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Chronic diseases and multimorbidity are common and expected to rise over the coming years. The objective of this study is to examine the time trend in the prevalence of chronic diseases and multimorbidity over the period 2001 till 2011 in the Netherlands, and the extent to which this can be ascribed to the aging of the population. Monitoring study, using two data sources: 1) medical records of patients listed in a nationally representative network of general practices over the period 2002-2011, and 2) national health interview surveys over the period 2001-2011. Regression models were used to study trends in the prevalence-rates over time, with and without standardization for age. An increase from 34.9% to 41.8% (p<0.01) in the prevalence of chronic diseases was observed in the general practice registration over the period 2004-2011 and from 41.0% to 46.6% (p<0.01) based on self-reported diseases over the period 2001-2011. Multimorbidity increased from 12.7% to 16.2% (p<0.01) and from 14.3% to 17.5% (p<0.01), respectively. Aging of the population explained part of these trends: about one-fifth based on general practice data, and one-third for chronic diseases and half of the trend for multimorbidity based on health surveys. The prevalence of chronic diseases and multimorbidity increased over the period 2001-2011. Aging of the population only explained part of the increase, implying that other factors such as health care and society-related developments are responsible for a substantial part of this rise.

Background Greater use of antibiotics during the past 50 years has exerted selective pressure on susceptible bacteria and may have favoured the survival of resistant strains. Existing information on antibiotic resistance patterns from pathogens circulating among community-based patients is substantially less than from hospitalized patients on whom guidelines are often based. We therefore chose to assess the relationship between the antibiotic resistance pattern of bacteria circulating in the community and the consumption of antibiotics in the community. Methods Both gray literature and published scientific literature in English and other European languages was examined. Multiple regression analysis was used to analyse whether studies found a positive relationship between antibiotic consumption and resistance. A subsequent meta-analysis and meta-regression was conducted for studies for which a common effect size measure (odds ratio) could be calculated. Results Electronic searches identified 974 studies but only 243 studies were considered eligible for inclusion by the two independent reviewers who extracted the data. A binomial test revealed a positive relationship between antibiotic consumption and resistance (p < .001) but multiple regression modelling did not produce any significant predictors of study outcome. The meta-analysis generated a significant pooled odds ratio of 2.3 (95% confidence interval 2.2 to 2.5) with a meta-regression producing several significant predictors (F(10,77) = 5.82, p < .01). Countries in southern Europe produced a stronger link between consumption and resistance than other regions. Conclusions Using a large set of studies we found that antibiotic consumption is associated with the development of antibiotic resistance. A subsequent meta-analysis, with a subsample of the studies, generated several significant predictors. Countries in southern Europe produced a stronger link between consumption and resistance than other regions so efforts at reducing antibiotic consumption may need to be strengthened in this area. Increased consumption of antibiotics may not only produce greater resistance at the individual patient level but may also produce greater resistance at the community, country, and regional levels, which can harm individual patients.

Purpose To examine the health-related needs of people with multiple chronic diseases in the Netherlands compared to people with one chronic disease, and to identify different subgroups of multimorbid patients based on differences in their health problems. Methods Participants were 1092 people with one or more chronic diseases of a nationwide prospective panel study on the consequences of chronic illness in the Netherlands. They completed the EQ-6D, a multi-dimensional questionnaire on health problems (October 2013). Chi-square tests and analyses of variance were performed to test for differences between multimorbid patients and patients with one chronic disease. To identify subgroups of multimorbid patients, cluster analysis was performed and differences in EQ-6D scores between clusters were tested with Chisquare tests. Results Multimorbid patients (51 % of the total sample) experience more problems in most health domains than patients with one chronic disease. Almost half (44 %) of the multimorbid people had many health problems in different domains. These people were more often female, had a smaller household size, had a lower health literacy, and suffered from more chronic diseases. Remarkably, a small subgroup of multimorbid patients (4 %, mostly elderly males) is characterized by all having cognitive problems. Conclusions Based on the problems they experience, we conclude that patients with multimorbidity have relatively many and diverse health-related needs. Extensive healthrelated needs among people with multimorbidity may relate not only to the number of chronic diseases they suffer from, but also to their patient characteristics. This should be taken into account, when identifying target groups for comprehensive support programmes.

