Publications by authors named "Hendriek C Boshuizen"

128 Publications

Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands.

BMC Public Health 2021 06 2;21(1):1039. Epub 2021 Jun 2.

RIVM (National Institute for Public Health and the Environment), Centre for Nutrition, Prevention and Health Services, P.O. Box 1, 3720, BA, Bilthoven, The Netherlands.

Background: Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease.

Methods: Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant.

Results: Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age.

Conclusion: Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12889-021-10754-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170948PMC
June 2021

Identification of Lifestyle Behaviors Associated with Recurrence and Survival in Colorectal Cancer Patients Using Random Survival Forests.

Cancers (Basel) 2021 May 18;13(10). Epub 2021 May 18.

Division of Human Nutrition and Health, Wageningen University & Research, 6708 WE Wageningen, The Netherlands.

Current lifestyle recommendations for cancer survivors are the same as those for the general public to decrease their risk of cancer. However, it is unclear which lifestyle behaviors are most important for prognosis. We aimed to identify which lifestyle behaviors were most important regarding colorectal cancer (CRC) recurrence and all-cause mortality with a data-driven method. The study consisted of 1180 newly diagnosed stage I-III CRC patients from a prospective cohort study. Lifestyle behaviors included in the current recommendations, as well as additional lifestyle behaviors related to diet, physical activity, adiposity, alcohol use, and smoking were assessed six months after diagnosis. These behaviors were simultaneously analyzed as potential predictors of recurrence or all-cause mortality with Random Survival Forests (RSFs). We observed 148 recurrences during 2.6-year median follow-up and 152 deaths during 4.8-year median follow-up. Higher intakes of sugary drinks were associated with increased recurrence risk. For all-cause mortality, fruit and vegetable, liquid fat and oil, and animal protein intake were identified as the most important lifestyle behaviors. These behaviors showed non-linear associations with all-cause mortality. Our exploratory RSF findings give new ideas on potential associations between certain lifestyle behaviors and CRC prognosis that still need to be confirmed in other cohorts of CRC survivors.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/cancers13102442DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157840PMC
May 2021

Lifestyle after colorectal cancer diagnosis in relation to recurrence and all-cause mortality.

Am J Clin Nutr 2021 06;113(6):1447-1457

Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands.

Background: An unhealthy lifestyle is associated with the risk of colorectal cancer (CRC), but it is unclear whether overall lifestyle after a CRC diagnosis is associated with risks of recurrence and mortality.

Objectives: To examine associations between postdiagnosis lifestyle and changes in lifestyle after a CRC diagnosis with risks of CRC recurrence and all-cause mortality.

Methods: The study population included 1425 newly diagnosed, stage I-III CRC patients from 2 prospective cohort studies enrolled between 2010 and 2016. Lifestyle, including BMI, physical activity, diet, and alcohol intake, was assessed at diagnosis and at 6 months postdiagnosis. We assigned lifestyle scores based on concordance with 2 sets of cancer prevention guidelines-from the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) and the American Cancer Society (ACS)-and national disease prevention guidelines. Higher scores indicate healthier lifestyles. We computed adjusted HRs and 95% CIs using Cox regression.

Results: We observed 164 recurrences during a 2.8-year median follow-up and 171 deaths during a 4.4-year median follow-up. No associations were observed for CRC recurrence. A lifestyle more consistent with the ACS recommendations was associated with a lower all-cause mortality risk (HR per +1 SD, 0.85; 95% CI: 0.73-0.995). The same tendency was observed for higher WCRF/AICR (HR, 0.92; 95% CI: 0.78-1.08) and national (HR, 0.90; 95% CI: 0.77-1.05) lifestyle scores, although these associations were statistically nonsignificant. Generally, no statistically significant associations were observed for BMI, physical activity, diet, or alcohol. Improving one's lifestyle after diagnosis (+1 SD) was associated with a lower all-cause mortality risk for the ACS (HR, 0.80; 95% CI: 0.67-0.96) and national (HR, 0.84; 95% CI: 0.70-0.999) scores, yet was statistically nonsignificant for the WCRF/AICR score (HR, 0.94; 95% CI: 0.78-1.13).

Conclusions: A healthy lifestyle after CRC diagnosis and improvements therein were not associated with the risk of CRC recurrence, but were associated with a decreased all-cause mortality risk.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/ajcn/nqaa394DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168353PMC
June 2021

Validity Coefficient of Repeated Measurements of Urinary Marker of Sugar Intake Is Comparable to Urinary Nitrogen as Marker of Protein Intake in Free-living Subjects.

Cancer Epidemiol Biomarkers Prev 2021 01 30;30(1):193-202. Epub 2020 Sep 30.

Wageningen University & Research, Human Nutrition & Health, Wageningen, the Netherlands.

Background: Studies do not show consistent relationships between self-reported intake of sugar and outcome of disease. To overcome the drawbacks of self-reported intake methods, we investigated whether there is an agreement in ranking of individuals between their self-reported sugar intake and urinary sucrose and fructose.

Methods: We used data of 198 Dutch adults (106 women) from the DUPLO study. Sugar intake of all foods and drinks consumed over 24-hour period was estimated by collecting duplicate portions (DP) and 24-hour recalls (24hR), telephone (24hRT) and Web-based (24hRW), while sugar excretion was based on 24-hour urine samples. Sugar content of 24hR was calculated using a newly developed sugar database and sugar content of DPs and urine samples was calculated using high-performance liquid chromatography-atomic emission spectrometry and LC/MS-MS, respectively. Measurement error models assessed validity coefficients (VC) and attenuation factors (AF). Coefficients were compared with those of protein biomarker.

Results: The VC for the marker, using DP as reference, showed comparability with substantially better ranking of participants (0.72 for women and 0.93 for men), than 24hRT (0.57 and 0.78) or 24hRW (0.70 and 0.78) as reference in the sucrose models. The VC of the sucrose models was within 10% of the protein models, except for the model with 24hRT as reference, among women. The AF started at higher values and increased by a greater factor compared with the VC.

Conclusions: Repeated measurements of urinary sucrose and fructose as a marker of daily sucrose intake had a ranking performance comparable to urinary nitrogen as marker of protein intake in free-living Dutch adults.

Impact: The validation of the sugar biomarker in a free-living population with three different dietary assessment methods and its comparable ranking ability with a good recovery biomarker (i.e., protein biomarker) have important research applications. The biomarker may be used for validating dietary assessment methods, for monitoring compliance in human feeding studies, for monitoring the effect of public health interventions, and as a surrogate for ranking subjects according to sucrose intake when information on sucrose in food composition databases is lacking.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1055-9965.EPI-20-0271DOI Listing
January 2021

Associations of Abdominal Skeletal Muscle Mass, Fat Mass, and Mortality among Men and Women with Stage I-III Colorectal Cancer.

