The Rotterdam Study: objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in the Netherlands. The study targets cardiovascular, neurological, ophthalmological and endocrine diseases. As of 2008 about 15,000 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in some 600 research articles and reports (see http://www.epib.nl/rotterdamstudy). This article gives the reasons for the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.
Introduction
The Rotterdam Study was designed in the mid-1980s as a response to the demographic changes that were leading to an increase of the proportion of elderly people in most populations [1]. It was clear that this would produce a strong rise in elderly people living with diseases, as most diseases cluster at the end of life, and that to discover the causes of diseases in the elderly one would have to study risk factors of those diseases [2]. A major approach to finding causes is the prospective follow-up study, which has proven quite effective in finding causes of heart disease and cancer. Remarkably, in the 1980s there were hardly any follow-up studies focussing on the elderly.
The design of the Rotterdam Study
The basic design of the study was straight-forward: a prospective cohort study among 7,983 persons living in the well-defined Ommoord district in the city of Rotterdam in the Netherlands (78% of 10,215 invitees). They were all 55 years of age or over and the oldest participant at the start was 106 years [3]. The study started with a pilot phase in the second half of 1989. From January 1990 onwards participants were recruited for the Rotterdam Study. Figure 1 gives a diagram of the various cycles of the study.
Fig. 1Diagram of examination cycles of the Rotterdam Study (RS). RS1 refers to the baseline examination of the original cohort (pilot phase 07/1989-12/1989; cohort recruitment 01/1990-09/1993). RS2, RS3 and RS4 refer to re-examination of the original cohort members. RSPlus1 refers to the extension of the cohort with persons in the study district that became 55 years since the start of the study or those of 55 years or over that migrated into the study district. RSPlus2 refers to the re-examination of the extension cohort. RSYoung1 refers to the baseline examination of all persons aged 45 and over living in the study district that had not been examined (i.e. mainly comprising those aged 45–55 years)
In 1999, 3,011 participants (out of 4,472 invitees) who had become 55 years of age or moved into the study district since the start of the study were added to the cohort.
In 2006, a further extension of the cohort was initiated in which about 6,000 subjects aged 45–54 years, living in the Ommoord district, were invited (expected number of participants about 4,000). By the end of 2008, the Rotterdam Study is therefore expected to comprise about 15,000 subjects aged 45 years or over.
The participants were all examined in some detail at baseline. They were interviewed at home (2 h) and then had an extensive set of examinations (a total of 5 h) in a specially built research facility in the centre of their district. These examinations focussed on possible causes of invalidating diseases in the elderly in a clinically state-of-the-art manner, as far as the circumstances allowed. The emphasis was put on imaging (of heart, blood vessels, eyes, skeleton and later brain) and on collecting bodily fluids that enabled further in-depth molecular and genetic analyses. These examinations were repeated every 3–4 years in characteristics that could change over time. And so we had examination cycles from 1990 to 1993, from 1993 to 1995, from 1997 to 1999, from 2000 to 2001, from 2002 to 2004, from 2004 to 2005 and from 2006 to 2008 (Fig. 1).
The participants in the Rotterdam Study are followed for a variety of diseases that are frequent in the elderly (and many are also in the not so elderly): coronary heart disease, heart failure and stroke, Parkinson disease, Alzheimer disease and other dementias, depression and anxiety disorders, macular degeneration and glaucoma, diabetes mellitus and osteoporosis.
The Rotterdam Study has been approved by the institutional review board (Medical Ethics Committee) of the Erasmus Medical Center and by the review board of the Netherlands Ministry of Health, Welfare and Sports. The approval has been renewed every 5 years. Separate approval has been obtained for the introduction of major new elements in the study (e.g. MRI investigations).
In the remainder of this article the objectives and major findings will be presented with an update of the methods for cardiovascular diseases, neurological diseases, ophthalmologic diseases, psychiatric diseases, endocrine diseases, as well as for genetic and for pharmaco-epidemiologic studies.
Cardiovascular diseases
Objectives
Research on the epidemiology of cardiovascular diseases focuses on three primary areas of interest: studies on risk factors for atherosclerosis and coronary heart disease, studies on the detection of subjects at high risk of coronary heart disease, and studies on cardiovascular conditions in older age.
Two groups of putative risk factors for atherosclerosis and coronary heart disease are included. The first are endocrine factors, including estrogens and androgens, insulin and insulin-like growth hormone I, and thyroid gland and adrenal gland hormones. The second group contains factors involved in haemostasis, inflammation and endothelial function. Research also focuses on genetic factors in these areas in relation to risk of coronary heart disease.
The ability of classical cardiovascular risk factors to identify subjects at high risk of coronary heart disease is limited. Risk stratification may be improved when based on the presence of atherosclerosis. To this end, repeated measurements of non-coronary atherosclerosis and measurements of coronary, carotid and aortic arch calcification have been included in the study.
Another line of research focuses on cardiovascular diseases in the elderly that are in large part the consequence of ischemic heart disease, like heart failure and atrial fibrillation. An important topic in this area is the early diagnosis of heart failure using echocardiographic assessment of asymptomatic systolic and diastolic dysfunction of the left ventricle. Atrial fibrillation is another major chronic condition frequent at older age. Examination of the determinants and prognosis of atrial fibrillation is part of this research line.
Major findings
Findings on determinants of atherosclerosis and coronary heart disease include the relation between hypothyroidism and myocardial infarction [4] and the association of genetic variation in the estrogen receptor alpha gene with risk of myocardial infarction in women [5]. The study demonstrated that high levels of CRP [6] and lipoprotein-associated phospholipase A2 activity [7] were associated with risk of coronary heart disease and stroke. Genetic findings in this area include a positive association of coronary heart disease with genetic variation in the complement factor H gene [8] but not with variation in the CRP gene [6].
The study showed that carotid intima-media thickness and plaques measured by ultrasound, the ankle-arm index, and aortic calcification detected by X-ray are independent predictors of risk of coronary heart disease [9]. The study also showed that vessel wall stiffness, as measured by aortic pulse wave velocity (PWV), improved prediction of cardiovascular disease beyond the classical risk factors [10]. The Rotterdam Study was the first to publish results from population-based research on the predictive value for coronary heart disease of coronary calcification assessed by electron-beam CT [11].
The study enabled accurate assessment of the incidence and lifetime risk of heart failure and atrial fibrillation in an elderly population [12, 13]. It was shown that atherosclerosis and inflammation are also involved in these conditions [14, 15].
Methods update
Repeated measures of non-coronary atherosclerosis included carotid intima-media thickness and plaques by ultrasound, the ankle-arm index and aortic calcification by X-ray [9]. Electron-beam CT and multi-detector CT were used to accurately quantify calcification in the coronary, aortic arch and carotid arteries [11]. In the second additional cohort of the Rotterdam Study (RS Young1, see Fig. 1), the measurement of plaque vulnerability with high-resolution MRI of the carotid arteries will be added. Other outcome measures include electrocardiography, echocardiography and measures of vessel wall stiffness (PWV and carotid distensibility) [10].
Determinants were assessed by physical examinations, collection of blood samples, and by questionnaires and interview. The role of genetic factors is studied using the candidate gene approach and more recently the genome wide association study.
Clinical cardiovascular outcomes are collected during our continuous follow-up and include non-fatal myocardial infarction and cardiac death, revascularizations, heart failure and atrial fibrillation. The methods of diagnosis of these outcomes have been described in previous papers [6, 12, 13].
Neurological diseases
Objectives
Neuroepidemiologic research in the Rotterdam Study focuses on the frequency, etiology and early recognition of the most frequent neurologic diseases in the elderly, including dementia (in particular Alzheimer disease), Parkinson disease and stroke. In neurodegenerative and cerebrovascular disorders clinical symptoms typically become manifest late in the disease course, the occurrence of clinical disease does not reflect the underlying spectrum of disease-related pathology, and most of the clinical syndromes are etiologically heterogeneous. Therefore, an additional research focus is on the causes and consequences of pre-symptomatic brain pathology that can be assessed with non-invasive imaging modalities.
Major findings
Neurodegenerative and cerebrovascular diseases are highly frequent in the elderly. The prevalence increases from age 55 to 65 years to age 90 years and above from less than 1% to over 40% for dementia [16], from less than 0.5% to more than 4% for Parkinson disease [17], and from approximately 1% to nearly 10% for stroke. The incidence figures follow this pattern of a strong increase with age over the entire age range, with the age-specific incidence of dementia being identical for men and women at least until the age of 85 [18] but with men having a higher age-specific incidence of both stroke and Parkinson disease than women throughout the age range [19, 20].
Vascular pathology and vascular risk factors are associated with worse cognitive performance [21], which also translates in people with vascular pathology or risk factors for vascular disease having an increased risk of dementia, including Alzheimer disease [22]. Moreover, several life style factors are associated with the risk of dementia and Alzheimer disease [23–25], suggesting that onset of dementia may at least partly be delayed or prevented. Commonly used drugs may have a role in this [26].
The classical risk factors for stroke also predict risk of stroke in the Rotterdam Study [27]. More recently identified risk factors, including inflammatory markers, may be etiologically relevant but thus far add little to the identification of people at risk [28]. Possibly underlying this is that a large amount of stroke goes clinically undetected [29]. Nearly 20% of elderly people have at least one silent brain infarct, and thereby a nearly fourfold increased risk of clinical stroke, a more than doubled risk of dementia including Alzheimer disease, and an increased risk of depression [29].
