Protocol

Home-built environment interventions and inflammation biomarkers: a systematic review and meta-analysis protocol

Eva Hernandez-Garcia, Evangelia Chrysikou, Larissa Nekhlyudov, Derek W Gilroy and José M Ordóñez-Mena
BJGP Open 2022; 6 (4): BJGPO.2022.0104. DOI: https://doi.org/10.3399/BJGPO.2022.0104

Abstract

Background Inflammation control is a fundamental part of chronic care in patients with a history of cancer and comorbidity. As the risk–benefit profile of anti-inflammatory drugs is unclear in survivors of cancer, GPs and patients could benefit from alternative non-pharmacological treatment options for dysregulated inflammation. There is a potential for home-built environment (H-BE) interventions to modulate inflammation; however, discrepancies exist between studies.

Aim To evaluate the effectiveness of H-BE interventions on cancer-associated inflammation biomarkers.

Design & setting A systematic review and meta-analysis of randomised and non-randomised trials in community-dwelling adults.

Method PubMed and MEDLINE, Embase, Web of Science, and Google Scholar will be searched for clinical trials published in January 2000 onwards. The study will include H-BE interventions modifying air quality, thermal comfort, non-ionising radiation, noise, nature, and water. No restrictions to study population will be applied to allow deriving expectations for effects of the interventions in cancer survivors from available source populations. Outcome measures will be inflammatory biomarkers clinically and physiologically relevant to cancer. The first reviewer will independently screen articles together with GPs and extract data that will be verified by a second reviewer. The quality of studies will be assessed using the Cochrane risk-of-bias tools. Depending on the clinical and methodological homogeneity of populations, interventions, and outcomes, a meta-analysis will be conducted using random-effects models.

Conclusion Findings will determine the effectiveness of H-BE interventions on inflammatory parameters, guide future directions for its provision in community-dwelling survivors of cancer and support GPs with safer anti-inflammatory treatment options in high-risk patients for clinical complications.

How this fits in

Provision of treatment options for inflammation control is a fundamental component for the management of common chronic diseases in primary and community care, especially of the complex medical and pathophysiological profile of survivors of cancer. The most compelling evidence for an association comes from randomised controlled trials that test drugs or exercise–nutritional programmes aimed at modulating inflammatory response. While non-steroidal anti-inflammatory drugs and systemic glucocorticoids are frequently prescribed in general practice, the effects are still controversial in survivors of cancer as these may be unsafe and hinder restoring the normal regulation of inflammation. The potential benefit of H-BE interventions on cancer-associated inflammation biomarkers may mean they are a reasonable treatment to improve quality of life and clinical outcomes in community-dwelling older patients and ultimately in survivors of cancer.

Introduction

Cancer survival in high-income countries continues to improve across almost all cancer types, even for those with the worst prognosis.1,2 The delivery of multiple evidence-based interventions has been an important driver of the progress in cancer control, particularly around the management of comorbidities.2 Compared with the cancer-free population, survivors of cancer are at significantly higher risk for mental health,3–5 cardiometabolic, musculoskeletal,6–8 somatic, and physical conditions9–11 years after primary treatment. Significant predictors of the number of comorbidities post-diagnosis include cancer type, treatment received, years since diagnosis, age, adiposity, physical activity, and level of deprivation.12–20 Unique multimorbidity clusters drive differences on cancer survival outcomes,17,18 drug prescriptions,19 GP contacts and home visits,21,22 and hospitalisations.11

Inflammatory biomarkers are postulated to derive a clinically relevant metric in the early prediction of multimorbidity, including diseases of various physiologic systems.23,24 Combined inflammatory markers have shown to predict treatment response,25–27 early recurrence,28,29 prognosis,30 and comorbidity development after cancer diagnosis.31 In primary care settings, prediction models for cancer that include inflammatory biomarkers demonstrate superior clinical utility compared with symptoms-only scores.32 GPs commonly request blood test combinations that check for abnormal inflammation in patients such as C-reactive protein (CRP), full blood count, glycated haemoglobin, ferritin and/or neutrophil count.33,34 Their usefulness as surrogate endpoints has been confirmed in clinical trials in multiple cancer types.35

Improvements to the H-BE lead to better health.36 Housing refurbishment of new energy-efficient combination (combi) boilers and double-glazed windows in social housing showed a reduction of 16% in healthcare service utilisation costs over 6-months and improved the residents’ health status, particularly in people aged ≥65 years.37 Multiple home improvements — electric systems, windows, wall insulation, and garden paths — to meet UK housing quality standards were associated with up to 35% and 52% fewer emergency admissions for cardiovascular and respiratory conditions among all-aged residents within a 10-year period compared with people who did not receive the intervention.38 Interventions modifying the household environmental quality — air, artificial lighting and nature — trigger changes on the residents’ inflammatory levels, particularly interleukin-6, CRP, high sensitivity-CRP, endothelial growth factor, granulocyte-colony stimulating factor, and eotaxin.39

This systematic review will examine the effectiveness of H-BE interventions modifying air quality, thermal comfort, non-ionising radiation, noise, nature, and water on inflammatory biomarkers in community-dwelling adults. This study is intended to provide the groundwork for future H-BE interventions as inflammation-targeting treatment in survivors of cancer for potential consideration in general practice (Figure 1).

Figure 1.
Figure 1. Conceptual framework describing the relationships between home-built environment intervention, systemic inflammatory response, clinical outcomes and general practice care of survivors of cancer.

*In daytime. Minimum melanopic equivalent daylight illuminance at the eye measured in the vertical plane at 1.2m height. 25(OH)D = 25 hydroxyvitamin D. 5-HT = 5-hydroxytryptamine/serotonin. AC = adrenal cortex. ALB = albumin. COR = cortisol. COX = cyclooxygenase. CRP = c-reactive protein. E = environmental. EDI = melanopic equivalent daylight illuminance. ESR = erythrocyte sedimentation rate. Fer = ferritin. FGN = fibrinogen. G-CSF = granulocyte-colony stimulating factor. GP = general practice. Hb = haemoglobin. H-BE = home-built environment. HDL-C = high-density lipoprotein cholesterol. IGF = insulin-like growth factor. IL = interleukin. LC = leukocyte count. LED = light-emitting diode. MEL = melatonin. NSAIDs = non-steroidal anti-inflammatory drugs. Personal E exposure = Personal environmental exposure. PG = prostaglandin. PLT = platelets. PV = plasma viscosity. RBC = red blood cells. SIR = systemic inflammatory response. TC = total cholesterol. TG = triglycerides. TP = total protein. TX = therapy. VEGF = vascular endothelial growth factor. WBC = white blood cells.