Cancer Epidemiol Biomarkers Prev 2020 05 4;29(5):956-965. Epub 2020 Mar 4.

Department of Research & Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands.

Background: The associations of abdominal skeletal muscle mass index (SMI), visceral and subcutaneous adipose tissue (VAT and SAT, respectively), and mortality among patients with stage I-III colorectal cancer may differ for men and women, but only few studies stratified their data into men and women. We investigated associations of abdominal SMI, VAT, and SAT with overall mortality among men and among women with stage I-III colorectal cancer.

Methods: SMI, VAT, and SAT were assessed from abdominal CT images for 1,998 patients with stage I-III colorectal cancer diagnosed between 2006 and 2015. Restricted cubic splines (RCS) were used to investigate associations of SMI, VAT, and SAT with overall mortality.

Results: Average age of the participants was 67.9 ± 10.6 years and 58% were men. During a median follow-up of 4.3 years, 546 (27%) patients died. Among men, the association of SMI and mortality was statistically significant in a nonlinear way in the RCS analyses, with lower SMI levels associated with higher mortality. SMI was not associated with mortality among women. SAT was associated with mortality in a nonlinear way for men and for women, with lower SAT levels being associated with higher mortality. VAT was not significantly associated with mortality in men or women.

Conclusion: Associations of abdominal skeletal muscle mass with mortality among patients with colorectal cancer were not the same for men and for women.

Impact: This study stresses the importance for more attention on sex-related differences in body composition and cancer outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1055-9965.EPI-19-1134DOI Listing
May 2020

Validity of Absolute Intake and Nutrient Density of Protein, Potassium, and Sodium Assessed by Various Dietary Assessment Methods: An Exploratory Study.

Nutrients 2019 Dec 31;12(1). Epub 2019 Dec 31.

Division of Human Nutrition and Health, Wageningen University, 6700 AA Wageningen, Gelderland, The Netherlands.

It is suggested that nutrient densities are less affected by measurement errors than absolute intake estimates of dietary exposure. We compared the validity of absolute intakes and densities of protein (kJ from protein/total energy (kJ)), potassium, and sodium (potassium or sodium (in mg)/total energy (kJ)) assessed by different dietary assessment methods. For 69 Dutch subjects, two duplicate portions (DPs), five to fifteen 24-h dietary recalls (24 hRs, telephone-based and web-based) and two food frequency questionnaires (FFQs) were collected and compared to duplicate urinary biomarkers and one or two doubly labelled water measurements. Multivariate measurement error models were used to estimate validity coefficients (VCs) and attenuation factors (AFs). This research showed that group bias diminished for protein and sodium densities assessed by all methods as compared to the respective absolute intakes, but not for those of potassium. However, the VCs and AFs for the nutrient densities did not improve compared to absolute intakes for all four methods; except for the AF of sodium density (0.71) or the FFQ which was better than that of the absolute sodium intake (0.51). Thus, using nutrient densities rather than absolute intakes does not necessarily improve the performance of the DP, FFQ, or 24 hR.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/nu12010109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019974PMC
December 2019

Evaluating the Validity of a Food Frequency Questionnaire in Comparison with a 7-Day Dietary Record for Measuring Dietary Intake in a Population of Survivors of Colorectal Cancer.

J Acad Nutr Diet 2020 02 3;120(2):245-257. Epub 2019 Dec 3.

Background: Food frequency questionnaires (FFQs) are a commonly used method to assess dietary intake in epidemiological studies. It is important to evaluate the validity of FFQs in the population of interest.

Objective: To evaluate the validity of an FFQ for measuring dietary intake in survivors of colorectal cancer (CRC), relative to a 7-day dietary record.

Design: Dietary intake was assessed 1 year after the end of CRC treatment. Participants first completed a 7-day dietary record and 2 weeks later a 253-item FFQ that measured intake in the preceding month.

Participants/setting: Data were used from a subsample of participants (n=100) enrolled in an ongoing prospective study (EnCoRe study) in the Netherlands, from 2015 to 2018.

Main Outcome Measures: Estimated intakes of total energy, 19 nutrients, and 20 food groups as well as scoring adherence to the dietary recommendations of the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) were compared between both dietary assessment methods.

Statistical Analyses Performed: Means and standard deviations, Spearman rank correlations corrected for within-person variation and total energy, and κ agreement between quintiles were assessed.

Results: The median Spearman correlation corrected for within-person variation for nutrients and total energy was 0.60. Correlations >0.50 were found for 15 of 19 nutrients, with highest agreement for vitamin B-12 (0.74), polysaccharides (0.75), and alcohol (0.91). On average, 73% (range=60% to 84%) of participants were classified into the exact same or adjacent nutrient quintile. The median Spearman correlation corrected for within-person variation for food groups was 0.62. Correlations >0.50 were found for 17 of 20 food groups, with highest agreement for cereals and cereal products (0.96), fish (0.96), and potatoes (0.99). The Spearman correlation between total scores of the WCRF/AICR dietary recommendations was 0.53.

Conclusions: Relative to a 7-day dietary record, the validity of an FFQ for measuring dietary intake among survivors of CRC appeared moderate to good for most nutrients and food groups.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jand.2019.09.008DOI Listing
February 2020

Potential impact of reduced tobacco use on life and health expectancies in Belgium.

Int J Public Health 2020 Mar 28;65(2):129-138. Epub 2019 Nov 28.

Department of Epidemiology and public health, Sciensano, Rue J Wytsman 14, 1050, Brussels, Belgium.

Objectives: We investigated the potential impact of reduced tobacco use scenarios on total life expectancy and health expectancies, i.e., healthy life years and unhealthy life years.

Methods: Data from the Belgian Health Interview Survey 2013 were used to estimate smoking and disability prevalence. Disability was based on the Global Activity Limitation Indicator. We used DYNAMO-HIA to quantify the impacts of risk factor changes and to compare the "business-as-usual" with alternative scenarios.

Results: The "business-as-usual" scenario estimated that in 2028 the 15-year-old men/women would live additional 50/52 years without disability and 14/17 years with disability. The "smoking-free population" scenario added 3.4/2.8 healthy life years and reduced unhealthy life years by 0.79/1.9. Scenarios combining the prevention of smoking initiation with smoking cessation programs are the most effective, yielding the largest increase in healthy life years (1.9/1.7) and the largest decrease in unhealthy life years (- 0.80/- 1.47).

