A systematic review and multilevel regression analysis reveals the comorbidity prevalence in cancer.
Cilla E J VrinzenLinn DelfgouNiek Waltherus StadhoudersRosella P M G HermensMatthias A W MerkxHaiko J BloemendalPatrick P T JeurissenPublished in: Cancer research (2023)
Comorbidities can have major implications for cancer care, as they might impact the timing of cancer diagnosis, compromise optimal care, affect treatment outcomes, and increase healthcare costs. Thus, it is important to comprehensively evaluate cancer comorbidities and examine trends over time. Here, we performed a systematic literature review on the prevalence and types of comorbidities for the five most common forms of cancer. Observational studies from Organisation for Economic Co-operation and Development (OECD) countries published between 1990 and 2020 in English or Dutch that used routinely collected data from a representative population were included. The search yielded 3,070 articles of which 161 were eligible for data analyses. Multilevel analyses were performed to evaluate determinants of variation in comorbidity prevalence and trends over time. The weighted average comorbidity prevalence was 33.4%, and comorbidities were the most common in lung cancer (46.7%) and colorectal cancer (40.0%), followed by prostate (28.5%), melanoma (28.3%), and breast (22.4%). The most common types of comorbidities were hypertension (29.7%), pulmonary diseases (15.9%), and diabetes (13.5%). After adjusting for gender, type of comorbidity index, age, data source (patient records versus claims), and country, a significant increase in comorbidities of 0.54% per year was observed. Overall, a large and increasing proportion of the oncological population is dealing with comorbidities, which could be used to inform and adapt treatment options to improve health outcomes and reduce healthcare costs.
Keyphrases
- healthcare
- papillary thyroid
- risk factors
- squamous cell
- prostate cancer
- blood pressure
- electronic health record
- lymph node metastasis
- palliative care
- big data
- pulmonary hypertension
- magnetic resonance
- machine learning
- computed tomography
- cross sectional
- rectal cancer
- pain management
- data analysis
- weight loss
- affordable care act
- arterial hypertension