Neuroimaging reveals that brain pathology is widespread [30] and can go clinically undetected for a long time. In addition to the silent infarcts, many apparently healthy elderly have ischemic changes in their cerebral white matter that are associated with an increased risk of dementia, stroke and depression [31]. Also brain atrophy, especially of the hippocampus, is already present years before onset of even the earliest sign of cognitive impairment or subjective complaints [32]. This emphasizes the need to shift the attention in etiologic research of neurodegenerative and cerebrovascular disease to the causes of pre-symptomatic and underlying brain changes.
Methods update
Assessment of dementia and Alzheimer disease
In the baseline and follow-up examinations participants undergo an initial screen for dementia with the Mini Mental State Examination (MMSE) and the Geriatric Mental Schedule (GMS), followed by an examination and informant interview with the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX) in screenpositives (MMSE <26 or GMS >0), and subsequent neurological, neuropsychological and neuroimaging examinations [16, 18]. Of subjects who cannot be reexamined in person, information is obtained from the GPs and the regional institute for outpatient mental health care. A consensus panel makes the final diagnoses in accordance with standard criteria (DSM-III-R criteria; NINCDS-ADRDA; NINDS-AIREN).
Assessment of parkinsonism and Parkinson disease
Participants are screened in the baseline and follow-up examinations for cardinal signs of parkinsonism (resting tremor, rigidity, bradykinesia or impaired postural reflexes). Persons with at least one sign present are examined with the Unified Parkinson Disease Rating Scale and a further neurologic exam. Parkinson disease is diagnosed if two or more cardinal signs are present in a subject not taking antiparkinsonian drugs, or if at least one sign has improved through medication, and when all causes of secondary parkinsonism (dementia, use of neuroleptics, cerebrovascular disease, multiple system atrophy or progressive supranuclear palsy) can be excluded [17, 19].
Assessment of stroke and stroke subtypes
History of stroke at baseline was assessed through interview and verified in medical records. Putative incident strokes get identified through the linkage of the study database with files from general practitioners, the municipality, and nursing home physicians’ files, after which additional information (including brain imaging) is collected from hospital records. A panel discusses all potential strokes and subclassifies strokes into ischemic, hemorrhagic or unspecified [20, 28].
Assessment of cognitive function
Global cognitive function is measured through the Mini Mental State Examination (MMSE) in all surveys. From the third survey onwards we added a 30 min test battery that was designed to assess executive function and memory function, and which includes a Stroop test, a Letter Digit Substitution Task, a Word Fluency Test, and a 15 words Word List Learning test.
Rotterdam Scan Study: brain imaging within the Rotterdam Study
In 1991, a random sample of 111 participants underwent axial T2-weighted magnetic resonance (MR) imaging to assess presence and severity of white matter lesions [33]. In 1995, a random sample of 563 non-demented participants underwent brain MR imaging in the context of the Rotterdam Scan Study. The scanning protocol included series of axial proton-density, T2-weighted and T1-weighted images, as well as a high-resolution 3D-HASTE sequence [31]. From August 2005 onwards, a dedicated 1.5 Tesla scanner is operational in the research centre of the Rotterdam Study, and brain imaging is performed in all study participants without contra-indications. The scanning protocol includes 4 high-resolution axial sequences (3D T1-weighted; 2D PD-weighted; 2D FLAIR; and 3D T2* GRE), 2D phase-contrast imaging, and diffusion tensor imaging (DTI).
Psychiatric diseases
Objectives
The aim of the psychiatric research in the Rotterdam Study is to investigate the determinants, correlates and consequences of common psychiatric problems in the elderly. The focus has been on depressive disorders but anxiety disorders, sleep disturbances and complicated grief are also being studied.
Major findings
The one-month prevalence of both major and minor depression at baseline was 1.5%, the prevalence of anxiety disorder was 8.2%.
We found evidence for the vascular depression hypothesis. More severe coronary and extra-coronary atherosclerosis were associated with a higher prevalence of depression, as were cerebral haemodynamic changes [34, 35]. However, we could not rule out that earlier depressive episodes may have contributed to the development of atherosclerosis. Moreover, our data did not support a specific symptom profile of vascular depression as previously defined [36].
Most of our studies of nutritional and metabolic factors provided no support for an etiological role in depression. Folate deficiency, homocysteine, fatty acid ratio (omega-6/omega-3) and vitamin E had little or no effect on the risk of depression after control for health and health related behaviour [37–39]. In contrast, low levels of vitamin B12 and high levels of inflammatory protein IL-6 convey a strong risk for depression, although the latter effect may be confined to high-risk groups [40].
Several SNPs have been investigated, mostly with negative results. However, we found that an ER-alpha polymorphism had a substantial effect on anxiety but not on depression in women [41].
Methods update
In the first years of the Rotterdam Study psychiatric data collection was very limited. However, from the third examination onwards, which began in 1997 (see Fig. 1), depressive symptoms and disorders are have been ascertained in all participants. The 4,603 participants with valid depression scores in this examination thus constitute the baseline population for longitudinal studies of depression. Because assessment of anxiety disorders, sleeping disturbances and complicated grief were added in the fourth examination, they can currently be studied only cross-sectionally.
Assessment of determinants
Psychiatric research in the Rotterdam Study focuses on biological risk factors. The vascular depression hypothesis was tested with different measures of atherosclerosis, arterial stiffness and cerebral blood flow [34, 35]. We also examined whether blood levels of vitamins and fatty acids, immune parameters, and markers of folate metabolism increased the likelihood of depression [37–40]. In one ongoing project, diurnal patterns of cortisol secretion are related to psychiatric and other disorders. Studies of genetic polymorphisms and brain morphology are underway. Current data collection includes a dexamethasone suppression test to measure hypothalamic-pituitary-adrenal axis activity in all participants, which is unique in a population-based study.
Assessment of outcomes
Depression is assessed using a two step procedure. First, participants completed the Center for Epidemiologic Studies Depression scale (CES-D) during the home interview to measure self-reported depressive symptoms [42]. As a second step, screen positive subjects, who scored above a well-established CES-D cut-off of 20 points, have a psychiatric interview using the Present State Examination (PSE/SCAN) [43]. This semi-structured interview is conducted by a clinician and yields DSM-IV diagnoses including major and minor depression and dysthymia.
The following anxiety disorders are assessed with a slightly adapted Munich version of the Composite International Diagnostic Interview: generalized anxiety disorder, specific and social phobia, agoraphobia without panic disorder, and panic disorder [44]. Sleep quality and disturbance is measured with the Pittsburgh Sleep Quality Index. In addition, sleep duration and fragmentation are assessed with actigraphy, a method that infers wakefulness and sleep from the presence or absence of limb movement [45]. The Inventory of Complicated Grief is used to identify traumatic grief [46]. This is a condition distinct from normal grief and bereavement-related depression, characterized by symptoms like disbelief about the death and searching for the deceased.
Ophthalmologic diseases
Objectives
The primary objectives of the ophthalmological part of the Rotterdam Study are to study frequency and risk factors of common and invalidating eye diseases with emphasis on age-related macular degeneration (AMD) and primary open angle glaucoma (POAG). Another line of research focuses on the associations between retinal vessel abnormalities and cardiovascular and neurologic diseases.
Major findings
AMD was the main cause of blindness in the high age-group [47]. Major risk factors that were found for AMD are smoking, atherosclerosis, hyperopia [48–50]. The APO-E ε-4 allele showed an inverse association with AMD [51]. First-degree relatives of patients with late ARM developed ARM at an increased rate at a relatively young age [52]. The heterogeneity of genetic risk among AMD families is considerable, and the proportion of high-risk families is relatively small [53]. The anticipated protective effect of cholesterol-lowering drugs on AMD could not be substantiated [54]. A protective effect on AMD was demonstrated in participants using a diet rich of antioxidants and zinc intake in our study population. Support for a multifactorial origin of AMD was found in the cumulative effects of aberrant CFH and CRP genes, inflammations and smoking as genetic and environmental stimulators of the complement cascade [55].
The prevalence estimates of POAG in Rotterdam was highly dependent on the applied criteria [56]. That was a major reason for proposing a classification system without final, subjective adjudication [57]. Systemic blood pressure and hypertension were associated with elevated intraocular pressure but not with prevalent POAG [58]. Relatives of patients with POAG have a strongly increased risk of glaucoma. Enlarged cup-disc ratio was the earliest and most prominent feature of familial aggregation [59]. The incidence of OAG rises significantly with age. Most of the patients with incident OAG were unaware of having OAG [60]. The incidence of visual field loss rises fivefold between 55 and 80 years to 20/1,000 person years in the general population [61]. Glaucoma is the main cause for visual field loss, followed by stroke [62].
Larger retinal venular diameters are associated with generalized atherosclerosis, inflammation and cholesterol levels. Retinal venular diameters are variable and may play their own independent role in predicting cardiovascular disorders [62]. Dilated retinal venules at baseline were predictive for stroke, cerebral infarction, dementia, white brain matter lesions, impaired glucose tolerance, diabetes mellitus and mortality [63, 64]. In systemic hypertension, the arteriolar diameters are narrow and arteriolar and venular narrowing precedes the onset of hypertension [65].