Conclusions: Health impact assessment tools provide different scenarios for evidence-informed public health actions. New anti-smoking strategies or stricter enforcement of existing policies potentially gain more healthy life years and reduce unhealthy life years in Belgium.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00038-019-01315-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7049546PMC
March 2020

Colorectal cancer survivors only marginally change their overall lifestyle in the first 2 years following diagnosis.

J Cancer Surviv 2019 Dec 23;13(6):956-967. Epub 2019 Oct 23.

Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, PO Box 17 6708, WE, Wageningen, the Netherlands.

Purpose: A healthy lifestyle after colorectal cancer (CRC) diagnosis may improve prognosis. Data related to lifestyle change in CRC survivors are inconsistent and potential interrelated changes are unknown.

Methods: We assessed dietary intake, physical activity, body mass index (BMI), waist circumference, and smoking among 1072 patients diagnosed with stages I-III CRC at diagnosis, 6 months and 2 years post-diagnosis. An overall lifestyle score was constructed based on the 2018 World Cancer Research Fund/American Institute of Cancer Research recommendations (range 0-7). We used linear mixed models to analyze changes in lifestyle over time.

Results: Participants had a mean (± SD) age of 65 ± 9 years and 43% had stage III disease. In the 2 years following CRC diagnosis, largest changes were noted for sugary drinks (- 45 g/day) and red and processed meat intake (- 62 g/week). BMI (+ 0.4 kg/m), waist circumference (+ 2 cm), and dietary fiber intake (- 1 g/day) changed slightly. CRC survivors did not statistically significant change their mean intake of fruits and vegetables, alcohol, or ultra-processed foods nor did they change their physical activity or smoking behavior. Half of participants made simultaneous changes that resulted in improved concordance with one component as well as deteriorated concordance with another component of the lifestyle score. Overall lifestyle score changed from a mean 3.4 ± 0.9 at diagnosis to 3.5 ± 0.9 2 years post-diagnosis.

Conclusions: CRC survivors hardly improve their overall lifestyle after diagnosis.

Implications For Cancer Survivors: Given the importance of a healthy lifestyle, strategies to effectively support behavior changes in CRC survivors need to be identified.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11764-019-00812-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881417PMC
December 2019

Using enhanced regression calibration to combine dietary intake estimates from 24 h recall and FFQ reduces bias in diet-disease associations.

Public Health Nutr 2019 10 2;22(15):2738-2746. Epub 2019 Jul 2.

Division of Human Nutrition, Wageningen University & Research, PO Box 17, 6700 AA Wageningen, The Netherlands.

Objective: To illustrate the impact of combining 24 h recall (24hR) and FFQ estimates using regression calibration (RC) and enhanced regression calibration (ERC) on diet-disease associations.

Setting: Wageningen area, the Netherlands, 2011-2013.

Design: Five approaches for obtaining self-reported dietary intake estimates of protein and K were compared: (i) uncorrected FFQ intakes (FFQ); (ii) uncorrected average of two 24hR ( $\overline {\rm R}$ ); (iii) average of FFQ and $\overline {\rm R}$ ( ${\overline {\rm F}}\,\overline {\rm R}}$ ); (iv) RC from regression of 24hR v. FFQ; and (v) ERC by adding individual random effects to the RC approach. Empirical attenuation factors (AF) were derived by regression of urinary biomarker measurements v. the resulting intake estimates.

Participants: Data of 236 individuals collected within the National Dietary Assessment Reference Database.

Results: Both FFQ and 24hR dietary intake estimates were measured with substantial error. Using statistical techniques to correct for measurement error (i.e. RC and ERC) reduced bias in diet-disease associations as indicated by their AF approaching 1 (RC 1·14, ERC 0·95 for protein; RC 1·28, ERC 1·34 for K). The larger sd and narrower 95% CI of AF obtained with ERC compared with RC indicated that using ERC has more power than using RC. However, the difference in AF between RC and ERC was not statistically significant, indicating no significantly better de-attenuation by using ERC compared with RC. AF larger than 1, observed for the ERC for K, indicated possible overcorrection.

Conclusions: Our study highlights the potential of combining FFQ and 24hR data. Using RC and ERC resulted in less biased associations for protein and K.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/S1368980019001563DOI Listing
October 2019

Importance of details in food descriptions in estimating population nutrient intake distributions.

Nutr J 2019 03 15;18(1):17. Epub 2019 Mar 15.

Division of Human Nutrition, Wageningen University, Wageningen, the Netherlands.

Background: National food consumption surveys are important policy instruments that could monitor food consumption of a certain population. To be used for multiple purposes, this type of survey usually collects comprehensive food information using dietary assessment methods like 24-h dietary recalls (24HRs). However, the collection and handling of such detailed information require tremendous efforts. We aimed to improve the efficiency of data collection and handling in 24HRs, by identifying less important characteristics of food descriptions (facets) and assessing the impact of disregarding them on energy and nutrient intake distributions.

Methods: In the Dutch National Food Consumption Survey 2007-2010, food consumption data were collected through interviewer-administered 24HRs using GloboDiet software in 3819 persons. Interviewers asked participants about the characteristics of each food item according to applicable facets. Food consumption data were subsequently linked to the food composition database. The importance of facets for predicting energy and each of the 33 nutrients was estimated using the random forest algorithm. Then a simulation study was performed to determine the influence of deleting less important facets on population nutrient intake distributions.

Results: We identified 35% facets as unimportant and deleted them from the total food consumption database. The majority (79.4%) of the percent difference between percentile estimates of the population nutrient intake distributions before and after facet deletion ranged from 0 to 1%, while 20% cases ranged from 1 to 5% and 0.6% cases more than 10%.

Conclusion: We concluded that our procedure was successful in identifying less important food descriptions in estimating population nutrient intake distributions. The reduction in food descriptions has the potential to reduce the time needed for conducting interviews and data handling while maintaining the data quality of the survey.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12937-019-0443-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419831PMC
March 2019

Potential gains in health expectancy by improving lifestyle: an application for European regions.

Popul Health Metr 2019 01 17;17(1). Epub 2019 Jan 17.

National Institute for Public Health and the Environment, Postbus 1, 3720 BA, Bilthoven, The Netherlands.

