Alzheimer’s disease neuropathologic change (ADNC) is defined by progressive accumulation of β-amyloid plaques and hyperphosphorylated tau (pTau) neurofibrillary tangles across diverse regions of brain. Non-demented individuals who reach advanced age without significant ADNC are considered to be resistant to AD, while those burdened with ADNC are considered to be resilient. Understanding mechanisms underlying ADNC resistance and resilience may provide important clues to treating and/or preventing AD associated dementia. ADNC criteria for resistance and resilience are not well-defined, so we developed stringent pathologic cutoffs for non-demented subjects to eliminate cases of borderline pathology. We identified 14 resistant (85+ years old, non-demented, Braak stage ≤ III, CERAD absent) and 7 resilient (non-demented, Braak stage VI, CERAD frequent) individuals out of 684 autopsies from the Adult Changes in Thought study, a long-standing community-based cohort. We matched each resistant or resilient subject to a subject with dementia and severe ADNC (Braak stage VI, CERAD frequent) by age, sex, year of death, and post-mortem interval. We expanded the neuropathologic evaluation to include quantitative approaches to assess neuropathology and found that resilient participants had lower neocortical pTau burden despite fulfilling criteria for Braak stage VI. Moreover, limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) was robustly associated with clinical dementia and was more prevalent in cases with high pTau burden, supporting the notion that resilience to ADNC may depend, in part, on resistance to pTDP-43 pathology. To probe for interactions between tau and TDP-43, we developed a C. elegans model of combined human (h) Tau and TDP-43 proteotoxicity, which exhibited a severe degenerative phenotype most compatible with a synergistic, rather than simply additive, interaction between hTau and hTDP-43 neurodegeneration. Pathways that underlie this synergy may present novel therapeutic targets for the prevention and treatment of AD.

The Affordable Care Act authorized, but did not fund, the Primary Care Extension Program (PCEP). Much like the Cooperative Extension Program of the US Department of Agriculture sped the modernization of farming a century ago, the PCEP could speed the transformation of primary care. It could also help achieve other goals such as integrating primary care with public health and translating research into practice. The urgency of these goals and their importance to achieving the Triple Aim for health care should increase interest in rapidly building the PCEP, much as the need to feed the country did a century ago. 2013;11:173-178. doi:10.1370/afm.1495. Ann Fam Med INTRODUCTIONP assage of the Affordable Care Act of 2010 (ACA) laid a foundation for unprecedented support of primary care, placing it at the core of a learning health care system that seeks to achieve the Center for Medicare and Medicaid Services' Triple Aim: improving the experience of care, improving the health of populations, and reducing per capita costs of health care.1 This article seeks to help clinical and policy leaders understand how critical the Primary Care Extension Program (PCEP) is to enhancing primary care effectiveness, to the integration of primary care and public health, and to translating research into practice, all with the goal of achieving the Triple Aim for health care. 2,3 Before the ACA, primary care leaders were already engaged in designing and testing new models of care, particularly the patient-centered medical home (PCMH). Evidence suggesting that these reformed models of primary care improve health outcomes while reducing costs has stimulated a surge of interest for widespread transformation of primary care. 4,5 Many of these high-performing models have increased capacity for monitoring and managing population health, and some have bridged the substantial gap between primary care and public health. 6,7 Despite early evidence and growing enthusiasm, primary care transformation has not yet arrived at a tipping point, and the United States lacks a mechanism for facilitating the change. PRIMARY CARE EXTENSION PROGRAMAnticipating these challenges to primary care transformation, the ACA authorized the Agency for Healthcare Research and Quality (AHRQ) to create a national the PCEP. This section of the law states that the principal charge of the PCEP is to "assist primary care providers to implement a patient-centered medical home to improve the accessibility, quality, and effi ciency of primary care services" through local deployment of community-based Health Extension Agents. In addition to their practice facilitation roles, these agents may "collaborate with local health departments, community health centers, tribes and tribal entities, and other community agencies to identify community health priorities and local health 7 This Institute of Medicine study specifi cally mentions the PCEP as an important model for developing these partnerships. Roots in the Department of Agriculture's Cooperative ExtensionThe PCEP builds upon...

Integrated behavioral health and primary care is emerging as a superior means by which to address the needs of the whole person, but we know neither the extent nor the distribution of integration. Using the Centers for Medicare and Medicaid Services' National Plan and Provider Enumeration System (NPPES) Downloadable File, this study reports where colocation exists for (a) primary care providers and any behavioral health provider and (b) primary care providers and psychologists specifically. The NPPES database offers new insights into where opportunities are limited for integration due to workforce shortages or nonproximity of providers and where possibilities exist for colocation, a prerequisite for integration.