Methods update
Repeated ophthalmic measurements include best corrected ETDRS visual acuity, refractive error, Goldmann applanation tonometry, keratometry, slitlamp examination of the anterior segment and visual field testing. In pharmacological mydriasis we make colour photographs of the lens, 35 degree photography of the macular area, and simultaneous stereoscopic imaging of the optic disc and macular area. Digital photography of the macular area and optic disc are added since the fourth follow-up examination. Scanning laser ophthalmoscopy (HRT II) measurements of the optic disc, macular pigment density measurements and optical coherence tomography of the macular area and optic disc were added during follow-up.
For the assessment of AMD we use stereoscopic 35° macular images centred on the fovea. The images are graded according to the International Classification and Grading System for AMD [66].
The procedure for assessing POAG includes Goldmann applanation tonometry, visual field screening, ophthalmoscopy and stereoscopic fundus photography [57].
Assessment of retinal vascular diameters is done by analyses of digitized fundus transparencies with a semi automated system [58, 67].
Genetic studies
Objectives
The first objective of the laboratory team is to collect, store and manage the biological tissues sampled in the Rotterdam Study. The second objective of the group concerns genotyping and assessment of biomarkers.
Major findings
Both from biomarker studies as well as molecular genetic studies important findings have emerged. For example, among the biomarker analyses the study documenting the relationship between homocysteine and osteoporosis has been novel [68] and since widely replicated. Several candidate gene studies have also yielded major new insights coming from both exploratory studies as well as from collaborative replication efforts across all disease-oriented research lines in the Rotterdam Study. A unique feature of the Rotterdam Study is exploited by studying the relationship between pleiotropic gene variants and not one but multiple diseases and disease-related endpoints. For example, the studies on the promoter region of the IGF-1 gene revealed a series of consistent associations ranging from birth weight to diabetes [69], while other consistent associations involve the estrogen receptor alpha (ESR1) gene in relation to osteoporosis [70], osteoarthritis, height, myocardial infarction [5], age-at-menopause, and depression.
The Rotterdam Study is involved as a major collaborative centre for studies for novel genes coming from genome-wide analysis (GWA) studies, including CFH in age-related macula degeneration [71], NOS1AP in QT interval [72], and several SNPs involved in height, type 2 diabetes, and breast cancer. Finally, Rotterdam Study investigators play a leading role in the emerging large global consortia focussing on the contribution of complex disease gene variants by prospective meta-analyses across many epidemiological cohorts, such as for osteoporosis in the GENOMOS Study and EUROSPAN.
Methods update
Data collection, storage and management
At each examination, blood, serum, plasma (citrate, heparine and EDTA based), sputum, and urine are collected. Fasting blood samples are collected along with challenged samples as part of a glucose tolerance test. Sputum is collected before and after a dexamethasone-suppression test. Sputum is frozen at −196°C before and after the challenge and stored at −80°C. To obtain serum and plasma, tubes are centrifuged according to a protocol standardising time and conditions from the drawing of blood to centrifugation. All samples are snap frozen at −196°C using liquid nitrogen and stored at −80°C. RNA is isolated from blood within 5 h after sampling and stored at −20°C. DNA is isolated from blood and extraction has been recently automated using a Hamilton STAR pipetting platform and AGOWA magnetic bead technology. DNA sample storage is in Matrix 2D-barcode tubes in 96-well format. Overnight urine samples are collected, frozen at −196°C and stored at −80°C. For data management, an in-house customized laboratory management system has been developed. Sample retrieval will be automated with an in-house customized laboratory track and trace system.
Blood assessments
For all participants, serum cholesterol, HDL, LDL, triglycerides, glucose and glucose levels are assessed. In urine, micro albumin and creatinine are determined in all participants. There have been a large number of specific blood/serum/plasma-based biomarker assessments including steroids (e.g. estrogens, androgens, vitamin D, cortisol), interleukins, CRP, IGF1, insulin, iron-parameters (iron, ferritin and transferrin saturation), fibrinogen, homocysteine, folic acid, riboflavine, pyridoxine, SAM/SAH ratio, cobalamine, Lp-PLA2, Fas/Fas-L, vitamins, a-beta42/40 and thyroid hormones (TSH).
Genotyping facilities
Affiliated laboratory facilities include a medium/high-throughput platform for candidate gene studies and genome wide association (GWA) analyses. The facilities use high-end automated machinery including a Caliper/Zymark ALH 3000 pipetting robot (including a TwisterII, and integrated plate sealer, plate reader (OD 260/280), a Tecan EVO 150 Freedom pipetting robot, a Deerac Equator NS808 nanoliter liquid dispenser, 15 electronic PCR machines (ABI 9700, 2 × 384), an ABI7900HT Taqman machine (running 1 ng gDNA in 2 μl reactions), a WAVE 3500HT dHPLC, and two ABI3100 sequencing machines. DNA sample handling is centred on 384-well plates. Candidate gene studies are done mostly using Taqman genotyping with throughputs at 30,000 genotypes per day. Continuous efforts are focussed on reducing the required amount of genomic DNA, which is now down to 1 ng per genotype. Genome-wide genotyping studies are based on 500/1,000 K Affymetrix arrays and 317, 550 and 1,000 K Illumina arrays with throughputs at 100–300 arrays per week.
The genotyping facility has been partly sponsored by NWO investment grants (911-03-012; 175.010.2005.011), is part of the ErasmusMC Biomics core facility, and serves as knowledge centre for polymorphism analysis attracting national and international interested parties, both academic and industrial.
Candidate gene studies
We have genotyped >200 polymorphisms across the complete cohort and conducted a large number of candidate gene studies in the Rotterdam Study. These mostly concern individual potentially functional single nucleotide polymorphisms (SNPs) per gene, but sometimes also haplotype tagging SNPs (e.g. ESR1, ESR2, HSD11B1, fibrinogen), and also high-density SNP screening (e.g. the vitamin D receptor gene [73]). The candidate genes studied include the apolipoprotein E gene (APOE), the angiotensin-converting enzyme (ACE), the gene encoding angiotensinogen (AGT), angiotensin II type 1 receptor (AT1R) gene, G protein beta3 (GNB3), adducine gene, Cholesteryl Ester Transfer Protein (CETP), Hepatic Lipase, Phosphodiesterase 4D (PDE4D), ALOX5AP encoding 5-lipoxygenase activating protein, a polymorphism in the regulatory region of the Insulin-like Growth Factor 1 (IGF-1) gene, the hemochromatosis (HFE) gene, Complement factor H gene (CFH), and several polymorphisms in genes from the estrogen-, thyroid-, cortisol-, vitamin D-, IGF-, and Wnt-signalling pathways, the homocysteine pathway, and several matrix molecules.
Genome wide association (GWA) studies
A recently developed approach to identify robust genetic factors for complex disease is the Genome Wide Association (GWA) analysis. This is based on genotyping epidemiological cohorts with ultra-high density SNP arrays with up to 1 million SNPs. The method has already been shown to successfully identify genetic factors for several traits and diseases including age-related macula degeneration, inflammatory bowel disease, body mass index, type 2 diabetes and breast cancer. Through a large grant from the Dutch research organization NWO one of the world’s largest GWA studies is conducted involving 10,000 DNA samples from the Rotterdam Study. This GWA is based on the Illumina 550 K arrays and finished by the end of 2007. The GWA data will be useful for all research lines within the Rotterdam Study, and will also serve as a control GWA dataset for other research centres in and outside the Netherlands for both SNP frequencies as well as copy number variations (CNVs). In addition to this, a pilot study has been performed on 500 women from the Rotterdam Study using the Affymetrix 500 K platform allowing direct platform comparisons and merging of data to further increase genome coverage. In addition, our group has been active in developing new software for GWA analyses [74].
Pharmaco-epidemiologic studies
Objectives
A major objective of the pharmaco-epidemiologic studies is to investigate the role of drugs as determinants of disease in the Rotterdam Study. This includes studying efficacy and effectiveness of drugs, as well as adverse reactions to drugs.
Major findings
Important findings have been published on pharmaco-epidemiological topics concerning the main outcomes in the Rotterdam Study. Studies about the association between dementia and antihypertensive drugs [75] and NSAIDs [26] have strongly suggested a protective effect of both groups of drugs. Several studies have been performed on cardiovascular topics [76–78]. In one of these studies, NSAIDs were associated with an increased risk of heart failure [79]. In line with the suspicion that QTc-prolonging drugs may cause sudden cardiac death, it was demonstrated in the Rotterdam Study that a prolonged QTc is indeed an important risk factor [77]. Furthermore, in one study it was demonstrated that high-dose corticosteroids increase the risk of atrial fibrillation [78]. In the important area of locomotor diseases, studies have demonstrated that thiazide diuretics protect against hip fracture [79] and that statins reduce the risk of vertebral fracture [80]. On the other hand, the risk that long-term use of certain NSAIDs may aggravate signs of osteoarthritis has been emphasized [81]. In the area of ophthalmological diseases, a protective effect of cholesterol-lowering agents on macular degeneration has been suggested [82]. In other areas, such as pharmacogenetics and other causes of interactions between drugs, several important findings have been published [83–91].
Methods update
For several reasons, a drug is a highly attractive determinant in clinical epidemiology. First, drugs are probably the most important therapeutic intervention in health care. Despite rigorous clinical research before registration, many important effects of drugs are discovered after marketing. Second, all marketed drugs have proven biological activity, meaning that it concerns a determinant which really matters. Third, and as a consequence of the availability of complete medication histories in Dutch health care, the role of drug exposure can be assessed in a detailed way.
