118 research outputs found
Measurement of health-related quality by multimorbidity groups in primary health care
[EN] Background: Increased life expectancy in Western societies does not necessarily mean better quality of life. To
improve resources management, management systems have been set up in health systems to stratify patients
according to morbidity, such as Clinical Risk Groups (CRG). The main objective of this study was to evaluate the
effect of multimorbidity on health-related quality of life (HRQL) in primary care.
Methods: An observational cross-sectional study, based on a representative random sample (n = 306) of adults
from a health district (N = 32,667) in east Spain (Valencian Community), was conducted in 2013. Multimorbidity was
measured by stratifying the population with the CRG system into nine mean health statuses (MHS). HRQL was
assessed by EQ-5D dimensions and the EQ Visual Analogue Scale (EQ VAS). The effect of the CRG system, age and
gender on the utility value and VAS was analysed by multiple linear regression. A predictive analysis was run by
binary logistic regression with all the sample groups classified according to the CRG system into the five HRQL
dimensions by taking the ¿healthy¿ group as a reference. Multivariate logistic regression studied the joint influence
of the nine CRG system MHS, age and gender on the five EQ-5D dimensions.
Results: Of the 306 subjects, 165 were female (mean age of 53). The most affected dimension was pain/discomfort
(53%), followed by anxiety/depression (42%). The EQ-5D utility value and EQ VAS progressively lowered for the MHS
with higher morbidity, except for MHS 6, more affected in the five dimensions, save self-care, which exceeded MHS
7 patients who were older, and MHS 8 and 9 patients, whose condition was more serious. The CRG system alone
was the variable that best explained health problems in HRQL with 17%, which rose to 21% when associated with
female gender. Age explained only 4%.
Conclusions: This work demonstrates that the multimorbidity groups obtained by the CRG classification system
can be used as an overall indicator of HRQL. These utility values can be employed for health policy decisions based
on cost-effectiveness to estimate incremental quality-adjusted life years (QALY) with routinely e-health data.
Patients under 65 years with multimorbidity perceived worse HRQL than older patients or disease severity.
Knowledge of multimorbidity with a stronger impact can help primary healthcare doctors to pay attention to these
population groups.The authors would like to thank the Conselleria de Sanitat Universal i Sanitat
Pública of the Generalitat Valenciana (the Regional Valencian Health
Government) for providing the study data. We would also like to thank
Helen Warbuton for editing the English.Milá-Perseguer, M.; Guadalajara Olmeda, MN.; Vivas-Consuelo, D.; Usó-Talamantes, R. (2019). Measurement of health-related quality by multimorbidity groups in primary health care. Health and Quality of Life Outcomes. 17(8):1-10. https://doi.org/10.1186/s12955-018-1063-zS110178Ministerio de Sanidad SS, Igualdad e. Indicadores de Salud 2013. Evolución de los indicadores del estado de salud en España y su magnitud en el contexto de la Unión Europea. Madrid: Ministerio de Sanidad, Servicios Sociales e Igualdad; 2014.OECD/EU: Health at a Glance: Europe 2016 – State of Health in the EU Cycle, OECD Publishing, Paris. In.; 2016.WHO: Disability and health. In. Edited by WHO; 2017.Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M. Multimorbidity and comorbidity revisited: refining the concepts for international health research. J Clin Epidemiol. 2018.Palmer K, Marengoni A, Forjaz MJ, Jureviciene E, Laatikainen T, Mammarella F, Muth C, Navickas R, Prados-Torres A, Rijken M, et al. Multimorbidity care model: recommendations from the consensus meeting of the joint action on chronic diseases and promoting healthy ageing across the life cycle (JA-CHRODIS). Health Policy. 2018;122(1):4–11.WHO: Innovative Care for Chronic Conditions. Building blocks for action. In.: WHO; 2014.Fortin M, Bravo G, Hudon C, Vanasse A, Lapointe L. Prevalence of multimorbidity among adults seen in family practice. Ann Fam Med. 2005;3(3):223–8.Inoriza JM, Coderch J, Carreras M, Vall-Llosera L, Garcia-Goni M, Lisbona JM, Ibern P. Measurement of morbidity attended in an integrated health care organization. Gac Sanit. 