OBJECTIVE:Pediatric hospital encounters related to asthma have been linked to failed housing inspections, but evidence at the individual- and parcel-level is absent. Our objective was to examine the impact of housing code violations on pediatric asthma exacerbations. METHODS:We conducted a retrospective cohort study based on electronic health records at Cincinnati Children's Hospital Medical Center in Hamilton County, Ohio, between July 2016 and July 2022. We followed 13 404 patients with asthma living at 22 762 unique addresses for 11 million cumulative patient-days. Study participants were exposed to poor housing conditions if they resided at a parcel within 1 year of the enforcement of a housing code infraction. Our outcome was defined as the time to asthma exacerbation (or censoring event) in days. RESULTS:Overall, 66% of patients with asthma were publicly insured and lived in homes with a median market total value of $104 000 with a parcel type of mostly single-family homes (67%) but also apartments (13%) and 2- or 3-family homes (9%). A total of 1327 study participants (9.9%) experienced an asthma exacerbation during the follow-up period, and 1651 (12%) were exposed to poor housing conditions as defined by infractions of local housing codes. In proportional hazards models adjusted for public insurance and total market value by housing type, living at a parcel with a housing infraction during the previous year was associated with a 34% increased individual-level hazard for an asthma exacerbation (hazard ratio, 1.34; 95% CI, 1.08-1.67). CONCLUSION:The impact of improving housing conditions merits further study.

BACKGROUND AND OBJECTIVES Population-wide racial inequities in child health outcomes are well documented. Less is known about causal pathways linking inequities and social, economic, and environmental exposures. Here, we sought to estimate the total inequities in population-level hospitalization rates and determine how much is mediated by place-based exposures and community characteristics. METHODS We employed a population-wide, neighborhood-level study that included youth <18 years hospitalized between July 1, 2016 and June 30, 2022. We defined a causal directed acyclic graph a priori to estimate the mediating pathways by which marginalized population composition causes census tract-level hospitalization rates. We used negative binomial regression models to estimate hospitalization rate inequities and how much of these inequities were mediated indirectly through place-based social, economic, and environmental exposures. RESULTS We analyzed 50 719 hospitalizations experienced by 28 390 patients. We calculated census tract-level hospitalization rates per 1000 children, which ranged from 10.9 to 143.0 (median 45.1; interquartile range 34.5 to 60.1) across included tracts. For every 10% increase in the marginalized population, the tract-level hospitalization rate increased by 6.2% (95% confidence interval: 4.5 to 8.0). After adjustment for tract-level community material deprivation, crime risk, English usage, housing tenure, family composition, hospital access, greenspace, traffic-related air pollution, and housing conditions, no inequity remained (0.2%, 95% confidence interval: -2.2 to 2.7). Results differed when considering subsets of asthma, type 1 diabetes, sickle cell anemia, and psychiatric disorders. CONCLUSIONS Our findings provide additional evidence supporting structural racism as a significant root cause of inequities in child health outcomes, including outcomes at the population level.

OBJECTIVES:We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. MATERIALS AND METHODS:We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. RESULTS:We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. DISCUSSION:Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. CONCLUSION:We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.

As the COVID-19 pandemic progressed, reliable, accessible, and equitable community-based testing strategies were sought that did not flood already overburdened hospitals and emergency departments. In Hamilton County, Ohio, home to similar to 800 000 people across urban, suburban, and rural areas, we sought to develop and optimize an accessible, equitable county-wide COVID-19 testing program. Using Coronavirus Aid, Relief, and Economic Security Act funding, multidisciplinary, multiorganization partners created the test and protect program to deliver safe, reliable testing in neighborhoods and organizations needing it most. Our approach involved: (1) use of geospatial analytics to identify testing locations positioned to optimize access; (2) community engagement to ensure sites were in trusted places; and (3) tracking of data over time to facilitate ongoing improvement. Between August 2020 and December 2021, more than 65 000 tests were completed for nearly 46 000 individuals at community-based testing sites. These methods could have application beyond COVID-19 and our region.