In the Rotterdam Study, there is an almost complete coverage of the population as of 1 January, 1991 thanks to the fact that all pharmacies which serve the Ommoord region are on one computer network. To date, almost 3 million prescriptions have been delivered to the population of the Rotterdam Study and of each prescription, details are available about the product name and contents, ATC-code, dosage and duration of drug therapy.
Drugs are a group of determinants, which can be studied in association with a large variety of diseases. In the Rotterdam Study, there is a strong interest in the association between drugs and the cardiovascular, neurological, endocrinological and ophthalmological diseases, which have been the main topics since starting. However, there is also important information about the association with psychiatric diseases, cancer, and chronic obstructive pulmonary disease. Moreover, important information about secondary outcomes, such as drug blood levels, other laboratory information, and information about hospital discharge diagnoses, is gathered on a continuous basis to facilitate pharmaco-epidemiological studies.
Management of the Rotterdam Study
The Rotterdam Study is directed by a Management Team comprising Jan Heeringa, MD, study coordinator, Eric Neeleman, head IT, Frank van Rooij, head data-management, and the scientific principal investigators Albert Hofman (PI Rotterdam Study, chairman), Monique Breteler (PI Neurological diseases), Cornelia van Duijn (PI Genetic studies), Gabriel Krestin (PI Radiology), Huibert Pols (PI Endocrinology), Bruno Stricker (PI Pharmaco-epidemiology), Henning Tiemeier (PI Psychiatric diseases), André Uitterlinden (PI Genome wide analysis), Johannes Vingerling (PI Ophthalmologic diseases) and Jacqueline Witteman (PI Cardiovascular diseases).

cohort study
heart failure
stroke
parkinson disease
alzheimer disease
dementia
macular degeneration
glaucoma
diabetes
osteoporosis
pharmaco-epidemiology
myocardial infarction
genome wide analysis


SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

Objective To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data.
Introduction
One of the crucial steps in the evaluation of risk-adjusted outcomes is the choice of the reference database for estimating adequate reference lines for the analyzed variables. For the SAPS 3 to reflect the standard of practices and outcome in intensive care at the beginning of the 21st century, we decided to collect data from a large sample of intensive care units (ICUs) worldwide. Other models have restricted data collection to large ICUs in Europe or North America—SAPS II [1], MPM II [2], APACHE II [3] and APACHE III [4], a strategy that minimizes the heterogeneity of the sample but restricts the generalization of the results.
At the statistical level, there is also a need for change, in order to take into account the hierarchic nature of our data [5, 6]. Current general outcome prediction models do not consider the existence of clinical and nonclinical factors, aggregated at the ICU level, that can have an important impact on prognosis. Instead, they assume that these factors are either not important or are randomly distributed throughout large samples and that the variation between ICUs is small. This assumption is not likely to be borne out at the ICU level for either nonclinical factors (e.g. organization and management, organizational culture) or clinical factors (e.g. clinical management, diagnostic and therapeutic strategies). If the variation between ICUs is not negligible, it will compromise the stability of the equations used to compute predicted mortality. Furthermore, the published models consider the relation between performance and severity of illness to be constant, and that may not be the case, since performance can vary within ICUs according to the severity of illness of the patients [7, 8]. To overcome this problem, we chose to adopt a new strategy for the development of the SAPS 3 score and to apply statistical modelling techniques that control for the clustering of patients within ICUs instead of assuming the independence of observations. Conceptually, the SAPS 3 admission core comprises the following parts:
First, the SAPS 3 ADMISSION SCORE, represented by the arithmetic sum of three subscores, or boxes:Box I: What we know about the patient characteristics before ICU admission: age, previous health status, co-morbidities, location before ICU admission, length of stay in the hospital before ICU admission, and use of major therapeutic options before ICU admission.Box II: What we know about the circumstances of ICU admission: reason(s) for ICU admission, anatomic site of surgery (if applicable), planned or unplanned ICU admission, surgical status and infection at ICU admission.Box III: What we know about the presence and degree of physiologic derangement at ICU admission (within 1 h before or after admission). Second, the SAPS 3 PROBABILITY OF DEATH during a certain period of time (in the case of the main model, the probability of death at hospital discharge).
Given our objective of evaluating not only individual patient outcome but also the effectiveness of ICU practices, we focused the model on data available at ICU admission or shortly thereafter. This model will be completely open and available free of any direct or indirect charges to the scientific community.
Methods and statistical analysis
Primary variable selection
Based on the SAPS 3 Hospital Outcome Cohort as described in Part 1 of this report, continuous predictive variables were categorized in mutually exclusive categories based on smoothed curves such as LOWESS [9], showing the univariate dependence of hospital mortality on the predictive variables. Classes of categorical variables were also collapsed according to their univariate hospital mortality levels using multidimensional tables and clinical judgment as appropriate, depending on the nature of the data. Additively, regression trees (MART) [10] were applied to check the cutoffs.
Missing values were coded as the reference or “normal” category for each variable. When dual data collection was used—maximum and minimum values recorded during a certain time period—missing maximum values of a variable were replaced by the minimum, if documented, and vice versa. Some regression imputations were performed if noticeable correlations to available values could be exploited. For a detailed description of data collection and handling, see Part 1 of this report.
Selection of variables was done according to their association with hospital mortality, together with expert knowledge and definitions used in other severity of illness scoring systems. The objective of using this combination of techniques rather than regression-based criteria alone was to reach a compromise between over-sophistication of the model and knowledge from sources beyond the sample with its specific case mix and ICU characteristics.
Cross validation
For being able to cross-validate the model, we randomly extracted five roughly equal-sized parts based on number of patients from the database, as suggested previously [11]. In a second approach, partitioning was based on ICUs and not on patients. It was thus possible to run the model-building procedure five times in each of the two approaches, each time taking four parts of the sample as a development set and the remaining one as the validation set. This allowed to estimate the variability of prediction resulting from the construction process of the prognostic score. A further check of the stability of the predictions was made by partitioning the sample according to major patient characteristics, such as surgical status and infection status.
The quality of predictions in the validation sets was assessed by looking at the goodness-of-fit in terms of the p values for the Hosmer-Lemeshow tests Ĉ and Ĥ [13] and the discriminative capability of the models by the use of the area under the receiver operating characteristic (aROC) curve [14, 15]. Another criterion to judge the appropriateness of the model was the fit in certain subsamples, defined according to major patient typologies [16].
Reducing model complexity
To reduce the complexity of the model classes, we concentrated on logistic regression. In the first step a stepwise logistic regression was used to identify the significant predictors in each of the five subsamples. A threshold of 0.01 for the p value was generally applied for inclusion in the model to separate irrelevant predictors [12]. At this stage we also evaluated if interactions among these predictors would influence results. Interactions, however, did not make a valuable contribution for the prediction.
Significant predictors (n=70) were in a second step entered into a logistic regression model. The criterion for a predictor to enter the model was homogeneity across the five model-building processes: in principle, predictors should enter the model in all five development sets, but depending on the frequency of the predictor in the samples, the magnitude of the effect, and medical reasoning, some predictors were included if they appeared in the model in at least three subsamples. An example is the presence of Acquired Immunodeficiency Syndrome (AIDS): it was selected as a comorbidity in only 81 patients (0.48%), but the mortality—without controlling for other variables—in these patients was 42%. By taking all the above steps to identify the set of predictors, although deliberately not using any formal numeric criterion, we reduced the complexity of the model to minimize the amount of overfitting: This process resulted in 61 item classes (representing 20 variables) remaining in the final model.
Using the parameter estimates from the logistic regression as starting values, a multilevel model was applied in the next step, using patient characteristics as fixed effects and ICUs as a random effect. Estimates were again calculated for the five development sets (for both, patient and ICU -based development subsamples).
At this stage it was checked if rounding of coefficients (which allows for an easier manual computation of the score) would influence results, which was found not to be the case. Consequently, this was the approach chosen for the final construction of the SAPS 3 admission score sheet.
The stability of the processes of variable selection and reducing complexity was further checked by bootstraping with replacement the total sample 100 times, both at patient level and at ICU level.
Predicting hospital mortality
After this step was completed, a shrinking power transformation was applied. This procedure uses log-transformation of the score to reduce the influence of extreme score values (outliers) on the mortality prediction. For this purpose, the SAPS 3 score and the transformed log (SAPS 3 + g) scores were used to predict hospital mortality. Conventional logistic regression was used in the evaluation of this step because of convergence problems for the corresponding multilevel model in a few subsamples. The best shrinkage model then was selected (excluding the trivial model with the SAPS 3 score as the single predictor) by checking which of the terms in the model contributed best to the prediction and was moreover stable over the respective validation sets and specific subsamples. This procedure was applied on both, patient and ICU -based subsamples.
After finishing these steps of cross-validation, the final estimates for the selected predictors of the SAPS 3 score as well as the selected shrinkage procedure were then calculated from the total sample of patients.
To arrive at the customised models for each major geographic region, specific customised equations were calculated, relating, by logistic regression, the transformed log (SAPS 3 + g) admission scores computed as described above to the vital status at hospital discharge. This process allows both the intercept and the slope of the curve relating the SAPS 3 admission score to change across different regions. The goodness-of-fit of these equations was evaluated by means of the same methodology used for the global sample.
SAS for Windows, version 8.02 (SAS Institute Inc., Cary, NC, USA) and MLwiN version 1.10.0007 (Centre for Multilevel Modelling, Institute of Education, London, UK) and the R Software Package (http://www.r-project.org) were used for the development of the model.
