Health-related quality of life in cancer immunotherapy: a systematic perspective, using causal loop diagrams

Abstract

Purpose System science offers a unique set of tools, including causal loop diagrams (CLDs), for stakeholders to better grasp the complexity of factors surrounding quality of life. Because the health-related quality of life (HRQoL) of cancer immunotherapy patients exists within an intricate system affected by and affecting many factors across multiple dimensions, the development of a systems-level model can provide a powerful framework to aid the understanding of this complexity. We developed a CLD for HRQoL of cancer immunotherapy patients. Methods We first applied a literature-based approach to construct a CLD for patients following immunotherapy. We then iteratively reviewed and enhanced the CLD through interviews with subject matter experts. Results Based on the reviewed literature and subject matter expert input, we produced a CLD representing the system surrounding cancer immunotherapy patients’ HRQoL. Several feedback loops are identified that span clinical experiences, oncology teams’ perceptions about immunotherapy, social support structures, and further research and development in cancer immunotherapy, in addition to other components. The CLD enables visualization of thought experiments regarding how a change anywhere in the system can ultimately worsen or improve patients’ HRQoL. Conclusion The CLD illustrates the valuable contribution of a systems perspective to quality-of-life research. This systems-based qualitative representation gives insight on strategies to inhibit harmful effects, enhance beneficial effects, and inherent tradeoffs within the system. The CLD identifies gaps in the literature and offers a communication tool for diverse stakeholders. Our research method provides an example for studying the complexities of quality of life in other health domains.

Investigating the Quality of Life for Cancer Patients and Estimating the Cost of Immunotherapy in Selected Cases

“…Similar studies, also, have found a statistically significant association between socioeconomic and demographic characteristics and quality of life in cancer patients, such as marital status, age, and gender [ 22 - 25 ]. The present study highlights the severity of the disease but also the benefits of checkpoint inhibitor therapy on quality of life during and after treatment and is consistent with related studies [ 26 - 28 ]. However, a systematic study is needed at the national level in Greece to collect the necessary critical information on the course of the disease in cases monitored by hospitals in the country.…”

System dynamics modeling for cancer prevention and control: A systematic review

“…Some studies used system dynamics modeling as part of a mixed methods study in conjunction with other methods, such as mixed integer programming [33] or qualitative interviews and surveys [34]. People were the primary unit being modeled in 50% of the studies (n = 16) [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49], while 16% modeled patient experience or behavior (n = 5) [50][51][52][53][54]. Other primary units modeled include cells (n = 4; 13%) [33, [55][56][57], environmental contaminants (n = 4; 13%) [58][59][60][61], currency (n = 2; 6%) [62,63], and web search queries (n = 1; 3%) [64].…”

“…Many studies included multiple diagrams to illustrate overall model structure or model subcomponents, while two studies did not include diagrams or descriptions of their model structure at all. Fig 2 illustrates examples of diagrams from two included studies, including a simplified causal-loop diagram related to cancer immunotherapy and patient quality of life [50], as well as a stockand-flow diagram corresponding to a simulation model of a Human Papillomavirus (HPV) vaccination program [43].…”

“…Most studies of both types indicated more than one purpose. [34,35,49,[52][53][54], publicly available data from cancer registries or federal statistics (n = 7; 22%) [40,41,43,44,46,47,61], patient or health system data (n = 5; 16%) [34,37,45,51,53], literature review (n = 4; 13%) [36,48,50,62], and expert interviews (n = 4; 13%) [41,42,50,51]. One study used search engine data [64] and another used unpublished data from a previously conducted clinical trial [46].…”

“…As seen in Fig 5, our search yielded studies relating to all stages in the cancer control continuum: fourteen (44%) related to treatment [33-37, 45, 49, 51, 53-57, 63], eleven (34%) to prevention [35,39,40,43,47,48,52,58,60,61,64], ten (31%) to detection [35-38, 41-44, 46, 62], eight (25%) to etiology [35,39,48,52,[57][58][59]61], four (13%) to diagnosis [35][36][37]51], and one (3%) to survivorship [50]. The majority of studies (n = 21; 66%) related most significantly to one focus area in the cancer control continuum, while seven (22%) related to two areas [39,43,44,48,52,58,61], two (6%) related to three areas [36,37] and one (3%) related to five [35].…”