Assessment of palliative care service utilization and determinant factors among adult cancer patients in Ethiopia: A systematic review and meta-analysis (PRISMA-compliant).

1. Introduction
Palliative care (PC) refers to the proper and specialized medical care given to patients suffering from any severe illness, including cancer, heart failure, and other diseases. PC is offered in addition to the patients’ planned medical therapy for their illnesses. PC also focuses on enhancing the well-being of patients by handling symptoms associated with life-threatening diseases and the adverse effects of treatment, as well as the needs of caregivers.[1]
Globally, only 14% of people receive the necessary PC services, although around 56.8 million people worldwide need them.[2] Among these in need of service, 76% reside in low- and middle-income countries (LMICs), with a higher proportion among these low-income nations. Approximately 67.1% of patients requiring PC are older than 50 years old.[2,3] Due to the higher prevalence of HIV/AIDS, the African region has a greater need for PC per population; nevertheless, <5% of them received PC services. Noncommunicable diseases (NCDs) account for 69% of people in need of PC. Cancer, HIV/AIDS, lung disease, cerebrovascular disease, and dementias are a few of the illnesses that can lead to significant illness and conditions that call for PC intervention.[2,3]
Although 76% of the need is in LMICs, the availability of excellent PCs is limited outside of North America, Europe, and Australia, and it is still in the early stages in the majority of the world.[4] As the world’s population ages and the prevalence of cancer and other NCDs rises, PC becomes further essential. It is anticipated that the need for PC service at the end of life will double by 2060.[4] Sixty-seventh World Health Assembly, 2014, adopted a resolution titled “Strengthening of Palliative Care as a Component of Comprehensive Care throughout the Life Course,” with unanimous support.[2,5] In accordance with the World Health Organization, this resolution urges all member nations to improve and combine the PC service throughout their medical care systems.[5] This resolution entails ensuring that regulations are followed, that thorough information about PC is given, that necessary drugs for PC are readily available and reasonably priced, and that initiatives are financed and delivered.[5]
In Ethiopia, PC services have been integrated with several healthcare guidelines, such as the national cancer control strategy, the hospital transformation guideline, the primary healthcare guideline, and the national strategic action plan for the prevention and control of NCDs. Furthermore, the government established a national goal to provide PCs to at least half of public health institutions by 2020 and published a national guideline on the subject.[3]
A survey conducted in various countries found that Ethiopia is the nation with the poorest PC implementation and utilization, as it heavily relies on donors, rendering the PC program implementation ineffective.[6,7] This finding also indicated that PC is not adequately incorporated into the traditional healthcare system and current healthcare systems in the country. Therefore, the implementation and utilization of PCs remain low, especially in rural areas of the country.[6,7]
The Ethiopian hospital services transformation guidelines on PC were published initially in September 2016. National Palliative Care Guidelines issued a PC course in June 2017 and have been provided to hospitals, health facilities, and all participants.[8] It has also become a component of the essential healthcare package and the fifth pillar of Ethiopia’s health policy. PC services are typically needed for advanced-stage cancer patients, end-stage AIDS, or other life-threatening illnesses. PC is often the primary option for cancer patients owing to late diagnosis and limited resources.[6–8]
Different literature indicates that even though the Ethiopian government provides training for healthcare providers and prepares guidelines on PC plan, the PC utilization of cancer patients remains at a low level because of many barriers and challenges, including the nonincorporating of the service into current healthcare delivery unit of the country, healthcare providers’ limited knowledge, cancer patients’ poor knowledge and awareness on PC services, and people’s geographical location (78% of population are living in the rural area).[6,9]
It is necessary to compile research for the utilization of PC service and determinant factors among adult patients with cancer because different studies report several findings. This systematic review and meta-analysis aimed to synthesize and compile studies that explored the utilization of PC service and its determinants among adult patients with cancer in Ethiopia.

