Population curation: The construction of mutual obligation between individual and state in Danish precision medicine.

How do precision medicine initiatives (re)organize relations between individuals and populations? In this article, we investigate how the curation of national genomic populations enacts communities and, in so doing, constructs mutual obligation between individuals and the state. Drawing on ethnographic fieldwork in the Danish National Genome Center (DNGC), we show how members of advisory bodies negotiated the inclusion criteria for two different genomic populations: a patient genome population and an envisioned 'Danish' reference genome population. The patient genome population was curated through a politics of inclusion, of as many genomes as possible, whereas the reference genome was to be curated through a politics of exclusion, to include only the genomes of 'ethnic' Danes. These two data populations configure differently the community of 'Danish patients' who might benefit from precision medicine, and thereby prescribe different moral continuities between person, state, and territory. We argue that the DNGC's patient genome population reinforces reciprocal relations of obligations and responsibility between the Danish welfare state and all individuals, while the proposed Danish reference genome population privileges the state's commitment to individuals with biographical-territorial belonging to the nation-state. Drawing on scholarship on social and health citizenship, as well as data solidarity in the Nordics, the discussion shows how population curation in national precision medicine initiatives might both construct and stratify political obligation. Whereas STS scholarship has previously deconstructed the concept of 'population', in the context of the troubling and violent effects of the management of human populations, we point to the importance of population curation as a vehicle for making the individual legible as part of a community to which the state is responsible and for which it is committed to care.

Novel integration of governmental data sources using machine learning to identify super-utilization among U.S. counties.

Background: Super-utilizers consume the greatest share of resource intensive healthcare (RIHC) and reducing their utilization remains a crucial challenge to healthcare systems in the United States (U.S.). The objective of this study was to predict RIHC among U.S. counties, using routinely collected data from the U.S. government, including information on consumer spending, offering an alternative method for identifying super-utilization among population units rather than individuals. Methods: Cross-sectional data from 5 governmental sources in 2017 were used in a machine learning pipeline, where target-prediction features were selected and used in 4 distinct algorithms. Outcome metrics of RIHC utilization came from the American Hospital Association and included yearly: (1) emergency rooms visit, (2) inpatient days, and (3) hospital expenditures. Target-prediction features included: 149 demographic characteristics from the U.S. Census Bureau, 151 adult and child health characteristics from the Centers for Disease Control and Prevention, 151 community characteristics from the American Community Survey, and 571 consumer expenditures from the Bureau of Labor Statistics. SHAP analysis identified important target-prediction features for 3 RIHC outcome metrics. Results: 2475 counties with emergency rooms and 2491 counties with hospitals were included. The median yearly emergency room visits per capita was 0.450 [IQR:0.318, 0.618], the median inpatient days per capita was 0.368 [IQR: 0.176, 0.826], and the median hospital expenditures per capita was $2104 [IQR: $1299.93, 3362.97]. The coefficient of determination (R2), calculated on the test set, ranged between 0.267 and 0.447. Demographic and community characteristics were among the important predictors for all 3 RIHC outcome metrics. Conclusions: Integrating diverse population characteristics from numerous governmental sources, we predicted 3-outcome metrics of RIHC among U.S. counties with good performance, offering a novel and actionable tool for identifying super-utilizer segments in the population. Wider integration of routinely collected data can be used to develop alternative methods for predicting RIHC among population units.

Intangible Cultural Heritage: 'Curating' the Human.

'Nostalgic environments' are increasingly being created in museums and institutional care settings for people with dementia, to support residents' capacities for memory and recognition. Drawing upon ethnography carried out in a public nursing home specialized in dementia care in Copenhagen, Denmark, this paper engages conceptually the employment of material heritage within dementia care environments, proposing dementia care as a 'curatorial' practice: caregivers act as 'curators' who re-establish and reorganize the 'meaning' of the residents by preserving their individual biographies and societal belonging. The analytical alignment of dementia care with the curating of cultural valuables reveals that the human is not only the subject within-and the creator of-cultural heritage, but also the object: the person with dementia is simultaneously an acting subject in care and an object for performances of the category of the human. As the curatorial care performed in nursing homes preserves not only individual, but also collective memories of what it takes to be human and belong in society, these institutions should be recognized as significant sites within society concerned with the production of meaning, value and cultural heritage.