Background: Prevention aiming at smoking, alcohol consumption, and BMI could potentially bring large gains in life expectancy (LE) and health expectancy measures such as Healthy Life Years (HLY) and Life Expectancy in Good Perceived Health (LEGPH) in the European Union. However, the potential gains might differ by region.

Methods: A Sullivan life table model was applied for 27 European countries to calculate the impact of alternative scenarios of lifestyle behavior on life and health expectancy. Results were then pooled over countries to present the potential gains in HLY and LEGPH for four European regions.

Results: Simulations show that up to 4 years of extra health expectancy can be gained by getting all countries to the healthiest levels of lifestyle observed in EU countries. This is more than the 2 years to be gained in life expectancy. Generally, Eastern Europe has the lowest LE, HLY, and LEGPH. Even though the largest gains in LEPGH and HLY can also be made in Eastern Europe, the gap in LE, HLY, and LEGPH can only in a small part be closed by changing smoking, alcohol consumption, and BMI.

Conclusion: Based on the current data, up to 4 years of good health could be gained by adopting lifestyle as seen in the best-performing countries. Only a part of the lagging health expectancy of Eastern Europe can potentially be solved by improvements in lifestyle involving smoking and BMI. Before it is definitely concluded that lifestyle policy for alcohol use is of relatively little importance compared to smoking or BMI, as our findings suggest, better data should be gathered in all European countries concerning alcohol use and the odds ratios of overconsumption of alcohol.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12963-018-0181-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6337827PMC
January 2019

FFQ versus repeated 24-h recalls for estimating diet-related environmental impact.

Nutr J 2019 01 8;18(1). Epub 2019 Jan 8.

Division of Human Nutrition and Health, Wageningen University, PO Box 8129, 6700, EV, Wageningen, The Netherlands.

Background: There is an increasing interest in estimating environmental impact of individuals' diets by using individual-level food consumption data. However, like assessment of nutrient intakes, these data are prone to substantial measurement errors dependent on the method of dietary assessment, and this often result in attenuation of associations.

Purpose: To investigate the performance of a food frequency questionnaire (FFQ) for estimating the environmental impact of the diet as compared to independent 24-h recalls (24hR), and to study the association between environmental impact and dietary quality for the FFQ and 24hR.

Methods: We analysed cross-sectional data from 1169 men and women, aged 20-76 years, who participated in the NQplus study, the Netherlands. They completed a 216-item FFQ and two replicates of web-based 24hR. Life cycle assessments of 207 food products were used to calculate greenhouse gas emissions, fossil energy and land use, summarised into an aggregated score, pReCiPe. Validity of the FFQ was evaluated against 24hRs using correlation coefficients and attenuation coefficients. Associations with dietary quality were based on Dutch Healthy Diet 15-index (DHD15-index) and Nutrient Rich Diet score (NRD9.3).

Results: For pReCiPe, correlation coefficient between FFQ and 24hR was 0.33 when adjusted for covariates age, gender and BMI, and increased to 0.76 when de-attenuated for within-subject variation in the 24hR. Energy-adjustment slightly reduced these correlations (r = 0.71 for residuals of observed values and 0.59 for residuals of density values). Covariate-adjusted attenuation coefficient for the FFQ was 0.56 (ʎ = 0.56 and ʎ = 0.65 for observed and density residuals), slightly lower than without covariate adjustment. Diet-related environmental impact was inversely associated with the food-based DHD15-index for both FFQ and 24hR, while associations with the nutrient-based NRD9.3 were inconsistent.

Conclusions: The FFQ slightly underestimated environmental impact when compared to 24hR. Associations with dietary quality are highly dependent on the diet score used, and less dependent on the method of dietary assessment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12937-018-0425-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323679PMC
January 2019

Estimating disease prevalence from drug utilization data using the Random Forest algorithm.

Eur J Public Health 2019 Aug;29(4):615-621

National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.

Background: Aggregated claims data on medication are often used as a proxy for the prevalence of diseases, especially chronic diseases. However, linkage between medication and diagnosis tend to be theory based and not very precise. Modelling disease probability at an individual level using individual level data may yield more accurate results.

Methods: Individual probabilities of having a certain chronic disease were estimated using the Random Forest (RF) algorithm. A training set was created from a general practitioners database of 276 723 cases that included diagnosis and claims data on medication. Model performance for 29 chronic diseases was evaluated using Receiver-Operator Curves, by measuring the Area Under the Curve (AUC).

Results: The diseases for which model performance was best were Parkinson's disease (AUC = .89, 95% CI = .77-1.00), diabetes (AUC = .87, 95% CI = .85-.90), osteoporosis (AUC = .87, 95% CI = .81-.92) and heart failure (AUC = .81, 95% CI = .74-.88). Five other diseases had an AUC >.75: asthma, chronic enteritis, COPD, epilepsy and HIV/AIDS. For 16 of 17 diseases tested, the medication categories used in theory-based algorithms were also identified by our method, however the RF models included a broader range of medications as important predictors.

Conclusion: Data on medication use can be a useful predictor when estimating the prevalence of several chronic diseases. To improve the estimates, for a broader range of chronic diseases, research should use better training data, include more details concerning dosages and duration of prescriptions, and add related predictors like hospitalizations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/eurpub/cky270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660107PMC
August 2019

Pre-to-post diagnosis weight trajectories in colorectal cancer patients with non-metastatic disease.

Support Care Cancer 2019 Apr 27;27(4):1541-1549. Epub 2018 Nov 27.

Division of Human Nutrition and Health, Wageningen University and Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.

Purpose: Previous studies have shown that > 50% of colorectal cancer (CRC) patients treated with adjuvant chemotherapy gain weight after diagnosis. This may affect long-term health. Therefore, prevention of weight gain has been incorporated in oncological guidelines for CRC with a focus on patients that undergo adjuvant chemotherapy treatment. It is, however, unknown how changes in weight after diagnosis relate to weight before diagnosis and whether weight changes from pre-to-post diagnosis are restricted to chemotherapy treatment. We therefore examined pre-to-post diagnosis weight trajectories and compared them between those treated with and without adjuvant chemotherapy.

Methods: We included 1184 patients diagnosed with stages I-III CRC between 2010 and 2015 from an ongoing observational prospective study. At diagnosis, patients reported current weight and usual weight 2 years before diagnosis. In the 2 years following diagnosis, weight was self-reported repeatedly. We used linear mixed models to analyse weight trajectories.