The Strengths and Difficulties Questionnaire (SDQ) is a screening tool widely used in both pediatric research and clinical practice. Few studies have examined the psychometric properties of the U.S. version of this measure, and none have examined the structure of the measure with children younger than 4 years of age. The goal of this study was to assess the psychometric properties of the parent informant version of the SDQ administered in a pediatric practice serving a low-income, predominately Black, urban population of children ages 2–5 years. Parents routinely completed the SDQ electronically during annual well-child visits from July 2016 to June 2017. Confirmatory factor analysis was used to test 3-factor, 5-factor, and modified 5-factor (with positive construal response style) models. Responses to age-appropriate SDQ forms were analyzed. There were N = 622 for the 4–12 year old version (only collected on 4–5 year olds) and N = 672 of the 2–3 year old version. In both groups, the modified 5-factor model was the best fitting model. The internal consistency for the modified 5-factor model was lower than the original 5-factor model with the alpha coefficient lower on the Conduct Problems, Emotional Problems and Peer Problems subscales. Measurement invariance testing found non-invariance due to gender among the 4–5 year olds on the emotional problems subscale. Our study found that the SDQ shows satisfactory psychometric properties in preschool-aged children in a low-income urban U.S. population. More research is needed to deepen our understanding of the measure’s clinical utility.

OBJECTIVE To assess whether integrated behavioral health (IBH) prevention encounters provided during well-child visits (WCVs) is associated with increased adherence to WCVs and timely immunizations in the first year. METHODS Data were collected in an urban pediatric primary care clinic serving a low-income population and using the HealthySteps model. Subjects were 813 children who attended a newborn well-child visit between January 13, 2016 and August 8, 2017. Data from the electronic health record was extracted on attendance at six well-child visits in the first year of life, IBH prevention encounters by the HealthySteps specialist, completion of immunizations at 5 and 14 months, and demographics and social and clinical risk factors. RESULTS After controlling for covariates, odds of attendance at 6, 9, and 12-month WCVs were significantly higher for those who had IBH prevention encounters at previous WCVs. Odds of immunization completion by 5 months was associated with number of IBH prevention encounters in the first 4 months (OR = 1.52, p = .001) but not immunization completion at 14 months (OR = 1.18, p = .059). CONCLUSIONS IBH prevention encounters were associated with increased adherence to WCVs in the first year and vaccine completion at 5 months of age. These findings are consistent with IBH having a broad positive effect on child health and health care through strong relational connections with families and providing value in addressing emotional and behavioral concerns in the context of WCVs.

OBJECTIVES:To assess the association between neighborhood socioeconomic deprivation and health care utilization in a cohort of children with medical complexity (CMC). METHODS:Cross-sectional study of children aged <18 years receiving care in our institution's patient-centered medical home (PCMH) for CMC in 2016 to 2017. Home addresses were assigned to census tracts and a tract-level measure of socioeconomic deprivation (Deprivation Index with range 0-1, higher numbers represent greater deprivation). Health care utilization outcomes included emergency department visits, hospitalizations, inpatient bed days, and missed PCMH clinic appointments. To evaluate the independent association between area-level socioeconomic deprivation and utilization outcomes, multivariable Poisson and linear regression models were used to control for demographic and clinical covariates. RESULTS:The 512 included CMC lived in neighborhoods with varying degrees of socioeconomic deprivation (median 0.32, interquartile range 0.26-0.42, full range 0.12-0.82). There was no association between area-level deprivation and emergency department visits (adjusted risk ratio [aRR] 0.98; 95% confidence interval [CI]: 0.93 to 1.04), hospitalizations (aRR 0.97; 95% CI: 0.92 to 1.01), or inpatient bed-days (aRR 1.00, 95% CI: 0.80 to 1.27). However, there was a 13% relative increase in the missed clinic visit rate for every 0.1 unit increase in Deprivation Index (95% CI: 8%-18%). CONCLUSIONS:A child's socioeconomic context is associated with their adherence to PCMH visits. Our PCMH for CMC includes children living in neighborhoods with a range of socioeconomic deprivation and may blunt effects from harmful social determinants. Incorporating knowledge of the socioeconomic context of where CMC and their families live is crucial to ensure equitable health outcomes.