Quantifying differences in packaged food and drink purchases among households with diet-related cardiometabolic multi-morbidity: a cross-sectional analysis.

BACKGROUND: Diet is important for chronic disease management, with limited research understanding dietary choices among those with multi-morbidity, the state of having 2 or more chronic conditions. The objective of this study was to identify associations between packaged food and drink purchases and diet-related cardiometabolic multi-morbidity (DRCMM). METHODS: Cross-sectional associations between packaged food and drink purchases and household DRCMM were investigated using a national sample of U.S. households participating in a research marketing study. DRCMM households were defined as household head(s) self-reporting 2 or more diet-related chronic conditions. Separate multivariable logistic regression models were used to model the associations between household DRCMM status and total servings of, and total calories and nutrients from, packaged food and drinks purchased per month, as well as the nutrient density (protein, carbohydrates, and fat per serving) of packaged food and drinks purchased per month, adjusted for household size. RESULTS: Among eligible households, 3795 (16.8%) had DRCMM. On average, households with DRCMM versus without purchased 14.8 more servings per capita, per month, from packaged foods and drinks (p < 0.001). DRCMM households were 1.01 times more likely to purchase fat and carbohydrates in lieu of protein across all packaged food and drinks (p = 0.002, p = 0.000, respectively). DRCMM households averaged fewer grams per serving of protein, carbohydrates, and fat per month across all food and drink purchases (all p < 0.001). When carbonated soft drinks and juices were excluded, the same associations for grams of protein and carbohydrates per serving per month were seen (both p < 0.001) but the association for grams of fat per serving per month attenuated. CONCLUSIONS: DRCMM households purchased greater quantities of packaged food and drinks per capita than non-DRCMM households, which contributed to more fat, carbohydrates, and sodium in the home. However, food and drinks in DRCMM homes on average were lower in nutrient-density. Future studies are needed to understand the motivations for packaged food and drink choices among households with DRCMM to inform interventions targeting the home food environment.

An Experiment in Denmark to Infect Wounded Sitka Spruce with the Rotstop Isolate of Phlebiopsis gigantea, and Its Implications for the Control of Heterobasidion annosum in Britain.

The formulation of a Finnish isolate of the saprotrophic wood-rotting fungus Phlebiopsis gigantea into the biocontrol agent (BCA) Rotstop, which is used to prevent infection of Norway spruce stumps by aerial basidiospores of H. annosum, has led to its application to more than 200,000 ha of forest in Scandinavia and North Europe. The success of this treatment opens the possibility of introducing the Rotstop strain into Britain for use on Sitka spruce stumps, which at present (2022) are lacking adequate prophylactic treatment. However, Rotstop is probably non-native to Britain and to North America (the ancestral home of this spruce), and we do not know if this xylem-decaying BCA can invade standing trees. In this paper, we describe a trial into this issue conducted for the U.K. Forestry Commission in Denmark, in a country where both Rotstop and Sitka spruce have been naturalised. It was preliminary to further stump treatment trials, and is relevant to long-term issues surrounding stump treatment in Britain. Inoculations into 44-year-old standing Sitka spruce with 20 mm wooden Scots pine plugs pre-colonised with Rotstop resulted in decay of the S1, S2, S3 and middle lamellae of sapwood above and below the wounds after 11-18 months. In contrast, infection of sapwood occurred in only one of 39 wounds on the same trees challenged with oidial spore inoculants adpressed to undamaged xylem sapwood during the same period. While the results suggest that release of Rotstop into the productive stands of Sitka spruce in Britain would be unlikely to cause long-term commercial losses to wounded trees, the work highlights issues relating to the assessment of risk associated with the introduction of non-native BCAs within the forest environment.

Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

BACKGROUND: Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a novel and actionable tool for identifying super-utilizer segments in the population. Consumer expenditures may offer additional value in predicting RIHC beyond typical population characteristics alone. METHODS: Cross-sectional data from 2017 was extracted from 5 unique sources. The outcome was RIHC and included emergency room (ER) visits, inpatient days, and hospital expenditures, all expressed as log per capita. Candidate predictors from 4 broad groups were used, including demographics, adults and child health characteristics, community characteristics, and consumer expenditures. Candidate predictors were expressed as per capita or per capita percent and were aggregated from zip-codes to HSAs using weighed means. Machine learning approaches (Random Forrest, LASSO) selected important features from nearly 1,000 available candidate predictors and used them to generate 4 distinct models, including non-regularized and LASSO regression, random forest, and gradient boosting. Candidate predictors from the best performing models, for each outcome, were used as independent variables in multiple linear regression models. Relative contribution of variables from each candidate predictor group to regression model fit were calculated. RESULTS: The median ER visits per capita was 0.482 [IQR:0.351-0.646], the median inpatient days per capita was 0.395 [IQR:0.214-0.806], and the median hospital expenditures per capita was $2,302 [1$,544.70-$3,469.80]. Using 1,106 variables, the test-set coefficient of determination (R2) from the best performing models ranged between 0.184-0.782. The adjusted R2 values from multiple linear regression models ranged from 0.311-0.8293. Relative contribution of consumer expenditures to model fit ranged from 23.4-33.6%. DISCUSSION: Machine learning models predicted RIHC among HSAs using diverse population data, including novel consumer expenditures and provides an innovative tool to predict population-based healthcare utilization and expenditures. Geographic variation in utilization and spending were identified.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial: Lessons Learned From ADAPTABLE.

BACKGROUND: Many large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events. METHODS AND RESULTS: A multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation. CONCLUSIONS: The ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.

Time and Personhood across Early and Late-Stage Dementia.

How do time and personhood become related when dementia sets in? This article brings together ethnographies from a memory clinic and a dementia nursing home in Copenhagen, Denmark, pursuing how personhood and time become intertwined across early and late-stage dementia. In the memory clinic, the dementia diagnosis is enacted and experienced simultaneously as an indispensable prophecy of discontinuity of personhood and life for the patients, and as a prognosis that renders the future indeterminate and open to intervention. In the nursing home, institutionalized care marks the fulfillment of the prophecy of decline, yet nursing home staff insist on practicing prognoses for the residents. Across our empirical sites, we enquire what the tension between prophecy and prognosis mean for personhood and the possibilities of the present, arguing that people with dementia are made and unmade through different understandings and enactments of future-oriented temporalities.

Lactase Persistence, Milk Intake, and Adult Acne: A Mendelian Randomization Study of 20,416 Danish Adults.

Whether there is a causal relationship between milk intake and acne is unknown. We tested the hypothesis that genetically determined milk intake is associated with acne in adults using a Mendelian randomization design. LCT-13910 C/T (rs4988235) is associated with lactase persistence (TT/TC) in Northern Europeans. We investigated the association between milk intake, LCT-13910 C/T (rs4988235), and acne in 20,416 adults (age-range: 20⁻96) from The Danish General Suburban Population Study (GESUS). The adjusted observational odds ratio for acne in any milk intake vs. no milk intake was 0.93(95% confidence interval: 0.48⁻1.78) in females and 0.49(0.22⁻1.08) in males aged 20⁻39 years, and 1.15(95% confidence interval: 0.66⁻1.99) in females and 1.02(0.61⁻1.72) in males above 40 years. The unadjusted odds ratio for acne in TT+TC vs. CC was 0.84(0.43⁻1.62) in the age group 20⁻39 years, and 0.99(0.52⁻1.88) above 40 years. We did not find any observational or genetic association between milk intake and acne in our population of adults.

Dairy Intake and Acne Vulgaris: A Systematic Review and Meta-Analysis of 78,529 Children, Adolescents, and Young Adults.

