Unlocking the Value of Variation in CKD Prevalence
Authors: Corri Black, Sabine N. van der Veer
Journal: Journal of the American Society of Nephrology (2015)

Darwin,1 publishing his seminal work ‘On the Origin of the Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life’ in 1859, presented his then revolutionary recognition of the importance of describing variation and, by studying variation, being able to identify potentially important mechanisms of change:“I have hitherto sometimes spoken as if the variations [...] had been due to chance. This, of course, is a wholly incorrect expression, but it serves to acknowledge plainly our ignorance of the cause of each particular variation.”1 Before the adoption of a common definition for CKD, the substantial variation in prevalence reported between studies was largely attributed to differences in methodology. The lack of a consistent definition distorted comparisons of prevalence between countries, with important differences being lost in methodologic noise.2 Since the introduction, in 2002, of the Kidney Disease Improving Global Outcomes definition for CKD, there has been a global recognition that CKD is common.3 What remains challenging, however, is to determine precisely how common. In 2013, the Global Burden of Disease Study ranked CKD 19th on the list of global causes of death.4 The associated risks of ESRD, cardiovascular morbidity, fractures, cognitive impairment, and anemia mark CKD as an important global health problem.5 Estimates from the United States population on the basis of national health surveys reported a prevalence of CKD of 13.1% in 1999–2004.6 The aging of the population combined with an epidemic-like increase in diabetes mellitus, obesity, and hypertension are anticipated to further drive substantial rises in CKD prevalence in the coming decades.5 In this issue of the Journal of the American Society of Nephrology, Brück et al.7 report CKD prevalence across 13 European countries. Brück et al.7 leveraged the power of 19 studies designed to be representative of the adult general population; 10 restricted inclusion to subjects ages ≥65 years old. The total study sample consisted of >225,000 people. The prevalence of risk factors varied substantially between included studies. For example, the percentage of people ≥65 years old with diabetes mellitus ranged from 6.9% in Groningen, The Netherlands to 28.2% in southwest Germany. Brück et al.7 performed analyses on individual patient–level data, which optimized comparability of prevalence estimates between studies. It enabled application of a single definition for CKD on the basis of the same equation to estimate the GFR; the majority of studies also used the same assays for measuring serum creatinine (Jaffe) and urinary albumin (immunoassays). Brück et al.7 further increased comparability by adjusting overall prevalence estimates to a standard European age and sex distribution and repeating their analyses stratified for age and the main CKD risk factors: hypertension, diabetes, and obesity. The study by Brück et al.7 suggests substantial geographic variation in CKD prevalence across Europe. For CKD stages 1–5, Brück et al.7 report overall adjusted prevalence ranging between 3.3% in Norway to >17% in northeast Germany. For CKD stages 3–5, CKD varied between 1.0% in Italy and 5.9% in northeast Germany. Stratifying the analyses for age and risk factors showed similar patterns. Another recent publication reported the prevalence of CKD globally.8 Mills et al.8 included 33 original population–based studies totaling >920,000 people, of whom 81% were from high-income countries. Mills et al.8 analyzed study-level (and not individual patient-level) data to provide pooled estimates of CKD prevalence for high-income and low/middle-income countries separately while stratifying by age and sex. Seven cohorts were included in both the work by Brück et al.7 and the work by Mills et al.,8 accounting for 39% of the total study population in the work by Brück et al.7 Using the same definition for CKD but a different standard global population for age and sex standardization, Mills et al.8 found an overall age–adjusted CKD stages 1–5 prevalence in high-income countries that was in line with that in the work by Brück et al.7: 10.1% in men and 12.1% in women; for CKD stages 3–5, it was somewhat higher (5.4% and 8.6% for men and women, respectively). Mills et al.8 convincingly show that CKD is a common public health problem worldwide, especially in low- and middle-income countries. The importance of their findings notwithstanding, Mills et al.8 adopted a simplification approach by pooling data into two large groups. The countries within each group may share their level of gross national income, but they are likely to have different health care systems and public health policies, which in turn, may affect CKD prevalence. However, the methodologic approach by Mills et al.8 left any meaningful between-country differences unrevealed. Furthermore, interpretation of between-country variation would have been hampered: first, 60% of included studies used the same equation to estimate GFR, and second, analyses were not adjusted for differences in CKD risk factors. The paper by Brück et al.7 reminds us that there is important knowledge to be gained by understanding variation better. In contrast to the work by Mills et al.,8 the work by Brück et al.7 aimed to minimize