Results: Mean pre-to-post diagnosis weight change was -0.8 (95% CI -1.1, -0.4) kg. Post-diagnosis weight gain was + 3.5 (95% CI 2.7, 4.3) kg in patients who had lost ≥ 5% weight before diagnosis, while on average clinically relevant weight gain after diagnosis was absent in the groups without pre-diagnosis weight loss. Pre-to-post diagnosis weight change was similar in patients treated with (-0.1 kg (95%CI -0.8, 0.6)) and without adjuvant chemotherapy (-0.9 kg (95%CI -1.4, -0.5)).

Conclusions: Overall, hardly any pre-to-post diagnosis weight change was observed among CRC patients, because post-diagnosis weight gain was mainly observed in patients who lost weight before diagnosis. This was observed independent of treatment with adjuvant chemotherapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00520-018-4560-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394719PMC
April 2019

Internal consistency of a synthetic population construction method for chronic disease micro-simulation models.

PLoS One 2018 15;13(11):e0205225. Epub 2018 Nov 15.

National Institute for Public Health and the Environment, Bilthoven, the Netherlands.

Background: Micro-simulation models of risk-factors and chronic diseases are built increasingly often, and each model starts with an initial population. Constructing such populations when no survey data covering all variables are available is no trivial task, often requiring complex methods based on several (untested) assumptions. In this paper, we propose a method for evaluating the merits of construction methods, and apply this to one specific method: the construction method used in the DYNAMO-HIA model.

Methods: The initial population constructed using the DYNAMO-HIA method is compared to another population constructed by starting a simulation with only newborns and simulating the course taken by one risk-factor and several diseases. In this simulation, the age- and sex-specific prevalence of the risk-factor is kept constant over time.

Results: Our simulations show that, in general, the DYNAMO-HIA method clearly outperforms a method that assumes independence of the risk-factor and the prevalence of diseases and independence between all diseases. In many situations the DYNAMO-HIA method performs reasonably well, but in some the proportion with the risk-factor for those with a disease is under- or overestimated by as much as 10 percentage points. For determining comorbidity between diseases linked by a common causal disease or a common risk-factor it also performs reasonably well. However, the current method performs poorly for determining the comorbidity between one disease caused by the other.

Conclusion: The DYNAMO-HIA methods perform reasonably well; they outperform a baseline assumption of independence between the risk-factor and diseases in the initial population. The method for determining the comorbidity between diseases that are causally linked needs improvement. Given the existing discrepancies for situations with high relative risks, however, developing more elaborate methods based on running simulation models to generate an initial population would be worthwhile.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205225PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237328PMC
April 2019

Validating fatty acid intake as estimated by an FFQ: how does the 24 h recall perform as reference method compared with the duplicate portion?

Public Health Nutr 2018 10 8;21(14):2568-2574. Epub 2018 May 8.

1Division of Human Nutrition,Wageningen University & Research,PO Box 17,6700 AA Wageningen,The Netherlands.

Objective: To compare the performance of the commonly used 24 h recall (24hR) with the more distinct duplicate portion (DP) as reference method for validation of fatty acid intake estimated with an FFQ.

Design: Intakes of SFA, MUFA, n-3 fatty acids and linoleic acid (LA) were estimated by chemical analysis of two DP and by on average five 24hR and two FFQ. Plasma n-3 fatty acids and LA were used to objectively compare ranking of individuals based on DP and 24hR. Multivariate measurement error models were used to estimate validity coefficients and attenuation factors for the FFQ with the DP and 24hR as reference methods.

Setting: Wageningen, the Netherlands.

Subjects: Ninety-two men and 106 women (aged 20-70 years).

Results: Validity coefficients for the fatty acid estimates by the FFQ tended to be lower when using the DP as reference method compared with the 24hR. Attenuation factors for the FFQ tended to be slightly higher based on the DP than those based on the 24hR as reference method. Furthermore, when using plasma fatty acids as reference, the DP showed comparable to slightly better ranking of participants according to their intake of n-3 fatty acids (0·33) and n-3:LA (0·34) than the 24hR (0·22 and 0·24, respectively).

Conclusions: The 24hR gives only slightly different results compared with the distinctive but less feasible DP, therefore use of the 24hR seems appropriate as the reference method for FFQ validation of fatty acid intake.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/S1368980018001131DOI Listing
October 2018

Are Metabolic Signatures Mediating the Relationship between Lifestyle Factors and Hepatocellular Carcinoma Risk? Results from a Nested Case-Control Study in EPIC.

Cancer Epidemiol Biomarkers Prev 2018 05 21;27(5):531-540. Epub 2018 Mar 21.

Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC), Lyon, France.

The "meeting-in-the-middle" (MITM) is a principle to identify exposure biomarkers that are also predictors of disease. The MITM statistical framework was applied in a nested case-control study of hepatocellular carcinoma (HCC) within European Prospective Investigation into Cancer and Nutrition (EPIC), where healthy lifestyle index (HLI) variables were related to targeted serum metabolites. Lifestyle and targeted metabolomic data were available from 147 incident HCC cases and 147 matched controls. Partial least squares analysis related 7 lifestyle variables from a modified HLI to a set of 132 serum-measured metabolites and a liver function score. Mediation analysis evaluated whether metabolic profiles mediated the relationship between each lifestyle exposure and HCC risk. Exposure-related metabolic signatures were identified. Particularly, the body mass index (BMI)-associated metabolic component was positively related to glutamic acid, tyrosine, PC aaC38:3, and liver function score and negatively to lysoPC aC17:0 and aC18:2. The lifetime alcohol-specific signature had negative loadings on sphingomyelins (SM C16:1, C18:1, SM(OH) C14:1, C16:1 and C22:2). Both exposures were associated with increased HCC with total effects (TE) = 1.23 (95% confidence interval = 0.93-1.62) and 1.40 (1.14-1.72), respectively, for BMI and alcohol consumption. Both metabolic signatures mediated the association between BMI and lifetime alcohol consumption and HCC with natural indirect effects, respectively, equal to 1.56 (1.24-1.96) and 1.09 (1.03-1.15), accounting for a proportion mediated of 100% and 24%. In a refined MITM framework, relevant metabolic signatures were identified as mediators in the relationship between lifestyle exposures and HCC risk. The understanding of the biological basis for the relationship between modifiable exposures and cancer would pave avenues for clinical and public health interventions on metabolic mediators. .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1055-9965.EPI-17-0649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444360PMC
May 2018

Limited salt consumption reduces the incidence of chronic kidney disease: a modeling study.

J Public Health (Oxf) 2018 09;40(3):e351-e358

Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), BA Bilthoven, The Netherlands.