2. Methods
A systematic review and meta-analysis was carried out to evaluate the utilization of PC services and their determinants among adult patients with cancer in Ethiopia. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses-2020 criteria were followed in the conduct of this systematic review and meta-analysis.[10] The protocol for this review was registered on prospective register of systematic reviews (CRD42024547395).

2.1. Ethical review
Ethical approval was not applicable because our study involved review of published articles, and patient consent was exempt for this type of study.

2.2. Search strategy, keywords, and Boolean operators
The central question that guides this review is: What is known about the utilization of PC care service as well as determinant factors for adult patients with cancer in Ethiopia? Extensive searching was conducted to identify pertinent studies from various sources in response to this question. We have employed electronic databases (PubMed and Cochrane Library) as a means of searching strategy to find publications about the research subject. Google Scholar and gray literature were also utilized for searching and retrieving relevant articles using keywords.
PC, end of life care, hospice care, end of life communication, cancer patient, adult cancer patient, cancer, determinant, social care, supportive care, pain, hospital care, utilization of PC, home care, oncology, determinant of PC, nursing, critically ill cancer patients, terminally ill patients, malignancy, and Ethiopia were the list essential keywords.
The essential keyword phrases were combined with Boolean operators and truncation (e.g., “OR,” “AND”) to electronically search across many databases. The search was customized for the years with a range between January 2010 and May 2024. In searching the PubMed database, we have searched for (“Palliative care” OR “end of life care” OR “hospice care” OR “end of life communication” OR “supportive care” OR “terminal care” OR “home care” OR “pain management” OR “hospital care” OR “social care”) AND (“cancer” OR “oncology” OR “malignancy” OR “cancer patient” OR “adult cancer patient”) AND (“utilization” OR “use” OR “access” OR “determinant” OR “factors”) AND (“Ethiopia”). While searching, filters such as all fields, English language, human subjects, and age over 18 years were applied. Then, we have recorded the total number of articles retrieved before screening (OA-Supplemental Digital 1, Supplemental Digital Content, https://links.lww.com/MD/R476).
Similarly, in Cochrane Library searching, we have searched for (“Palliative care” OR “hospice care” OR “supportive care” OR “terminal care” OR “pain management” OR “hospital care” OR “social care”) AND (“cancer” OR “oncology” OR “malignancy” OR “cancer patient” OR “adult cancer patient”) AND (“utilization” OR “use” OR “access” OR “determinant” OR “factors”) AND (“Ethiopia”). Then, we have recorded the total hits retrieved. In Google Scholar searching, we have searched for (“palliative care” OR “hospice care” OR “supportive care” OR “terminal care” OR “pain management” OR “hospital care” OR “social care”) AND (“cancer” OR “oncology” OR “malignancy” OR “cancer patient” OR “adult cancer patient”) AND (“utilization” OR “use” OR “access” OR “determinant” OR “factors”) AND (“Ethiopia”). The first 200 articles were screened by sorting based on their relevance. Finally, duplicates were removed, and the retrieved articles were recorded.
Furthermore, in gray literature, we have used different websites such as ProQuest Theses and Dissertations Global, Ethiopian university repositories, the Ethiopian Minister of Health, World Health Organization Institutional Repository for Information Sharing, the African Palliative Care Association, and Google. We have used the above essential keywords in these websites and databases. Then, we have recorded the total hits screened from all sources.
Citation tracking and manual searching of the publications found in the reference lists.

2.3. Selection of study
The study selection process was based on clearly defined inclusion and exclusion criteria as described below.

2.3.1. Inclusion criteria
This systematic review includes studies focusing on PC service utilization and determinants for adult patients with cancer in Ethiopia, published between January 2010 and May 2024, written in the English language, and research papers that used quantitative and mixed-methods, as well as systematic and meta-analyses focused on PC for cancer patients in Ethiopia.

