Impact of Socioeconomic Status on Prostate Cancer Outcomes Globally: A Protocol for Systematic Review and Meta-analysis


 BackgroundOne in every four men will be affected by prostate cancer. Choice of treatment depends on factors including grade and stage of the disease, age of the patient, availability of treatment options and socioeconomic status. We aimed to develop a protocol to assess the impact of socioeconomic status on prostate cancer outcomes globally.Methods A search strategy is developed using MeSH, text words, and entry terms. Nine databases will be searched, including PubMed, African Journals Online (AJOL), Google Scholar, Scopus, Cochrane Library, CINAHL, Web of Science, Embase and ResearchGate.Only observational studies, retrievable in the English language will be included. The primary outcome of this study is the socioeconomic status of prostate cancer patients. Secondary outcomes include mortality due to prostate cancer, health related quality of life, prostate cancer recurrence, need for secondary treatment, time to return to work, treatment choice regret and hospice enrollment. Identified studies will be screened and selected based on inclusion criteria. Data items will be managed in Zotero software, Microsoft Excel and CMA software. Both quality scores and the risk of bias for individual studies will be reported. Studies will be assessed for methodological, clinical, and statistical heterogeneity. Funnel Plots will be used for assessing publication bias. DiscussionThis protocol will enable a transparent, reliable and accurate method for assessing the impact of socioeconomic status on the global prostate cancer outcomes. It will allow discussions on outcomes such as mortality due to prostate cancer and how income disparity and availability of treatment options can influence prostate cancer outcomes. The final report of this study will be published in a peer-reviewed journal and the findings will be made available to health authorities.Systematic review RegistrationThis protocol has been registered in PROSPERO, with registration number CRD42020213700


Abstract Background
One in every four men will be affected by prostate cancer. Choice of treatment depends on factors including grade and stage of the disease, age of the patient, availability of treatment options and socioeconomic status. We aimed to develop a protocol to assess the impact of socioeconomic status on prostate cancer outcomes globally.

Methods
A search strategy is developed using MeSH, text words, and entry terms. Nine databases will be searched, including PubMed, African Journals Online (AJOL), Google Scholar, Scopus, Cochrane Library, CINAHL, Web of Science, Embase and ResearchGate.Only observational studies, retrievable in the English language will be included. The primary outcome of this study is the socioeconomic status of prostate cancer patients. Secondary outcomes include mortality due to prostate cancer, health related quality of life, prostate cancer recurrence, need for secondary treatment, time to return to work, treatment choice regret and hospice enrollment. Identi ed studies will be screened and selected based on inclusion criteria. Data items will be managed in Zotero software, Microsoft Excel and CMA software. Both quality scores and the risk of bias for individual studies will be reported. Studies will be assessed for methodological, clinical, and statistical heterogeneity. Funnel Plots will be used for assessing publication bias.

Discussion
This protocol will enable a transparent, reliable and accurate method for assessing the impact of socioeconomic status on the global prostate cancer outcomes. It will allow discussions on outcomes such as mortality due to prostate cancer and how income disparity and availability of treatment options can in uence prostate cancer outcomes. The nal report of this study will be published in a peer-reviewed journal and the ndings will be made available to health authorities.