Method

This review will be developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.40 This protocol conforms with the PRISMA-Protocols checklist41 and is registered with PROSPERO (CRD42022310680).

Eligibility criteria

Types of study

All types of clinical trials will be included (randomised and pseudo-randomised controlled and uncontrolled trials, and so on) if these were published, peer reviewed, and reported primary research and quantitative data. Trials will refer to experimental or interventional studies in which the researchers intervened to modify the H-BE. Mixed-methods studies employing quantitative data will be included if meeting the inclusion criteria.

The operational definition of clinical trial and further discussion on the eligible study types is reported in Supplementary Box 1.

Participants and setting

Given the limited studies addressing the research question in survivors of cancer, no restrictions will be applied to participants other than those applied by the included primary publications itself. This is intended to avoid missing relevant data that identify the interactions between H-BE interventions and inflammation, and translate the potential benefits to survivors of cancer. Thus the study will include adults (aged ≥18 years) living in the community at any H-BE as their place of usual residence. The term H-BE is described in the Supplementary Box 2.

Interventions

A H-BE intervention is defined as any change of baseline housing conditions for a modified environmental quality by using architectural elements or devices. Architecture elements are referred to as a new installation, supply, or retrofitting related to any building physical characteristic, design configurations, and engineering system within homes ; for example, double-glazing of windows and air filtration. Household environmental quality will be considered — air quality, thermal comfort, non-ionising radiation, noise, nature, and water — and will be monitored using quantitative measurement equipment (Supplementary Table 1). Potential H-BE interventions by environmental exposure category are described in the Supplementary Table 2. No restrictions will apply to interventions in terms of delivery, dose, duration, intensity, frequency, and co-interventions.

Comparison

Studies with or without any comparative group will be considered for the review.

Outcome measures

The outcome will be inflammatory biomarkers in blood, urine, and saliva, either examined individually or in combination as part of a score. A comprehensive review of clinical and preclinical data was done to identify the cancer-associated inflammation markers for this study (Table 1; Supplementary Table 3).

View this table:
Table 1. Summary of cancer-associated systemic inflammatory response biomarkers

Information sources

Articles will be sought using PubMed and MEDLINE, Embase, Web of Science databases, and Google Scholar.42⇓–44 Additional non-indexed citations will be identified by handsearching and scrutiny of reference lists of eligible studies to minimise potential reporting bias.45 The authors will also retrieve registered clinical trials from Cochrane Central Register of Controlled Trials, International Clinical Trials Registry Platform, and ClinicalTrials.gov that are not indexed in bibliographic databases.46,47

Search strategy

The search algorithm combines the terms 'home settings' and 'environmental attributes' and 'inflammatory biomarkers' using Boolean operators, truncation, and proximity operators. Controlled vocabulary terms and free-text words were identified and refined through an iterative process of preliminary searches in databases and snowballing technique. The initial search strategy developed for PubMed (Supplementary Table 4) will be tailored appropriately as required for each database. Harzing’s Publish or Perish software (version 7)48 will be used to retrieve the first collated 300 records titles from Google Scholar.49 Databases will be searched for the period 1 January 2000 onwards, since investigations that address H-BE interventions and inflammatory biomarkers are scarce before this timeframe.

There will be no filtering for study design as these may not achieve sufficient sensitivity and miss potential studies.50,51 Validated search query filters for Humans will be added to the final search algorithm as the Cochrane Handbook for Systematic Reviews of Interventions recommends.52 Given the non-English language studies rarely impact on the effect estimates and conclusions of systematic reviews and meta-analyses,43,53 these will be only labelled as 'studies awaiting classification' in the PRISMA flow diagram to inform its availability.

Data records, management, and extraction

All records identified will be stored in Mendeley software. The Systematic Review Assistant-Deduplication Module will be used to automatically remove duplicated references,54 and the screening process will be undertaken in Rayyan tool.55,56

One review author (EHG) will independently screen titles and abstracts of records in duplicate with a GP group, crowdsourcing citation-screening.57 Identified articles will be randomly split among the GPs involved (≤300 articles each). If studies remain, these will be distributed among the review team or a second reviewer(s). The same method will be used to screen the full-text (≤3 articles each GP). If no abstract or not enough information is available, the study will be retained for full-text screening. Discrepancies will be resolved through discussion until consensus is reached.

Data will be extracted by one reviewer (EHG) and verified by a second reviewer for quality assurance. A predefined data-extraction form will be initially developed using Cochrane58 and JBI manuals,59 including study information, methodology, participant characteristics, interventions, and outcomes (Supplementary Table 5). Whenever necessary, the corresponding author will be contacted by email to request information.

Involvement of GPs

The process of study selection will be done in collaboration with non-academic GPs, adopting the National Institute for Health Research involvement activity framework.60 Despite the validity of crowdsourced citation-screening by untrained workers,57 additional quality assurance tests will be conducted as part of this review to improve confidence in the results.61 The strategy for the involvement and inclusion criteria of GPs and quality control mechanisms in crowdsourcing are outlined in Supplementary Box 3.

The Guidance for Reporting Involvement of Patients and the Public version 2 (GRIPP2) will be used to ensure the overall quality and transparency of the involvement activity in this research.62

Methodological quality assessment

Risk-of-bias assessment

The risk of bias will be assessed with the Cochrane risk-of-bias tool for randomised controlled trials (RoB 2 RCTs), across several features of trial design, management, and reporting.63 Additionally, the test version of the RoB 2 tool will be used for crossover trials with specific considerations required in this study design.64 Judgement is assigned as 'Low' or 'High' risk-of-bias, or 'Some concerns'.

For the other experimental studies, controlled or uncontrolled trials, the Cochrane risk of bias in non-randomised studies of interventions (ROBINS-I) guideline will be used.65,66 Bias domains include confounding, participant selection, classification of the interventions, deviations from intended interventions, missing data, outcome measurements, and reported results. Judgement is classified as 'Critical', 'Serious', 'Moderate', and 'Low' risk of bias.