OBJECTIVE:We sought to determine whether census tract poverty, race, and insurance status were associated with the likelihood and severity of diabetic ketoacidosis (DKA) hospitalization among youth with type 1 diabetes (T1D).METHODS:We conducted a retrospective population-based cohort study using Cincinnati Children's Hospital electronic medical record (EMR) data from January 1, 2011, to December 31, 2017, for T1D patients ≤18 years old. The primary outcome was admission for DKA. Secondary outcomes included DKA severity, defined by initial pH and bicarbonate, and length of stay. Exposures were the poverty rate for the youth's home census tract, parent-reported race, and insurance status. We used multivariable logistic regression to analyze effects on odds of admission.RESULTS:We identified 439 patients with T1D; 152 were hospitalized. The cohort was 48% female, 25% Black, and 36% publicly insured; the median age was 14 years. For every 10% increase in a youth's census tract poverty rate, the adjusted odds of admission increased by 22% (95% CI, 1.03-1.47). Public insurance status was associated with DKA admission (adjusted odds ratio [AOR], 2.71, 95% CI, 1.62-4.55) while race was not. There were no clinically meaningful differences in pH or bicarbonate by census tract poverty, race, or insurance status; however, Black patients experienced differences in care (eg, longer length of stay).CONCLUSION:Youth with T1D living in high poverty areas and on public insurance were significantly more likely to be admitted for DKA. Severity upon presentation was similar across exposures. Understanding contextual mechanisms by which disparities emerge will inform changes aimed at equitably improving care.

