Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 9 de 9
Filtrar
1.
MMWR Morb Mortal Wkly Rep ; 70(36): 1249-1254, 2021 Sep 10.
Artículo en Inglés | MEDLINE | ID: mdl-34499628

RESUMEN

Although COVID-19 generally results in milder disease in children and adolescents than in adults, severe illness from COVID-19 can occur in children and adolescents and might require hospitalization and intensive care unit (ICU) support (1-3). It is not known whether the B.1.617.2 (Delta) variant,* which has been the predominant variant of SARS-CoV-2 (the virus that causes COVID-19) in the United States since late June 2021,† causes different clinical outcomes in children and adolescents compared with variants that circulated earlier. To assess trends among children and adolescents, CDC analyzed new COVID-19 cases, emergency department (ED) visits with a COVID-19 diagnosis code, and hospital admissions of patients with confirmed COVID-19 among persons aged 0-17 years during August 1, 2020-August 27, 2021. Since July 2021, after Delta had become the predominant circulating variant, the rate of new COVID-19 cases and COVID-19-related ED visits increased for persons aged 0-4, 5-11, and 12-17 years, and hospital admissions of patients with confirmed COVID-19 increased for persons aged 0-17 years. Among persons aged 0-17 years during the most recent 2-week period (August 14-27, 2021), COVID-19-related ED visits and hospital admissions in the states with the lowest vaccination coverage were 3.4 and 3.7 times that in the states with the highest vaccination coverage, respectively. At selected hospitals, the proportion of COVID-19 patients aged 0-17 years who were admitted to an ICU ranged from 10% to 25% during August 2020-June 2021 and was 20% and 18% during July and August 2021, respectively. Broad, community-wide vaccination of all eligible persons is a critical component of mitigation strategies to protect pediatric populations from SARS-CoV-2 infection and severe COVID-19 illness.


Asunto(s)
COVID-19/epidemiología , COVID-19/terapia , Servicio de Urgencia en Hospital/estadística & datos numéricos , Utilización de Instalaciones y Servicios/tendencias , Hospitalización/tendencias , Adolescente , COVID-19/prevención & control , Vacunas contra la COVID-19/administración & dosificación , Niño , Preescolar , Humanos , Lactante , Recién Nacido , Índice de Severidad de la Enfermedad , Estados Unidos/epidemiología , Cobertura de Vacunación/estadística & datos numéricos
2.
MMWR Morb Mortal Wkly Rep ; 70(37): 1284-1290, 2021 Sep 17.
Artículo en Inglés | MEDLINE | ID: mdl-34529637

RESUMEN

COVID-19 vaccine breakthrough infection surveillance helps monitor trends in disease incidence and severe outcomes in fully vaccinated persons, including the impact of the highly transmissible B.1.617.2 (Delta) variant of SARS-CoV-2, the virus that causes COVID-19. Reported COVID-19 cases, hospitalizations, and deaths occurring among persons aged ≥18 years during April 4-July 17, 2021, were analyzed by vaccination status across 13 U.S. jurisdictions that routinely linked case surveillance and immunization registry data. Averaged weekly, age-standardized incidence rate ratios (IRRs) for cases among persons who were not fully vaccinated compared with those among fully vaccinated persons decreased from 11.1 (95% confidence interval [CI] = 7.8-15.8) to 4.6 (95% CI = 2.5-8.5) between two periods when prevalence of the Delta variant was lower (<50% of sequenced isolates; April 4-June 19) and higher (≥50%; June 20-July 17), and IRRs for hospitalizations and deaths decreased between the same two periods, from 13.3 (95% CI = 11.3-15.6) to 10.4 (95% CI = 8.1-13.3) and from 16.6 (95% CI = 13.5-20.4) to 11.3 (95% CI = 9.1-13.9). Findings were consistent with a potential decline in vaccine protection against confirmed SARS-CoV-2 infection and continued strong protection against COVID-19-associated hospitalization and death. Getting vaccinated protects against severe illness from COVID-19, including the Delta variant, and monitoring COVID-19 incidence by vaccination status might provide early signals of changes in vaccine-related protection that can be confirmed through well-controlled vaccine effectiveness (VE) studies.