Systematic review Registration
This protocol has been registered in PROSPERO, with registration number CRD42020213700 Background Globally, the incidence of prostate cancer has been on the increase over the years. Marked by differences in epidemiology, the prevalence varies with geographical location and its incidence progressively increases with age [1]. The incidence was reported to be higher in countries with higher socioeconomic development [1]. It constitutes a signi cant burden of non-skin cancers in men as the second most commonly diagnosed cancer in adult males and ranked the sixth leading cause of cancer-associated male mortality globally in 2018 [2]. It is a common condition and the lifetime risk of being diagnosed with prostate cancer was observed in a study to be approximately 1 in 8 for White men, 1 in 4 for Black men, and 1 in 13 for Asian men, whereas that of dying from prostate cancer is approximately 1 in 24 for White men, 1 in 12 for Black men, and 1 in 44 for Asian men [3]. It is also thought to be more aggressive in black men compared to men of other races [4]. This disease has attendant morbidity, mortality, poor quality of life, psychological challenges [5], high cost of treatment, poor treatment free survival rate [6] which are some of the sources of concern in these patients. These concerns are at times compounded depending on the socioeconomic status of the patient.
The treatments can be nancially demanding, involving but not limited to radical prostatectomy, radiotherapy, both medical and surgical castration [7] among others and depend on the disease risk category derived from combination of patient's prostate speci c antigen level (PSA), Gleason score and Tumour stage with various modi cations [8,9]. Therefore, it poses a threat to overall health related quality of life of the subject and his dependents including the spouse [10]. In the early stage disease, treatment is aimed at cure thus preventing morbidity and mortality while for late stage, it is palliation with improvement in overall quality of life.
Socioeconomic status especially where there is no health insurance coverage may constitute a barrier to decision to seek early intervention, proper patient evaluation, choice of treatment and post treatment follow up which can negatively affect the disease outcome in terms of prognosis and patient overall treatment free survival.
Numerous factors are associated with prostate cancer outcome evaluation. These include proportion of prostate cancer speci c deaths, degree of Health related quality of life (HRQOL) [10][11][12][13], proportion who regret choice of treatment for prostate cancer [14][15][16], incidence of and mortality rate of those with 'unknown risk' category for prostate cancer, incidence rate of prostate cancer, disease-free time, positive surgical margin, proportion of those needing hospice enrollment [17], proportion of those who needed secondary treatment [18], socioeconomic class, medical Insurance status [11,12,18], income and education [19]. Lack of health insurance coverage [18] and low socioeconomic class are related to poor HRQOL and worse prostate cancer outcome [18,20,21].
The negative impact of a patient's socioeconomic status with regard to prostate cancer outcome has been reported in some studies [11]. But even where access to health care coverage was provided, it has been reported that socioeconomic status did not correlate with prostate cancers outcome [22]. Furthermore, it was observed that with access to health care coverage and irrespective of treatment option offered to men with low risk localized prostate cancer, a higher number of men still died from lower compared to higher socioeconomic groups, though the nding was not signi cant [23]. Another study also noted in their analysis that sociodemographic factors were not important prognostic factors in determining outcome after External Beam Radiation Therapy (EBRT) for prostate cancer [24]. While another study found increased relative risk for presenting with advanced-stage prostate cancer in Hispanic but not in African-American men to be related to traditional socioeconomic, clinical, and pathologic factors [25,26]. Therefore, there is a need to determine the signi cance of variation in patient socioeconomic status and prostate cancer outcomes. This will ll the gap in knowledge on the relationship between patients' socioeconomic status and prostate cancer outcome. This protocol is therefore developed to conduct a systematic review and meta-analysis on the impact of socio-economic status of prostate cancer patients on their disease outcomes using published primary studies.

Methods And Design
The main objective of this protocol is to determine the impact of socioeconomic status on the outcomes of prostate cancer globally. There is no time frame/restriction in the inclusion of publications.

Search strategy
The search strategy included MeSH terms, text words and entry words. The search strategies that were used are shown in Table 1.