Beneath top-down national and state directives and recommendations, communities must respond to the many phases of coronavirus disease 2019 (COVID-19). The pandemic has unfolded differently across those communities with outcomes dependent on context, infrastructure, capacity, and how assets are organized, linked, and deployed. Achieving control requires real-time multisector data sharing, learning, and adaptation. Leaders from health care, public health, congregate care, elected offices, neighborhoods, schools, and businesses must work together to create systems that can respond to a pathogen that does not respect geographic, jurisdictional, or disciplinary boundaries.1Romanelli R.J. Azar K.M.J. Sudat S. Hung D. Frosch D.L. Pressman A.R. The learning health system in crisis: lessons from the novel coronavirus disease pandemic.Mayo Clin Proc Innov Qual Outcomes. 2020; 5: 171-176Abstract Full Text Full Text PDF PubMed Google Scholar Response capabilities have been compromised by limited cross-sector coordination and decades-long disinvestment in public health.2Calonge N. Brown L. Downey A. Evidence-based practice for public health emergency preparedness and response: recommendations from a National Academies of Sciences, Engineering, and Medicine Report.JAMA. 2020; 324: 629-630Crossref Scopus (6) Google Scholar, 3Endsley M.R. The Role of Situation Awareness in Naturalistic Decision Making.in: Zsambok C.E. Klein G. Naturalistic Decision Making. Psychology Press, New York, NY1997Google Scholar, 4Schneider E.C. Failing the test — the tragic data gap undermining the US pandemic response.N Engl J Med. 2020; 383: 299-302Crossref PubMed Scopus (49) Google Scholar The Pandemic All-Hazards Preparedness Act of 2006 was passed to overcome these limitations by establishing an "electronic nationwide public health situational awareness capability through an interoperable network of systems to share data and information."5Public Law 109–417—December 19, 2006 — Pandemic and All-Hazards Preparedness Act. 2006.https://www.govinfo.gov/content/pkg/PLAW-109publ417/pdf/PLAW-109publ417.pdfDate accessed: November 25, 2020Google Scholar This goal has not been achieved,6Public Health Information Technology: HHS Has Made Little Progress toward Implementing Enhanced Situational Awareness Network Capabilities. 2017.https://www.gao.gov/assets/690/686971.pdfDate accessed: November 25, 2020Google Scholar and communities continue to rely on insights pieced together, often manually, from multiple isolated sources.7Knieser L. The Case for A Situational Awareness Network for Emergency Response. 2020.https://www.healthitanswers.net/the-case-for-a-situational-awareness-network-for-emergency-response/Date accessed: November 3, 2020Google Scholar Data are often at too large a scale (ie, national or state) or too incomplete (ie, single sector or jurisdiction) to be useful for decision-making. As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) found its way to Greater Cincinnati, it became clear that we, like many communities, lacked processes and infrastructure to optimize pandemic control. We were confronted with difficult, urgent decisions without up-to-date data and coordination capabilities.8Sittig D.F. Singh H. COVID-19 and the Need for a National Health Information Technology Infrastructure.JAMA. 2020; 323: 2373-2374Crossref PubMed Scopus (47) Google Scholar, 9Inglesby T.V. Public health measures and the reproduction number of SARS-CoV-2.JAMA. 2020; 323: 2186-2187Crossref PubMed Scopus (82) Google Scholar, 10Hartley D.M. Perencevich E.N. Public health interventions for COVID-19: emerging evidence and implications for an evolving public health crisis.JAMA. 2020; 323: 1908-1909Crossref PubMed Scopus (163) Google Scholar, 11Pan A. Liu L. Wang C. et al.Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China.JAMA. 2020; 323: 1915-1923Crossref PubMed Scopus (1053) Google Scholar We sought to catalyze an agile and adaptive regional response using a learning health system (LHS) lens. Much as a hurricane disrupts travel, utilities, and access to needed services, so too has COVID-19 wreaked havoc across regions. One sector cannot respond to a hurricane; neither can one sector respond to COVID-19. Here, we describe how we used LHS principles to lower boundaries across sectors, promote collaborative sense-making, and grow coordinated infrastructure. Our pre-existing regional emergency preparedness coalition defined Greater Cincinnati as including 14 counties in three states, with 22 hospitals and 17 local health departments serving more than 2 million people. Although response plans existed across jurisdictions and institutions, they were insufficiently linked. Thus, we quickly saw a need to use design and change management strategies, and a network organizational model,12Fjeldstad O.D. Snow C.C. Miles R.E. Lettl C. The architecture of collaboration.Strategic Manag J. 2012; 33: 734-750Crossref Scopus (291) Google Scholar to catalyze an LHS "team of teams" to empower stakeholders to act with shared purpose. We applied the following guiding principles: Initial discussions focused on pressing problems of hospital surge capacity and personal protective equipment availability. By delineating scope, scale, and boundaries that made sense epidemiologically and pragmatically, decision-makers came to understand cross-sector interconnections. The result was goals, measures, and a recognition of critical stakeholders not yet connected to the response (eg, congregate care leaders). Widely available data streams provided the foundation for a complete, holistic, and accurate regional picture, beyond its component parts. By bringing together data into a single community-wide report, stakeholders developed a more holistic view of the pandemic. For instance, they were able to observe the relationship of community incidence to hospital and congregate care facility admission, not one or the other. In normal circumstances, stakeholders compete — hospitals for patients, social service agencies and universities for grant dollars. System-level measures revealed opportunities for learning, catalyzing alignment, and collective action. The pandemic resulted in a spontaneous outpouring of contributions by scientists, organizations, and citizens. Harnessing such expertise added capacity and accelerated innovation (eg, voluntary participation of media companies in developing communication strategies). What was appropriate on day 1 was not on day 30. Structured improvement methods facilitated adaptation to rapidly changing context and identification of answers to emerging questions (eg, how to establish outdoor testing sites in cold weather, where to locate sites to optimize equitable access, and how to communicate to diverse populations). Small-scale testing and cross-sector learning generated the know-how needed to identify and scale up solutions. Aims and theory emerged from these principles. In mid-March 2020, a coalition of health care, public health, and community leaders came together, convened by the Regional Health Information Organization that serves as the hub for health information exchange and emergency preparedness. A team with expertise in design, change management, improvement, epidemiology, analytics, and community health was recruited from local academic medical centers to help assembled stakeholders create regional situational awareness and strategy. Initial participants were hospital leaders, but participation evolved and grew over time, ultimately becoming a regional multi-agency coalition (MAC) composed of leaders from stakeholder organizations and sectors. Within ∼10 days of Greater Cincinnati's first SARS-CoV-2 case, our situational awareness and strategy team worked with the growing MAC to 1) develop and agree on a shared aim — to suppress regional SARS-CoV-2 transmission to reduce disease burden while maintaining economic productivity; 2) delineate scope of activities by defining populations, geographies, and partners; and 3) identify drivers of a successfully networked system comprised of effective health care delivery, public health-driven prevention, and coordinated cross-sector planning and service delivery (Figure 1). We then defined measures related to our aim, drawing on practices from around the world. Officials in Wuhan, China, used municipal public health measures such as daily case incidence and effective reproductive ratios to inform and evaluate effectiveness of nonpharmaceutical interventions over time.11Pan A. Liu L. Wang C. et al.Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China.JAMA. 2020; 323: 1915-1923Crossref PubMed Scopus (1053) Google Scholar Taiwan demonstrated how integrated "timely, accurate, and transparent" data meaningfully informed responses.13Wang C.J. Ng C.Y. Brook R.H. Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing.JAMA. 2020; 323: 1341-1342Crossref PubMed Scopus (1016) Google Scholar By early April, we were producing a shared dashboard daily for MAC members — leaders from area hospitals, public health jurisdictions, and congregate care facilities alongside subject matter experts and support teams like ours (Figure 2). The process of agreeing upon and then using measures built shared commitment and deepening understanding of the interdependent components of the system these leaders were seeking to manage. We related incidence and spread to downstream pandemic effects: health care system impact (ie, hospital occupancy, ventilator use, personal protective equipment availability, and death) and community capability (ie, access to testing, test turnaround time, participation in contact tracing, and time-lags from symptom to isolation).14Kretzschmar M.E.R.G. Bootsma M.C.J. van Boven M. van de Wijgert J.H.H.M. Bonten M.J.M. Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.Lancet Public Health. 