Background: In addition to blood pressure and cardiovascular disease, high-salt intake has been associated with renal diseases. The aim of this study is to estimate the potential health impact of salt reduction on chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in the Netherlands.

Methods: We developed a dynamic population health modeling tool to estimate the health impact of salt reduction on CKD and ESKD. We used data from the PREVEND study and extrapolated that to the Dutch population aged 30-75 years. We estimated the potential health impact of salt reduction comparing the current situation with the health impact of the adherence to the recommended maximum salt intake of 6 g/d.

Results: In the recommended maximum intake scenario, a cumulative reduction in CKD of 1.1% (N = 290 000; interquartile range (IQR) = 249 000) and in ESKD of 3.2% (N = 470; IQR = 5080) would occur over a period of 20 years.

Conclusions: Our health impact estimation showed that health benefits on CKD might be achieved when salt intake is reduced to the recommended maximum intake of 6 g/d.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/pubmed/fdx178DOI Listing
September 2018

Identification of differences in health impact modelling of salt reduction.

PLoS One 2017 28;12(11):e0186760. Epub 2017 Nov 28.

National Institute for Public Health and the Environment, Bilthoven, the Netherlands.

We examined whether specific input data and assumptions explain outcome differences in otherwise comparable health impact assessment models. Seven population health models estimating the impact of salt reduction on morbidity and mortality in western populations were compared on four sets of key features, their underlying assumptions and input data. Next, assumptions and input data were varied one by one in a default approach (the DYNAMO-HIA model) to examine how it influences the estimated health impact. Major differences in outcome were related to the size and shape of the dose-response relation between salt and blood pressure and blood pressure and disease. Modifying the effect sizes in the salt to health association resulted in the largest change in health impact estimates (33% lower), whereas other changes had less influence. Differences in health impact assessment model structure and input data may affect the health impact estimate. Therefore, clearly defined assumptions and transparent reporting for different models is crucial. However, the estimated impact of salt reduction was substantial in all of the models used, emphasizing the need for public health actions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186760PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5705127PMC
December 2017

Taking multi-morbidity into account when attributing DALYs to risk factors: comparing dynamic modeling with the GBD2010 calculation method.

BMC Public Health 2017 02 14;17(1):197. Epub 2017 Feb 14.

National Institute for Public Health and the Environment, P.O. Box 13720 BA, Bilthoven, The Netherlands.

Background: Disability Adjusted Life Years (DALYs) quantify the loss of healthy years of life due to dying prematurely and due to living with diseases and injuries. Current methods of attributing DALYs to underlying risk factors fall short on two main points. First, risk factor attribution methods often unjustly apply incidence-based population attributable fractions (PAFs) to prevalence-based data. Second, it mixes two conceptually distinct approaches targeting different goals, namely an attribution method aiming to attribute uniquely to a single cause, and an elimination method aiming to describe a counterfactual situation without exposure. In this paper we describe dynamic modeling as an alternative, completely counterfactual approach and compare this to the approach used in the Global Burden of Disease 2010 study (GBD2010).

Methods: Using data on smoking in the Netherlands in 2011, we demonstrate how an alternative method of risk factor attribution using a pure counterfactual approach results in different estimates for DALYs. This alternative method is carried out using the dynamic multistate disease table model DYNAMO-HIA. We investigate the differences between our alternative method and the method used by the GBD2010 by doing additional analyses using data from a synthetic population in steady state.

Results: We observed important differences between the outcomes of the two methods: in an artificial situation where dynamics play a limited role, DALYs are a third lower as compared to those calculated with the GBD2010 method (398,000 versus 607,000 DALYs). The most important factor is newly occurring morbidity in life years gained that is ignored in the GBD2010 approach. Age-dependent relative risks and exposures lead to additional differences between methods as they distort the results of prevalence-based DALY calculations, but the direction and magnitude of the distortions depend on the particular situation.

Conclusions: We argue that the GBD2010 approach is a hybrid of an attributional and counterfactual approach, making the end result hard to understand, while dynamic modelling uses a purely counterfactual approach and thus yields better interpretable results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12889-017-4024-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310082PMC
February 2017

A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data.

BMC Med Res Methodol 2016 10 13;16(1):139. Epub 2016 Oct 13.

Department of Statistics, mathematical modelling and data logistics, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.

Background: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data.

Methods: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study.

Results: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations.

Conclusions: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12874-016-0240-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5064985PMC
October 2016

BMI was found to be a consistent determinant related to misreporting of energy, protein and potassium intake using self-report and duplicate portion methods.

Public Health Nutr 2017 03 11;20(4):598-607. Epub 2016 Oct 11.

1Division of Human Nutrition,Wageningen University,PO Box 8129,6700 EV Wageningen,The Netherlands.

Objective: As misreporting, mostly under-reporting, of dietary intake is a generally known problem in nutritional research, we aimed to analyse the association between selected determinants and the extent of misreporting by the duplicate portion method (DP), 24 h recall (24hR) and FFQ by linear regression analysis using the biomarker values as unbiased estimates.

Design: For each individual, two DP, two 24hR, two FFQ and two 24 h urinary biomarkers were collected within 1·5 years. Also, for sixty-nine individuals one or two doubly labelled water measurements were obtained. The associations of basic determinants (BMI, gender, age and level of education) with misreporting of energy, protein and K intake of the DP, 24hR and FFQ were evaluated using linear regression analysis. Additionally, associations between other determinants, such as physical activity and smoking habits, and misreporting were investigated.

Setting: The Netherlands.

Subjects: One hundred and ninety-seven individuals aged 20-70 years.

Results: Higher BMI was associated with under-reporting of dietary intake assessed by the different dietary assessment methods for energy, protein and K, except for K by DP. Men tended to under-report protein by the DP, FFQ and 24hR, and persons of older age under-reported K but only by the 24hR and FFQ. When adjusted for the basic determinants, the other determinants did not show a consistent association with misreporting of energy or nutrients and by the different dietary assessment methods.

Conclusions: As BMI was the only consistent determinant of misreporting, we conclude that BMI should always be taken into account when assessing and correcting dietary intake.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/S1368980016002743DOI Listing
March 2017

Accounting for multimorbidity can affect the estimation of the Burden of Disease: a comparison of approaches.

Arch Public Health 2016 22;74:37. Epub 2016 Aug 22.

National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands.

