Self-reported cancer has been validated with heterogeneous results across populations. The aim was to assess the validity of self-reported cancer in the Lifelines population-based cohort and to search for explanations for not reporting cancer. Data from adult participants (n = 152,780) from Lifelines was linked to the Dutch-Nationwide pathology databank (PALGA), which has nearly 100% coverage of cancer diagnoses in the Netherlands and is considered as the gold standard for ascertainment of cancer diagnosis in this study. Sensitivity and positive predictive value (PPV) for self-reported cancers -reported as hand-written free text- were described. Logistic regressions analyses were performed to evaluate whether socio-demographic factors were associated with the presence of self-reported cancer when there was a diagnosis in PALGA. 6611 (4.50%) participants had at least one self-reported diagnosis of cancer, where 9960 (6.97%) participants had at least one cancer diagnosis in PALGA. The sensitivity of self-reported cancer was 64.68% [95%CI:63.71-65.66], and 70.18% [95%CI:68.83-71.56] after excluding skin and cervical cancers. Skin and cervical cancers represented 61.24% of non-self-reported cancers. The overall PPV was 97.45% [95%CI:97.45-97.81], and 97.33% [95%CI:96.72-97.82] after the exclusion of skin and cervical cancers. Participants who did not self-report their cancer were more likely to be male, had longer time since diagnosis and lower educational level. Overall, the reports of cancer in Lifelines have a high positive predictive value and moderate sensitivity. One third of the cancers were not reported, mainly skin and cervical cancers. Male participants, those with a lower educational level and those with longer time since diagnosis were less likely to self-report a diagnosed cancer.

Diet quality among short- and long-term gastrointestinal (GI) cancer survivors with different tumor sites was investigated compared to a reference population cohort. Diet quality of GI cancer survivors (n = 307) was compared to an age- and sex-matched reference population with no history of cancer (n = 3070). All were selected from Lifelines, a population-based cohort. GI cancers were defined as having a history of cancer of the bowel, esophagus, or stomach. Diet quality was assessed by a self-administrated food frequency questionnaire in terms of: (i) Lifelines Diet (LLD) scores, where higher scores indicate higher diet quality; (ii) the adherence to dietary guidelines, quantified by the percentage of meeting dietary recommendations, as given by Dutch dietary guidelines; and (iii) the mean daily intake of food components. All analyses were adjusted for lifestyle factors. Diet scores in GI cancer survivors were not different from the reference population (OR = 0.97, 95% CI: 0.73–1.23). Stratification for time since diagnosis and tumor site gave similar results. The intake of vegetables, unsweetened dairies, and nuts and legumes was almost 50% lower than the recommended amount, and the mean intake of unhealthy food components was at least one serving/day among GI cancer survivors, as well as in the reference population. In the long run, GI cancer survivors do not differ from the reference population in their diet quality. In conclusion, both groups can improve their diet quality.

Purpose: To compare the differences in lifestyle behaviours between cancer survivors (CSs) and cancer-free participants in a large and representative population-based cohort. Methods: We included 115,257 adults from the Lifelines cohort. Cancer status was self-reported, and health behaviours were measured (e.g. body mass index [BMI]) or assessed by questionnaire (e.g. physical activity, smoking, alcohol consumption, sedentary behaviour and diet). The data were then categorised for logistic regression analysis, stratified and adjusted by sex and age (< 55 vs ≥ 55 years). Results: CSs (5473; 4.7%) were diagnosed 9 ± 8.5 years before data collection, were older (mean age 55.4 vs 44.4 years) and more often female (66.6% vs 33.4%) than the cancer-free participants. They were also more likely to be physically active and to have a better diet, and also less likely to be alcohol drinkers; but, were more likely to have a higher BMI, be former smokers and to be sedentary. After adjustment for sex and age, however, BMI was more likely to be normal, physical activity was more likely to be higher and smoking to be prevalent in CSs. Current smoking was also significantly higher among females and those aged < 55 years who were CSs than for those with no history of cancer. Conclusions: In this population-based cohort, CSs have health behaviour comparable to those without a cancer diagnosis. Implications for cancer survivors: Smoking cessation strategies should target all CSs, but efforts could yield greatest benefit if they target females and those younger than 55 years.

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) can affect anyone, however, it is often mixed with other respiratory diseases. This study aimed to identify the factors associated with SARS-COV-2 positive test. Methods: Participants from the Northern Netherlands representative of the general population were included if filled in the questionnaire about well-being between June 2020-April 2021 and were tested for SARS-COV-2. The outcome was a self-reported test as measured by polymerase chain reaction. The data were collected on age, sex, household, smoking, alcohol use, physical activity, quality of life, fatigue, symptoms and medications use. Participants were matched on sex, age and the timing of their SARS-COV-2 tests maintaining a 1:4 ratio and classified into those with a positive and negative SARS-COV-2 using logistic regression. The performance of the model was compared with other machine-learning algorithms by the area under the receiving operating curve. Results: 2564 (20%) of 12786 participants had a positive SARS-COV-2 test. The factors associated with a higher risk of SARS-COV-2 positive test in multivariate logistic regression were: contact with someone tested positive for SARS-COV-2, ≥1 household members, typical SARS-COV-2 symptoms, male gender and fatigue. The factors associated with a lower risk of SARS-COV-2 positive test were higher quality of life, inhaler use, runny nose, lower back pain, diarrhea, pain when breathing, sore throat, pain in neck, shoulder or arm, numbness or tingling, and stomach pain. The performance of the logistic models was comparable with that of random forest, support vector machine and gradient boosting machine. Conclusions: Having a contact with someone tested positive for SARS-COV-2 and living in a household with someone else are the most important factors related to a positive SARS-COV-2 test. The loss of smell or taste is the most prominent symptom associated with a positive test. Symptoms like runny nose, pain when breathing, sore throat are more likely to be indicative of other conditions.

Health behaviors affect health status in cancer survivors. We hypothesized that nonlinear algorithms would identify distinct key health behaviors compared to a linear algorithm and better classify cancer survivors. We aimed to use three nonlinear algorithms to identify such key health behaviors and compare their performances with that of a logistic regression for distinguishing cancer survivors from those without cancer in a population-based cohort study. We used six health behaviors and three socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified into a cancer-survivors group and a cancer-free group using either nonlinear algorithms or logistic regression, and their performances were compared by the area under the curve (AUC). In addition, we performed case–control analyses (matched by age, sex, and education level) to evaluate classification performance only by health behaviors. Data were collected for 107,624 cancer free participants and 2760 cancer survivors. Using all variables resulted an AUC of 0.75 ± 0.01, using only six health behaviors, the logistic regression and nonlinear algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 ± 0.01 and 0.60 ± 0.01, respectively. The main distinctive classifier was age. Though not relevant to classification, the main distinctive health behaviors were body mass index and alcohol consumption. In the case–control analyses, algorithms produced AUCs of 0.52 ± 0.01. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants in this population-based cohort.