2020; 5: e452-e459Abstract Full Text Full Text PDF PubMed Scopus (428) Google Scholar We depicted measures at different levels of aggregation — entire region, county, neighborhood, health care system, and hospital — and across vulnerable subpopulations (those residing in congregate care facilities, living in impoverished neighborhoods, and of minority race or ethnicity). We used statistical process control methods to differentiate significant change from random variation.15Benneyan J.C. Lloyd R.C. Plsek P.E. Statistical process control as a tool for research and healthcare improvement.Qual Saf Health Care. 2003; 12: 458-464Crossref PubMed Scopus (756) Google Scholar Geospatial approaches identified case clusters and enhanced awareness of background context.16Chowkwanyun M. Reed Jr., A.L. Racial health disparities and COVID-19 — Caution and Context.N Engl J Med. 2020; 383: 201-203Crossref PubMed Scopus (455) Google Scholar Annotations on charts and maps helped the MAC relate changes in intervention strategies to changes in measures (Figure 3).Figure 3Annotated chart depicting daily case incidence, measured per 100,000 population, with a 7-day moving average. Each line indicates an event or change expanded upon with the list on the right side. Light blue lines indicate mitigation-oriented interventions. Dark blue lines indicate background changes likely influencing viral transmission.View Large Image Figure ViewerDownload Hi-res image Download (PPT) By early May, we were producing a subset of dashboard measures to share publicly.17The Health Collaborative Situational Dashboard.https://www.cctst.org/covid19Date accessed: December 4, 2020Google Scholar This enhanced transparency extended the reach of the LHS, further enabling real-time learning and action across still more sectors (eg, schools, universities, and businesses).9Inglesby T.V. Public health measures and the reproduction number of SARS-CoV-2.JAMA. 2020; 323: 2186-2187Crossref PubMed Scopus (82) Google Scholar,10Hartley D.M. Perencevich E.N. Public health interventions for COVID-19: emerging evidence and implications for an evolving public health crisis.JAMA. 2020; 323: 1908-1909Crossref PubMed Scopus (163) Google Scholar,18Krieger N. Gonsalves G. Bassett M.T. Hanage W. Krumholz H.M. The Fierce Urgency Of Now: Closing Glaring Gaps In US Surveillance Data On COVID-19. Health Affairs Blog.https://www.healthaffairs.org/do/10.1377/hblog20200414.238084/full/Date accessed: April 29, 2020Google Scholar,19Ng Y. Li Z. Chua Y.X. et al.Evaluation of the effectiveness of surveillance and containment measures for the first 100 patients with COVID-19 in Singapore — January 2–February 29, 2020.MMWR Morb Mortal Wkly Rep. 2020; 69: 307-311Crossref PubMed Google Scholar These measures continue to provide the single trusted regional COVID-19 picture looked to by those in health-relevant sectors, the media, and public alike. Data within the dashboard have evolved over time, changing because of needs identified and feedback obtained. The discussions data generate inform important decisions and expedite structured, continuous improvement processes. In the pandemic's initial surge phase, data about stable hospital capacity informed a decision to not open Cincinnati's convention center as a field hospital, saving tens of millions of dollars. Identification of increasing case incidence following reopening in May and June directly stimulated community-wide "mask on" communication campaigns supported by the regional Chamber of Commerce and business community. Granular depictions of geographic case clusters helped detect outbreaks in congregate care facilities and emergent racial or ethnic inequities. Geospatial analytics informed decisions about locations for community testing. Networked leaders also began using the data to drive improvement. Health departments reduced lags in contact tracing. Community testing teams worked with partnered organizations and community centers to increase demand for and accessibility of tests. Schools brought data to decisions about re-opening and improvement methods to efforts to maximize students reached while on virtual instruction.20COVID-19. Hamilton County Public Health. 2020.https://www.hamiltoncountyhealth.org/covid19/Date accessed: November 30, 2020Google Scholar Regular use of available data also mitigated data shortcomings (eg, inaccurate or incomplete data or inconsistent operational definitions). A local health care leader commented that routinely reflecting on and critically evaluating data "allowed [stakeholders] to look outside [their] span of control, toward bigger community issues" reflective of the broader regional system. Despite the presence of the Regional Health Information Organization and preparedness infrastructure, we encountered several important challenges (Figure 2). First, regional health care institutions are active competitors, historically reluctant to share data. Second, like in many regions, our health information exchanges tend to be siloed, excluding public health, congregate care, and social services data.21Kierkegaard P. Kaushal R. Vest J.R. Applications of health information exchange information to public health practice.AMIA Annu Symp Proc. 2014; 2014: 795-804PubMed Google Scholar,22Holmgren A.J. Adler-Milstein J. Health information exchange in US Hospitals: the current landscape and a path to improved information sharing.J Hosp Med. 2017; 12: 193-198Crossref PubMed Scopus (41) Google Scholar Third, the sheer number of jurisdictions, organizations, and sectors creates immense alignment, measurement, and improvement challenges. Finally, delays in data entry, manual entry, and unclear data definitions make interpretation difficult. To overcome challenges and connect disconnected sectors, our situational awareness and strategy team used data, analytics, and modeling to identify solutions and meet decision-making needs of stakeholders. We routinely identified data sources and developed prototype measures relevant to immediate needs. Reviewing data relative to regional goals was the first topic on the agenda of every MAC and subgroup meeting (often multiple times weekly). During these meetings, we elicited feedback on measure utility and presentation23James B. Information system concepts for quality measurement.Med Care. 2003; 41: I71-I79PubMed Google Scholar and responded with revisions and new analyses as needed (often within 24 hours). As possible, we used existing infrastructure. Continuous measure reviews helped identify and mitigate reporting errors and unearth immediate needs. In parallel, we provided coaching in systems improvement, knowledge sharing, and community connection. We facilitated rapid learning cycles where stakeholder organizations learned from small-scale tests of changes and from one another. We continuously identified best practices from other regions, sharing knowledge back with regional partners. We identified contextual realities within neighborhoods and on the front lines, facilitating the co-design of tailored solutions. Transparent data sharing, learning from variation, sharing best practices, and connecting with community members deepened trust, stimulated action, and enabled participants to see themselves as part of one LHS with common objectives.24Diez Roux A.V. Population Health in the Time of COVID-19: Confirmations and Revelations.Milbank Q. 2020; 98: 629-640Crossref PubMed Scopus (22) Google Scholar This approach, built from the bottom-up, facilitated the identification, contextualization, and alignment of responses to new challenges or top-down directives. There is now an opportunity to build better national infrastructure by learning from hundreds of small-scale responses like ours. Future phases of this pandemic, other pandemics, climate events, and economic disasters all could benefit from such infrastructure and learning. Complex challenges demand coordinated, integrated, and adaptive functionalities across relevant sectors. Recognizing the power of LHS approaches like ours has the potential to inform policy and support better systems for emergency preparedness and for population health. Indeed, COVID-19 highlights the urgency to achieve data interoperability and trustworthy integration of data, programs, and ideas.6Public Health Information Technology: HHS Has Made Little Progress toward Implementing Enhanced Situational Awareness Network Capabilities. 2017.https://www.gao.gov/assets/690/686971.pdfDate accessed: November 25, 2020Google Scholar Building a national system by capturing regional innovations (and learning from failures) may seem daunting, but it is a tractable problem. There are fewer than 400 metropolitan statistical areas similar to Greater Cincinnati across the United States. It is possible to gather and curate the best of what is taking place in such regions, share it broadly, and provide mechanisms to access not just technical experts, but also peers who have solved similar problems.25COVID Local. 2020.https://covid-local.org/Date accessed: November 25, 2020Google Scholar Regions must also have the resources and supports necessary to optimize existing capabilities and then extend them. Policies and incentives should promote cross-jurisdictional and cross-sector alignment. Learning health system development will require support for technical assistance to learn and apply new methods of system change and collaborative learning. It will also require investments in educating the next generation of LHS improvers, and researching how LHS design, technology, and data and digital governance shape communities' ability to respond at the speed and scale of epidemics. To enhance likelihood of success, health care systems, public health jurisdictions, congregate care facilities, elected officials, neighborhood leaders, schools, and businesses must work together to pursue solutions to complex problems such as COVID-19. Learning health system approaches and principles facilitate broad community alignment and dynamic, collaborative action. An enduring, dynamic, adaptive population health situational awareness and action LHS, built from the bottom-up, can help us to emerge stronger, enabling swift, comprehensive responses to future phases of this pandemic and to other epidemics sure to follow.