ROBINS-I tool is frequently misapplied in practice.67 The risk-of-bias assessment will be performed by the first reviewer (EHG) and a random sample will be verified by the review member with methodological expertise (JMOM) to ensure that they do not disregard more intricate domains of bias.

Quality assessment

The quality of evidence for an association between intervention(s) and inflammatory biomarker concentrations will be rated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach across five domains: study limitations, imprecision, indirectness, inconsistency, and publication bias.68 The relevant risk-of-bias tool will be integrated within GRADE assessment and it will be accepted that both randomised and non-randomised experimental studies are the reference initial for highest feasible certainty.69

Data synthesis

The findings will be reported narratively and supplemented with summary tables structured by the type of intervention. The criteria used to prioritise the reporting results will be based on the type of study design and will be separate for randomised, pseudo-randomised, and crossover trials (RoB 2) and other experimental studies controlled or uncontrolled trials (ROBINS-I, which has a separate domain to address confounding). To enhance transparency in reporting the quantitative effects of H-BE interventions, the Synthesis Without Meta-analysis (SWiM) guideline will be followed.70

Statistical analysis

If inflammatory biomarkers are reported at the end of the study or as a change from baseline, raw or adjusted unstandardised mean differences with a 95% confidence interval (CI) will be extracted or calculated to compare intervention and comparator arms. If means and standard deviations are not available, these will be calculated from medians and interquartile ranges using Wan et al’s equations.71 If inflammatory biomarkers are reported as below or above a certain threshold, as categorical outcomes, raw or adjusted odds ratio or risk ratios will be extracted or calculated with 95% CI. Random-effects models will be used to pool study-specific effect size measures using the Paule and Mandel estimator for the between-study variance.72

The authors' previous analysis on the topic39 showed that studies reported multiple or single biomarker(s) at multiple time points. Therefore, subgroup analyses will be conducted by length of follow-up: short term (≤2 weeks), mid-term (>2 weeks to ≤6 months), and long term (>6 months).

The robustness of the findings will be evaluated with sensitivity analyses (excluding studies at high risk of bias).

The I 2 statistic will be computed with 95% CIs to quantify the proportion of heterogeneity not attributable to sampling error. The Cochrane thresholds of I 2 will be used for unimportant heterogeneity (0%–40%); moderate (30–60%); substantial (50–90%) and, considerable heterogeneity (>75%).73 If the number of studies is small in the meta-analysis, the I 2 statistic will be interpreted cautiously as it can be biased owing to low statistical power.74 The significance of the heterogeneity will be tested with the χ2 test.73 Statistical significance will be set at P<0.05.

Publication bias will be evaluated using Begg and Mazumdar’s funnel plot75 and the Egger’s linear regression test.76,77

Analysis will be conducted using statistical software of R,78 with the R packages meta and metafor.79

Discussion

Summary

This systematic review of clinical trials will provide insights on the effectiveness of H-BE interventions on reducing inflammatory parameters of community-dwelling adults, the quality of the evidence provided by these studies, and their reliability to inform the potential adoption by GP surgeries, clinical commissioning groups, and patients themselves.

Strengths and limitations

Given survivors of cancer are vastly underrepresented in this research area,39,80 the generalisability of the findings from ‘adults’ to ‘survivors of cancer’ will be considered.81 Biomarker endpoints that are physiologically relevant to disease pathology and reflect earlier phase of disease progression are a useful approach to support extrapolation.81,82 A panel of inflammatory biomarkers was derived from clinical and preclinical research. These biomarkers have been shown to predict comorbidity development, treatment response, recurrence, and prognosis in survivors of cancer. Cancer-associated inflammatory mediators from preclinical data are also relevant since there is biologically plausibility to treat them as surrogate endpoints in clinical trials.

While this systematic review may not generate immediate recommendations for clinical practice that are specific to survivors of cancer, evidence from high quality RCTs will generate meaningful information about the effects of H-BE interventions on the systemic inflammatory responses. The findings may identify promising H-BE interventions that will need further investigation in trials with long-term non-surrogate hard outcomes and multimodal treatment programmes. As a major limitation, clinical and methodological heterogeneity between studies is anticipated, with different study designs, populations included, interventions administered, and outcome definitions; as such, appropriate interpretation of results will require caution. Another weakness is that the assessment of risk of bias will only be conducted in duplicate in a random sample of included studies.

Implications for research and practice

Cancer treatments lead to long-lasting immune dysfunction, chronic non-resolving inflammation,83,84 increased comorbidity burden,31 and epigenetic age acceleration associated with an elevated inflammatory profile.85 Interventions to mitigate inflammation may benefit survivors of cancer. In general practice, non-steroidal anti-inflammatory drugs are widely prescribed.86,87 However, their regular use on cancer course is still controversial, ranging from promising chemopreventive effects of aspirin use;88,89 to little or no effect of celecobix use on cancer recurrence, progression, and death; and cardiovascular toxic effects.90,91 While the effect of glucocorticoids as anti-inflammatory agents on survival outcomes remains inconclusive,92 the steroid regimen administered may cause long-term adverse metabolic events.93 Against this, GPs are calling for alternative treatments to the routine use of anti-inflammatory drugs in patients with comorbidities, including non-pharmacological therapies.94 Treatment options promoting pro-resolution processes of inflammation may be superior to standard anti-inflammatory strategies.95

One potential non-pharmacological area for intervention is the H-BE. There is, therefore, a need for evidence-based information around which H-BE interventions are effective to improve inflammation-related outcomes and what available knowledge translation tools could efficiently support its delivery. This study will provide further understanding of H-BE interventions as potential therapeutics for inflammation control. Given that the knowledge is scarce around care through H-BE for survivors of cancer,80 findings will serve as a resource for a potential applied research field in survivors of cancer and for which interventions may be implemented into primary care. Thus, this review may support GPs against the increasing demand of other safer inflammation-modulating treatment options, especially when considering prescribing anti-inflammatory drugs in patients at risk of clinical complications. Overall, primary care professionals and researchers may optimise the standard chronic care by understanding this evidence for and against their use.

Notes

Funding

This research did not receive any funding.

Ethical approval

Not applicable.

Data

The dataset on which the conclusions of the proposed research will rely can be obtained from the corresponding author on reasonable request.

