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      National Use of Safety-Net Clinics for Primary Care among Adults with Non-Medicaid Insurance in the United States

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          Abstract

          Objective

          To describe the prevalence, characteristics, and predictors of safety-net use for primary care among non-Medicaid insured adults (i.e., those with private insurance or Medicare).

          Methods

          Cross-sectional analysis using the 2006–2010 National Ambulatory Medical Care Surveys, annual probability samples of outpatient visits in the U.S. We estimated national prevalence of safety-net visits using weighted percentages to account for the complex survey design. We conducted bivariate and multivariate logistic regression analyses to examine characteristics associated with safety-net clinic use.

          Results

          More than one-third (35.0%) of all primary care safety-net clinic visits were among adults with non-Medicaid primary insurance, representing 6,642,000 annual visits nationally. The strongest predictors of safety-net use among non-Medicaid insured adults were: being from a high-poverty neighborhood (AOR 9.53, 95% CI 4.65–19.53), being dually eligible for Medicare and Medicaid (AOR 2.13, 95% CI 1.38–3.30), and being black (AOR 1.97, 95% CI 1.06–3.66) or Hispanic (AOR 2.28, 95% CI 1.32–3.93). Compared to non-safety-net users, non-Medicaid insured adults who used safety-net clinics had a higher prevalence of diabetes (23.5% vs. 15.0%, p<0.001), hypertension (49.4% vs. 36.0%, p<0.001), multimorbidity (≥2 chronic conditions; 53.5% vs. 40.9%, p<0.001) and polypharmacy (≥4 medications; 48.8% vs. 34.0%, p<0.001). Nearly one-third (28.9%) of Medicare beneficiaries in the safety-net were dual eligibles, compared to only 6.8% of Medicare beneficiaries in non-safety-net clinics (p<0.001).

          Conclusions

          Safety net clinics are important primary care delivery sites for non-Medicaid insured minority and low-income populations with a high burden of chronic illness. The critical role of safety-net clinics in care delivery is likely to persist despite expanded insurance coverage under the Affordable Care Act.

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          Most cited references20

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          Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project.

          N Krieger (2002)
          Despite the promise of geocoding and use of area-based socioeconomic measures to overcome the paucity of socioeconomic data in US public health surveillance systems, no consensus exists as to which measures should be used or at which level of geography. The authors generated diverse single-variable and composite area-based socioeconomic measures at the census tract, block group, and zip code level for Massachusetts (1990 population: 6,016,425) and Rhode Island (1990 population: 1,003,464) to investigate their associations with mortality rates (1989-1991: 156,366 resident deaths in Massachusetts and 27,291 in Rhode Island) and incidence of primary invasive cancer (1988-1992: 140,610 resident cases in Massachusetts; 1989-1992: 19,808 resident cases in Rhode Island). Analyses of all-cause and cause-specific mortality rates and all-cause and site-specific cancer incidence rates indicated that: 1) block group and tract socioeconomic measures performed comparably within and across both states, but zip code measures for several outcomes detected no gradients or gradients contrary to those observed with tract and block group measures; 2) similar gradients were detected with categories generated by quintiles and by a priori categorical cutpoints; and 3) measures including data on economic poverty were most robust and detected gradients that were unobserved using measures of only education and wealth.
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            The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study.

            Multimorbidity, the presence of more than 1 long-term disorder, is associated with increased use of health services, but unplanned admissions to hospital may often be undesirable. Furthermore, socioeconomic deprivation and mental health comorbidity may lead to additional unplanned admissions. We examined the association between unplanned admission to hospital and physical multimorbidity, mental health and socioeconomic deprivation. We conducted a retrospective cohort study using data from 180 815 patients aged 20 years and older who were registered with 40 general practices in Scotland. Details of 32 physical and 8 mental health morbidities were extracted from the patients' electronic health records (as of Apr. 1, 2006) and linked to hospital admission data. We then recorded the occurrence of unplanned or potentially preventable unplanned acute (nonpsychiatric) admissions to hospital in the subsequent 12 months. We used logistic regression models, adjusting for age and sex, to determine associations between unplanned or potentially preventable unplanned admissions to hospital and physical multimorbidity, mental health and socioeconomic deprivation. We identified 10 828 (6.0%) patients who had at least 1 unplanned admission to hospital and 2037 (1.1%) patients who had at least 1 potentially preventable unplanned admission to hospital. Both unplanned and potentially preventable unplanned admissions were independently associated with increasing physical multimorbidity (for ≥4 v. 0 conditions, odds ratio [OR] 5.87 [95% confidence interval (CI) 5.45-6.32] for unplanned admissions, OR 14.38 [95% CI 11.87-17.43] for potentially preventable unplanned admissions), mental health conditions (for ≥1 v. 0 conditions, OR 2.01 [95% CI 1.92-2.09] for unplanned admissions, OR 1.80 [95% CI 1.64-1.97] for potentially preventable unplanned admissions) and socioeconomic deprivation (for most v. least deprived quintile, OR 1.56 [95% CI 1.43-1.70] for unplanned admissions, OR 1.98 [95% CI 1.63-2.41] for potentially preventable unplanned admissions). Physical multimorbidity was strongly associated with unplanned admission to hospital, including admissions that were potentially preventable. The risk of admission to hospital was exacerbated by the coexistence of mental health conditions and socioeconomic deprivation.
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              Interventions to improve the appropriate use of polypharmacy for older people.