2.3.2. Exclusion criteria
Studies written in a language other than English, newspaper reports, those published before 2010, and articles focused on the utilization of PC in noncancer adults and children were excluded. Studies that did not report the required data for analysis have also excluded.

2.4. Outcome of interest
In this review, the primary outcome was the PC services utilization among adult patients with cancer in Ethiopia, whereas the secondary outcome was the associated factors with PC utilization.

2.5. Quality assessment
A quality assessment was done for all selected studies. The quality of papers in this study was assessed with the help of the Joanna Briggs Institute critical appraisal checklist for studies reporting prevalence data.[11] The authors have evaluated the quality of selected papers. According to the Joanna Briggs Institute criteria, study papers that scored 60% and above were deemed of acceptable quality and were chosen for the meta-analysis.

2.6. Data extraction
Duplicate articles were removed from the database search results. The paper’s title and abstract were carefully assessed for the significance of the study findings. We then extracted data from each selected, eligible article. We have contacted the authors to check if any missing data are present in the included paper. We have retrieved necessary data from each review, such as author name, publication year, study design, study place, sample size, and magnitude of PC utilization.

2.7. Statistical analysis
After data extraction using Microsoft Excel (Microsoft Corporation, Redmond), it has been exported to Statistical Package for Social Science (SPSS) software version 29 (IBM, Armonk) and Review Manager (RevMan) version 5.4.1 (Cochrane, London) for meta-analysis. To do this, the author’s name, the total number of participants, and the year of publication were tabulated. Every result was double-entered into the system to ensure accuracy.
In this meta-analysis process, SPSS was used for identifying missing data/outliers and descriptive analysis to ensure data quality for the analysis. The RevMan version 5.4.1 was used for conducting and reporting the meta-analysis, including the pooled effect sizes of different studies, random-effect modeling, I2 calculation, Egger test, funnel plot, and forest plots. Tables, figures, and forest plots have been used to summarize the acquired data and initiate the synthesis process. The I2 statistical test has been employed for verifying heterogeneity. A meta-analysis was carried out to calculate the associated impact of the pooled prevalence of PC service use with a 95% confidence interval (CI).
To assess the pooled effect estimates for determinants of PC utilization among adult cancer patients, several analytical steps were undertaken. This entails the extraction of all adjusted odds ratios (AORs) along with their respective 95% CIs for each study exhibiting significant determinants. We converted all AORs into their natural logarithms (log[AOR]) to achieve a normalized distribution and then calculated the standard error for each study using the 95% CIs. We employed the generic inverse variance method to derive pooled effect estimates of the transformed log(AOR)s. This method allowed us to combine the results across different studies that reported on similar significant variables. This method is appropriate for meta-analysis involving continuous and odds ratio data. To account for the anticipated variability among studies, we utilized a random-effects model to account for study variances, and the I2 statistic was used to assess the level of heterogeneity. Finally, to display the pooled effect estimates in a more interpretable form, we exponentiated the combined log(AOR) to obtain the pooled AORs with 95% CIs for the determinants.

2.8. Operational definition
PC service utilization was assessed based on adult cancer patients’ utilization of at least 1 PC service, such as pain and symptom relief treatment services, rehabilitation services, spiritual care, and addressing physical and psychological needs. PC services encompass providing care for patients as well as their families or caregivers. The PC service can be available at any location, that is, community health facilities, hospices, and hospitals.[12]

3. Results

3.1. Selection of literatures
Initially, we have identified a total of 1446 studies. Following careful assessment, 831 duplicate studies were removed, and 615 papers were screened. Eighty-seven relevant studies were screened for full-text assessment after excluding incomplete articles based on title and abstract evaluation. After a full-text assessment, 73 studies were excluded due to poor quality, differing target populations, and incomplete reports. Finally, eleven studies have been included in the review, and 3 papers were excluded because they were qualitative studies only. Figure 1 shows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-2020 flow diagram for selection of PC utilization papers (Fig. 1).