Provenance

Freely submitted; externally peer reviewed.

Trial registration number

PROSPERO: CRD42022310680.

Acknowledgement

Competing interests

The authors declare that no competing interests exist.

  • Received July 5, 2022.
  • Revision received September 15, 2022.
  • Accepted September 20, 2022.

This article is Open Access: CC BY license (https://creativecommons.org/licenses/by/4.0/)

References

  1. 1.↵
    1. Allemani C,
    2. Matsuda T,
    3. Di Carlo V,
    4. et al.
    (2018) Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 391 (10125):1023–1075, doi:10.1016/S0140-6736(17)33326-3.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Arnold M,
    2. Rutherford MJ,
    3. Bardot A,
    4. et al.
    (2019) Progress in cancer survival, mortality, and incidence in seven high-income countries 1995–2014 (ICBP SURVMARK-2): a population-based study. Lancet Oncol 20 (11):1493–1505, doi:10.1016/S1470-2045(19)30456-5.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Huang W-K,
    2. Juang Y-Y,
    3. Chung C-C,
    4. et al.
    (2018) Timing and risk of mood disorders requiring psychotropics in long-term survivors of adult cancers: a nationwide cohort study. J Affect Disord 236 80–87, doi:10.1016/j.jad.2018.04.107.
    OpenUrlCrossRef
  4. 4.
    1. Ji X,
    2. Cummings JR,
    3. Gilleland Marchak J,
    4. et al.
    (2020) Mental health among nonelderly adult cancer survivors: a national estimate. Cancer 126 (16):3768–3776, doi:10.1002/cncr.32988.
    OpenUrlCrossRef
  5. 5.↵
    1. Carreira H,
    2. Williams R,
    3. Müller M,
    4. et al.
    (2018) Associations between breast cancer survivorship and adverse mental health outcomes: a systematic review. J Natl Cancer Inst 110 (12):1311–1327, doi:10.1093/jnci/djy177.
    OpenUrlCrossRef
  6. 6.↵
    1. Xiao Y,
    2. Wang H,
    3. Tang Y,
    4. et al.
    (2021) Increased risk of diabetes in cancer survivors: a pooled analysis of 13 population-based cohort studies. ESMO Open 6 (4):100218, doi:10.1016/j.esmoop.2021.100218.
    OpenUrlCrossRef
  7. 7.
    1. Strongman H,
    2. Gadd S,
    3. Matthews A,
    4. et al.
    (2019) Medium and long-term risks of specific cardiovascular diseases in survivors of 20 adult cancers: a population-based cohort study using multiple linked UK electronic health records databases. Lancet 394 (10203):1041–1054, doi:10.1016/S0140-6736(19)31674-5.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Khan NF,
    2. Mant D,
    3. Carpenter L,
    4. et al.
    (2011) Long-term health outcomes in a British cohort of breast, colorectal and prostate cancer survivors: a database study. Br J Cancer 105 (Suppl 1):S29–S37, doi:10.1038/bjc.2011.420.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Leysen L,
    2. Lahousse A,
    3. Nijs J,
    4. et al.
    (2019) Prevalence and risk factors of sleep disturbances in breast cancer survivors: systematic review and meta-analyses. Support Care Cancer 27 (12):4401–4433, doi:10.1007/s00520-019-04936-5.
    OpenUrlCrossRefPubMed
  10. 10.
    1. Baden M,
    2. Lu L,
    3. Drummond FJ,
    4. et al.
    (2020) Pain, fatigue and depression symptom cluster in survivors of prostate cancer. Support Care Cancer 28 (10):4813–4824, doi:10.1007/s00520-019-05268-0.
    OpenUrlCrossRef
  11. 11.↵
    1. Kjaer TK,
    2. Andersen EAW,
    3. Winther JF,
    4. et al.
    (2019) Long-term somatic disease risk in adult Danish cancer survivors. JAMA Oncol 5 (4):537–545, doi:10.1001/jamaoncol.2018.7192.
    OpenUrlCrossRef
  12. 12.↵
    1. Koo MM,
    2. Swann R,
    3. McPhail S,
    4. et al.
    (2020) The prevalence of chronic conditions in patients diagnosed with one of 29 common and rarer cancers: a cross-sectional study using primary care data. Cancer Epidemiol 69 doi:10.1016/j.canep.2020.101845. 101845.
    OpenUrlCrossRefPubMed
  13. 13.
    1. Leach CR,
    2. Weaver KE,
    3. Aziz NM,
    4. et al.
    (2015) The complex health profile of long-term cancer survivors: prevalence and predictors of comorbid conditions. J Cancer Surviv 9 (2):239–251, doi:10.1007/s11764-014-0403-1.
    OpenUrlCrossRefPubMed
  14. 14.
    1. Kenzik KM,
    2. Kent EE,
    3. Martin MY,
    4. et al.
    (2016) Chronic condition clusters and functional impairment in older cancer survivors: a population-based study. J Cancer Surviv 10 (6):1096–1103, doi:10.1007/s11764-016-0553-4.
    OpenUrlCrossRefPubMed
  15. 15.
    1. Götze H,
    2. Taubenheim S,
    3. Dietz A,
    4. et al.
    (2018) Comorbid conditions and health-related quality of life in long-term cancer survivors-associations with demographic and medical characteristics. J Cancer Surviv 12 (5):712–720, doi:10.1007/s11764-018-0708-6.
    OpenUrlCrossRef
  16. 16.
    1. Holmes HM,
    2. Nguyen HT,
    3. Nayak P,
    4. et al.
    (2014) Chronic conditions and health status in older cancer survivors. Eur J Intern Med 25 (4):374–378, doi:10.1016/j.ejim.2013.12.003.
    OpenUrlCrossRef
  17. 17.↵
    1. Jansana A,
    2. Poblador-Plou B,
    3. Gimeno-Miguel A,
    4. et al.
    (2021) Multimorbidity clusters among long-term breast cancer survivors in Spain: results of the SURBCAN study. Int J Cancer 149 (10):1755–1767, doi:10.1002/ijc.33736.
    OpenUrlCrossRef
  18. 18.↵
    1. Warner DF,
    2. Schiltz NK,
    3. Stange KC,
    4. et al.
    (2017) Complex multimorbidity and health outcomes in older adult cancer survivors. Fam Med Community Heal 5 (2):129–138, doi:10.15212/FMCH.2017.0127.
