Douglas J Noble’s research while affiliated with Weill Cornell Medicine and other places

This qualitative interview study explored perceptions of the phrases 'population health', 'public health' and 'community health'. Accountable care organisations (ACOs), and public health or similar agencies in different parts of the USA. Purposive sample of 29 interviewees at four ACOs, and 10 interviewees at six public health or similar agencies. Interviewees working for ACOs most often viewed 'population health' as referring to a defined group of their organisation's patients, though a few applied the phrase to people living in a geographical area. In contrast, interviewees working for public health agencies were more likely to consider 'population health' from a geographical perspective. Conflating geographical population health with the health of ACOs' patients may divert attention and resources away from organisations that use non-medical means to improve the health of geographical populations. As ACOs battle to control costs of their population of patients, it would be more accurate to consider using a more specific phrase, such as 'population of attributed patients', to refer to ACOs' efforts to care for the health of their defined group of patients.

In Reply Drs Sanford and Handley point out the assistance that the work of the USPSTF might provide to those interested in population health. Further research is needed to establish how the leaders of ACOs perceive the meaning of population health and how they may form partnerships with public health agencies.Section Editor: Jody W. Zylke, MD, Senior Editor.Article InformationCorresponding Author: Douglas J. Noble, MD, MPH, Weill Cornell Medical College, 402 E. 67th St, New York, NY 10065 (djn2004@med.cornell.edu).Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Noble reported being a Commonwealth Fund Harkness Fellow for 2012-2013 and is sponsored financially by the Commonwealth Fund for 1 year to perform research on accountable care organizations and population health. Dr Casalino reported serving from 2010-2011 as an unpaid member of the Accountable Care Organization Task Force of the National Committee for Quality Assurance; receiving honoraria for lectures from Primary Care Summit of the Connecticut Center for Primary Care and Mission Health System and the American Medical Group Association; and being an unpaid board member of the American Medical Group Association Foundation.

The number of accountable care organizations (ACOs) increased rapidly during 2012. There are now more than 250. This increase is likely to accelerate: commercial health insurers are signing ACO-like contracts with health care organizations, and the return of President Obama to the White House, as well as the Supreme Court ruling upholding the Affordable Care Act (ACA), have removed uncertainty about the Medicare ACO program. The goals for ACOs are well known: to control health care costs, to drive quality in health care, and to improve population health.

Risk scores calculated from electronic patient records can be used to predict the risk of adults developing diabetes in the future. To use a risk-prediction model on GPs' electronic health records in three inner-city boroughs, and to map the risk of diabetes by locality for commissioners, to guide possible interventions for targeting groups at high risk. Cross-sectional analysis of electronic general practice records from three deprived and ethnically diverse inner-city boroughs in London. A cross-sectional analysis of 519 288 electronic primary care records was performed for all people without diabetes aged 25-79 years. A validated risk score, the QDScore, was used to predict 10-year risk of developing type 2 diabetes. Descriptive statistics were generated, including subanalysis by deprivation and ethnicity. The proportion of people at high risk (≥20% risk) per general practice was geospatially mapped. Data were obtained from 135 out of 145 general practices (91.3%); 1 in 10 people in this population were at high risk (≥20%) of developing type 2 diabetes within 10 years. Of those with known cardiovascular disease or hypertension, approximately 50% were at high risk. Male sex, increasing age, South Asian ethnicity, deprivation, obesity, and other comorbidities increased the risk. Geospatial mapping revealed hotspots of high risk. Individual risk scores calculated from electronic records can be aggregated to produce population risk profiles to inform commissioning and public health planning. Specific localities were identified (the 'East London diabetes belt'), where preventive efforts should be targeted. This method could be used for other diseases and risk states, to inform targeted commissioning and preventive research.

Siontis and colleagues’ conclusion that comparison of risk prediction models for cardiovascular disease “would benefit from standardised reporting and formal, consistent statistical comparisons” is only part of the story.1 …

To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams. Cross-sectional geospatial analysis using routinely collected general practitioner electronic record data. Tower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases. The authors used type 2 diabetes as an example. The data set was drawn from electronic general practice records on all non-diabetic individuals aged 25-79 years in the district (n=163 275). The authors used a validated instrument, QDScore, to calculate 10-year risk of developing type 2 diabetes. Using specialist mapping software (ArcGIS), the authors produced visualisations of how these data varied by lower and middle super output area across the district. The authors enhanced these maps with information on examples of locality-based social determinants of health (population density, fast food outlets and green spaces). Data were piloted as three types of geospatial map (basic, heat and ring). The authors noted practical, technical and information governance challenges involved in producing the maps. Usable data were obtained on 96.2% of all records. One in 11 adults in our cohort was at 'high risk' of developing type 2 diabetes with a 20% or more 10-year risk. Small-area geospatial mapping illustrated 'hot spots' where up to 17.3% of all adults were at high risk of developing type 2 diabetes. Ring maps allowed visualisation of high risk for type 2 diabetes by locality alongside putative social determinants in the same locality. The task of downloading, cleaning and mapping data from electronic general practice records posed some technical challenges, and judgement was required to group data at an appropriate geographical level. Information governance issues were time consuming and required local and national consultation and agreement. Producing small-area geospatial maps of diabetes risk calculated from general practice electronic record data across a district-wide population was feasible but not straightforward. Geovisualisation of epidemiological and environmental data, made possible by interdisciplinary links between public health clinicians and human geographers, allows presentation of findings in a way that is both accessible and engaging, hence potentially of value to commissioners and policymakers. Impact studies are needed of how maps of chronic disease risk might be used in public health and urban planning.