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  • 1.
    Kallestal, Carina
    et al.
    Uppsala Univ, Sweden.
    Blandon, Elmer Zelaya
    Asociac Desarrollo Econ and Sostenible El Espino AP, Nicaragua; Nicaraguan Autonomous Natl Univ Leon UNAN Leon, Nicaragua.
    Pena, Rodolfo
    Uppsala Univ, Sweden; Pan Amer Hlth Org, Honduras.
    Perez, Wilton
    Uppsala Univ, Sweden.
    Contreras, Mariela
    Uppsala Univ, Sweden.
    Persson, Lars-Ake
    Uppsala Univ, Sweden; London Sch Hyg and Trop Med, England.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Selling, Katarina Ekholm
    Uppsala Univ, Sweden.
    Assessing the Multiple Dimensions of Poverty. Data Mining Approaches to the 2004-14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua2020In: Frontiers In Public Health, ISSN 2296-2565, FRONTIERS IN PUBLIC HEALTH, Vol. 7, article id 409Article in journal (Refereed)
    Abstract [en]

    We identified clusters of multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance database, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems, prioritizing suggested actions, was initiated in 1997 and generated a community action plan 2002-2015. Interventions were school breakfasts, environmental protection, water and sanitation, preventive healthcare, home gardening, microcredit, technical training, university education stipends, and use of the Internet. In 2004, a survey of basic health and demographic information was performed in the whole population, followed by surveillance updates in 2007, 2009, and 2014 linking households and individuals. Information included the house material (floor, walls) and services (water, sanitation, electricity) as well as demographic data (birth, deaths, migration). Data on participation in interventions, food security, household assets, and womens self-rated health were collected in 2014. A K-means algorithm was used to cluster the household data (56 variables) in six clusters. The poverty ranking of household clusters using the unsatisfied basic needs index variables changed when including variables describing basic capabilities. The households in the fairly rich cluster with assets such as motorbikes and computers were described as modern. Those in the fairly poor cluster, having different degrees of food insecurity, were labeled vulnerable. Poor and poorest clusters of households were traditional, e.g., in using horses for transport. Results displayed a society transforming from traditional to modern, where the forerunners were not the richest but educated, had more working members in household, had fewer children, and were food secure. Those lagging were the poor, traditional, and food insecure. The approach may be useful for an improved understanding of poverty and to direct local policy and interventions.

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  • 2.
    Wretborn, Jens
    et al.
    Region Östergötland, Local Health Care Services in Central Östergötland, Department of Emergency Medicine in Linköping.
    Ekelund, Ulf
    Lund Univ, Sweden.
    Wilhelms, Daniel
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Emergency Medicine in Linköping.
    Emergency Department Workload and Crowding During a Major Electronic Health Record Breakdown2019In: Frontiers In Public Health, ISSN 2296-2565, FRONTIERS IN PUBLIC HEALTH, Vol. 7, article id 267Article in journal (Refereed)
    Abstract [en]

    Background: Emergency Departments (EDs) today rely heavily on Electronic Health Records (EHRs) and associated support systems. EHR updates are known to be associated with adverse events, but reports on the consequences of breakdowns in EDs are lacking.

    Objectives: To describe the effects on workload, occupancy, patient Length Of Stay (LOS), and admissions at three EDs (a regional trauma center, a community hospital and a rural community hospital) during a 96 h period of EHR downtime, of which 48 h represented an unexpected breakdown.

    Methods: Assessments of workload, on a scale from 1 (no workload) to 6 (very high workload), were obtained from all staff before, during and after the downtime period. Occupancy, LOS and hospital admissions were extracted from data recorded in the fallback system at each ED during the downtime, and compared with the period before and after (uptime).

    Results: Workload increased considerably at two EDs during the downtime whereas the third ED lacked resources to assess workload due to the breakdown. The proportion of assessments 4 were 28.5% during uptime compared to 38.4% during downtime at the regional trauma center ED (difference 9.9%, p = 0.006, 95% CI 2.7–17%), and 22.9% compared to 41% at the rural community ED (difference 18.1%, p = 0.0002, 95%CI 7.9–28.3%). Median LOS increased by 19 min (3:56 vs. 4:15, p < 0.004) at the regional trauma center ED, by 76 min (3:34 vs. 4:50, p < 0.001) at the community ED and was unaltered at the rural community ED (2:47 vs. 2:51, p = 0.3) during downtime. Occupancy increased significantly at the community ED (1.59 vs. 0.71, p < 0.0001). Admissions rates remained unchanged during the breakdown. Fallback systems and initiatives to manage the effects of the breakdown differed between the EDs.

    Conclusions: EHR downtime or unexpected breakdowns increased staff workload, and had variable effects on ED crowding as measured by LOS and occupancy. Additional staff and digital fallback systems may reduce the effects on ED crowding, but this descriptive study cannot determine causality.

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