    OpenUrlCrossRef
  19. 19.↵
    1. Keats MR,
    2. Cui Y,
    3. DeClercq V,
    4. et al.
    (2021) Burden of multimorbidity and polypharmacy among cancer survivors: a population-based nested case–control study. Support Care Cancer 29 (2):713–723, doi:10.1007/s00520-020-05529-3.
    OpenUrlCrossRef
  20. 20.↵
    1. Mahumud RA,
    2. Alam K,
    3. Dunn J,
    4. Gow J
    (2020) The burden of chronic diseases among Australian cancer patients: evidence from a longitudinal exploration, 2007–2017. PLoS One 15 (2), doi:10.1371/journal.pone.0228744. e0228744.
    OpenUrlCrossRef
  21. 21.↵
    1. Fidjeland HL,
    2. Vistad I,
    3. Gjelstad S,
    4. Brekke M
    (2019) Exploring why patients with cancer consult GPs: a 1-year data extraction. BJGP Open 3 (4), doi:10.3399/bjgpopen19X101663. bjgpopen19X101663.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    1. Jabaaij L,
    2. van den Akker M,
    3. Schellevis FG
    (2012) Excess of health care use in general practice and of comorbid chronic conditions in cancer patients compared to controls. BMC Fam Pract 13 doi:10.1186/1471-2296-13-60. 60.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Sayed N,
    2. Huang Y,
    3. Nguyen K,
    4. et al.
    (2021) An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging 1 598–615, doi:10.1038/s43587-021-00082-y.
    OpenUrlCrossRef
  24. 24.↵
    1. Ferreira GD,
    2. Simões JA,
    3. Senaratna C,
    4. et al.
    (2018) Physiological markers and multimorbidity: a systematic review. J Comorb 8 (1):2235042X18806986, doi:10.1177/2235042X18806986.
    OpenUrlCrossRef
  25. 25.↵
    1. Zhang X,
    2. Gari A,
    3. Li M,
    4. et al.
    (2022) Combining serum inflammation indexes at baseline and post treatment could predict pathological efficacy to anti‑PD‑1 combined with neoadjuvant chemotherapy in esophageal squamous cell carcinoma. J Transl Med 20 (1), doi:10.1186/s12967-022-03252-7. 61.
    OpenUrlCrossRef
  26. 26.
    1. Katayama Y,
    2. Yamada T,
    3. Chihara Y,
    4. et al.
    (2020) Significance of inflammatory indexes in atezolizumab monotherapy outcomes in previously treated non-small-cell lung cancer patients. Sci Rep 10 (1), doi:10.1038/s41598-020-74573-0. 17495.
    OpenUrlCrossRef
  27. 27.↵
    1. Patil NS,
    2. Zou W,
    3. Mocci S,
    4. et al.
    (2021) C-reactive protein reduction post treatment is associated with improved survival in atezolizumab (anti-PD-L1) treated non-small cell lung cancer patients. PLoS One 16 (2), doi:10.1371/journal.pone.0246486. e0246486.
    OpenUrlCrossRef
  28. 28.↵
    1. Ying HQ,
    2. Liao YC,
    3. Sun F,
    4. et al.
    (2021) The role of cancer-elicited inflammatory biomarkers in predicting early recurrence within stage II-III colorectal cancer patients after curable resection. J Inflamm Res 14 115–129, doi:10.2147/JIR.S285129.
    OpenUrlCrossRef
  29. 29.↵
    1. Sparano JA,
    2. O’Neill A,
    3. Graham N,
    4. et al.
    (2022) Inflammatory cytokines and distant recurrence in HER2-negative early breast cancer. NPJ Breast Cancer 8 (1):16, doi:10.1038/s41523-021-00376-9.
    OpenUrlCrossRef
  30. 30.↵
    1. Dolan RD,
    2. Lim J,
    3. McSorley ST,
    4. et al.
    (2017) The role of the systemic inflammatory response in predicting outcomes in patients with operable cancer: systematic review and meta-analysis. Sci Rep 7 (1), doi:10.1038/s41598-017-16955-5. 16717.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Alfano CM,
    2. Peng J,
    3. Andridge RR,
    4. et al.
    (2017) Inflammatory cytokines and comorbidity development in breast cancer survivors versus noncancer controls: evidence for accelerated aging? J Clin Oncol 35 (2):149–156, doi:10.1200/JCO.2016.67.1883.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Nicholson BD,
    2. Aveyard P,
    3. Koshiaris C,
    4. et al.
    (2021) Combining simple blood tests to identify primary care patients with unexpected weight loss for cancer investigation: clinical risk score development, internal validation, and net benefit analysis. PLoS Med 18 (8), doi:10.1371/journal.pmed.1003728. e1003728.
    OpenUrlCrossRef
  33. 33.↵
    1. Fanshawe TR,
    2. Ordóñez-Mena JM,
    3. Turner PJ,
    4. et al.
    (2018) Frequencies and patterns of laboratory test requests from general practice: a service evaluation to inform point-of-care testing. J Clin Pathol 71 (12):1065–1071, doi:10.1136/jclinpath-2018-205242.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    1. Ordóñez-Mena JM,
    2. Fanshawe TR,
    3. McCartney D,
    4. et al.
    (2019) C-reactive protein and neutrophil count laboratory test requests from primary care: what is the demand and would substitution by point-of-care technology be viable? J Clin Pathol 72 (7):474–481, doi:10.1136/jclinpath-2018-205688.
    OpenUrlAbstract/FREE Full Text
  35. 35.↵
    1. Dolan RD,
    2. Laird BJA,
    3. Horgan PG,
    4. McMillan DC
    (2018) The prognostic value of the systemic inflammatory response in randomised clinical trials in cancer: a systematic review. Crit Rev Oncol Hematol 132 130–137, doi:10.1016/j.critrevonc.2018.09.016.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Thomson H,
    2. Thomas S,
    3. Sellstrom E,
    4. Petticrew M
    (2013) Housing improvements for health and associated socio-economic outcomes. Cochrane Database Syst Rev 2 doi:10.1002/14651858.CD008657.pub2. CD008657.