Results
Based on the methodology described, 20 variables were selected for the SAPS 3 admission score (Tables 1 and 2):Five variables for evaluating Box I: age, co-morbidities, use of vasoactive drugs before ICU admission, intrahospital location before ICU admission, and length of stay in the hospital before ICU admission;Five variables for evaluating Box II: reason(s) for ICU admission, planned/unplanned ICU admission, surgical status at ICU admission, anatomic site of surgery, and presence of infection at ICU admission and place acquired;Ten variables for evaluating Box III: lowest estimated Glasgow coma scale, highest heart rate, lowest systolic blood pressure, highest bilirubine, highest body temperature, highest creatinine, highest leukocytes, lowest platelets, lowest hydrogen ion concentration (pH), and ventilatory support and oxygenation.Table 1 SAPS 3 admission scoresheet—Part 1Box I035678911131518Age, years<40>=40<60>60<70>=70<75>=75<80>=80Co-MorbiditiesCancer therapy 2)Chron, HF (NYHA IV),Haematological cancer 3),4)Cirrhosis, AIDS 3)Cancer 5)Length of stay before ICU admission, days 1)<14>=14<28>=28Intra-hospital location before ICU admissionEmergency roomOther ICUOther 6)Use of major therapeutic options before ICU admissionVasoactive drugsBox II034569ICU admission: Planned or UnplannedUnplannedReason(s) for ICU admissionplease see Part 2 of the scoresheetSurgical status at ICU admissionScheduled surgeryNo surgery 7)Emergency surgeryAnatomical site of surgeryplease see Part 2 of the scoresheetAcute infection at ICU admissionNosocomial 8)Respiratory 9)Box III1513111087532024578Estimated Glasgow Coma Scale (lowest), points3–4567–12>=13Total bilirubine (highest), mg/dL<2>=2<6>=6Total bilirubine (highest), µmol/L<34.2>=34.2<102.6>=102.6Body temperature (highest), Degrees Celsius<35>=35Creatinine (highest), mg/dL<1.2>=1.2<2>=2<3.5>=3.5Creatinine (highest), µmol/L3–456<106.1>=106.1<176.8>=176.8<309.4>=309.4Heart rate (highest), beats/minute<120>=120<160>=160Leukocytes (highest), G/L<15>=15Hydrogen ion concentration (lowest), pH<=7.25>7.25Plateletes (lowest), G/L<20>=20<50>=50<100>=100Systolic blood pressure (lowest), mm Hg<40>=40<70>=70<120>=120Oxygenation 10), 11)PaO2/FiO2<100 and MVPaO2/FiO2>=100 and MVPaO2<60 and no MVPaO2>=60 and no MVThe definition for all variables can be found in detail in Appendix C of the ESM. For names and abbreviations which are differing from those in the ESM, explanations are given below. Generally, it should be noted that no mutually exclusive conditions exist for the following fields: Comorbidities, Reasons for ICU admission, and Acute infection at ICU admission. Thus, if a patient has more than one condition listed for a specific variable, points are assigned for all applicable combinations.1 This variable is calculated from the two data fields: ICU Admission date and time—Hospital admission date and time (see Appendix C of the ESM) 2 Cancer Therapy refers to the data definitions in Appendix C of the ESM: Co-Morbidities: Chemotherapy, Immunosupression other, Radiotherapy, Steroid treatment3 If a patient has both conditions he/she gets double points.4 Chronic HF (NYHA IV)/Haematological cancer refer both to the data definitions in Appendix C of the ESM: Co-Morbidities: Chronic heart failure class IV NYHA, Haematological cancer. 5 Cancer refers to the data definitions in Appendix C of the ESM: Co-Morbidities: Metastatic cancer.6 Other refers to the data definitions in Appendix C of the ESM: Intra-hospital location before ICU admission: Ward, Other.7 No surgery refers to the data definitions in Appendix C of the ESM: Surgical Status at ICU Admission: Patient not submitted to surgery.8 Nosocomial refers to the data definitions in Appendix C of the ESM: Acute infection at ICU admission—Acquisition: Hospital-acquired.9 Respiratory refers to the data definition in Appendix C of the ESM: Acute infection at ICU admission—Site: Lower respiratory tract: Pneumonia, Lung asbcess, other. 10 PaO2, FIO2 refer to the data definitions in Appendix C of the ESM: Arterial oxygen partial pressure (lowest), Inspiratory oxygen concentration.11 MV refers to the data definition in Appendix C of the ESM: Ventilatory support and mechanical ventilation.Table 2SAPS 3 admission scoresheet – Part 2Box II – continuedICU admission 12)16Reason(s) for ICU admission   Cardiovascular: Rhythm disturbances 13)–5   Neurologic: Seizures 13)–4   Cardiovascular: Hypovolemic hemorrhagic shock, Hypovolemic non hemorrhagic shock. / Digestive: Acute abdomen, Other 3)3   Neurologic: Coma, Stupor, Obtuned patient, Vigilance disturbances, Confusion, Agitation, Delirium4   Cardiovascular: Septic shock. / Cardiovascular: Anaphylactic shock, mixed and undefined shock 3)5   Hepatic: Liver failure6   Neurologic: Focal neurologic deficit7   Digestive: Severe pancreatitis9   Neurologic: Intracranial mass effect10   All others0Anatomical site of surgery   Transplantation surgery: Liver, Kidney, Pancreas, Kidney and pancreas, Transplantation other–11   Trauma – Other, isolated: (includes Thorax, Abdomen, limb); Trauma – Multiple–8   Cardiac surgery: CABG without valvular repair–6   Neurosurgery: Cerebrovascular accident5   All others012)Every patient gets an offset of 16 points for being admitted (to avoid negative SAPS 3 Scores).13) If both reasons for admission are present, only the worse valve (–4) is scored.
An estimation of the variability of the coefficients in the overall sample and in the five disjoint subsamples is given in Table E8 of the Electronic Supplementary Material (ESM), together with their respective coefficients (unrounded and rounded) and p values. The SAPS 3 admission score can thus, in theory, vary from a minimum of 0 points to a maximum of 217 points. The distribution of the SAPS 3 admission score in our sample is presented in Fig. 1. The minimum value observed was 5, and the maximum value was 124, with a mean of 49.9±16.6 (mean ± SD) and a median of 48 (38–60). The highest explanatory power came from Box I, with Box II and Box III being less important for the outcome; the three boxes represent 50%, 22.5% and 27.5%, respectively, of the total Nagelkerke’s R-Square. The relationship between the SAPS 3 and vital status at hospital discharge is given by the equation:
Logit = −32.6659 +ln(SAPS 3 score +20.5958) ×7.3068
and the probability of mortality by the equation:
Probability of death = elogit/(1+elogit).Fig. 1 Distribution of the SAPS 3 admission score in the SAPS 3 database
The relationship between the SAPS 3 admission score and the respective probability of death in the hospital is described in Fig. 2. Overall, no combined discrepancy between observed and expected outcomes across all of the strata was outside sampling variability as demonstrated a Hosmer-Lemeshow goodness-of-fit test Ĥ of 10.56 (p=0.39) and a Hosmer-Lemeshow goodness-of-fit test Ĉ of 14.29 (p=0.16) (Figs. 3, 4 and Table E9, ESM). The overall discriminatory capability of the model, as measured by aROC curve, was 0.848. The goodness-of-fit according to major patient typologies (surgical status, trauma, and infection) can be found in Table 3. Calibration and discrimination presented differences across different geographic areas: the best predictive results were achieved in patients from Northern Europe (observed-to-expected [O/E] mortality ratio 0.96 [0.83–1.09]) and the worst predictive results were obtained in patients from Central and South America (O/E mortality ratio, 1.30 [1.23–1.37]); see also Table 4 and Fig. 5 and Appendix B in the ESM.Fig. 2 Relationship between the SAPS 3 admission score and the respective probabilities of hospital mortalityFig. 3 Hosmer-Lemeshow goodness-of-fit test Ĉ in the overall sample. Predicted risk of hospital death, observed hospital mortality rate, and the corresponding number of patients per decile are shown. Columns: Number of patients; squares: mean SAPS 3-predicted mortality per decile; circles: mean observed mortality per decileFig. 4Hosmer-Lemeshow goodness-of-fit test Ĥ in the overall sample. Predicted risk of hospital death, observed hospital mortaliy rate, and the corresponding number of patients per decile are shown. Columns: Number of patients; squares: mean SAPS 3-predicted mortality per decile; circles: mean observed mortality per decileTable 3 Performance of the model across major patient typologiesPatient characteristics GOF test ĤpGOF test ĈpO/E ratio95% CIaROCTrauma patients19.920.039.030.531.030.93–1.120.854Non-operative admissionsa14.860.1417.80.061.010.98–1.040.825Scheduled surgerya11.50.3227.39<0.010.970.90–1.030.825Emergency surgerya4.970.8912.880.231.000.95–1.050.809No infectionb8.570.5714.770.141.000.97–1.020.846Community-acquired infectionc8.40.5911.760.31.000.96–1.050.786Hospital-acquired infectiond15.210.127.110.721.020.97–1.070.77GOF: Hosmer-Lemeshow goodness-of-fit; O/E: observed-to-expected mortality; CI: 95% confidence interval; aROC: area under receiver operating characteristic (curve)aNon-operative admissions, scheduled surgery emergency surgery: see data definitions appendix C, ESMbNo infection: Patients not infected at ICU admissioncCommunity-acquired infection: Patients with community-acquired infection at ICU admissiondHospital-acquired infection: Patients with hospital-acquired infection at ICU admissionTable 4 Performance of the model in the global sample and in different geographic areasRegionsGOF test ĤpGOF test ĈpO/E ratio95% CIaROCAustralasia15.250.128.090.620.920.85–0.990.839Central, South America78.01<0.0180.82<0.011.301.23–1.370.855Central, Western Europe56.45<0.0147.89<0.010.840.79–0.900.861Eastern Europe19.450.0318.690.041.091.00–1.190.903North Europe2.440.992.340.990.960.83–1.090.814Southern Europe, Mediterranean countries14.180.1620.780.021.020.98–1.050.834North America10.570.399.630.470.910.78–1.040.812Global database10.560.3914.290.1610.98–1.020.848GOF: Hosmer-Lemeshow goodness-of-fit; O/E: observed-to-expected mortality; CI: 95% confidence interval; aROC: area under the receiver operating characteristic (curve).Fig. 5 Observed-to-expected (O/E) mortality ratios by region. Observed-to-expected (O/E) mortality ratios are shown by region. Bars indicate 95% confidence intervals