3.2. Basic characteristics of selected papers
In our final meta-analysis, we selected a total of 11 research articles reporting on PC services in Ethiopia.[13–23] The total number of study participants in the included articles was 3405, with the largest sample size being 404 and the smallest being 83. The majority (8, 72.7%) of the articles were carried out at the capital city of the country, Addis Ababa. Among those included articles, the most significant PC utilization was 69%, whereas the smallest was 10.6%. All selected articles were cross-sectional studies. All featured articles were published between 2015 and 2024 (Table 1).

3.3. Sensitivity analysis and publication bias
To all selected papers, publication bias has been evaluated with the help of a funnel plot (subjective) and Egger regression (objective). A funnel plot showed a symmetrical distribution for this review. The Egger regression-based test confirmed the presence of publication bias, yielding a P-value of .002, indicating strong publication bias, based on the conventional threshold of P-value < .05 or <.1 suggests bias. The presence of publication bias suggested that the pooled estimates may overstate the true prevalence of PC utilization (Fig. 2).
To evaluate the impact of each research paper on the pooled magnitude of PC service utilization, a sensitivity analysis was carried out using a random-effects model. No studies were found to fall beyond the pooled confidence range for overall PC service utilization. Finally, it was shown that all papers had a roughly identical impact on the pooled fraction of PC service utilization.

3.4. Prevalence of PC service utilization
Regarding the PC usage among cancer patients, the pooled magnitude of PC service utilization was analyzed using a random-effect model. Our review showed that the overall pooled magnitude of PC service utilization was 44.32% (95% CI: 33.33%–55.32%). The heterogeneity test revealed significant variation (I2 = 99%, P-value = .00), indicating the need to use the random-effects model in the study (Fig. 3).

3.5. Determinant of PC utilization
Based on our meta-analysis findings, the age of patients, being male, monthly income, educational status, having family support, satisfaction with PC service, and previous knowledge of PC were identified as determinants of PC support utilization among adult patients with cancer.
Two studies assessed the association between the age of cancer patients and PC utilization.[14,17] While 1 article indicated that participants whose age was >61 years were associated with greater odds of using PC service, the other article reported the opposite. Using the random-effect model, the pooled effect estimates from 2 studies indicated that participants aged over 60 years had 1.31 times higher odds of utilizing PC services compared with those aged 60 and below (AOR = 1.31; 95% CI: 1.02–2.63; I2 = 84%), indicating considerable heterogeneity and inconsistent evidence across studies.
According to the pooled effect of 2 articles, participants being male sex had 5.44 times higher odds of using PC than their counterparts (AOR = 5.44; 95% CI: 2.14–12.70; I2 = 29%).[14,18] In this meta-analysis, 3 research studies reveal that participants with a monthly income of more than $50 were 1.77 times higher odds of utilizing PC service compared with those with a $50 monthly income (AOR = 1.77; 95% CI: 0.65–3.50; I2 = 57%).[15,17,23]
In the meta-analysis, 4 articles examined the association between participants’ educational levels and their PC utilization. However, 2 of these articles found that higher education was associated with a greater odds of utilizing PC service,[16,17] while the other 2 articles reported the opposite.[19,23] Using the random-effect model, the pooled effect estimates 4 studies showed a statistically significant association, indicating that participants with educational levels of high school and above had 1.52 times higher odds of utilizing PC utilization compared with their counterparts (AOR = 1.52; 95% CI: 1.01–2.70; I2 = 89%).
A pooled effect estimate from 2 studies indicated that participants satisfied with PC service had 2.65 times better odds of using PC service compared with those who were not satisfied (AOR = 2.65; 95% CI: 1.35–4.67; I2 = 61%).[16,18] Moreover, this meta-analysis showed that participants who have previous knowledge of PC had 15 times greater odds of utilizing PC service compared with their counterparts (AOR = 15.22; 95% CI: 6.69–35.21; I2 = 98%).[13,15]
Two studies reveal that participants visiting the hospital with the availability of PC service were associated with the utilization of PC service.[13,17] The pooled effect estimates of the 2 articles showed that participants who visited the hospital with available PC service had 3 times higher odds of using PC service when compared with the counterpart (AOR = 3.10; 95% CI: 1.02–4.36; I2 = 68%). Furthermore, 2 articles indicated that participants with family support had been associated with increased odds of utilizing PC services.[13,18] Our pooled effect estimates analysis of the 2 studies showed that participants with family support had 2 times greater odds of utilizing PC service as compared with the counterpart (AOR = 2.20; 95% CI: 1.09–4.62; I2 = 79%).