2009;23(1):29–37.Hunger M, Thorand B, Schunk M, Döring A, Menn P, Peters A, Holle R. Multimorbidity and health-related quality of life in the older population: results from the German KORA-age study. Health Qual Life Outcomes. 2011;9:53.de Miguel P, Caballero I, Rivas FJ, Manera J, de Vicente MA, Gómez Á. Morbidity observed in a health area: impact on professionals and funding. Aten Primaria. 2015;47(5):301–7.Agborsangaya CB, Lau D, Lahtinen M, Cooke T, Johnson JA. Multimorbidity prevalence and patterns across socioeconomic determinants: a cross-sectional survey. BMC Public Health. 2012;12:201.Orueta JF, García-Álvarez A, García-Goñi M, Paolucci F, Nuño-Solinís R. Prevalence and costs of multimorbidity by deprivation levels in the Basque Country: a population based study using health administrative databases. PLoS One. 2014;9(2):e89787.Mujica-Mota RE, Roberts M, Abel G, Elliott M, Lyratzopoulos G, Roland M, Campbell J. Common patterns of morbidity and multi-morbidity and their impact on health-related quality of life: evidence from a national survey. Qual Life Res. 2015;24(4):909–18.Caballer Tarazona V, Guadalajara Olmeda N, Vivas Consuelo D, Clemente Collado A. Impact of morbidity on health care costs of a Department of Health through clinical risk groups. Valencian Community, Spain. Rev Esp Salud Publica. 2016;90:e1–e15.Calderon-Larranaga A, Abrams C, Poblador-Plou B, Weiner JP, Prados-Torres A. Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration. BMC Health Serv Res. 2010;10:22.Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC. Clinical risk groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42(1):81–90.Vivas-Consuelo D, Uso-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martinez I, Buigues-Pastor L. Predictability of pharmaceutical spending in primary health services using clinical risk groups. Health Policy. 2014;116(2–3):188–95.Milla Perseguer M, Guadalajara Olmeda N, Vivas Consuelo D. Impact of cardiovascular risk factors on the consumption of resources in primary care according to clinical risk groups. Aten Primaria. 2018.WHOQOL. The World Health Organization quality of life assessment (WHOQOL): development and general psychometric properties. Soc Sci Med. 1998;46(12):1569–85.Badia X, Carne X. The evaluation of quality of life in clinical trials. Medicina Clinica. 1998;110(14):550–6.Revicki DA. Health-related quality of life in the evaluation of medical therapy for chronic illness. J Fam Pract. 1989;29(4):377–80.Agborsangaya CB, Lau D, Lahtinen M, Cooke T, Johnson JA. Health-related quality of life and healthcare utilization in multimorbidity: results of a cross-sectional survey. Qual Life Res. 2013;22(4):791–9.Romero M, Vivas-Consuelo D, Alvis-Guzman N. Is health related quality of life (HRQoL) a valid indicator for health systems evaluation? Springerplus. 2013;2:664.Hanmer J, Feeny D, Fischhoff B, Hays RD, Hess R, Pilkonis PA, Revicki DA, Roberts MS, Tsevat J, Yu L. The PROMIS of QALYs. Health Qual Life Outcomes. 2015;13.Herdman M, Badia X, Berra S. EuroQol-5D: a simple alternative for measuring health-related quality of life in primary care. Atencion primaria / Sociedad Espanola de Medicina de Familia y Comunitaria. 2001;28(6):425–30.EuroQol G. EuroQol-a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(199–208).Agborsangaya CB, Lahtinen M, Cooke T, Johnson JA. Comparing the EQ-5D 3L and 5L: measurement properties and association with chronic conditions and multimorbidity in the general population. Health Qual Life Outcomes. 2014;12:7.Real Decreto Legislativo 8/2015, de 30 de octubre, por el que se aprueba el texto refundido de la Ley General de la Seguridad Social. In. «BOE» núm. 261, de 31/10/2015.: Ministerio de Empleo y Seguridad Social.; 2015.Ministry of Health and Social Policy:. Estudios sobre la calidad de vida de pacientes afectados por determinadas patologías. [ http://www.mscbs.gob.es/organizacion/sns/planCalidadSNS/ ].Ministry of Health and Social Policy: Encuesta Nacional de Salud. España 2011/12. Calidad de vida relacionada con la salud en adultos: EQ-5D-5L. Serie Informes monográficos n° 3. Madrid: Ministerio de Sanidad, Servicios Sociales e Igualdad; 2014.Fortin M, Bravo G, Hudon C, Lapointe L, Almirall J, Dubois MF, Vanasse A. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83–91.Fortin M, Dubois MF, Hudon C, Soubhi H, Almirall J. Multimorbidity and quality of life: a closer look. Health Qual Life Outcomes. 2007;5:52.Brazier JE, Yang Y, Tsuchiya A, Rowen DL. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ. 2010;11.Peak J, Goranitis I, Day E, Copello A, Freemantle N, Frew E. Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. Health Qual Life Outcomes. 2018;16(1):106.Rivero-Arias O, Ouellet M, Gray A, Wolstenholme J, Rothwell PM, Luengo-Fernandez R. Mapping the modified Rankin scale (mRS) measurement into the generic EuroQol (EQ-5D) health outcome. Med Decis Mak. 2010;30.Argimon Pallás JM, Jiménez Villa J: Métodos de investigación clínica y epidemiológica, vol. Capítulo 15. Tamaño de la muestra; 2013.Yepes-Núñez JJ, García García HI: Preferencias de estados de salud y medidas de utilidad. In., vol. 24. Iatreia; 2011: 365–377.Attema AE, Edelaar-Peeters Y, Versteegh MM, Stolk EA. Time trade-off: one methodology, different methods. Eur J Health Econ. 2013;14(Suppl 1):S53–64.Badia X, Roset M, Herdman M, Kind P. A comparison of United Kingdom and Spanish general population time trade-off values for EQ-5D health states. Med Decis Mak. 2001;21(1):7–16.Garin N, Olaya B, Moneta MV, Miret M, Lobo A, Ayuso-Mateos JL, Haro JM. Impact of multimorbidity on disability and quality of life in the Spanish older population. PLoS One. 2014;9(11):e111498.Mielck A, Vogelmann M, Leidl R. Health-related quality of life and socioeconomic status: inequalities among adults with a chronic disease. Health Qual Life Outcomes. 2014;12:58.Usó Talamantes R: Análisis y desarrollo de un modelo predictivo del gasto farmacéutico ambulatorio ajustado a morbilidad y riesgo clínico [tesis doctoral]. Universidad Politécnica de Valencia; 2015. https://www.educacion.gob.es/teseo/mostrarRef.do?ref=1183638 .Sánchez Mollá M, Candela García I, Gómez-Romero FJ, Orozco Beltrán D, Ollero Baturone M. Concordance between stratification systems and identification of patients with multiple chronic diseases in primary care. Rev Calid Asist. 2017;32(1):10–6.Vivas-Consuelo D, Uso-Talamantes R, Guadalajara-Olmeda N, Trillo-Mata J-L, Sancho-Mestre C, Buigues-Pastor L. Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool. BMC Health Serv Res. 2014;14:462.Coderch J, Sánchez-Pérez I, Ibern P, Carreras M, Pérez-Berruezo X, Inoriza JM. Predicting individual risk of high healthcare cost to identify complex chronic patients. Gac Sanit. 2014;28(4):292–300.Osca Guadalajara M, Guadalajara Olmeda N, Escartín Martínez R. Impact of Teriparatide on quality of life in osteoporotic patients. Rev Esp Salud Publica. 2015;89(2):215–25.Prazeres F, Santiago L. Relationship between health-related quality of life, perceived family support and unmet health needs in adult patients with multimorbidity attending primary care in Portugal: a multicentre cross-sectional study. Health Qual Life Outcomes. 2016;14(1):156.Brettschneider C, Leicht H, Bickel H, Dahlhaus A, Fuchs A, Gensichen J, Maier W, Group MS. Relative impact of multimorbid chronic conditions on health-related quality of life--results from the MultiCare cohort study. PLoS One. 2013;8(6):e66742
HIV Risks and Seroprevalence Among Mexican American Injection Drug Users in California
Latinos in the United States are an ethnically diverse group disproportionately affected by HIV/AIDS. We describe HIV seroprevalence, HIV risk behaviors and utilization of health services among Mexican American injection drug users (IDUs) in California (n = 286) and compare them to White (n = 830) and African American (n = 314) IDUs. Study participants were recruited from syringe exchange programs (n = 24) in California. HIV seroprevalence among Mexican Americans (0.5%) was dramatically lower than Whites (5%) and African Americans (8%). Mexican Americans reported fewer sex-related risks than Whites and African Americans though injection-related risks remained high. Compared to Whites, Mexican Americans were more likely to participate in drug treatment during a 6 month period (AOR 1.5, 95% CI 1.1, 2.0) but less likely to receive any health care (AOR 0.6, 95% CI 0.5, 0.8). Exploring cultural and structural factors among Mexican American IDUs may offer new insights into how to maintain low rates of HIV seroprevalence and reduce barriers to health care utilization