For a more precise estimation of the probability of death in the hospital across the different geographic regions, specific customised equations were calculated (Table 5). This customised approach allows each ICU to choose its own reference line for the prediction of hospital mortality: either the overall SAPS 3 hospital mortality sample or its own regional subsample. This approach can be supplemented in the future by customised equations at the country level if data are available and if a more precise estimation of outcome in a specific setting is needed. The overall goodness-of-fit of these customised equations for each region is presented in Table 5. A complete list of the number of patients and the respective O/E mortality ratios by country, according to the global equation and the regional equations, are presented in Tables E10 and E11 of the ESM, with point estimates varying at the global level from 0.68 (0.56–0.80) to 2.05 (1.27–2.82). Most O/E ratios are close to the identity line, as expected for a stable model.Table 5 Customized SAPS 3 admission equations for the different geographic areasAreaEquationGOF ĤpGOF ĈpO/ECIAustralasiaLogit=−22.5717 + ln (SAPS 3 score + 1) ×5.316310.430.402.200.991.000.93–1.07Central, South AmericaLogit=−64.5990 + ln (SAPS 3 score + 71.0599) ×13.23228.940.547.030.721.000.94–1.06Central, Western EuropeLogit=−36.0877 + ln (SAPS 3 score + 22.2655) ×7.986715.130.1312.150.271.000.94–1.06Eastern EuropeLogit=−60.1771 + ln (SAPS 3 score + 51.4043) ×12.684710.130.437.120.711.000.92–1.08North EuropeLogit=−26.9065 + ln (SAPS 3 score + 5.5077) ×6.27463.450.972.220.991.000.86–1.14Southern Europe, Mediterranean countriesLogit=−23.8501 + ln (SAPS 3 score + 5.5708) ×5.57095.280.8713.120.221.000.97–1.03North AmericaLogit=−18.8839 + ln (SAPS 3 score + 1) ×4.39794.220.934.470.921.000.86–1.14GOF Ĥ: Hosmer-Lemeshow goodness-of-fit Ĥ test; GOF Ĉ: Hosmer-Lemeshow goodness-of-fit Ĉ test; p: respective p-values;  O/E: observed-to-expected mortality ratio; CI: 95% confidence interval
Discussion
We have presented the results of a large multicentric, multinational study aimed at updating the SAPS II model. This study was necessary for several reasons. First, the reference line used by SAPS II was derived from a database collected in the early 1990s; since that time, there have been changes in the prevalence of major diseases and in the availability and use of major diagnostic and therapeutic methods that are associated with a shift toward poor calibration of older models such as SAPS II and APACHE III [17, 18]. Second, SAPS II was developed from a database built exclusively from patients in Europe and North America. This sample may not be representative of the case mix and medical practices that constitute the reality of intensive care medicine in the rest of the world (e.g. Australasia or South America), where variability in structures and organization is probably related to outcome [19].
Third, since computation of predicted mortality is based on a reference database, the user should be able to choose between them, i.e., a global database, which provides a broader comparison at the potential cost of less relevance to local conditions, and a regional database, which provides a better comparison with ICUs in geographic proximity but at the cost of losing comparability with ICUs in other parts of the world. A third possibility could be added—a country-representative database—but such a database would raise the problem of whether the ICUs selected were representative of a certain country.
Fourth, the development of computers in recent years has created easy access to strong computational power. One of the implications of this is that it is now possible to develop a new outcome prediction model, based on digital data acquisition and analysis, with minimal differences in definitions and application criteria. These advances were coupled with extensive automatic logical and error-checking capabilities and the availability of data collection manuals online. Moreover, developers of the SAPS 3 model could take advantage of computer-intensive methods of data selection and analysis, such as the use of additive partition trees and logistic regression with random effects. Several new statistical techniques have been used in recent years to allow a more stable prediction of outcome, such as genetic algorithms and artificial neural networks [20, 21], dynamic microsimulation techniques [22], and first- and second-level customization strategies [23–25]. However, the value of these techniques is for the moment limited, usually because they are based on regional databases [24–26] that prevent extrapolation to other settings; moreover, their superiority in even the regional setting still needs to be established.
Finally, the SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU. Thus, for individual patient assessment, the system separates the relative contributions to prognosis of (i) chronic health status and previous therapy, (ii) the circumstances related to ICU admission, and (iii) the presence and degree of physiologic dysfunction. It is interesting to note that one half of the predictive power of the model is achieved with Box I, i.e., with the information that is available before ICU admission. The prognostic capabilities of the model can be further improved by 22.5% by using data related to the circumstances of the ICU admission (Box II), and by another 27.5% by the incorporation of physiologic data (Box III). These numbers are different from those published by Knaus et al. [4] but are based on what we have learned in the last years about prognostic determinants in the critically ill patient.
For performance evaluation, several reference lines should be used, with risk-adjusted mortality in different patient typologies and not only O/E mortality ratios at hospital discharge in the overall ICU population [27]. The results of the SAPS 3 study showing that different O/E ratios were observed in different regions of the world should be explored further, since, apart from regional differences in case mix (not taken into account by the model), they can also be related to regional variations in structures and organization of acute medical care, to different lifestyles (e.g., prevalence of obesity, or alcohol and tobacco use) and/or—though less likely—to genetic differences among populations.
We would like to re-emphasize that the model presented here is based exclusively on data (including physiologic data) available within 1 h of ICU admission and calibrated for manual data acquisition; consequently, it should be expected to overestimate mortality when an automatic patient data management system with a high sampling rate is used [28, 29]. Limiting acquisition of physiologic data to the hour of ICU admission should minimise the impact of this factor when compared with models based on the most deranged data from the first 24 h after ICU admission, probably at the expense of a small decrease in the ROC curve, a greater sensitivity to the exact time point at which admission to ICU occurs, and therefore more reliant on the assumption that measured physiology alone (as opposed to changes in physiology) predict outcome. It also allows the prediction of mortality to be done before ICU interventions take place. This gives the SAPS 3 admission model a major advantage over existing systems, such as the SAPS II or the APACHE II and III, since all these systems can be affected by the so-called Boyd and Grounds effect: the occurrence of more abnormal physiologic values during the first 24 h in the ICU, leading to an increase in computed severity of illness and a corresponding increase in predicted mortality. These increases may, however, be due not to a greater intrinsic severity of illness of the patient but to the provision of suboptimal care in the first 24 h of ICU admission, when a stable patient may be allowed to deteriorate [30].
Further studies should be done of factors occurring after ICU admission that influence risk-adjusted mortality. We should keep however in mind that this approach comes with one potential pitfall: a possible decrease in the amount of data available for the computation of the model; also, the shorter time period for data collection can eventually increase the likelihood of missing physiological data and the reliance on the assumption that missing physiological data are normal. This effect should be small, considering the widespread availability of monitoring and point-of-case analysers.
Having demonstrated the internal validity of the SAPS 3 admission model by the extensive use of cross-validation techniques, we should stress that external validation is also necessary. The fact that the overall database was not collected to be representative of the global case-mix (and especially the case-mix of specific regional areas or patient typologies such as specific diseases) should be empirically tested. Furthermore, the rate of deterioration of our estimates over time should be followed by the appropriate use of temporal validation, especially to avoid what Popovich called grade inflation [18].
The SAPS 3 system was developed to be used free of charge by the scientific community; no proprietary information regarding the scientific content is retained. All the coefficients needed for the computation of outcome probabilities are available in the published material. The SAPS 3 can even be computed manually, using a simple scoresheet, although it was designed to be integrated into computerised data acquisition and storage systems that allow the automatic check of the quality of the registered data.
In conclusion, we can say that at the end of this stage of the project, we have been able to overcome some of the problems inherent in current risk-adjustment systems. We have minimized user-dependent problems through the publication of careful, detailed definitions and criteria for data collection [31]. We have also addressed the patient-dependent problems by expanding the reference database and making it more representative of reality, in order to include the maximum possible range of variations for patient-centred variables and resulting patient-centred outcomes. This approach was complemented by the development of specific customised equations for major areas of the world, allowing ICUs to choose a reference line for outcome prediction—the global database or the regional database for their own area.
Users of these models should keep in mind that benchmarking is a process of comparing an ICU with a reference population. The appropriate choice of reference population is difficult, and we cannot simply change it because the observed-to-predicted mortality rate is not the one we want. For this reason, the choice should depend on the objective of the benchmark: more precise estimation will need local or regional equations, developed from a more homogeneous case mix. A generalisable estimation will, on the other hand, need more global equations developed from a more representative case mix.