BackgroundFederally Qualified Health Centers are expanding to increase access for millions of more Americans with a goal of doubling capacity to serve 40 million people. Health centers provide a lot of behavioral health services but many have difficulty accessing mental health and substance use professionals for their patients. To meet the needs of the underserved and newly insured it is important to better estimate how many behavioral health professionals are needed.MethodsUsing health center staffing data and behavioral health service patterns from the 2010 Uniform Data System and the 2010 National Survey on Drug Use and Health, we estimated the number of patients likely to need behavioral health care by insurance type, the number of visits likely needed by health center patients annually, and the number of full time equivalent providers needed to serve them.ResultsMore than 2.5 million patients, 12 or older, with mild or moderate mental illness, and more than 357,000 with substance abuse disorders, may have gone without needed behavioral health services in 2010. This level of need would have required more than 11,600 full time providers. This translates to approximately 0.9 licensed mental health provider FTE, 0.1 FTE psychiatrist, 0.4 FTE other mental health staff, and 0.3 FTE substance abuse provider per 2,500 patients. These estimates suggest that 90% of current centers could not access mental health services or provide substance abuse services to fully meet patients’ needs in 2010. If needs are similar after health center expansion, more than 27,000 full time behavioral health providers will be needed to serve 40 million medical patients, and grantees will need to increase behavioral health staff more than four-fold.ConclusionsMore behavioral health is seen in primary care than in any other setting, and health center clients have greater behavioral health needs than typical primary care patients. Most health centers needed additional behavioral health services in 2010, and this need will be magnified to serve 40 million patients. Further testing of these workforce models are needed, but the degree of current underservice suggests that we cannot wait to move on closing the gap.

These findings suggest that traditional and genetic risk factors in middle age may be useful for identifying those at risk of gout in older age.

Objectives Gout prevalence is high in older adults and those affected are at risk of physical disability, yet it is unclear whether they have worse physical function. Methods We studied gout, hyperuricemia, and physical function in 5,819 older adults (age ≥ 65) attending the 2011–2013 Atherosclerosis Risk in Communities Study visit, a prospective US population-based cohort. Differences in lower extremity [Short Physical Performance Battery (SPPB) and 4 meter walking speed] and upper extremity function (grip strength) by gout status and by hyperuricemia prevalence were estimated in adjusted ordinal logistic regression (SPPB) and linear regression (walking speed and grip strength) models. Lower scores or times signify worse function. The prevalences of poor physical performance (first quartile) by gout and hyperuricemia were estimated using adjusted modified Poisson regression. Results 10% of participants reported a history of gout and 21% had hyperuricemia. There was no difference in grip strength by history of gout (P=0.77). Participants with gout performed worse on the SPPB test; they had 0.77-times (95%CI:0.65,0.90; P=0.001) the prevalence odds of 1-unit increase in SPPB score and were 1.18-times (95%CI:1.07,1.32; P=0.002) more likely to have poor SPPB performance. Participants with a history of gout had slower walking speed (mean difference = −0.03, 95%CI: −0.05, −0.01; P<0.001) and were 1.19-times (95%CI:1.06,1.34; P=0.003) more likely to have poor walking speed. Similarly, SPPB score and walking speed, but not grip strength, were worse in participants with hyperuricemia. Conclusion Older adults with gout and hyperuricemia are more likely to have worse lower but not upper extremity function.

Network methods have been applied to obesity to map connections between obesity-related genes, model biological feedback mechanisms and potential interventions, and to understand the spread of obesity through social networks. However, network methods have not been applied to understanding the obesogenic environment. Here, we created a network of 32 features of communities hypothesized to be related to obesity. Data from an existing study of determinants of obesity among 1,288 communities in Pennsylvania were used. Spearman correlation coefficients were used to describe the bivariate association between each pair of features. These correlations were used to create a network in which the nodes are community features and weighted edges are the strength of the correlations among those nodes. Modules of clustered features were identified using the walktrap method. This network was plotted, and then examined separately for communities stratified by quartiles of child obesity prevalence. We also examined the relationship between measures of network centrality and child obesity prevalence. The overall structure of the network suggests that environmental features geographically co-occur, and features of the environment that were more highly correlated with body mass index were more central to the network. Three clusters were identified: a crime-related cluster, a foodenvironment and land use-related cluster, and a physical activityrelated cluster. The structure of connections between features of the environment differed between communities with the highest and lowest burden of childhood obesity, and a higher degree of average correlation was observed in the heaviest communities. Network methods may help to explicate the concept of the obesogenic environment, and ultimately to illuminate features of the environment that may serve as levers of community-level intervention.

The AT(N) research framework categorizes eight biomarker profiles using amyloid (A), tauopathy (T), and neurodegeneration (N), regardless of dementia status. We evaluated associations with dementia risk in a community‐based cohort by approximating AT(N) profiles using autopsy‐based neuropathology correlates, and considered cost implications for clinical trials for secondary prevention of dementia based on AT(N) profiles. We used Consortium to Establish a Registry for Alzheimer's Disease (moderate/frequent) to approximate A+, Braak stage (IV‐VI) for T+, and temporal pole lateral ventricular dilation for (N)+. Outcomes included dementia prevalence at death and incidence in the last 5 years of life. A+T+(N)+ was the most common profile (31%). Dementia prevalence ranged from 14% (A–T–[N]–) to 79% (A+T+[N]+). Between 8% (A+T–[N]–) and 68% (A+T+[N]–) of decedents developed incident dementia in the last 5 years of life. Clinical trials would incur substantial expense to characterize AT(N). Many people with biomarker‐defined preclinical Alzheimer's disease will never develop clinical dementia during life, highlighting resilience to clinical expression of AD neuropathologic changes and the need for improved tools for prediction beyond current AT(N) biomarkers.