Background: Various Burden of Disease (BoD) studies do not account for multimorbidity in their BoD estimates. Ignoring multimorbidity can lead to inaccuracies in BoD estimations, particularly in ageing populations that include large proportions of persons with two or more health conditions. The objective of this study is to improve BoD estimates for the Netherlands by accounting for multimorbidity. For this purpose, we analyzed different methods for 1) estimating the prevalence of multimorbidity and 2) deriving Disability Weights (DWs) for multimorbidity by using existing data on single health conditions.

Methods: We included 25 health conditions from the Dutch Burden of Disease study that have a high rate of prevalence and that make a large contribution to the total number of Years Lived with a Disability (YLD). First, we analyzed four methods for estimating the prevalence of multimorbid conditions (i.e. independent, independent age- and sex-specific, dependent, and dependent sex- and age-specific). Secondly, we analyzed three methods for calculating the Combined Disability Weights (CDWs) associated with multimorbid conditions (i.e. additive, multiplicative and maximum limit). A combination of these two approaches was used to recalculate the number of YLDs, which is a component of the Disability-Adjusted Life Years (DALY).

Results: This study shows that the YLD estimates for 25 health conditions calculated using the multiplicative method for Combined Disability Weights are 5 % lower, and 14 % lower when using the maximum limit method, than when calculated using the additive method. Adjusting for sex- and age-specific dependent co-occurrence of health conditions reduces the number of YLDs by 10 % for the multiplicative method and by 26 % for the maximum limit method. The adjustment is higher for health conditions with a higher prevalence in old age, like heart failure (up to 43 %) and coronary heart diseases (up to 33 %). Health conditions with a high prevalence in middle age, such as anxiety disorders, have a moderate adjustment (up to 13 %).

Conclusions: We conclude that BoD calculations that do not account for multimorbidity can result in an overestimation of the actual BoD. This may affect public health policy strategies that focus on single health conditions if the underlying cost-effectiveness analysis overestimates the intended effects. The methodology used in this study could be further refined to provide greater insight into co-occurrence and the possible consequences of multimorbid conditions in terms of disability for particular combinations of health conditions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13690-016-0147-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993005PMC
August 2016

Potential health gains and health losses in eleven EU countries attainable through feasible prevalences of the life-style related risk factors alcohol, BMI, and smoking: a quantitative health impact assessment.

BMC Public Health 2016 08 5;16:734. Epub 2016 Aug 5.

Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.

Background: Influencing the life-style risk-factors alcohol, body mass index (BMI), and smoking is an European Union (EU) wide objective of public health policy. The population-level health effects of these risk-factors depend on population specific characteristics and are difficult to quantify without dynamic population health models.

Methods: For eleven countries-approx. 80 % of the EU-27 population-we used evidence from the publicly available DYNAMO-HIA data-set. For each country the age- and sex-specific risk-factor prevalence and the incidence, prevalence, and excess mortality of nine chronic diseases are utilized; including the corresponding relative risks linking risk-factor exposure causally to disease incidence and all-cause mortality. Applying the DYNAMO-HIA tool, we dynamically project the country-wise potential health gains and losses using feasible, i.e. observed elsewhere, risk-factor prevalence rates as benchmarks. The effects of the "worst practice", "best practice", and the currently observed risk-factor prevalence on population health are quantified and expected changes in life expectancy, morbidity-free life years, disease cases, and cumulative mortality are reported.

Results: Applying the best practice smoking prevalence yields the largest gains in life expectancy with 0.4 years for males and 0.3 year for females (approx. 332,950 and 274,200 deaths postponed, respectively) while the worst practice smoking prevalence also leads to the largest losses with 0.7 years for males and 0.9 year for females (approx. 609,400 and 710,550 lives lost, respectively). Comparing morbidity-free life years, the best practice smoking prevalence shows the highest gains for males with 0.4 years (342,800 less disease cases), whereas for females the best practice BMI prevalence yields the largest gains with 0.7 years (1,075,200 less disease cases).

Conclusion: Smoking is still the risk-factor with the largest potential health gains. BMI, however, has comparatively large effects on morbidity. Future research should aim to improve knowledge of how policies can influence and shape individual and aggregated life-style-related risk-factor behavior.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12889-016-3299-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4975898PMC
August 2016

Trend in and predictors for cardiovascular mortality in patients with rheumatoid arthritis over a period of 15 years: a prospective cohort study.

Clin Exp Rheumatol 2016 Sep-Oct;34(5):813-819. Epub 2016 Aug 2.

Jan van Breemen Research Institute/Reade, Amsterdam, The Netherlands.

Objectives: To investigate a) the cardiovascular (CV) mortality in a clinical cohort of patients with established rheumatoid arthritis (RA) in comparison with the general population over 15 years, b) the trend in this CV mortality during the study period, and c) for a broad range of predictors, which baseline variables predict CV mortality.

Methods: In 1997, a sample of 1222 patients was randomly selected from the register of a rheumatology outpatient clinic in Amsterdam. Their CV mortality between 1997 and 2012 was obtained from Statistics Netherlands. The standardised mortality ratio (SMR) for CV mortality was calculated. A linear poisson regression analysis was performed to investigate if there was a trend in SMR over time. A Cox regression analysis was performed to determine which baseline variables predicted CV mortality.

Results: Mean age of the population at baseline was 60.4 (SD 15.4) years and 72.6% of the patients were women. Estimated SMR (95% confidence interval) for CV mortality was 1.24 (1.05, 1.43). The SMR decreased with 3% annually (p=0.16). Higher age, higher erythrocyte sedimentation rate, having CV comorbidity and diabetes mellitus (DM) were predictors for CV mortality.

Conclusions: CV mortality among patients with RA in the past 15 years was still higher than in the general population. CV mortality decrease was not statistically significant. As CV mortality in RA is still higher than in the general population, continued attention for CV diseases in RA is important. Both tight control of disease activity and good care for comorbid conditions (CV diseases and DM) are advocated.
View Article and Find Full Text PDF

Download full-text PDF

Source
January 2017

You Only Die Once: Accounting for Multi-Attributable Mortality Risks in Multi-Disease Models for Health-Economic Analyses.

Med Decis Making 2017 05 12;37(4):403-414. Epub 2016 Jul 12.

Erasmus University Rotterdam, institute of Health Policy & Management, Rotterdam, Netherlands (PHMV).