Last but not least, we have successfully addressed some of the problems of prognostic model development, especially those related to the underlying statistical assumptions for the use of specific methods for selection and weighting of variables and the conceptual development of outcome prediction models. In the future, multi-level modelling with varying slopes (and not just random intercepts) might be able to give a better answer to researchers but for the moment they would make the models to complex to be managed outside a research environment.
Electronic Supplementary Material
(PDF 794 KB)

intensive care unit
hospital mortality
severity of illness
icu mortality
risk adjustment


Preconception Care in International Settings

Objectives: This literature review briefly describes international programs, policies, and activities related to preconception care and resulting pregnancy outcomes. Methods: Electronic databases were searched and findings supplemented with secondary references cited in the original articles as well as textbook chapters, declarations, reports, and recommendations. Results: Forty-two articles, book chapters, declarations, and other published materials were reviewed. Policies, programs, and recommendations related to preconceptional health promotion exist worldwide and comprise a readily identifiable component of historic and modern initiatives pertaining to women's health, reproductive freedom, and child survival. Conclusions: The integration of preconception care services within a larger maternal and child health continuum of care is well aligned with a prevention-based approach to enhancing global health.
Introduction
Preconception health is widely recognized as a critical component of domestic and international maternal and child health promotion. Broadly defined as the provision of biomedical and behavioral interventions prior to conception in order to optimize women's wellness and subsequent pregnancy outcomes [1], the notion of preconception care can be found in various global policy and practice recommendations concerning women's health and child survival. Although programs and guidelines may vary in response to local needs, the overarching concept of preconception care is present among developing and industrialized countries, within socialized or market-based health care systems, and independent of political, cultural, or religious beliefs. Furthermore, the incorporation of various preconception care strategies and ideologies within international maternal and child health programs and policies spans nearly 30 years, indicating a long-term recognition of the relative importance of such interventions as a means of optimizing pregnancy outcomes.
This article describes international efforts to reduce adverse maternal and infant outcomes through programs, policies, and activities related to preconception care. Because widespread support for preconception health promotion is comparatively recent, explicit mention of preconception care programs and policies per se is often difficult to identify within the international literature. Furthermore, because many developing countries adopt strategies guided by the declarations of international agencies or other coalitions [2], the development of preconception health initiatives must be traced through the history of various international health movements, starting with the promotion of global primary health care, followed by Safe Motherhood initiatives, and culminating with the women's rights and health movements. Thus, whereas the activities described herein may not be immediately identifiable as preconception care, they represent essential pieces of the preconception health puzzle.
Materials and methods
A MEDLINE search with English language and human subject restriction was conducted for the years 1980 through May 9, 2005 using the reference terms “preconception (pre-conception) care,” “preconception counseling,” “preconceptional care,” “periconception care,” and “periconceptional care.” A total of 756 citations were identified using the initial search parameters. References for which preconception care programs or activities outside of the United States comprised the primary topic of interest were included. Articles detailing clinical or scientific evidence of specific periconceptional interventions were excluded. Also reviewed were other published articles as well as textbook chapters, declarations, reports, and recommendations not retrievable from these databases. The search terms were also entered in various internet search engines and relevant web-based information was used to supplement the findings. Pertinent abstracts from the National Summit on Preconception Care (CDC, June 2005) were also reviewed.
Results
A total of 42 articles, book chapters, declarations, and other published materials were reviewed. The findings were categorized into three main areas in order to present the material in a coherent fashion: international conferences and accords, professional organizations, and international preconception care programs.
International conferences and accords
As preconception health is closely linked to women's health, language in support of preconception care is found in declarations and agreements derived at international conventions on the topic of women's wellness and reproductive health. For example, although the primary goal of the 1978 International Conference on Primary Health Care was to promote the health of all by outlining essential primary health care strategies to be implemented throughout the world, one important provision of the declaration outlined the importance of maternal and child health care and family planning as integral components of primary health care [2–4].
The Safe Motherhood Initiative, initially drafted in 1987 at the first international Safe Motherhood Conference in Nairobi, Kenya, focused on reducing morbidity and mortality associated with pregnancy and childbirth among developing countries [3]. The Safe Motherhood Inter-Agency Group (IAG) cosponsored the event and is now comprised of various international and national agencies whose mission is to enhance maternal and neonatal survival through the development and promotion of cost-effective interventions. In a global technical consultation held 10 years after the launch of this initiative, the IAG assessed relevant health indicators and developed a package of services to be offered to all women as a means of ensuring safe motherhood. Various preconception care services were outlined, including family planning, adolescent reproductive health education services, and community-based education pertaining to sexual and reproductive health as well as safe motherhood [5].
Covenants related to preconception care were adopted by the 1988 World Congress of Obstetrics and Gynaecology and its predecessor, the General Assembly of the International Federation of Gynaecology and Obstetrics. The 1988 World Congress heralded the promotion of women's health with the first step being prevention as a social responsibility; the General Assembly of the International Federation advocated governments undertake concrete measures to improve women's health and social status [6]. The second International Congress for Maternal and Neonatal Health, held in Monastir, Tunisia, in November 1984, included in its recommendations the need to promote the expansion of health services including family planning services and other preconception interventions such as smoking cessation, increased birth spacing, and tetanus vaccination [7].
The 1993 Report of the World Conference on Human Rights reaffirmed the need for equal access to health care for women, including a wide range of family planning services and called for reductions in infant and maternal mortality rates and overall improvements in women's health and nutrition [5, 8]. Similarly, the International Day of Action for Women's Health, first held on May 28, 1990, through the collaborative efforts of the Women's Global Network for Reproductive Rights and the Latin American and Caribbean Women's Health Network/ISIS International, reiterated the need for significant reductions in maternal mortality, particularly in Latin American countries [3].
International conferences held in subsequent years reflected an important paradigm shift in which pregnancy outcomes and maternal health were considered within a broader context encompassing reproductive health and women's health as well as socio-cultural factors [3]. Held in Cairo in 1994, the third decennial International Conference on Population and Development (ICPD) emphasized the importance of reproductive freedom and developed a definition of reproductive health that included “access to appropriate health care services that enable women to go safely through pregnancy and childbirth and provide couples with the best chance of having a healthy infant” [9]. In 1995, two additional international meetings took place—the World Summit for Social Development and the Fourth World Conference on Women—in which the tenets set forth by the ICPD with regard to maternal mortality and morbidity were reaffirmed and new goals for improving access to maternal health services were also derived [3, 5, 9, 10].
More recent international activities related to preconception health include the 1998 World Health Day and the Millennium Declaration. World Health Day 1998 focused on safe motherhood with the assertion that every pregnancy faces risks that may be ameliorated by enhancing access to quality maternal health services, delayed childbearing, and preventing unwanted pregnancies [11]. The Millennium Declaration, adopted by all member states of the United Nations in September 2000, included eight millennium goals to be achieved on a global level. The goals were designed as a measure of development against which countries may compare health indicators, programs, and policies. Maternal health and child mortality are explicitly detailed in two main goals and are closely related to other key areas including poverty, gender equality, nutrition, education, and infectious diseases [3, 12].
References to preconception health can also be found in the declarations of conferences addressing the issues of child health and survival. For example, the 1989 Convention on the Rights of the Child recognized the right of children to attain the highest possible standard of health and contained provisions for reductions in infant mortality, prenatal and postnatal care for mothers, family planning education, and services and health education for children and parents regarding nutrition, clean drinking water, and environmental contaminants [13]. In addition, the World Declaration on the Survival, Protection, and Development of Children, developed at the 1990 World Summit for Children, included tasks pertaining to the reduction of maternal mortality with a specific emphasis on family planning and child spacing as well as efforts to reduce infant and child mortality [14].
Professional organizations and associations
International health care professionals also have long recognized the utility of preconception care, as evidenced by directives and recommendations supported by various professional organizations. In 1952, after significant changes to the system of reporting maternal deaths sparked concern within the medical community about maternal mortality rates in the United Kingdom, a series of inquiries were initiated that ultimately resulted in the ongoing Confidential Enquiries into Maternal and Child Health [3, 15]. In its current manifestation, the program seeks to improve the health of mothers, infants, and children through careful investigation of all maternal and perinatal deaths as well as issues associated with diabetes during pregnancy and child health.
In 1982, the International Federation of Gynaecologists and Obstetricians (FIGO) joined the World Health Organization (WHO) in creating a task force to assess safe motherhood within regional and international communities. Since then, FIGO has conducted needs assessments and funded demonstration projects to ascertain cost-effective methods of identifying and managing pregnancy complications [3, 16, 17]. One such project, the Averting Maternal Death and Disability Program (AMDD), was established at the Columbia University Mailman School of Public Health and has partnered with numerous international agencies and communities to improve access to quality emergency obstetric care [17].
Nurses and midwives also have played an important role in the promotion of preconception care among the medical community. Themes associated with International Nurses Day have incorporated various components of the preconception care paradigm, including safe motherhood, school health, healthy families, women's health, community health, domestic violence, and the AIDS stigma. The International Confederation of Midwives, a member of the FIGO working group on Safe Motherhood and Newborn Health, has hosted a number of workshops related to safe motherhood and midwifery. Topics addressed at the workshops have included HIV/AIDS, gender-based violence, and most recently, maternal and newborn health during birth and the postnatal period [3, 18].