Data Extraction and Management
Three tools will be used for data extraction and management: i) Zotero software, ii) Microsoft Excel and iii) Comprehensive Meta-analysis software CMA version 3 a) Screening: Studies will be screened at four levels: Level 1: screening to select only observational studies while other study designs are excluded.
Level 2: screening titles and abstracts of observational studies using MeSH terms, keywords, and entry terms.
Level 3: screening of the selected studies by full-text reading, using the same strategy Level 4: snowballing of the literature using included studies b) Reviewers: Fifteen reviewers are involved in this study. Two reviewers will independently screen studies from each database and assess studies for inclusion and or exclusion. Con icts will be resolved by a third reviewer. All reviews are blinded.
C) Selection process: The screening and deduplication will be done in Zotero software. Studies will be selected based on eligibility criteria and primary measurable outcome. Authors of studies with missing data will be contacted via email and telephone.
d) Data collection: The following data items will be extracted from each eligible study into Microsoft Excel: i. rst author's surname and year of publication of the study, ii. socioeconomic status: income, educational level, insurance status iii. prostate outcomes: mortality due to prostate cancer, HRQOL including depression and time of returnto-work, recurrence of the prostate cancer, need for secondary treatment, treatment choice regret, unknown risk category, and hospice enrollment, iv. sample size.
Data from Microsoft Excel will be exported to CMA software for meta-analysis Data items (Main measurable outcomes) The measurable data items in this study are: i. socioeconomic status including numerical data such as income, categorical data such as educational status, and categorical data such as health insurance status. The effect size is mean or median income status.
ii. prostate cancer-speci c mortality rate (categorical data), prostatic cancer recurrence rates (numerical data), health related quality of life (numerical data), recurrence of prostate cancer (categorical), choice of treatment regrets (categorical), and time of return to work (numerical)

Risk of bias
The risk of bias in included studies will be accessed for the individual studies using the National Institute of Health (NIH) Quality assessment tool for observational cohort and cross-sectional studies. This will be cross-checked with the Cochrane tool of risk of bias assessment for the strength of the body of evidence, i.e., using speci c relevant items from this tool to assess the strength of the body of evidence.
Studies with extreme bias may be excluded after assessment in the following areas: 1. Method of testing and reporting at the outcome level 2. Heterogeneity will be assessed at the study level 3. Publication bias will be assessed at the study level 4. Sensitivity test using include/exclude function in the CMA software will be done at the study level

Data synthesis
A. Studies that passed the methodological quality assessment using the NIH quality assessment tool will be extracted. The results will be presented in tabular format in addition to a narrative synthesis.
B. The following shall be included in the meta-analysis: i) Report on socioeconomic status. Mean income status will be expressed in cohen's d as the effect size.
ii) Subgroups analysis will be done using moderators such as educational status, insurance status, treatment option, race, recurrence of prostate cancer and choice of treatment regret.
Eligible studies will be quantitatively analyzed using the Comprehensive Meta-analysis CMA Software, Version 3 (BioStat USA). The random effect model will be used for computation.
In addition to subgroup analysis, a meta regression will be performed using numerical independent variables such as time to return to work, patients age and health related quality of life while income status will be the outcome variable.
A cumulative meta-analysis will be performed to check for trends on SES and prostate outcomes over years.

Assessment of Meta-bias
To test for heterogeneity Q value, I 2 , Τ 2 will be used. I² values of less than 40% will be considered low heterogeneity, values > 40 to ≤75 % moderate while values > 75% are high. Publication bias will be assessed using a funnel plot and test of assymmetry. Sensitivity test using include /exclude function will be performed in the CMA software.

Results
The study selection process will be summarized in a ow diagram according to the PRISMA 2015 Statement and PRISMA-P Checklist (attached). A table of the search strategy in various databases showing text words, MeSH, and entry terms will be included. A list of included studies will be summarized in a table. Pooled cohen's d for socioeconomic status, standard error, and 95% CI, P values, and relative weights assigned to studies and heterogeneity tests will be included in forest plots. A table of quality scores and risk of bias of each eligible study will be included. Forest plots to show sub-group analysis will be included. A cumulative meta-analysis to check for trends will also be included.

Discussion
The impact of socioeconomic status on prostate cancer outcomes will be discussed. The regression model from meta-regression will be examined for the predictive factors. The various changes in effect size due to sensitivity test will also be discussed. The nal study will be published in a peer-review journal and the ndings will be submitted to the Ministry of Health to inform policy decision making.