Background In the future, an increasing number of elderly people will be asked to accept care delivered through the Internet. For example, health-care professionals can provide treatment or support via telecare. But do elderly people intend to use such so-called e-Health applications? The objective of this study is to gain insight into the intention of older people, i.e. the elderly of the future, to use e-Health applications. Using elements of the Unified Theory of Acceptance and Use of Technology (UTAUT), we hypothesized that their intention is related to the belief that e-Health will help (performance expectancy), the perceived ease of use (effort expectancy), the beliefs of important others (social influence), and the self-efficacy concerning Internet usage. Methods A pre-structured questionnaire was completed by 1014 people aged between 57 and 77 (response 67%). The hypothesized relationships were tested using nested linear regression analyses. Results If offered an e-Health application in the future, 63.1% of the respondents would definitely or probably use it. In general, people with a lower level of education had less intention of using e-Health. The majority of respondents perceived e-Health as easy to use (60.8%) and easy to learn (68.4%), items that constitute the scale for effort expectancy. Items in the performance expectancy scale generally scored lower: 45.8% perceived e-Health as useful and 38.2% perceived it as a pleasant way to interact. The tested model showed that expected performance and effort were highly related to intention to use e-Health. In addition, self-efficacy was related to intention to use while social influence was not. Conclusions Acceptance of e-Health can be increased by informing people about the potential benefits of e-Health and letting them practice with the application. Special attention should be paid to people with less education and people who have not used the Internet before.

Objectives. We sought to determine whether sexual orientation is related to mental and physical health and health behaviors in the general population. Methods. Data was derived from a health interview survey that was part of the second Dutch National Survey of General Practice, carried out in 2001 among an all-age random sample of the population. Of the 19685 persons invited to participate, 65% took part in the survey. Sexual orientation was assessed in persons aged 18 years and older and reported by 98.2% of 9684 participants. The respondents’ characteristics are comparable with those of the Dutch general population. Results. Gay/lesbian participants reported more acute mental health symptoms than heterosexual people and their general mental health also was poorer. Gay/lesbian people more frequently reported acute physical symptoms and chronic conditions than heterosexual people. Differences in smoking, alcohol use, and drug use were less prominent. Conclusions. We found that sexual orientation was associated with mental as well as physical health. The causal processes responsible for these differences by sexual orientation need further exploration.

Continuity of care is widely acknowledged as a core value in family medicine. In this systematic review, we aimed to identify the instruments measuring continuity of care and to assess the quality of their measurement properties. We did a systematic review using the PubMed, Embase and PsycINFO databases, with an extensive search strategy including 'continuity of care', 'coordination of care', 'integration of care', 'patient centered care', 'case management' and its linguistic variations. We searched from 1995 to October 2011 and included articles describing the development and/or evaluation of the measurement properties of instruments measuring one or more dimensions of continuity of care (1) care from the same provider who knows and follows the patient (personal continuity), (2) communication and cooperation between care providers in one care setting (team continuity), and (3) communication and cooperation between care providers in different care settings (cross-boundary continuity). We assessed the methodological quality of the measurement properties of each instrument using the COSMIN checklist. We included 24 articles describing the development and/or evaluation of 21 instruments. Ten instruments measured all three dimensions of continuity of care. Instruments were developed for different groups of patients or providers. For most instruments, three or four of the six measurement properties were assessed (mostly internal consistency, content validity, structural validity and construct validity). Six instruments scored positive on the quality of at least three of six measurement properties. Most included instruments have problems with either the number or quality of its assessed measurement properties or the ability to measure all three dimensions of continuity of care. Based on the results of this review, we recommend the use of one of the four most promising instruments, depending on the target population Diabetes Continuity of Care Questionnaire, Alberta Continuity of Services Scale-Mental Health, Heart Continuity of Care Questionnaire, and Nijmegen Continuity Questionnaire.

Abstract Background Low socioeconomic position (SEP) is related to higher healthcare use in out-of-hours primary care services (OPCSs). We aimed to determine whether inequalities persist when taking the generally poorer health status of socioeconomically vulnerable individuals into account. To put OPCS use in perspective, this was compared with healthcare use in daytime general practice (DGP). Methods Electronic health record (EHR) data of 988 040 patients in 2017 (251 DGPs, 27 OPCSs) from Nivel Primary Care Database were linked to socio-demographic data (Statistics, The Netherlands). We analyzed associations of OPCS and DGP use with SEP (operationalized as patient household income) using multilevel logistic regression. We controlled for demographic characteristics and the presence of chronic diseases. We additionally stratified for chronic disease groups. Results An income gradient was observed for OPCS use, with higher probabilities within each lower income group [lowest income, reference highest income group: odds ratio (OR) = 1.48, 95% confidence interval (CI): 1.45–1.51]. Income inequalities in DGP use were considerably smaller (lowest income: OR = 1.17, 95% CI: 1.15–1.19). Inequalities in OPCS were more substantial among patients with chronic diseases (e.g. cardiovascular disease lowest income: OR = 1.60, 95% CI: 1.53–1.67). The inequalities in DGP use among patients with chronic diseases were similar to the inequalities in the total population. Conclusions Higher OPCS use suggests that chronically ill patients with lower income had additional healthcare needs that have not been met elsewhere. Our findings fuel the debate how to facilitate adequate primary healthcare in DGP and prevent vulnerable patients from OPCS use.