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Bray N,
    2. Burns P,
    3. Jones A,
    4. et al.
    (2017) Costs and outcomes of improving population health through better social housing: a cohort study and economic analysis. Int J Public Health 62 (9):1039–1050, doi:10.1007/s00038-017-0989-y.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Rodgers SE,
    2. Bailey R,
    3. Johnson R,
    4. et al.
    (2018) Emergency hospital admissions associated with a non-randomised housing intervention meeting national housing quality Standards: a longitudinal data linkage study. J Epidemiol Community Health 72 (10):896–903, doi:10.1136/jech-2017-210370.
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    1. Hernandez-Garcia E,
    2. Chrysikou E,
    3. Kalea AZ
    (2021) The interplay between housing environmental attributes and design exposures and psychoneuroimmunology profile-an exploratory review and analysis paper in the cancer survivors’ mental health morbidity context. Int J Environ Res Public Health 18 (20), doi:10.3390/ijerph182010891. 10891.
    OpenUrlCrossRef
  40. 40.↵
    1. Page MJ,
    2. McKenzie JE,
    3. Bossuyt PM,
    4. et al.
    (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 doi:10.1136/bmj.n71. n71.
    OpenUrlFREE Full Text
  41. 41.↵
    1. Shamseer L,
    2. Moher D,
    3. Clarke M,
    4. et al.
    (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 350 doi:10.1136/bmj.g7647. g7647.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Bramer WM,
    2. Rethlefsen ML,
    3. Kleijnen J,
    4. Franco OH
    (2017) Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Syst Rev 6 (1), doi:10.1186/s13643-017-0644-y. 245.
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Hartling L,
    2. Featherstone R,
    3. Nuspl M,
    4. et al.
    (2017) Grey literature in systematic reviews: a cross-sectional study of the contribution of non-English reports, unpublished studies and dissertations to the results of meta-analyses in child-relevant reviews. BMC Med Res Methodol 17 (1), doi:10.1186/s12874-017-0347-z. 64.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Falagas ME,
    2. Pitsouni EI,
    3. Malietzis GA,
    4. Pappas G
    (2008) Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J 22 (2):338–342, doi:10.1096/fj.07-9492LSF.
    OpenUrlCrossRefPubMed
  45. 45.↵
    1. Ziai H,
    2. Zhang R,
    3. Chan AW,
    4. Persaud N
    (2017) Search for unpublished data by systematic reviewers: an audit. BMJ Open 7 (10), doi:10.1136/bmjopen-2017-017737. e017737.
    OpenUrlAbstract/FREE Full Text
  46. 46.↵
    1. Glanville JM,
    2. Duffy S,
    3. McCool R,
    4. Varley D
    (2014) Searching ClinicalTrials.gov and the International Clinical Trials Registry platform to inform systematic reviews: what are the optimal search approaches. J Med Libr Assoc 102 (3):177–183, doi:10.3163/1536-5050.102.3.007.
    OpenUrlCrossRefPubMed
  47. 47.↵
    1. Adam GP,
    2. Springs S,
    3. Trikalinos T,
    4. et al.
    (2018) Does information from ClinicalTrials.gov increase transparency and reduce bias? Results from a five-report case series. Syst Rev 7 (1):59, doi:10.1186/s13643-018-0726-5.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Oppenheim C
    (2011) The publish or perish book. Prometheus 29 (2):181–183, doi:10.1080/08109028.2011.567849.
    OpenUrlCrossRef
  49. 49.↵
    1. Haddaway NR,
    2. Collins AM,
    3. Coughlin D,
    4. Kirk S
    (2015) The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PLoS One 10 (9), doi:10.1371/journal.pone.0138237. e0138237.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Cooper C,
    2. Varley-Campbell J,
    3. Carter P
    (2019) Established search filters may miss studies when identifying randomized controlled trials. J Clin Epidemiol 112 12–19, doi:10.1016/j.jclinepi.2019.04.002.
    OpenUrlCrossRef
  51. 51.↵
    1. Hausner E,
    2. Metzendorf MI,
    3. Richter B,
    4. et al.
    (2018) Study filters for non-randomized studies of interventions consistently lacked sensitivity upon external validation. BMC Med Res Methodol 18 (1), doi:10.1186/s12874-018-0625-4. 171.
    OpenUrlCrossRef
  52. 52.↵
    1. Higgins J,
    2. Thomas J,
    3. Chandler J
    1. Lefebvre C,
    2. Glanville J,
    3. Briscoe S,
    4. et al.
    (2022) in Cochrane Handbook for Systematic Reviews of Interventions version 6.3, eds Higgins J, Thomas J, Chandler J, In. Chapter 4: Searching for and selecting studies. https://training.cochrane.org/handbook/current/chapter-04.
  53. 53.↵
    1. Dobrescu AI,
    2. Nussbaumer-Streit B,
    3. Klerings I,
    4. et al.
    (2021) Restricting evidence syntheses of interventions to English-language publications is a viable methodological shortcut for most medical topics: a systematic review. J Clin Epidemiol 137 209–217, doi:10.1016/j.jclinepi.2021.04.012.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Rathbone J,
    2. Carter M,
    3. Hoffmann T,
    4. Glasziou P
    (2015) Better duplicate detection for systematic reviewers: evaluation of Systematic Review Assistant-Deduplication Module. Syst Rev 4 (1), doi:10.1186/2046-4053-4-6. 6.
    OpenUrlCrossRef
  55. 55.↵
    1. Ouzzani M,
    2. Hammady H,
    3. Fedorowicz Z,
    4. Elmagarmid A
    (2016) Rayyan—a web and mobile APP for systematic reviews. Syst Rev 5 (1), doi:10.1186/s13643-016-0384-4. 210.
    OpenUrlCrossRefPubMed
  56. 56.↵
    1. Harrison H,
    2. Griffin SJ,
    3. Kuhn I,
