Co-occurrence of diabetes, myocardial infarction, stroke, and cancer: quantifying age patterns in the Dutch population using health survey data

<p>Abstract</p> <p>Background</p> <p>The high prevalence of chronic diseases in Western countries implies that the presence of multiple chronic diseases within one person is common. Especially at older ages, when the likelihood of having a chronic disease increases, the co-occurrence of distinct diseases will be encountered more frequently. The aim of this study was to estimate the age-specific prevalence of multimorbidity in the general population. In particular, we investigate to what extent specific pairs of diseases cluster within people and how this deviates from what is to be expected under the assumption of the independent occurrence of diseases (i.e., sheer coincidence).</p> <p>Methods</p> <p>We used data from a Dutch health survey to estimate the prevalence of pairs of chronic diseases specified by age. Diseases we focused on were diabetes, myocardial infarction, stroke, and cancer. Multinomial P-splines were fitted to the data to model the relation between age and disease status (single versus two diseases). To assess to what extent co-occurrence cannot be explained by independent occurrence, we estimated observed/expected co-occurrence ratios using predictions of the fitted regression models.</p> <p>Results</p> <p>Prevalence increased with age for all disease pairs. For all disease pairs, prevalence at most ages was much higher than is to be expected on the basis of coincidence. Observed/expected ratios of disease combinations decreased with age.</p> <p>Conclusion</p> <p>Common chronic diseases co-occur in one individual more frequently than is due to chance. In monitoring the occurrence of diseases among the population at large, such multimorbidity is insufficiently taken into account.</p

The polypill in the primary prevention of cardiovascular disease: cost-effectiveness in the Dutch population

OBJECTIVES: The aim of the present study was to estimate the cost-effectiveness of the polypill in the primary prevention of cardiovascular disease. DESIGN: A health economic modeling study. SETTING: Primary health care in the Netherlands. PARTICIPANTS: Simulated individuals from the general Dutch population, aged 45-75 years of age. INTERVENTIONS: Opportunistic screening followed by prescription of the polypill to eligible individuals. Eligibility was defined as having a minimum 10-year risk of cardiovascular death as assessed with the SCORE function of alternatively 5%, 7.5%, or 10%. Different versions of the polypill were considered, depending on composition: 1) the Indian polycap, with three different types of blood pressure lowering drugs, a statin, and aspirin; 2) as 1) but without aspirin; 3) as 2) but with a double statin dose. In addition, a scenario of (targeted) separate antihypertensive and/or statin medication was simulated. PRIMARY OUTCOME MEASURES: Cases of acute myocardial infarction or stroke prevented, QALYs gained, and the costs per QALY gained. All interventions were compared with usual care. RESULTS: All scenarios were cost-effective with an incremental cost-effectiveness ratio between €7,900-12,300 per QALY compared with usual care. Most health gains were achieved with the polypill without aspirin and containing a double dose of statins. With a 10-year risk of 7.5% as threshold, this pill would prevent approximately 3.5% of all cardiovascular events. CONCLUSIONS: Opportunistic screening based on global cardiovascular risk assessment followed by polypill prescription to those with increased risk offers a cost-effective strategy. Most health gain is achieved by the polypill without aspirin and a double statin dose

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

Background: Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. Methods. Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. Results: Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. Conclusion: Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences

High-risk subtypes of chronic lymphocytic leukemia are detectable as early as 16 years prior to diagnosis

Chronic lymphocytic leukemia (CLL) is preceded by monoclonal B-cell lymphocytosis (MBL), a CLL precursor state with a prevalence of up to 12% in aged individuals; however, the duration of MBL and the mechanisms of its evolution to CLL remain largely unknown. In this study, we sequenced the B-cell receptor (BcR) immunoglobulin heavy chain (IGH) gene repertoire of 124 patients with CLL and 118 matched controls in blood samples taken up to 22 years prior to diagnosis. Significant skewing in the BcR IGH gene repertoire was detected in the majority of patients, even before the occurrence of lymphocytosis and irrespective of the clonotypic IGH variable gene somatic hypermutation status. Furthermore, we identified dominant clonotypes belonging to major stereotyped subsets associated with poor prognosis up to 16 years before diagnosis in 14 patients with CLL. In 22 patients with longitudinal samples, the skewing of the BcR IGH gene repertoire increased significantly over time to diagnosis or remained stable at high levels. For 14 of 16 patients with available samples at diagnosis, the CLL clonotype was already present in the prediagnostic samples. Overall, our data indicate that the preclinical phase of CLL could be longer than previously thought, even in adverse-prognostic cases

Rendering hospital budgets volume based and open ended to reduce waiting lists: Does it work?