the methodologic variation between studies, which is a salient strength of their approach. As a result, their findings contribute to our understanding of CKD prevalence by showing that the observed variation was unlikely to be driven by methodologic issues alone and that it could be explained by neither population-based differences in age and sex distributions nor risk factor prevalence.7 So, how do we move forward? Implicit within the variation is the potential to unlock a better understanding of the natural history of CKD and the opportunity for natural experiments. Brück et al.7 express an ambitious vision building on their work: a vision for rigorous data collection across Europe that can be used to evaluate the effect of care strategies. In essence, Brück et al.7 call for the infrastructure to support efficient natural experiments. Although randomized, controlled trials are the gold standard for experimental design to evaluate comparative effectiveness, there has been a long established use of observational epidemiology to tackle situations where randomized, controlled trials are impractical. Natural experiments seek to exploit variation, whether variation in exposure between individuals or between populations. All natural experiments have in common that the exposure or intervention under study has not been manipulated by the researchers.9 As such, natural experiments have an important role to play in situations where the exposure or intervention is difficult to study experimentally for either practical or ethical reasons (for example, determining the effect of legislation or policy, understanding the effect of environmental exposures, or studying very rare or long-term outcomes).9 Previous natural experiments have successfully exploited variation to understand the effect of policy, practice, or exposure to risk factors.10,11 For example, in 2006, Scotland implemented a law prohibiting smoking in enclosed public places ahead of the other three United Kingdom nations. Researchers used the before and after variation in exposure as well as a difference in differences approach to harness variation in geographic implementation.10 In nephrology, natural experiments have been used to study the effect of population-level interventions or health policies. For example, an experiment evaluating the effectiveness of influenza vaccination in adults on hemodialysis exploited variation between years when the vaccine was known to be a good match for the circulating strain and years when it was not.12 The introduction of a payment for performance initiative lent itself to a natural experiment using time-series analysis to study variation in markers of care before and after a national incentive scheme was rolled out.13 Bello et al.14 set out clear recommendations for policy and population-wide intervention to prevent and promote the best care for people with CKD. Variation between countries in the types of interventions and the timing of their implementation will be inevitable. How do we then create an effective and efficient system to identify best practices? Brück et al.7 recognize that their work, which establishes that variation exists, is a starting point. Importantly, Brück et al.7 also recognize the scale of the challenge, identifying that the perfect natural experiment is unlikely to be achievable without significant investment into multicountry population–wide surveillance systems with centralized collection, testing, and collation of data. The United Kingdom Medical Research Council methodologic framework for natural experiments reiterates this challenge and recommends that such quantitative endeavors should only be undertaken where variation in exposure can be identified, using samples large enough to detect effect, and where accurate data can be obtained about the exposure, outcomes, and potential confounders.9 Therefore, if natural experiments are the panacea to better understand kidney disease epidemiology, risk factors, and the effect of interventions, the reality is that we need efficient and sustainable solutions to capture detailed international population–based data. High–quality population health survey approaches may be part of this solution. However, a complementary and as yet, underused resource exists in the growing array of routinely collected health information. Although now still mainly pertaining to the clinical arena, health data collection is moving toward a more coproduced model, where patients and the general public are increasingly encouraged and facilitated to contribute their health data for clinical and research purposes.15 Ainsworth and Buchan16 recently described a vision for a learning health system. Such a system brings together data from multiple sources to describe the health of a community and enables distributed analysis of variation through a network of similar data clusters. Perhaps such an exciting future for a learning kidney health community is on the horizon, but whether through the investment in robust specialist surveillance systems or effective harnessing of routine health data, key will be the collection and curation of high-quality data. Disclosures We acknowledge the support from The Farr Institute of Health Informatics Research. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates), the Wellcome Trust, (MRC Grant Nos: CIPHER MR/K006525/1, HeRC MR/K006665/1, London MR/ K006584/1, Scotland MR/K007017/1).