    4. Usher-Smith JA
    (2020) Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol 20 (1), doi:10.1186/s12874-020-0897-3. 7.
    OpenUrlCrossRefPubMed
  57. 57.↵
    1. Nama N,
    2. Sampson M,
    3. Barrowman N,
    4. et al.
    (2019) Crowdsourcing the citation screening process for systematic reviews: validation study. J Med Internet Res 21 (4), doi:10.2196/12953. e12953.
    OpenUrlCrossRefPubMed
  58. 58.↵
    1. Moher D,
    2. Liberati A,
    3. Tetzlaff J,
    4. et al.
    (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6 (7), doi:10.1371/journal.pmed.1000097. e1000097.
    OpenUrlCrossRefPubMed
  59. 59.↵
    1. Pearson A,
    2. White H,
    3. Bath-Hextall F,
    4. et al.
    (2015) A mixed-methods approach to systematic reviews. Int J Evid Based Healthc 13 (3):121–131, doi:10.1097/XEB.0000000000000052.
    OpenUrlCrossRefPubMed
  60. 60.↵
    1. National Institute for Health Research
    (2014) Patient and public involvement in health and social care research: a handbook for researchers. accessed. https://www.rds-yh.nihr.ac.uk/wp-content/uploads/2015/01/RDS_PPI-Handbook_2014-v8-FINAL-11.pdf. 23 Sep 2022.
  61. 61.↵
    1. Mortensen ML,
    2. Adam GP,
    3. Trikalinos TA,
    4. et al.
    (2017) An exploration of crowdsourcing citation screening for systematic reviews. Res Synth Methods 8 (3):366–386, doi:10.1002/jrsm.1252.
    OpenUrlCrossRef
  62. 62.↵
    1. Staniszewska S,
    2. Brett J,
    3. Simera I,
    4. et al.
    (2017) GRIPP2 reporting checklists: tools to improve reporting of patient and public involvement in research. BMJ 358 doi:10.1136/bmj.j3453. j3453.
    OpenUrlAbstract/FREE Full Text
  63. 63.↵
    1. Sterne JAC,
    2. Savović J,
    3. Page MJ,
    4. et al.
    (2019) RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 366 doi:10.1136/bmj.l4898. l4898.
    OpenUrlFREE Full Text
  64. 64.↵
    1. Higgins JPT,
    2. Li T,
    3. Sterne J
    (2021) Revised Cochrane risk of bias tool for randomized trials (RoB 2): Additional considerations for crossover trials. accessed. https://www.riskofbias.info/welcome/rob-2-0-tool/rob-2-for-crossover-trials. 23 Sep 2022.
  65. 65.↵
    1. Sterne JA,
    2. Hernán MA,
    3. Reeves BC,
    4. et al.
    (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355 doi:10.1136/bmj.i4919. i4919.
    OpenUrlFREE Full Text
  66. 66.↵
    1. Sterne JAC,
    2. Higgins JPT,
    3. Elbers RG,
    4. et al.
    (2016) Risk of bias In Non-randomized Studies of Interventions (ROBINS-I): detailed guidance. accessed. https://www.riskofbias.info/welcome/home/current-version-of-robins-i/robins-i-detailed-guidance-2016. 23 Sep 2022.
  67. 67.↵
    1. Igelström E,
    2. Campbell M,
    3. Craig P,
    4. Katikireddi SV
    (2021) Cochrane's risk of bias tool for non-randomized studies (ROBINS-I) is frequently misapplied: a methodological systematic review. J Clin Epidemiol 140 22–32, doi:10.1016/j.jclinepi.2021.08.022.
    OpenUrlCrossRef
  68. 68.↵
    1. Guyatt GH,
    2. Oxman AD,
    3. Schünemann HJ,
    4. et al.
    (2011) GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol 64 (4):380–382, doi:10.1016/j.jclinepi.2010.09.011.
    OpenUrlCrossRefPubMed
  69. 69.↵
    1. Schünemann HJ,
    2. Cuello C,
    3. Akl EA,
    4. et al.
    (2019) Grade guidelines: 18. how ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. J Clin Epidemiol 111 105–114, doi:10.1016/j.jclinepi.2018.01.012.
    OpenUrlCrossRefPubMed
  70. 70.↵
    1. Campbell M,
    2. McKenzie JE,
    3. Sowden A,
    4. et al.
    (2020) Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ 368 doi:10.1136/bmj.l6890. l6890.
    OpenUrlFREE Full Text
  71. 71.↵
    1. Wan X,
    2. Wang W,
    3. Liu J,
    4. Tong T
    (2014) Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 14 doi:10.1186/1471-2288-14-135. 135.
    OpenUrlCrossRefPubMed
  72. 72.↵
    1. Veroniki AA,
    2. Jackson D,
    3. Viechtbauer W,
    4. et al.
    (2016) Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods 7 (1):55–79, doi:10.1002/jrsm.1164.
    OpenUrlCrossRefPubMed
  73. 73.↵
    1. Higgins JPT,
    2. Thomas J,
    3. Chandler J
    1. Deeks JJ,
    2. Higgins JPT,
    3. Altman DG,
    4. et al.
    (2019) in Cochrane Handbook for Systematic Reviews of Interventions, eds Higgins JPT, Thomas J, Chandler J (John Wiley & Sons, Chichester) In, 2nd edn, pp 241–284, doi:10.1002/9781119536604.ch10. Chapter 10: analysing data and undertaking meta-analyses.
    OpenUrlCrossRef
  74. 74.↵
    1. von Hippel PT
    (2015) The heterogeneity statistic I (2) can be biased in small meta-analyses. BMC Med Res Methodol 15 doi:10.1186/s12874-015-0024-z. 35.
    OpenUrlCrossRefPubMed
  75. 75.↵
    1. Begg CB,
    2. Mazumdar M
    (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50 (4):1088–1101.
    OpenUrlCrossRefPubMed
  76. 76.↵
    1. Egger M,
    2. Davey Smith G,
    3. Schneider M,
    4. Minder C
    (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315 (7109):629–634, doi:10.1136/bmj.315.7109.629.
    OpenUrlAbstract/FREE Full Text
  77. 77.↵
    1. Sterne JAC,
    2. Gavaghan D,
    3. Egger M
    (2000) Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol 53 (11):1119–1129, doi:10.1016/s0895-4356(00)00242-0.
    OpenUrlCrossRefPubMed
  78. 78.↵
    1. R Core Team
    (2017) R: A language and environment for statistical computing (Vienna: R Foundation for Statistical Computing). accessed. https://www.r-project.org/. 26 Sep 2022.