              Inappropriate polypharmacy is a particular concern in older people and is associated with negative health outcomes. Choosing the best interventions to improve appropriate polypharmacy is a priority, hence interest in appropriate polypharmacy, where many medicines may be used to achieve better clinical outcomes for patients, is growing. This review sought to determine which interventions, alone or in combination, are effective in improving the appropriate use of polypharmacy and reducing medication-related problems in older people. In November 2013, for this first update, a range of literature databases including MEDLINE and EMBASE were searched, and handsearching of reference lists was performed. Search terms included 'polypharmacy', 'medication appropriateness' and 'inappropriate prescribing'. A range of study designs were eligible. Eligible studies described interventions affecting prescribing aimed at improving appropriate polypharmacy in people 65 years of age and older in which a validated measure of appropriateness was used (e.g. Beers criteria, Medication Appropriateness Index (MAI)). Two review authors independently reviewed abstracts of eligible studies, extracted data and assessed risk of bias of included studies. Study-specific estimates were pooled, and a random-effects model was used to yield summary estimates of effect and 95% confidence intervals (CIs). The GRADE (Grades of Recommendation, Assessment, Development and Evaluation) approach was used to assess the overall quality of evidence for each pooled outcome. Two studies were added to this review to bring the total number of included studies to 12. One intervention consisted of computerised decision support; 11 complex, multi-faceted pharmaceutical approaches to interventions were provided in a variety of settings. Interventions were delivered by healthcare professionals, such as prescribers and pharmacists. Appropriateness of prescribing was measured using validated tools, including the MAI score post intervention (eight studies), Beers criteria (four studies), STOPP criteria (two studies) and START criteria (one study). Interventions included in this review resulted in a reduction in inappropriate medication usage. Based on the GRADE approach, the overall quality of evidence for all pooled outcomes ranged from very low to low. A greater reduction in MAI scores between baseline and follow-up was seen in the intervention group when compared with the control group (four studies; mean difference -6.78, 95% CI -12.34 to -1.22). Postintervention pooled data showed a lower summated MAI score (five studies; mean difference -3.88, 95% CI -5.40 to -2.35) and fewer Beers drugs per participant (two studies; mean difference -0.1, 95% CI -0.28 to 0.09) in the intervention group compared with the control group. Evidence of the effects of interventions on hospital admissions (five studies) and of medication-related problems (six studies) was conflicting. It is unclear whether interventions to improve appropriate polypharmacy, such as pharmaceutical care, resulted in clinically significant improvement; however, they appear beneficial in terms of reducing inappropriate prescribing.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 March 2016
                2016
                : 11
                : 3
                : e0151610
                Affiliations
                [1 ]Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, United States of America
                [2 ]Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas, United States of America
                Geisel School of Medicine at Dartmouth College, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: OKN ANM EAH. Performed the experiments: OKN. Analyzed the data: OKN. Contributed reagents/materials/analysis tools: OKN ANM EAH. Wrote the paper: OKN ANM EAH.

                Article
                PONE-D-15-28239
                10.1371/journal.pone.0151610
                4814117
                27027617
                5bfa45f8-fed1-4f47-8ccb-2f50a3fe5f97
                © 2016 Nguyen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 June 2015
                : 1 March 2016
                Page count
                Figures: 1, Tables: 3, Pages: 14
                Funding
                This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (1R24HS022418-01). Drs. Nguyen and Makam received funding from the UT Southwestern KL2 Scholars Program (NIH/NCATS KL2 TR001103). The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; decision to publish; or preparation, review, or approval of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Management Engineering
                Risk Management
                Insurance
                Social Sciences
                Political Science
                Public Policy
                Medicare
                Medicine and Health Sciences
                Health Care
                Primary Care
                People and Places
                Population Groupings
                Age Groups
                Adults
                People and Places
                Population Groupings
                Ethnicities
                Hispanic People
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Vascular Medicine
                Blood Pressure
                Hypertension
                Earth Sciences
                Geography
                Geographic Areas
                Rural Areas
                Custom metadata
                All National Ambulatory Medical Care Survey (NAMCS) public-use data files are available for download from the National Center for Health Statistics Ambulatory Health Data website ( http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm#public_use).

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                Uncategorized

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