In the past decades fixed budgets for hospitals were replaced by reimbursement based on outputs in several countries in order to bring down waiting lists. This was also the case in the Netherlands where fixed global budgets were replaced by budgets that are to a large extent volume based and in practice open-ended. The objective of this study was to examine the effectiveness of this Dutch policy measure, which was implemented in 2001. We carried out a statistical analysis and interpretation of trends in Dutch hospital admission rates. We observed a significant turn in the development of in-patient admission rates after the abolition of budget caps in 2001: decreasing admission rates turned into an internationally exceptional increase of more than 3% per year. Day care admissions had already been rising explosively for two decades, but the pace increased after 2001. The increase in the number of admissions includes a broad range of patient categories that were not in the first place associated with long waiting times. The growth was attributable for a large part to admissions for observation of the patient and the evaluation of symptoms, not resulting in a definite medical diagnosis. We considered several factors, other than the availability of more resources, to explain the growth: the ageing of the population, making up for waiting list arrears, ditto for "under consumption" of unplanned care and, as to the growth of day care, substitution for inpatient care. However, these factors were all found to fall short as an explanation. Although waiting times have dropped since the change in the budget system, they continue to be long for several procedures. Our study indicates that making available more resources to admit patients, or otherwise an increase in hospital activity, do not in itself lead to equilibrium between demand and supply because the volume and composition of demand are partly induced by supply. We conclude that abolishing budget caps to solve waiting list problems is not efficient. Instead of a generic measure, a more focused approach is necessary. We suggest ingredients for such an approach.Hospital Admissions Case mix Reimbursement mechanisms Payment systems Waiting lists

Disease Prevention: Saving Lives or Reducing Health Care Costs?

<div><p>Background</p><p>Disease prevention has been claimed to reduce health care costs. However, preventing lethal diseases increases life expectancy and, thereby, indirectly increases the demand for health care. Previous studies have argued that on balance preventing diseases that reduce longevity increases health care costs while preventing non-fatal diseases could lead to health care savings. The objective of this research is to investigate if disease prevention could result in both increased longevity and lower lifetime health care costs.</p><p>Methods</p><p>Mortality rates for Netherlands in 2009 were used to construct cause-deleted life tables. Data originating from the Dutch Costs of Illness study was incorporated in order to estimate lifetime health care costs in the absence of selected disease categories. We took into account that for most diseases health care expenditures are concentrated in the last year of life.</p><p>Results</p><p>Elimination of diseases that reduce life expectancy considerably increase lifetime health care costs. Exemplary are neoplasms that, when eliminated would increase both life expectancy and lifetime health care spending with roughly 5% for men and women. Costs savings are incurred when prevention has only a small effect on longevity such as in the case of mental and behavioural disorders. Diseases of the circulatory system stand out as their elimination would increase life expectancy while reducing health care spending.</p><p>Conclusion</p><p>The stronger the negative impact of a disease on longevity, the higher health care costs would be after elimination. Successful treatment of fatal diseases leaves less room for longevity gains due to effective prevention but more room for health care savings.</p></div

Healthy lifestyle over the life course: Population trends and individual changes over 30 years of the Doetinchem Cohort Study.

For five health-related lifestyle factors (physical activity, weight, smoking, sleep, and alcohol consumption) we describe both population trends and individual changes over a period of 30 years in the same adult population. Dichotomous indicators (healthy/unhealthy) of lifestyle were analyzed for 3,139 participants measured every 5 years in the Doetinchem Cohort Study (1987–2017). Population trends over 30 years in physical inactivity and “unhealthy” alcohol consumption were flat (i.e., stable); overweight and unhealthy sleep prevalence increased; smoking prevalence decreased. The proportion of the population being healthy on all five lifestyle factors declined from 17% in the round 1 to 10.8% in round 6. Underlying these trends a dynamic pattern of changes at the individual level was seen: sleep duration and physical activity level changed in almost half of the individuals; Body Mass Index (BMI) and alcohol consumption in one-third; smoking in one-fourth. Population trends don't give insight into change at the individual level. In order to be able to gauge the potential for change of health-related lifestyle, it is important to take changes at the individual level into account

Overview results disease elimination on life expectancy and health care costs for men.

<p>Overview results disease elimination on life expectancy and health care costs for men.</p