4. Discussion
Our review aimed to estimate the pooled magnitude of PC service utilization and its determinants among adult patients with cancer in Ethiopia. The overall pooled prevalence of PC service utilization was 44.32% (95% CI: 33.33%–55.32%). Based on this review analysis, age of adult cancer patients, sex being male, monthly income, educational status, having family support, service satisfaction, and previous knowledge on PC were identified as determinants of PC service utilization among adult patients with cancer. The pooled prevalence finding was very low compared with the goal of Ethiopian minster of health, which was “access to quality of PC service and evidence based to all in need of it” and to continue scaling up and implementing a sustainable, Ethiopian-led model palliative care services in partnership with our partners, which provides and shows a high-quality evidence-based service while also doing training, capacity building, and research.[8]
The finding of this pooled prevalence is consistent with single nationwide surveillance studies conducted in Asian countries and South Korea, which reported the prevalence rates of 35%[24] and 53.9%,[25] respectively. Nonetheless, our review finding is less than a single surveillance studies carried out in Taiwan, the United States, Australia, China, Tanzania, and France, which were 60.2%,[26] 57%,[27] 82%,[28] 80%,[29] 60.6%,[30] and 57%,[31] respectively. However, our review finding is higher than that of single surveillance studies carried out in Zimbabwe and Japan, which were 13%[32] and 24%,[33] respectively.
The consistent findings between our pooled prevalence and those from Asian countries and South Korea may be explained by the fact that, in Ethiopia and the majority of Asian countries, particularly for LMIC settings, there may be a lack of PC-trained healthcare workers, inadequate infrastructure, and poor integration of PC into mainstream oncology care. The cancer is often diagnosed at an advanced stage in both settings. The health system may be placing more emphasis on curative services relative to PC, restricting PC service usage, as well as facing cultural barriers to discussing end-of-life care; this may limit families’ and patients’ engagement.
Plausible explanations for variation in the pooled prevalence might include differences in the research population (sample size), methodological differences between articles, and study period differences. Other reasons for the difference could also be the health system policies and government structures of countries, educational curricula related to PC training for healthcare providers, the developmental status of countries, cancer population awareness and knowledge levels toward use of PC provision, and the population burden that requires PC services. The other main variation could be the kind of cancers included in the study, as this review encompasses all kinds of cancers in adult patients. However, some studies have been conducted on 1 or 2 types of cancers; for example, some articles have focused on patients with breast cancer only.
In this meta-analysis using the random-effect model, the pooled effect estimates from 2 studies indicated that participants aged over 60 years had 1.31 times higher odds of utilizing PC services compared with those aged 60 and below. Our finding is inconsistent with studies reported in Korea,[25] Australia, 82%,[28] Tanzania,[30] France,[31] the United Kingdom,[34] and the United States.[35] The participant’s age was a significant determinant for PC utilization. As patients age, their maturity level and awareness of PC service utilization may increase. Older people are well-informed about their choices and have a positive attitude toward PC services because they have access to relevant information.
According to this review, participants of male sex had a 5.44 times increased chance of PC usage than their counterparts. Our review result is consistent with single studies done in France,[31] the United States,[35] and Singapore.[36] This reason could be due to gender-related differences in access to care, health-seeking behavior, and provider referral patterns. Men may have more control and access to healthcare in most low-resource settings, which may lead to increased utilization of PC. Cultural expectations can also deter women from seeking health services to address their own health needs, with possible resulting underutilization of PC services.
In this meta-analysis finding, participants with a monthly income of more than $50 had 1.77 times the probable rate of PC service usage compared with those with a monthly income of less than $50. The finding might be that monthly income is frequently associated with improved access to healthcare services, especially PC. Patients with higher salaries may have better health insurance coverage or financial resources to cover out-of-pocket expenses for PC. Patients with higher means may have greater access to information about PC services and are more inclined to seek them out. By contrast, patients with lesser earnings might be unaware of PC choices or encounter hurdles to getting information about these treatments.
In this meta-analysis, the pooled effect estimates reveal that the participants with secondary school or above educational levels had 1.52 times greater odds of utilizing PC service than the other levels of educational status. Our finding is similar to other papers in Asian country,[24] the United States,[35] Pennsylvania,[37] and Kenya.[38] This educational achievement may also be related to regional mobility, which can influence the opportunity for low-income communities to utilize PC services. Participants with an excellent educational status may be highly willing to relocate to a place with improved healthcare infrastructure. Patients with higher educational attainment may have better health literacy, enabling them to understand the importance of PC services and effectively advocate for themselves when seeking these services.
Based on this review of studies, participants who were satisfied with PC services had a 2.65 times higher probability of using PC provision compared with those who were not happy with the service. Our result has been confirmed by a single study done in Zimbabwe.[39] This result is because patient satisfaction shows how people perceive the quality of service they receive. Patients who are satisfied with the PC service, such as prompt referrals, complete symptom management, and emotional support, have a higher chance of using better PC support.
Similarly, this review indicated that participants with previous knowledge of PC services had a 15 times greater chance of using PC services compared with those with no prior knowledge. This finding is in agreement with a single report in Tanzania.[30] It could be that patients who have had past experience with PC are more likely to appreciate its benefits and scope. This understanding may encourage people to seek PC treatments sooner in their disease, resulting in better symptom control, higher quality of life, and maybe more prolonged survival. Patients who are aware of PC choices can actively participate in shared decision-making with their healthcare professionals. They may voice choices for PC measures, including management of pain or mental health assistance, resulting in more tailored care plans that better meet their needs and aspirations.
The findings reveal that participants who visit the hospital with good accessible PC service had 2.99 times higher probability of utilizing the PC service compared with not available service, and participants who visit the hospital with the unavailable PC service had 85% lower chance of PC support usage as compared with the hospital visits with the available service. The availability of PC provision in a particular area has a direct impact on access. Patients who reside in areas with more PC facilities, whether in hospitals, hospices, or community settings, have a greater chance of using these PC support services compared with those living in areas with fewer options. Hospitals and healthcare systems that incorporate PC within their services make it easier for patients to use these resources as part of their overall cancer treatment.
According to this review article, participants with family support had a 2 times increased chance of using PC services compared with those without family support, and participants who lacked family support had a 93% lower probability of using PC services. Our result has been congruent with a single study in Singapore.[36] This finding may be because family support can help maintain continuity of care by providing ongoing support and assistance throughout the patient’s illness journey. This continuity is critical for the timely use of PC provision and for providing patients with consistent backing and symptom control. Overall, family support helps cancer patients access PC services by providing practical assistance, emotional support, advocacy, and communication.
This systematic review and meta-analysis has several methodological constraints. A high level of heterogeneity among studies (I2 = 99%) reduces the accuracy of combined estimates and suggests varying effect sizes across contexts. The Egger regression test showed significant publication bias (P = .002), indicating potential overrepresentation of positive outcomes. While initial data management used SPSS, meta-analytic procedures were reevaluated with RevMan version 5.4.1 for methodological soundness. Factors such as age and educational level showed inconsistent associations across studies, possibly due to contextual variations. Despite these limitations, our findings offer helpful information about factors influencing PC utilization among adult cancer patients in Ethiopia and highlight areas needing policy focus.

