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.
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.
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.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical ...methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA‐sequencing, while others are custom‐made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data.
Abstract
Due to the wealth of exposome data from longitudinal cohort studies that is currently available, the need for methods to adequately analyze these data is growing. We propose an approach in ...which machine learning is used to identify longitudinal exposome-related predictors of health, and illustrate its potential through an application. Our application involves studying the relation between exposome and self-perceived health based on the 30-year running Doetinchem Cohort Study. Random Forest (RF) was used to identify the strongest predictors due to its favorable prediction performance in prior research. The relation between predictors and outcome was visualized with partial dependence and accumulated local effects plots. To facilitate interpretation, exposures were summarized by expressing them as the average exposure and average trend over time. The RF model’s ability to discriminate poor from good self-perceived health was acceptable (Area-Under-the-Curve = 0.707). Nine exposures from different exposome-related domains were largely responsible for the model’s performance, while 87 exposures seemed to contribute little to the performance. Our approach demonstrates that ML can be interpreted more than widely believed, and can be applied to identify important longitudinal predictors of health over the life course in studies with repeated measures of exposure. The approach is context-independent and broadly applicable.
To develop public health intervention models using micro-simulations, extensive personal information about inhabitants is needed, such as socio-demographic, economic and health figures. ...Confidentiality is an essential characteristic of such data, while the data should reflect realistic scenarios. Collection of such data is possible only in secured environments and not directly available for open-source micro-simulation models. The aim of this paper is to illustrate a method of construction of synthetic data by predicting individual features through models based on confidential data on health and socio-economic determinants of the entire Dutch population. Administrative records and health registry data were linked to socio-economic characteristics and self-reported lifestyle factors. For the entire Dutch population (n = 16,778,708), all socio-demographic information except lifestyle factors was available. Lifestyle factors were available from the 2012 Dutch Health Monitor (n = 370,835). Regression model was used to sequentially predict individual features. The synthetic population resembles the original confidential population. Features predicted in the first stages of the sequential procedure are virtually similar to those in the original population, while those predicted in later stages of the sequential procedure carry the accumulation of limitations furthered by data quality and previously modelled features. By combining socio-demographic, economic, health and lifestyle related data at individual level on a large scale, our method provides us with a powerful tool to construct a synthetic population of good quality and with no confidentiality issues.
BACKGROUND: Dietary fiber may play a role in obesity prevention. Until now, the role that fiber from different sources plays in weight change had rarely been studied. OBJECTIVE: Our aim was to ...investigate the association of total dietary fiber, cereal fiber, and fruit and vegetable fiber with changes in weight and waist circumference. DESIGN: We conducted a prospective cohort study with 89,432 European participants, aged 20-78 y, who were free of cancer, cardiovascular disease, and diabetes at baseline and who were followed for an average of 6.5 y. Dietary information was collected by using validated country-specific food-frequency questionnaires. Multiple linear regression analysis was performed in each center studied, and estimates were combined by using random-effects meta-analyses. Adjustments were made for follow-up duration, other dietary variables, and baseline anthropometric, demographic, and lifestyle factors. RESULTS: Total fiber was inversely associated with subsequent weight and waist circumference change. For a 10-g/d higher total fiber intake, the pooled estimate was -39 g/y (95% CI: -71, -7 g/y) for weight change and -0.08 cm/y (95% CI: -0.11, -0.05 cm/y) for waist circumference change. A 10-g/d higher fiber intake from cereals was associated with -77 g/y (95% CI: -127, -26 g/y) weight change and -0.10 cm/y (95% CI: -0.18, -0.02 cm/y) waist circumference change. Fruit and vegetable fiber was not associated with weight change but had a similar association with waist circumference change when compared with intake of total dietary fiber and cereal fiber. CONCLUSION: Our finding may support a beneficial role of higher intake of dietary fiber, especially cereal fiber, in prevention of body-weight and waist circumference gain.