Background Incidence rates and prevalence proportions are commonly used to express the populations health status. Since there are several methods used to calculate these epidemiological measures, good comparison between studies and countries is difficult. This study investigates the impact of different operational definitions of numerators and denominators on incidence rates and prevalence proportions. Methods Data from routine electronic health records of general practices contributing to NIVEL Primary Care Database was used. Incidence rates were calculated using different denominators (person-years at-risk, person-years and midterm population). Three different prevalence proportions were determined: 1 year period prevalence proportions, point-prevalence proportions and contact prevalence proportions. Results One year period prevalence proportions were substantially higher than point-prevalence (58.3 - 206.6%) for long-lasting diseases, and one year period prevalence proportions were higher than contact prevalence proportions (26.2 - 79.7%). For incidence rates, the use of different denominators resulted in small differences between the different calculation methods (-1.3 - 14.8%). Using person-years at-risk or a midterm population resulted in higher rates compared to using person-years. Conclusions All different operational definitions affect incidence rates and prevalence proportions to some extent. Therefore, it is important that the terminology and methodology is well described by sources reporting these epidemiological measures. When comparing incidence rates and prevalence proportions from different sources, it is important to be aware of the operational definitions applied and their impact.

BackgroundTelephone triage is used to facilitate efficient and adequate acute care allocation, for instance in out-of-hours primary care services (OPCSs). Remote assessment of health problems is challenging and could be impeded by a patient’s ambiguous formulation of his or her healthcare need. Socioeconomically vulnerable patients may experience more difficulty in expressing their healthcare need. We aimed to assess whether income differences exist in the patient’s presented symptoms, assessed urgency and allocation of follow-up care in OPCS.MethodData were derived from Nivel Primary Care Database encompassing electronic health record data of 1.3 million patients from 28 OPCSs in 2017 in the Netherlands. These were linked to sociodemographic population registry data. Multilevel logistic regression analyses (contacts clustered in patients), adjusted for patient characteristics (eg, age, sex), were conducted to study associations of symptoms, urgency assessment and follow-up care with patients’ income (standardised for household size as socioeconomic status (SES) indicator).ResultsThe most frequently presented symptoms deduced during triage slightly differed across SES groups, with a larger relative share of trauma in the high-income groups. No SES differences were observed in urgency assessment. After triage, low income was associated with a higher probability of receiving telephone advice and home visits, and fewer consultations at the OPCS.ConclusionsSES differences in the patient’s presented symptom and in follow-up in OPCS suggest that the underlying health status and the ability to express care needs affect the telephone triage process . Further research should focus on opportunities to better tailor the telephone triage process to socioeconomically vulnerable patients.

Routinely recorded electronic health records (EHRs) from general practitioners (GPs) are increasingly available and provide valuable data for estimating incidence and prevalence rates of diseases in the population. This paper describes how we developed an algorithm to construct episodes of illness based on EHR data to calculate morbidity rates. The goal of the research was to develop a simple and uniform algorithm to construct episodes of illness based on electronic health record data and develop a method to calculate morbidity rates based on these episodes of illness. The algorithm was developed in discussion rounds with two expert groups and tested with data from the Netherlands Institute for Health Services Research Primary Care Database, which consisted of a representative sample of 219 general practices covering a total population of 867,140 listed patients in 2012. All 685 symptoms and diseases in the International Classification of Primary Care version 1 were categorized as acute symptoms and diseases, long-lasting reversible diseases, or chronic diseases. For the nonchronic diseases, a contact-free interval (the period in which it is likely that a patient will visit the GP again if a medical complaint persists) was defined. The constructed episode of illness starts with the date of diagnosis and ends at the time of the last encounter plus half of the duration of the contact-free interval. Chronic diseases were considered irreversible and for these diseases no contact-free interval was needed. An algorithm was developed to construct episodes of illness based on routinely recorded EHR data to estimate morbidity rates. The algorithm constitutes a simple and uniform way of using EHR data and can easily be applied in other registries.