5. Limitations
Although the study addresses a critical and under-researched topic in a low-resource setting, this meta-analysis reveals several potential methodological and other limitations. Notably, these include the limited number of studies and the inconsistency in outcome measurements, among the various potential methodological limitations included below:
First, the high statistical heterogeneity found among studies indicated significant variability of effect sizes, suggesting that studies in this meta-analysis were heterogeneous. Such heterogeneity may weaken the generalizability of pooled estimates, making it challenging to interpret the overall effect. Although a random-effects model accounted for variation, significant heterogeneity still limits drawing firm conclusions about the association between determinants and PC utilization across contexts. Second, there is a possibility of publication bias. The Egger regression test showed significant publication bias, which could indicate smaller or less statistically significant studies. This bias may lead to overestimation of associations between PC utilization and its determinants, as studies with null or contrary results can be poorly represented.
The third limitation is including only English studies, which limits the study to English-speaking high-income countries, excluding relevant research conducted within Ethiopia and other low-income settings. The findings may not represent the heterogeneous experiences and determinants that shape PC utilization across Ethiopian and non-English-speaking settings. Fourth, the study included only cross-sectional studies, which are limited in inferring causality. Cross-sectional studies provide a snapshot of associations but cannot establish cause-and-effect relations between determinants and PC utilization. Therefore, findings should be treated with caution as they do not account for temporal changes or causal pathways.
Finally, there were methodological challenges in pooling determinants from studies that adjusted for different sets of confounding variables. Studies included in the meta-analysis controlled for various confounders. This differential accounting for confounders may introduce bias into pooled estimates since the effect of a determinant may vary depending on which confounders are accounted for in each study. This limits the interpretation of how the pooled findings reflect the true effect of each determinant on PC utilization because confounding adjustment was not uniform across studies.