Asunto(s)
Vacunas contra la COVID-19/administración & dosificación , COVID-19/epidemiología , COVID-19/prevención & control , Hospitalización/estadística & datos numéricos , Vacunación/estadística & datos numéricos , Adolescente , Adulto , Anciano , COVID-19/mortalidad , COVID-19/terapia , Humanos , Incidencia , Persona de Mediana Edad , Estados Unidos/epidemiología , Adulto Joven
3.
MMWR Morb Mortal Wkly Rep ; 70(32): 1075-1080, 2021 Aug 13.
Artículo en Inglés | MEDLINE | ID: mdl-34383729

RESUMEN

Population-based analyses of COVID-19 data, by race and ethnicity can identify and monitor disparities in COVID-19 outcomes and vaccination coverage. CDC recommends that information about race and ethnicity be collected to identify disparities and ensure equitable access to protective measures such as vaccines; however, this information is often missing in COVID-19 data reported to CDC. Baseline data collection requirements of the Office of Management and Budget's Standards for the Classification of Federal Data on Race and Ethnicity (Statistical Policy Directive No. 15) include two ethnicity categories and a minimum of five race categories (1). Using available COVID-19 case and vaccination data, CDC compared the current method for grouping persons by race and ethnicity, which prioritizes ethnicity (in alignment with the policy directive), with two alternative methods (methods A and B) that used race information when ethnicity information was missing. Method A assumed non-Hispanic ethnicity when ethnicity data were unknown or missing and used the same population groupings (denominators) for rate calculations as the current method (Hispanic persons for the Hispanic group and race category and non-Hispanic persons for the different racial groups). Method B grouped persons into ethnicity and race categories that are not mutually exclusive, unlike the current method and method A. Denominators for rate calculations using method B were Hispanic persons for the Hispanic group and persons of Hispanic or non-Hispanic ethnicity for the different racial groups. Compared with the current method, the alternative methods resulted in higher counts of COVID-19 cases and fully vaccinated persons across race categories (American Indian or Alaska Native [AI/AN], Asian, Black or African American [Black], Native Hawaiian or Other Pacific Islander [NH/PI], and White persons). When method B was used, the largest relative increase in cases (58.5%) was among AI/AN persons and the largest relative increase in the number of those fully vaccinated persons was among NH/PI persons (51.6%). Compared with the current method, method A resulted in higher cumulative incidence and vaccination coverage rates for the five racial groups. Method B resulted in decreasing cumulative incidence rates for two groups (AI/AN and NH/PI persons) and decreasing cumulative vaccination coverage rates for AI/AN persons. The rate ratio for having a case of COVID-19 by racial and ethnic group compared with that for White persons varied by method but was <1 for Asian persons and >1 for other groups across all three methods. The likelihood of being fully vaccinated was highest among NH/PI persons across all three methods. This analysis demonstrates that alternative methods for analyzing race and ethnicity data when data are incomplete can lead to different conclusions about disparities. These methods have limitations, however, and warrant further examination of potential bias and consultation with experts to identify additional methods for analyzing and tracking disparities when race and ethnicity data are incomplete.


Asunto(s)
COVID-19/etnología , Grupos de Población Continentales/estadística & datos numéricos , Análisis de Datos , Grupos Étnicos/estadística & datos numéricos , Sesgo , COVID-19/prevención & control , COVID-19/terapia , Vacunas contra la COVID-19/administración & dosificación , Recolección de Datos/normas , Disparidades en el Estado de Salud , Disparidades en Atención de Salud/etnología , Humanos , Resultado del Tratamiento , Estados Unidos/epidemiología , Cobertura de Vacunación/estadística & datos numéricos
4.
Subst Use Misuse ; 56(3): 396-403, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33446000

RESUMEN

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., "unsolicited reporting") for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches.


Asunto(s)
Sobredosis de Droga , Sobredosis de Opiáceos , Programas de Monitoreo de Medicamentos Recetados , Analgésicos Opioides/uso terapéutico , Sobredosis de Droga/tratamiento farmacológico , Humanos , Maryland , Prescripciones , Estudios Retrospectivos
5.
Addiction ; 115(9): 1683-1694, 2020 09.
Artículo en Inglés | MEDLINE | ID: mdl-32096302