A meta-analysis can help inform the debate about the epidemiological evidence on dairy intake and development of acne. A systematic literature search of PubMed from inception to 11 December 2017 was performed to estimate the association of dairy intake and acne in children, adolescents, and young adults in observational studies. We estimated the pooled random effects odds ratio (OR) (95% CI), heterogeneity (I²-statistics, Q-statistics), and publication bias. We included 14 studies (n = 78,529; 23,046 acne-cases/55,483 controls) aged 7⁻30 years. ORs for acne were 1.25 (95% CI: 1.15⁻1.36; p = 6.13 × 10-8) for any dairy, 1.22 (1.08⁻1.38; p = 1.62 × 10-3) for full-fat dairy, 1.28 (1.13⁻1.44; p = 8.23 × 10-5) for any milk, 1.22 (1.06⁻1.41; p = 6.66 × 10-3) for whole milk, 1.32 (1.16⁻1.52; p = 4.33 × 10-5) for low-fat/skim milk, 1.22 (1.00⁻1.50; p = 5.21 × 10-2) for cheese, and 1.36 (1.05⁻1.77; p = 2.21 × 10-2) for yogurt compared to no intake. ORs per frequency of any milk intake were 1.24 (0.95⁻1.62) by 2⁻6 glasses per week, 1.41 (1.05⁻1.90) by 1 glass per day, and 1.43 (1.09⁻1.88) by ≥2 glasses per day compared to intake less than weekly. Adjusted results were attenuated and compared unadjusted. There was publication bias (p = 4.71 × 10-3), and heterogeneity in the meta-analyses were explained by dairy and study characteristics. In conclusion, any dairy, such as milk, yogurt, and cheese, was associated with an increased OR for acne in individuals aged 7⁻30 years. However, results should be interpreted with caution due to heterogeneity and bias across studies.

Resisting decay: On disposal, valuation, and care in a dementia nursing home in Denmark.

This paper approaches institutionalized dementia care as a site of societal disposal, valuation, and care for human life. Drawing upon six weeks of ethnographic fieldwork and ten qualitative interviews carried out in a Danish dementia nursing home in 2014, we analyze how nursing home staff, through everyday care, uphold the value of life for residents in severe mental and physical decline. We argue that life's worth is established when residents gain qualities of personhood and agency through substitution processes carried out by staff. Yet the persistent absence of conventional personhood and autonomous agency in residents (i.e. capacities for memory, consciousness, language, and mobility) evokes experiences of ambiguity in staff and relatives of residents. We close the article with a discussion of this ambiguity and the significance of the nursing home as care institution in the welfare state. Dementia care, we propose, is not only about preserving the lives of people with dementia. At stake in the daily care practices around severely disabled residents in the nursing home is the very continuance of the main principles of the welfare society.

The Follicular Skin Microbiome in Patients With Hidradenitis Suppurativa and Healthy Controls.

Importance: Although the pathogenesis of hidradenitis suppurativa (HS) remains enigmatic, several factors point to potential involvement of the cutaneous microbiome. Insight into the cutaneous microbiome in HS using next-generation sequencing may provide novel data on the microbiological diversity of the skin. Objective: To investigate the follicular skin microbiome in patients with HS and in healthy controls. Design, Setting, and Participants: This case-control study obtained punch biopsy specimens from patients with HS (lesional and nonlesional) and healthy controls between October 1, 2014, and August 1, 2016. Data were analyzed from March to November 2016. Patients with HS were recruited from the Department of Dermatology, Zealand University Hospital, Roskilde, Denmark. Biopsy specimens were analyzed at the Department of Microbiology and Infection Control, Statens Serum Institut, Copenhagen, Denmark. None of the participants received any antibiotics (systemic or topical therapy) within 1 month before the study. In patients with HS, biopsy specimens were obtained from lesional skin (axilla or groin) and nonlesional skin. Only nodules containing at least 1 visible hair follicle were biopsied. Biopsy specimens from healthy controls were obtained from the axilla only. Main Outcomes and Measures: The different microbiomes were investigated using next-generation sequencing targeting 16S and 18S ribosomal RNA. Results: The skin microbiome was characterized in 30 patients with HS (mean [SD] age, 46.9 [14.0] years; 19 [63% female]) and 24 healthy controls (mean [SD] age, 32.2 [12.0] years; 13 [54% female]). The next-generation sequencing data provided a previously unreported (to our knowledge) characterization of the skin microbiome in HS. The study demonstrated that the microbiome in HS differs significantly from that in healthy controls in lesional and nonlesional skin. Overall, the following 5 microbiome types were identified: Corynebacterium species (type I), Acinetobacter and Moraxella species (type II), Staphylococcus epidermidis (type III), Porphyromonas and Peptoniphilus species (type IV), and Propionibacterium acnes (type V). In lesional skin, microbiome types consisted predominantly of type I or type IV. Microbiome type IV was not detected in healthy controls. Several taxa, including Propionibacterium, showed a significantly higher relative abundance in healthy controls vs HS skin, indicating that Propionibacterium may be part of the pathogenesis in HS. Conclusions and Relevance: The study findings suggest a link between a dysbiotic cutaneous microbiome and HS.