Mortality rates in Markov models, as used in health economic studies, are often estimated from summary statistics that allow limited adjustment for confounders. If interventions are targeted at multiple diseases and/or risk factors, these mortality rates need to be combined in a single model. This requires them to be mutually adjusted to avoid 'double counting' of mortality. We present a mathematical modeling approach to describe the joint effect of mutually dependent risk factors and chronic diseases on mortality in a consistent manner. Most importantly, this approach explicitly allows the use of readily available external data sources. An additional advantage is that existing models can be smoothly expanded to encompass more diseases/risk factors. To illustrate the usefulness of this method and how it should be implemented, we present a health economic model that links risk factors for diseases to mortality from these diseases, and describe the causal chain running from these risk factors (e.g., obesity) through to the occurrence of disease (e.g., diabetes, CVD) and death. Our results suggest that these adjustment procedures may have a large impact on estimated mortality rates. An improper adjustment of the mortality rates could result in an underestimation of disease prevalence and, therefore, disease costs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/0272989X16658661DOI Listing
May 2017

Anthropometry and the Risk of Lung Cancer in EPIC.

Am J Epidemiol 2016 07 1;184(2):129-39. Epub 2016 Jul 1.

The associations of body mass index (BMI) and other anthropometric measurements with lung cancer were examined in 348,108 participants in the European Investigation Into Cancer and Nutrition (EPIC) between 1992 and 2010. The study population included 2,400 case patients with incident lung cancer, and the average length of follow-up was 11 years. Hazard ratios were calculated using Cox proportional hazard models in which we modeled smoking variables with cubic splines. Overall, there was a significant inverse association between BMI (weight (kg)/height (m)(2)) and the risk of lung cancer after adjustment for smoking and other confounders (for BMI of 30.0-34.9 versus 18.5-25.0, hazard ratio = 0.72, 95% confidence interval: 0.62, 0.84). The strength of the association declined with increasing follow-up time. Conversely, after adjustment for BMI, waist circumference and waist-to-height ratio were significantly positively associated with lung cancer risk (for the highest category of waist circumference vs. the lowest, hazard ratio = 1.25, 95% confidence interval: 1.05, 1.50). Given the decline of the inverse association between BMI and lung cancer over time, the association is likely at least partly due to weight loss resulting from preclinical lung cancer that was present at baseline. Residual confounding by smoking could also have influenced our findings.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/aje/kwv298DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945700PMC
July 2016

Evaluation of a two-part regression calibration to adjust for dietary exposure measurement error in the Cox proportional hazards model: A simulation study.

Biom J 2016 Jul 22;58(4):766-82. Epub 2016 Mar 22.

Biometris, Wageningen University and Research Centre, Postbus 16, 6700 AA, Wageningen, The Netherlands.

Dietary questionnaires are prone to measurement error, which bias the perceived association between dietary intake and risk of disease. Short-term measurements are required to adjust for the bias in the association. For foods that are not consumed daily, the short-term measurements are often characterized by excess zeroes. Via a simulation study, the performance of a two-part calibration model that was developed for a single-replicate study design was assessed by mimicking leafy vegetable intake reports from the multicenter European Prospective Investigation into Cancer and Nutrition (EPIC) study. In part I of the fitted two-part calibration model, a logistic distribution was assumed; in part II, a gamma distribution was assumed. The model was assessed with respect to the magnitude of the correlation between the consumption probability and the consumed amount (hereafter, cross-part correlation), the number and form of covariates in the calibration model, the percentage of zero response values, and the magnitude of the measurement error in the dietary intake. From the simulation study results, transforming the dietary variable in the regression calibration to an appropriate scale was found to be the most important factor for the model performance. Reducing the number of covariates in the model could be beneficial, but was not critical in large-sample studies. The performance was remarkably robust when fitting a one-part rather than a two-part model. The model performance was minimally affected by the cross-part correlation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/bimj.201500009DOI Listing
July 2016

Physical and Mental Functioning in Patients with Established Rheumatoid Arthritis over an 11-year Followup Period: The Role of Specific Comorbidities.

J Rheumatol 2016 Feb 15;43(2):307-14. Epub 2016 Jan 15.

From the Amsterdam Rehabilitation Research Center | Reade; Department of Social Medicine, Academic Medical Center, University of Amsterdam; Departments of Rehabilitation and Psychiatry, EMGO Institute, Vrije Universiteit (VU) University Medical Center Amsterdam, Amsterdam; National Institute of Public Health and the Environment, Bilthoven; Biometrics, Wageningen University and Research Centre, Wageningen, the Netherlands.J. van den Hoek, MSc, Amsterdam Rehabilitation Research Center | Reade, and Department of Social Medicine, Academic Medical Center, University of Amsterdam; L.D. Roorda, MD, PT, PhD, Amsterdam Rehabilitation Research Center | Reade; H.C. Boshuizen, PhD, National Institute of Public Health and the Environment, and Biometrics, Wageningen University and Research Centre; G.J. Tijhuis, MD, PhD, Amsterdam Rehabilitation Research Center | Reade; G.A. van den Bos, PhD, Department of Social Medicine, Academic Medical Center, University of Amsterdam; J. Dekker, PhD, Amsterdam Rehabilitation Research Center | Reade, and Departments of Rehabilitation and Psychiatry, EMGO Institute, VU University Medical Center Amsterdam.

Objective: To investigate the longterm association of a wide range of comorbidities with physical and mental functioning in patients with rheumatoid arthritis (RA).

Methods: Longitudinal data over a period of 11 years were collected from 882 patients with RA. Somatic comorbidity and comorbid depression were measured at baseline, with a questionnaire including 20 chronic diseases and with the Center for Epidemiologic Depression Scale, respectively. Physical functioning was measured at 5 timepoints with a disease-specific measure [Health Assessment Questionnaire (HAQ)] and a generic measure [physical scales of the Medical Outcomes Study Short Form-36 (SF-36)]. Mental functioning was measured with the mental scales of the SF-36. To determine the association of baseline-specific comorbidities with functioning over time, we performed longitudinal analyses.

Results: At baseline, 72% percent of the patients were women, mean age ± SD was 59.3 ± 14.8 years, median RA disease duration was 5.0 years, and 68% had ≥ 1 comorbid condition. The effect of comorbid conditions was more apparent when physical functioning was measured with SF-36, a disease-generic measure, compared with the HAQ, a disease-specific measure. Circulatory conditions and depression were associated (p < 0.05) with worse physical functioning according to the HAQ. Respiratory conditions, musculoskeletal conditions, cancer, and depression were associated (p < 0.05) with worse physical functioning according to the SF-36. Respiratory conditions and depression were associated with worse mental functioning.

Conclusion: Patients with specific comorbid conditions have an increased risk of low functioning in the long term. Targeted attention for these specific comorbid conditions by clinicians is recommended.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3899/jrheum.150536DOI Listing
February 2016
-->