Excessive salt intake is associated with hypertension and cardiovascular diseases. Salt intake exceeds the World Health Organization population nutrition goal of 5 grams per day in the European ...region. We assessed the health impact of salt reduction in nine European countries (Finland, France, Ireland, Italy, Netherlands, Poland, Spain, Sweden and United Kingdom). Through literature research we obtained current salt intake and systolic blood pressure levels of the nine countries. The population health modeling tool DYNAMO-HIA including country-specific disease data was used to predict the changes in prevalence of ischemic heart disease and stroke for each country estimating the effect of salt reduction through its effect on blood pressure levels. A 30% salt reduction would reduce the prevalence of stroke by 6.4% in Finland to 13.5% in Poland. Ischemic heart disease would be decreased by 4.1% in Finland to 8.9% in Poland. When salt intake is reduced to the WHO population nutrient goal, it would reduce the prevalence of stroke from 10.1% in Finland to 23.1% in Poland. Ischemic heart disease would decrease by 6.6% in Finland to 15.5% in Poland. The number of postponed deaths would be 102,100 (0.9%) in France, and 191,300 (2.3%) in Poland. A reduction of salt intake to 5 grams per day is expected to substantially reduce the burden of cardiovascular disease and mortality in several European countries.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BACKGROUND: Little is known about the effects of dietary fiber intake on long-term mortality. OBJECTIVE: We aimed to study recent and long-term dietary fiber intake in relation to coronary heart ...disease and all-cause mortality. DESIGN: The effects of recent and long-term dietary fiber intakes on mortality were investigated in the Zutphen Study, a cohort of 1373 men born between 1900 and 1920 and examined repeatedly between 1960 and 2000. During that period, 1130 men died, 348 as a result of coronary heart disease. Hazard ratios were obtained from time-dependent Cox regression models. RESULTS: Every additional 10 g of recent dietary fiber intake per day reduced coronary heart disease mortality by 17% (95% CI: 2%, 30%) and all-cause mortality by 9% (0%, 18%). The strength of the association between long-term dietary fiber intake and all-cause mortality decreased from age 50 y (hazard ratio: 0.71; 95% CI: 0.55, 0.93) until age 80 y (0.99; 0.87, 1.12). We observed no clear associations for different types of dietary fiber. CONCLUSIONS: A higher recent dietary fiber intake was associated with a lower risk of both coronary heart disease and all-cause mortality. For long-term intake, the strength of the association between dietary fiber and all-cause mortality decreased with increasing age.
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.
Wageningen area, ...the Netherlands, 2011-2013.
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.
Data of 236 individuals collected within the National Dietary Assessment Reference Database.
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.
Our study highlights the potential of combining FFQ and 24hR data. Using RC and ERC resulted in less biased associations for protein and K.
Obesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this ...study was to estimate the annual and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention.
With a simulation model, lifetime health-care costs were estimated for a cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and "healthy-living" persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions.
Although effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Conventional dietary assessment methods are affected by measurement errors. We developed a smartphone-based 2-h recall (2hR) methodology to reduce participant burden and memory-related bias.
...Assessing the validity of the 2hR method against traditional 24-h recalls (24hRs) and objective biomarkers.
Dietary intake was assessed in 215 Dutch adults on 6 randomly selected nonconsecutive days (i.e., 3 2hR-days and 3 24hRs) during a 4-wk period. Sixty-three participants provided 4 24-h urine samples, to assess urinary nitrogen and potassium concentrations.
Intake estimates of energy (2052±503 kcal vs. 1976±483 kcal) and nutrients (e.g., protein: 78±23 g vs. 71±19 g; fat: 84±30 g vs. 79±26 g; carbohydrates: 220±60 g vs. 216±60 g) were slightly higher with 2hR-days than with 24hRs. Comparing self-reported protein and potassium intake to urinary nitrogen and potassium concentrations indicated a slightly higher accuracy of 2hR-days than 24hRs (protein: −14% vs. −18%; potassium: −11% vs. −16%). Correlation coefficients between methods ranged from 0.41 to 0.75 for energy and macronutrients and from 0.41 to 0.62 for micronutrients. Generally, regularly consumed food groups showed small differences in intake (<10%) and good correlations (>0.60). Intake of energy, nutrients, and food groups showed similar reproducibility (intraclass correlation coefficient) for 2hR-days and 24hRs.
Comparing 2hR-days with 24hRs showed a relatively similar group-level bias for energy, most nutrients, and food groups. Differences were mostly due to higher intake estimates by 2hR-days. Biomarker comparisons showed less underestimation by 2hR-days as compared with 24hRs, suggesting that 2hR-days are a valid approach to assess the intake of energy, nutrients, and food groups.
This trial was registered at the Dutch Central Committee on Research Involving Human Subjects (CCMO) registry as ABR. No. NL69065.081.19.
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