Recognizing Dementia: Constructing Deconstruction in a Danish Memory Clinic.

This article investigates how a person with dementia is made up through intersubjective acts of recognition. Based on ethnographic fieldwork in a Danish memory clinic, we show that identification of disease requires patients to be substituted by their relatives in constructing believable medical narratives; yet during memory testing, patients are not allowed any substitution to clearly expose cognitive shortcomings. In combining works of theorists Ian Hacking and Paul Ricoeur, we argue that the clinical identification of dementia unmakes the knowing subject, a deconstruction that threatens to misrecognize and humiliate the person under examination. The article ends by proposing that dementia be the condition that forces us to rethink our ways of recognizing persons more generally. Thus, dementia diagnostics provide insights into different enactments of the person that invite us to explore practices of substitution and modes of interaction emerging when our fundamental dependency becomes unquestionable.

Humanity at the Edge: The Moral Laboratory of Feeding Precarious Lives.

At the heart of anthropology and the social sciences lies a notion of human existence according to which humans and animals share the basic need for food, but only humans have the capacity for morality. Based on fieldwork in a pig laboratory, a neonatal intensive care unit (NICU), and a dementia nursing home, we follow practices of feeding precarious lives lacking most markers of human personhood, including the exercise of moral judgment. Despite the absence of such markers, laboratory researchers and caregivers in these three sites do not abstain from engaging in questions about the moral status of the piglets, infants, and people with dementia in their care. They continually negotiate how their charges belong to the human collectivity and thereby challenge the notion of 'the human' that is foundational to anthropology. Combining analytical approaches that do not operate with a fixed boundary between human and animal value and agency with approaches that focus on human experience and virtue ethics, we argue that 'the human' at stake in the moral laboratory of feeding precarious lives puts 'the human' in anthropology at disposal for moral experimentation.

Normal Skin Microbiota is Altered in Pre-clinical Hidradenitis Suppurativa.

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease defined by recurrent nodules, tunnels (sinus tracts) and scarring involving the intertriginous regions. The clinical course of HS is compatible with a biofilm-driven disease, and biofilm has been described in lesional HS skin. We therefore hypothesized that clinically unaffected HS skin would also have an increased presence of biofilm compared with that of healthy controls. We conducted a case-control study, investigating the morphology of the axillary skin microbiota. Peptide nucleic acid - fluorescence in situ hybridization probes were used in combination with confocal laser scanning microscopy. Significant differences were found in both distribution and quantity of the cutaneous microbiota in clinically non-affected axillary skin of patients with HS compared with healthy controls. Surprisingly, we detected fewer bacteria and less biofilm in patients with HS. The reduced microbiota in patients with HS may play an important role in the early course of the disease.

Validation of a Screening Questionnaire for Chronic Leg Ulcers.

The use of a validated screening questionnaire to identify individuals with chronic leg ulcers allows large-scale population-based studies to be conducted that measure and monitor the prevalence of the disease. The aim of this study was to design and validate such a screening questionnaire to identify patients with chronic leg ulcers. A simple 3-item questionnaire was developed at the Department of Dermatology, University Hospital of Zealand, Denmark. In total, 90 patients attending the department's outpatient clinic for dermatological diseases and chronic wounds were included in this study. All included participants completed the questionnaire and were subsequently examined by dermatologists. We found that the constructed 3-item questionnaire in this study had a sensitivity and specificity of 95% and 93% and a positive predictive value and negative predictive value of 78% and 95%, respectively. Moreover, we found that the use of the 3-item questionnaire, as compared with a single question, in which the participants were asked whether they currently have a leg ulcer, resulted in significantly higher positive predictive value (+11.6%, P = .035) and specificity (+5.6%, P = .046) of the diagnostic test. Future studies are merited to investigate the diagnostic accuracy of the questionnaire in other populations and settings.

Renal amyloid A amyloidosis as a complication of hidradenitis suppurativa.

Rheumatic disease is the dominant cause of amyloid A (AA) amyloidosis, but other chronic inflammatory diseases may have similar consequences. Hidradenitis suppurativa (HS) is a relatively common, but little known skin disease characterized by chronic inflammation. Here we present a case of chronic HS leading to biopsy-verified severe renal AA amyloidosis and dialysis dependency.