  79. 79.↵
    1. Viechtbauer W
    (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36 (3):1–48, doi:10.18637/jss.v036.i03.
    OpenUrlCrossRefPubMed
  80. 80.↵
    1. Chan RJ,
    2. Hollingdrake O,
    3. Bui U,
    4. et al.
    (2021) Evolving landscape of cancer survivorship research: an analysis of the Journal of cancer survivorship, 2007–2020. J Cancer Surviv 15 (4):651–658, doi:10.1007/s11764-021-01042-6.
    OpenUrlCrossRef
  81. 81.↵
    1. European Medicines Agency
    (2013) Concept paper on extrapolation of efficacy and safety in medicine development (London: EMA). accessed. https://www.ema.europa.eu/en/documents/scientific-guideline/concept-paper-extrapolation-efficacy-safety-medicine-development_en.pdf. 26 Sep 2022.
  82. 82.↵
    1. Conklin LS,
    2. Hoffman EP,
    3. van den Anker J
    (2019) Developmental pharmacodynamics and modeling in pediatric drug development. J Clin Pharmacol 59 (Suppl 1):S87–S94, doi:10.1002/jcph.1482.
    OpenUrlCrossRef
  83. 83.↵
    1. Verma R,
    2. Foster RE,
    3. Horgan K,
    4. et al.
    (2016) Lymphocyte depletion and repopulation after chemotherapy for primary breast cancer. Breast Cancer Res 18 (1):10, doi:10.1186/s13058-015-0669-x.
    OpenUrlCrossRefPubMed
  84. 84.↵
    1. van Meir H,
    2. Nout RA,
    3. Welters MJP,
    4. et al.
    (2017) Impact of (chemo) radiotherapy on immune cell composition and function in cervical cancer patients. Oncoimmunology 6 (2), doi:10.1080/2162402X.2016.1267095. e1267095.
    OpenUrlCrossRef
  85. 85.↵
    1. Xiao C,
    2. Beitler JJ,
    3. Peng G,
    4. et al.
    (2021) Epigenetic age acceleration, fatigue, and inflammation in patients undergoing radiation therapy for head and neck cancer: a longitudinal study. Cancer 127 (18):3361–3371, doi:10.1002/cncr.33641.
    OpenUrlCrossRef
  86. 86.↵
    1. Bouck Z,
    2. Mecredy GC,
    3. Ivers NM,
    4. et al.
    (2018) Frequency and associations of prescription nonsteroidal anti-inflammatory drug use among patients with a musculoskeletal disorder and hypertension, heart failure, or chronic kidney disease. JAMA Intern Med 178 (11):1516–1525, doi:10.1001/jamainternmed.2018.4273.
    OpenUrlCrossRef
  87. 87.↵
    1. Chen Y,
    2. Bedson J,
    3. Hayward RA,
    4. Jordan KP
    (2018) Trends in prescribing of non-steroidal anti-inflammatory drugs in patients with cardiovascular disease: influence of national guidelines in UK primary care. Fam Pract 35 (4):426–432, doi:10.1093/fampra/cmx142.
    OpenUrlCrossRefPubMed
  88. 88.↵
    1. Loomans-Kropp HA,
    2. Pinsky P,
    3. Umar A
    (2021) Evaluation of aspirin use with cancer incidence and survival among older adults in the prostate, lung, colorectal, and ovarian cancer screening trial. JAMA Netw Open 4 (1), doi:10.1001/jamanetworkopen.2020.32072. e2032072.
    OpenUrlCrossRef
  89. 89.↵
    1. Rothwell PM,
    2. Wilson M,
    3. Price JF,
    4. et al.
    (2012) Effect of daily aspirin on risk of cancer metastasis: a study of incident cancers during randomised controlled trials. Lancet 379 (9826):1591–1601, doi:10.1016/S0140-6736(12)60209-8.
    OpenUrlCrossRefPubMed
  90. 90.↵
    1. Coombes RC,
    2. Tovey H,
    3. Kilburn L,
    4. et al.
    (2021) Effect of celecoxib vs placebo as adjuvant therapy on disease-free survival among patients with breast cancer: the react randomized clinical trial. JAMA Oncol 7 (9):1291–1301, doi:10.1001/jamaoncol.2021.2193.
    OpenUrlCrossRef
  91. 91.↵
    1. Strasser-Weippl K,
    2. Higgins MJ,
    3. Chapman J-A,
    4. et al.
    (2018) Effects of celecoxib and low-dose aspirin on outcomes in adjuvant aromatase inhibitor-treated patients: CCTG MA.27. J Natl Cancer Inst 110 (9):1003–1008, doi:10.1093/jnci/djy017.
    OpenUrlCrossRef
  92. 92.↵
    1. Rosenkrantz Hölmich E,
    2. Petring Hasselager R,
    3. Tvilling Madsen M,
    4. et al.
    (2019) Long-Term outcomes after use of perioperative glucocorticoids in patients undergoing cancer surgery: a systematic review and meta-analysis. Cancers (Basel) 12 (1):76, doi:10.3390/cancers12010076.
    OpenUrlCrossRef
  93. 93.↵
    1. Attard G,
    2. Merseburger AS,
    3. Arlt W,
    4. et al.
    (2019) Assessment of the safety of glucocorticoid regimens in combination with abiraterone acetate for metastatic castration-resistant prostate cancer: a randomized, open-label phase 2 study. JAMA Oncol 5 (8):1159–1167, doi:10.1001/jamaoncol.2019.1011.
    OpenUrlCrossRef
  94. 94.↵
    1. McDonald J,
    2. McBain L,
    3. Dowell AC,
    4. Morris C
    (2017) GPs’ views and experiences of prescribing non-steroidal anti-inflammatory drugs: a qualitative study. BJGP Open 1 (2), doi:10.3399/bjgpopen17X100869. bjgpopen17X100869.
    OpenUrlAbstract/FREE Full Text
  95. 95.↵
    1. Fullerton JN,
    2. Gilroy DW
    (2016) Resolution of inflammation: a new therapeutic frontier. Nat Rev Drug Discov 15 (8):551–567, doi:10.1038/nrd.2016.39.
    OpenUrlCrossRefPubMed
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BJGP Open
Vol. 6, Issue 4
December 2022
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Home-built environment interventions and inflammation biomarkers: a systematic review and meta-analysis protocol
Eva Hernandez-Garcia, Evangelia Chrysikou, Larissa Nekhlyudov, Derek W Gilroy, José M Ordóñez-Mena
BJGP Open 2022; 6 (4): BJGPO.2022.0104. DOI: 10.3399/BJGPO.2022.0104
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