Background. Neighborhood socioeconomic deprivation is associated with adverse health outcomes. We sought to determine if neighborhood socioeconomic deprivation was associated with adherence to immunosuppressive medications after liver transplantation. Methods. We conducted a secondary analysis of a multicenter, prospective cohort of children enrolled in the medication adherence in children who had a liver transplant study (enrollment 2010–2013). Participants (N = 271) received a liver transplant ≥1 year before enrollment and were subsequently treated with tacrolimus. The primary exposure, connected to geocoded participant home addresses, was a neighborhood socioeconomic deprivation index (range 0–1, higher indicates more deprivation). The primary outcome was the medication level variability index (MLVI), a surrogate measure of adherence to immunosuppression in pediatric liver transplant recipients. Higher MLVI indicates worse adherence behavior; values ≥2.5 are predictive of late allograft rejection. Results. There was a 5% increase in MLVI for each 0.1 increase in deprivation index (95% confidence interval, −1% to 11%; P = 0.08). Roughly 24% of participants from the most deprived quartile had an MLVI ≥2.5 compared with 12% in the remaining 3 quartiles (P = 0.018). Black children were more likely to have high MLVI even after adjusting for deprivation (adjusted odds ratio 4.0 95% confidence interval, 1.7-10.6). Conclusions. This is the first study to evaluate associations between neighborhood socioeconomic deprivation and an objective surrogate measure of medication adherence in children posttransplant. These findings suggest that neighborhood context may be an important consideration when assessing adherence. Differential rates of medication adherence may partly explain links between neighborhood factors and adverse health outcomes following pediatric liver transplantation.

