wider research programme.

Author contributions

JS designed the review, supported analysis and interpretation of data, and drafted the manuscript. ES executed the review, designed search terms, led data extraction and analysis, and write up of methods and results sections. KC and LG contributed to review design. All authors read and approved the final manuscript.

Funding

This review was funded by Macmillan Cancer Support as part of a wider programme of work to evaluate the implementation of holistic needs assessments and access to support for identified needs across three sites in North Central London, North East London, and Essex, to ensure people with cancer have equitable access to the practical, emotional and physical support they need to progress their clinical treatment and keep their lives on track.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

First line, the first cancer treatment the patient receives

Second line, the second cancer treatment the patient receives

Third line, the third cancer treatment the patient receives

Fourth line, the fourth cancer treatment the patient receives

Fifth line, the fifth cancer treatment the patient receives

Androgen deprivation therapy

Autologous stem cell transplant

Cytoxan, adriamycin and 5-flurouricil

Epidermal growth factor receptor

EGFR tyrosine kinase inhibitor

Health-related quality of life

Large-cell neuroendocrine carcinoma

Line of treatment

Multiple myeloma

Non-small cell lung cancer

Programmed death-ligand 1

Radiotherapy

Small cell lung cancer

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