Which medical error to disclose to patients and by whom? Public preference and perceptions of norm and current practice
<p>Abstract</p> <p>Background</p> <p>Disclosure of near miss medical error (ME) and who should disclose ME to patients continue to be controversial. Further, available recommendations on disclosure of ME have emerged largely in Western culture; their suitability to Islamic/Arabic culture is not known.</p> <p>Methods</p> <p>We surveyed 902 individuals attending the outpatient's clinics of a tertiary care hospital in Saudi Arabia. Personal preference and perceptions of norm and current practice regarding which ME to be disclosed (5 options: don't disclose; disclose if associated with major, moderate, or minor harm; disclose near miss) and by whom (6 options: any employee, any physician, at-fault-physician, manager of at-fault-physician, medical director, or chief executive director) were explored.</p> <p>Results</p> <p>Mean (SD) age of respondents was 33.9 (10) year, 47% were males, 90% Saudis, 37% patients, 49% employed, and 61% with college or higher education. The percentage (95% confidence interval) of respondents who preferred to be informed of harmful ME, of near miss ME, or by at-fault physician were 60.0% (56.8 to 63.2), 35.5% (32.4 to 38.6), and 59.7% (56.5 to 63.0), respectively. Respectively, 68.2% (65.2 to 71.2) and 17.3% (14.7 to 19.8) believed that as currently practiced, harmful ME and near miss ME are disclosed, and 34.0% (30.7 to 37.4) that ME are disclosed by at-fault-physician. Distributions of perception of norm and preference were similar but significantly different from the distribution of perception of current practice (P < 0.001). In a forward stepwise regression analysis, older age, female gender, and being healthy predicted preference of disclosure of near miss ME, while younger age and male gender predicted preference of no-disclosure of ME. Female gender also predicted preferring disclosure by the at-fault-physician.</p> <p>Conclusions</p> <p>We conclude that: 1) there is a considerable diversity in preferences and perceptions of norm and current practice among respondents regarding which ME to be disclosed and by whom, 2) Distributions of preference and perception of norm were similar but significantly different from the distribution of perception of current practice, 3) most respondents preferred to be informed of ME and by at-fault physician, and 4) one third of respondents preferred to be informed of near-miss ME, with a higher percentage among females, older, and healthy individuals.</p
Guidelines for Disclosing Genetic Information to Family Members: from Development to Use
[À l'origine dans / Was originally part of : CRDP - Droit, biotechnologie et rapport au milieu
Association between neighborhood safety and overweight status among urban adolescents
<p>Abstract</p> <p>Background</p> <p>Neighborhood safety may be an important social environmental determinant of overweight. We examined the relationship between perceived neighborhood safety and overweight status, and assessed the validity of reported neighborhood safety among a representative community sample of urban adolescents (who were racially and ethnically diverse).</p> <p>Methods</p> <p>Data come from the 2006 Boston Youth Survey, a cross-sectional study in which public high school students in Boston, MA completed a pencil-and-paper survey. The study used a two-stage, stratified sampling design whereby schools and then 9<sup>th</sup>–12<sup>th </sup>grade classrooms within schools were selected (the analytic sample included 1,140 students). Students reported their perceptions of neighborhood safety and several associated dimensions. With self-reported height and weight data, we computed body mass index (BMI, kg/m<sup>2</sup>) for the adolescents based on CDC growth charts. Chi-square statistics and corresponding <it>p</it>-values were computed to compare perceived neighborhood safety by the several associated dimensions. Prevalence ratios (PRs) and 95% confidence intervals (CI) were calculated to examine the association between perceived neighborhood safety and the prevalence of overweight status controlling for relevant covariates and school site.