ABSTRACTBackground and Objectives:Nonalcoholic fatty liver disease (NAFLD) is linked to obesity. Obesity is associated with lower socioeconomic status (SES). An independent link between pediatric NAFLD and SES has not been elucidated. The objective of this study was to evaluate the distribution of socioeconomic deprivation, measured using an area‐level proxy, in pediatric patients with known NAFLD and to determine whether deprivation is associated with liver disease severity.Methods:Retrospective study of patients <21 years with NAFLD, followed from 2009 to 2018. The patients’ addresses were mapped to census tracts, which were then linked to the community deprivation index (CDI; range 0‐‐1, higher values indicating higher deprivation, calculated from six SES‐related variables available publicly in US Census databases).Results:Two cohorts were evaluated; 1 with MRI (magnetic resonance imaging) and/or MRE (magnetic resonance elastography) findings indicative of NAFLD (n = 334), and another with biopsy‐confirmed NAFLD (n = 245). In the MRI and histology cohorts, the majority were boys (66%), non‐Hispanic (77%–78%), severely obese (79%–80%), and publicly insured (55%–56%, respectively). The median CDI for both groups was 0.36 (range 0.15–0.85). In both cohorts, patients living above the median CDI were more likely to be younger at initial presentation, time of MRI, and time of liver biopsy. MRI‐measured fat fraction and liver stiffness, as well as histologic characteristics were not different between the high‐ and low‐deprivation groups.Conclusions:Children with NAFLD were found across the spectrum of deprivation. Although children from more deprived neighborhoods present at a younger age, they exhibit the same degree of NAFLD severity as their peers from less deprived areas.