International preconception care guidelines and programs
The structure of preconception care programs in the international community differs considerably according to the type of health system employed and the level of economic support. Wealthier countries tend to have better organized health systems [19] and therefore use broader policies and guidelines to direct the provision of preconception services within the health care sector. Thus, countries such Canada [1], the United Kingdom [20, 21], Spain [22], Australia, Hungary [23], and the Netherlands [24] are more likely to adopt various recommendations related to preconception health, whereas less-developed countries in the regions of Latin America [25], Africa, India, and the Middle East tend to use more targeted interventions [26]. For example, many African countries provide continuing education to community health care workers to help them deal with issues in preconception counseling and reproductive choices for patients with HIV.
Within the developing countries of Latin America, preconception care has afforded such nations as Ecuador, Honduras, Nicaragua, Paraguay, and Peru an opportunity to address inequitable (as compared with more developed countries) maternal and perinatal morbidity and mortality rates [25]. Maternal mortality in these countries is cited as one of the primary causes of death among women 15 to 39 years of age. Using a $250,000 grant provided by WHO, these countries have undertaken a demonstration project to deliver preconception health education via community programs. The specific aims of the project are to develop and deliver a comprehensive preconception care package that includes (1) risk assessment (identify individual, family, and social risks and barriers to prenatal care); (2) health promotion (ensure proper nutrition; avoid substance, tobacco, and alcohol use; provide family planning; perform PAP smear screening, and provide ongoing care); and (3) treatment delivery (treat medical conditions and infections such as malaria and sexually transmitted diseases, update immunizations, provide nutritional supplementation such as folic acid, and conduct home visits).
In China before 2003, a premarital health check was required of all couples planning to marry and consisted of medical examinations and testing as well as health education. The requirement has since been abolished and this change has been followed by concomitant declines in the rates of premarital examinations [27]. As a result, pilot studies have been designed to test the feasibility and content of a social marketing campaign for preconception care in China. The findings indicate that although women are interested in information about preconception health, numerous barriers to implementing a national program exist, including vertical health systems, a lack of coordinated efforts among governmental organizations providing family planning and primary health care, and an abundance of potentially confusing media health messages [27]. Preconception care services are sometimes provided in private clinics such as the Pre-Pregnancy Preparation Service (PPPS) in Hong Kong which provides pre-pregnancy counseling, medical testing, health assessments, and educational services to approximately 4,000 couples each year [28].
To reach women and children residing in remote regions of the Philippines, a mobile health services program has been developed whereby a multidisciplinary team of physicians and nurse midwives travel to specified areas and provide services for 1 to 3 days per month. The team screens pregnant women for various risk factors and nonpregnant women of reproductive age for chronic medical conditions. Family planning services are also provided [29].
The Russian Association of Gynecologists and Obstetricians has developed and implemented the People's Health Movement with a primary objective of promoting health and preventing disease throughout the stages of a woman's reproductive life. Based on policy recommendations arising from an alert that the state of maternal and child health was one of crisis proportion, preconception care services inclusive of family planning are now being initiated [30].
The Office de la Naissance et de l’Enfance in Belgium is currently establishing a national preconception care campaign to educate all women of reproductive age and all health professionals engaged in the care of women and children regarding the importance of preconception health care. The marketing tools and plans for disseminating the information are now being developed [31].
In France, preconception care has focused on more targeted recommendations, such as good glycemic control prior to conception. It had previously been established that preterm delivery among diabetic mothers was much higher than among the general population and that fetal loss and congenital malformations were assoicated with poor glycemic control [32]. Using one of the goals of the 1989 International Diabetes Federation meeting in St. Vincent’s, Italy, tertiary perinatal centers began offering preconception care in an attempt to reduce the level of adverse pregnancy outcomes of diabetic mothers [33]. Services included risk assessment of potential diabetic complications, education on nutritional and glycemic self-monitoring, and optimization of insulin treatment regimens as directed by treatment guidelines. Data from a cross-sectional study of 12 perinatal centers in France indicate that nearly half of all women with Type 1 diabetes received preconception care during 2000–2001 as compared to 24% of women with Type 2 diabetes. Because rates of adverse outcomes among infants of diabetic mothers were similar to those during 1986–1988, it was concluded that more effort was needed to achieve the targets set forth in the St. Vincent's declaration [33].
Canada's National Guidelines on Family-Centred Maternity and Newborn Care devotes an entire chapter to preconception care and describes the multitude of intrinsic and extrinsic factors that influence preconception health. Accordingly, various settings appropriate for the administration of preconception care interventions are discussed as well as various social and medical issues, including stress, social support, abuse and violence, healthy lifestyle practices, and nutrition [1].
Although information about the development of international preconception care clinics is scant, such programs have been noted in Hungary, the United Kingdom, and the Netherlands. In 1989, Hungary established the Optimal Family Planning Service (OFPS) under the direction of the WHO Collaborating Centre for the Community Control of Hereditary Diseases. The OFPS was comprised of 32 regional health care centers providing periconceptional care free of charge. The aim of this program was to reduce adverse perinatal outcomes and protect maternal health among all women. In 1996, the Hungarian government increased the number and scope of the centers to incorporate these services within primary health care [23]. Assessments of various indicators in Hungary 10 years after the creation of the OFPS indicate that the rates of major congenital anomalies decreased and that the use of protective factors such as folic acid supplementation, rubella vaccination, and infection screening increased. Barriers to the use of preconception care services included unnecessary medical examinations, an inability to effect change among unintended pregnancies, and additional costs [24].
Despite the existence of numerous clinical genetic centers in the Netherlands, only two preconception clinics have been described in the literature. The first, an outpatient preconception care clinic established by the University Medical Center of Nijmegen, provides thorough evaluations of potential pregnancy risk and preconceptional interventions for women referred by health care practitioners [34]. Treatment for hyperhomocysteinemia and changes in drug therapies for women taking potentially teratogenic medications were the most common preconception interventions at the clinic. A pilot preconception clinic in Maastricht was developed as a forum in which women planning a pregnancy could address questions and concerns. Couples attending the clinic responded favorably to the program and indicated that they would not have asked their general practitioner such questions [35]. These findings are supported by the results of recent surveys of Dutch women in which more than 70% of the respondents reported an interest in preconception counseling when offered by their own general practitioner [36]. However, although the majority of Dutch health care providers are in favor of establishing preconception care clinics [37, 38], lack of specific knowledge and time constraints have been cited as barriers to the provision of adequate preconception counseling [38].
Preconception clinics have also been described in London and Glasgow. The London clinic opened in 1978 and consisted of one obstetrician who advised women with previous pregnancy complications about the management of subsequent pregnancies [39, 40]. During the first 18 months of the clinic's operation, 56 women were seen. Opened in 1982, the Glasgow clinic also served women affected by prior adverse pregnancy outcomes. The clinic remained operational for 9 years and relied on a research nurse for initial evaluation and screening followed by a physician consultation 4 weeks later [40, 41]. The most common reasons for referral to the clinic were previous miscarriage, previous fetal abnormality, and chronic maternal disease. Improvements in pregnancy outcome were noted only among the women with chronic conditions [41].
Conclusions
Global recognition of the potential benefits associated with preconception health promotion is not new. International policy directives and practice recommendations related to women's health, reproductive freedom, and child survival almost always include provisions for the enhancement of women's wellness and social status as a means of reducing adverse pregnancy outcomes. However, the relative success of preconception care programs in both developed and developing countries is directly related to the availability and accessibility of health care for women. Accordingly, WHO's World Health Report 2005: Make Every Mother and Child Count details the inherent interrelation of the needs of the mother and child, indicates that reproductive health comprises an essential element of the continuum of maternal and child health, and calls for a reformulation of interventions from vertical programs to those offering a wider range of services [42]. A similar longitudinal approach to women's wellness and reproductive health has been promoted in the U.S. in light of increasing rates of low birth weight and persistent racial disparities in maternal and infant outcomes [43–45].
The present description of international programs and perspectives on preconception care is limited in scope by numerous factors. The literature search was as comprehensive as possible but not exhaustive because of language restrictions, difficulty in retrieving documentation of international preconception care programs and policies, the relative scarcity of published information about international preconception care practices, and the myriad of maternal and child health outcomes that fall under the umbrella of preconception health and for which both population-based and targeted interventions exist. Finally, data pertaining to maternal and infant outcomes after the introduction of preconception care activities is often unavailable. Thus, this review provides only a broad overview of international activities related to preconception health. However, the findings are instructive as a preliminary step in the compilation and dissemination of information related to international preconception care recommendations and practices. Future research should include a regionalized inspection and comparison of preconception care programs, policies, and recommendations within and among various countries. The impact of these programs on community-specific indicators of maternal and perinatal health should also be evaluated.
Irrespective of setting, the wider application of the core components of preconception care (i.e., risk assessment, health promotion, and intervention) has the potential to enhance the health and overall well-being of women, infants, and children around the globe. In light of the numerous factors influencing pregnancy outcome, an integrated approach to preconception health promotion has been proposed as perhaps the most effective and efficient means of implementing preconception health care [43–47]. The benefits of service integration are apparent in even the most resource-poor settings, as evidenced by the introduction and expansion of the WHO's strategy, the Integrated Management of Childhood Illness, in various countries [48]. Indeed, the perspective that preconception care is part of a larger continuum of care is well aligned with a prevention-focused public health paradigm. As such, the widespread promotion of preconception health may be the most timely and logical step toward ensuring global health.

preconception care
international health
reproductive health