</p> <p>Results</p> <p>More than one-third (35.6%) of students said they always felt safe in their neighborhood, 43.9% said they sometimes felt safe, 11.6% rarely felt safe, and 8.9% never felt safe. Those students who reported that they rarely or never feel safe in their neighborhoods were more likely than those who said they always or sometimes feel safe to believe that gang violence was a serious problem in their neighborhood or school (68.0% vs. 44.1%, <it>p </it>< 0.001), and to have seen someone in their neighborhood assaulted with a weapon (other than a firearm) in the past 12 months (17.8% vs. 11.3%, <it>p </it>= 0.025). In the fully adjusted model (including grade and school) stratified by race/ethnicity, we found a statistically significant association between feeling unsafe in one's own neighborhood and overweight status among those in the Other race/ethnicity group [(PR = 1.56, (95% CI: 1.02, 2.40)].</p> <p>Conclusion</p> <p>Data suggest that perception of neighborhood safety may be associated with overweight status among urban adolescents in certain racial/ethnic groups. Policies and programs to address neighborhood safety may also be preventive for adolescent overweight.</p
A before-after implementation trial of smoking cessation guidelines in hospitalized veterans
Abstract
Background
Although most hospitalized smokers receive some form of cessation counseling during hospitalization, few receive outpatient cessation counseling and/or pharmacotherapy following discharge, which are key factors associated with long-term cessation. US Department of Veterans Affairs (VA) hospitals are challenged to find resources to implement and maintain the kind of high intensity cessation programs that have been shown to be effective in research studies. Few studies have applied the Chronic Care Model (CCM) to improve inpatient smoking cessation.
Specific objectives
The primary objective of this protocol is to determine the effect of a nurse-initiated intervention, which couples low-intensity inpatient counseling with sustained proactive telephone counseling, on smoking abstinence in hospitalized patients. Key secondary aims are to determine the impact of the intervention on staff nurses' attitudes toward providing smoking cessation counseling; to identify barriers and facilitators to implementation of smoking cessation guidelines in VA hospitals; and to determine the short-term cost-effectiveness of implementing the intervention.
Design
Pre-post study design in four VA hospitals
Participants
Hospitalized patients, aged 18 or older, who smoke at least one cigarette per day.
Intervention
The intervention will include: nurse training in delivery of bedside cessation counseling, electronic medical record tools (to streamline nursing assessment and documentation, to facilitate prescription of pharmacotherapy), computerized referral of motivated inpatients for proactive telephone counseling, and use of internal nursing facilitators to provide coaching to staff nurses practicing in non-critical care inpatient units.
Outcomes
The primary endpoint is seven-day point prevalence abstinence at six months following hospital admission and prolonged abstinence after a one-month grace period. To compare abstinence rates during the intervention and baseline periods, we will use random effects logistic regression models, which take the clustered nature of the data within nurses and hospitals into account. We will assess attitudes of staff nurses toward cessation counseling by questionnaire and will identify barriers and facilitators to implementation by using clinician focus groups. To determine the short-term incremental cost per quitter from the perspective of the VA health care system, we will calculate cessation-related costs incurred during the initial hospitalization and six-month follow-up period.
Trial number
NCT00816036http://deepblue.lib.umich.edu/bitstream/2027.42/112349/1/13012_2009_Article_190.pd
- …