6. Conclusion
This systematic review and meta-analysis reveals that the pooled prevalence of PC service utilization among Ethiopian adult patients with cancer was low. Based on the meta-analysis findings, patient age, male sex, monthly income, educational status, family support, service satisfaction, and previous knowledge of PC were determinant factors for the use of PC provision among adult patients with cancer. To improve the use of PC support for adult patients with cancer, a multifaceted approach is necessary, focusing on early integration, education, multidisciplinary collaboration, effective communication, caregiver support, integration with oncology care, and expansion of community-based services. There is an urgent need to incorporate PC provision within the cancer care continuum. Education and awareness campaigns are also necessary to increase the use of PC services among adult cancer patients. Supporting family caregivers is critical to ensuring the most effective utilization of PC assistance. Furthermore, increasing access to community-based PC services can improve PC utilization among adult cancer patients.

Acknowledgments
The authors would like to thank all study authors and publishers.

Author contributions
Conceptualization: Gebrhud Berihu Haile, Tomas Amare Abraha.
Data curation: Gebrhud Berihu Haile, Tomas Amare Abraha.
Formal analysis: Gebrhud Berihu Haile, Tomas Amare Abraha.
Investigation: Gebrhud Berihu Haile.
Methodology: Gebrhud Berihu Haile, Tomas Amare Abraha.
Project administration: Gebrhud Berihu Haile, Tomas Amare Abraha.
Resources: Gebrhud Berihu Haile, Tomas Amare Abraha.
Software: Gebrhud Berihu Haile, Tomas Amare Abraha.
Supervision: Gebrhud Berihu Haile.
Validation: Gebrhud Berihu Haile, Tomas Amare Abraha.
Visualization: Gebrhud Berihu Haile, Tomas Amare Abraha.
Writing – original draft: Gebrhud Berihu Haile, Tomas Amare Abraha.
Writing – review & editing: Gebrhud Berihu Haile, Tomas Amare Abraha.

Supplementary Material