Maxwell, Andrea; Yayah Jones, Nana-Hawa; Taylor, Stuart; Kichler, Jessica; Corathers, Sarah; Riley, Carley; Beck, Andrew Author Information

Pediatricians aspire to optimize overall health and development, but there are no comprehensive measures of well-being to guide pediatric primary care redesign. The objective of this article is to describe the Cincinnati Kids Thrive at 5 outcome measure, along with a set of more proximal outcome and process measures, designed to drive system improvement over several years. In this article, we describe a composite measure of “thriving” at age 66 months, using primary care data from the electronic health record. Thriving is defined as immunizations up-to-date, healthy BMI, free of dental pain, normal or corrected vision, normal or corrected hearing, and on track for communication, literacy, and social-emotional milestones. We discuss key considerations and tradeoffs in developing the measure. We then summarize insights from applying this measure to 9544 patients over 3 years. Baseline rates of thriving were 13% when including all patients and 31% when including only patients with complete data available. Interpretation of results was complicated by missing data in 50% of patients and nonindependent success rates among bundle components. There was considerable enthusiasm among other practices and sectors to learn with us and to measure system performance using time-linked trajectories. We learned to present our data in ways that balanced aspirational long-term or multidisciplinary goal-setting with more easily attainable short-term aims. On the basis of our experience with the Thrive at 5 measure, we discuss future directions and place a broader call to action for pediatricians, researchers, policy makers, and communities.

BACKGROUND: Disparities in health service use have been described across a range of sociodemographic factors. Patterns of PICU use have not been thoroughly assessed. METHODS: This was a population-level, retrospective analysis of admissions to the Cincinnati Children’s Hospital Medical Center PICU between 2011 and 2016. Residential addresses of patients were geocoded and spatially joined to census tracts. Pediatric patients were eligible for inclusion if they resided within Hamilton County, Ohio. PICU admission and bed-day rates were calculated by using numerators of admissions and bed days, respectively, over a denominator of tract child population. Relationships between tract-level PICU use and child poverty were assessed by using Spearman’s ρ and analysis of variance. Analyses were event based; children admitted multiple times were counted as discrete admissions. RESULTS: There were 4071 included admissions involving 3129 unique children contributing a total of 12 297 PICU bed days. Child poverty was positively associated with PICU admission rates (r = 0.59; P < .001) and bed-day rates (r = 0.47; P < .001). When tracts were grouped into quintiles based on child poverty rates, the PICU bed-day rate ranged from 23.4 days per 1000 children in the lowest poverty quintile to 81.9 days in the highest poverty quintile (P < .001). CONCLUSIONS: The association between poverty and poor health outcomes includes pediatric intensive care use. This association exists for children who grow up in poverty and around poverty. Future efforts should characterize the interplay between patient- and neighborhood-level risk factors and explore neighborhood-level interventions to improve child health.

Improving population health requires a focus on neighborhoods with high rates of illness. We aimed to reduce hospital days for children from two high-morbidity, high-poverty neighborhoods in Cincinnati, Ohio, to narrow the gap between their neighborhoods and healthier ones. We also sought to use this population health improvement initiative to develop and refine a theory for how to narrow equity gaps across broader geographic areas. We relied upon quality improvement methods and a learning health system approach. Interventions included the optimization of chronic disease management; transitions in care; mitigation of social risk; and use of actionable, real-time data. The inpatient bed-day rate for the two target neighborhoods decreased by 18 percent from baseline (July 2012-June 2015) to the improvement phase (July 2015-June 2018). Hospitalizations decreased by 20 percent. There was no similar decrease in demographically comparable neighborhoods. We see the neighborhood as a relevant frame for achieving equity and building a multisector culture of health.

Building a culture of health in hospitals means more than participating in community partnerships. It also requires an enhanced capacity to recognize and respond to disparities in utilization patterns across populations. We identified all pediatric hospitalizations at Cincinnati Children's Hospital Medical Center, in the period 2011-16. Each hospitalized child's address was geocoded, allowing us to calculate inpatient bed-day rates for each census tract in Hamilton County, Ohio, across all causes and for specific conditions and pediatric subspecialties. We then divided the census tracts into quintiles based on their underlying rates of child poverty and calculated bed-day rates per quintile. Poorer communities disproportionately bore the burden of pediatric hospital days. If children from all of the county's census tracts spent the same amount of time in the hospital each year as those from the most affluent tracts, approximately twenty-two child-years of hospitalization time would be prevented. Of particular note were "hot spots" in high-poverty census tracts neighboring the hospital, where bed-day rates were more than double the county average. Hospitals that address disparities would benefit from a more comprehensive understanding of the culture of health-a culture that is more cohesive inside the hospital and builds bridges into the community.