RESUMEN

BACKGROUND AND AIMS: Evidence from randomized controlled trials establishes that medication treatment with methadone and buprenorphine reduces opioid use and improves treatment retention. However, little is known about the role of such medications compared with non-medication treatments in mitigating overdose risk among US patient populations receiving treatment in usual care settings. This study compared overdose mortality among those in medication versus non-medication treatments in specialty care settings. DESIGN: Retrospective cohort study using state-wide treatment data linked to death records. Survival analysis was used to analyze data in a time-to-event framework. SETTING: Services delivered by 757 providers in publicly funded out-patient specialty treatment programs in Maryland, USA between 1 January 2015 and 31 December 2016. PARTICIPANTS: A total of 48 274 adults admitted to out-patient specialty treatment programs in 2015-16 for primary diagnosis of opioid use disorder. MEASUREMENTS: Main exposure was time in medication treatment (methadone/buprenorphine), time following medication treatment, time exposed to non-medication treatments and time following non-medication treatment. Main outcome was opioid overdose death during and after treatment. Hazard ratios were calculated using Cox proportional hazard regression. Propensity score weights were adjusted for patient information on sex, age, race, region of residence, marital and veteran status, employment, homelessness, primary opioid, mental health treatment, arrests and criminal justice referral. FINDINGS: The study population experienced 371 opioid overdose deaths. Periods in medication treatment were associated with substantially reduced hazard of opioid overdose death compared with periods in non-medication treatment [adjusted hazard ratio (aHR) = 0.18, 95% confidence interval (CI) = 0.08-0.40]. Periods after discharge from non-medication treatment (aHR = 5.45, 95% CI = 2.80-9.53) and medication treatment (aHR = 5.85, 95% CI = 3.10-11.02) had similar and substantially elevated risks compared with periods in non-medication treatments. CONCLUSIONS: Among Maryland patients in specialty opioid treatment, periods in treatment are protective against overdose compared with periods out of care. Methadone and buprenorphine are associated with significantly lower overdose death compared with non-medication treatments during care but not after treatment is discontinued.


Asunto(s)
Sobredosis de Droga/mortalidad , Antagonistas de Narcóticos/uso terapéutico , Tratamiento de Sustitución de Opiáceos/mortalidad , Trastornos Relacionados con Opioides/rehabilitación , Adolescente , Adulto , Analgésicos Opioides/uso terapéutico , Buprenorfina/uso terapéutico , Causas de Muerte , Estudios de Cohortes , Sobredosis de Droga/terapia , Femenino , Humanos , Masculino , Maryland , Metadona/uso terapéutico , Persona de Mediana Edad , Naltrexona/uso terapéutico , Modelos de Riesgos Proporcionales , Estudios Retrospectivos , Factores de Tiempo , Estados Unidos/epidemiología , Adulto Joven
6.
Ann Emerg Med ; 75(1): 1-12, 2020 01.
Artículo en Inglés | MEDLINE | ID: mdl-31515181

RESUMEN

STUDY OBJECTIVE: Persons with substance use disorders frequently utilize emergency department (ED) services, creating an opportunity for intervention and referral to addiction treatment and harm-reduction services. However, EDs may not have the appropriate tools to distinguish which patients are at greatest risk for negative outcomes. We link hospital ED and medical examiner mortality databases in one state to identify individual-level risk factors associated with overdose death among ED patients with substance-related encounters. METHODS: This retrospective cohort study linked Maryland statewide ED hospital claims records for adults with nonfatal overdose or substance use disorder encounters in 2014 to 2015 with medical examiner mortality records in 2015 to 2016. Logistic regression was used to identify factors in hospital records associated with risk of opioid overdose death. Predicted probabilities for overdose death were calculated for hypothetical patients with different combinations of overdose and substance use diagnostic histories. RESULTS: A total of 139,252 patients had substance-related ED encounters in 2014 to 2015. Of these patients, 963 later experienced an opioid overdose death, indicating a case fatality rate of 69.2 per 10,000 patients, 6 times higher than that of patients who used the ED for any cause. Factors most strongly associated with death included having both an opioid and another substance use disorder (adjusted odds ratio 2.88; 95% confidence interval 2.04 to 4.07), having greater than or equal to 3 previous nonfatal overdoses (adjusted odds ratio 2.89; 95% confidence interval 1.54 to 5.43), and having a previous nonfatal overdose involving heroin (adjusted odds ratio 2.24; 95% confidence interval 1.64 to 3.05). CONCLUSION: These results highlight important differences in overdose risk among patients receiving care in EDs for substance-related conditions. The findings demonstrate the potential utility of incorporating routine data from patient records to assess risk of future negative outcomes and identify primary targets for initiation and linkage to lifesaving care.


Asunto(s)
Sobredosis de Droga/mortalidad , Servicios Médicos de Urgencia/estadística & datos numéricos , Adolescente , Adulto , Comorbilidad , Bases de Datos Factuales , Femenino , Humanos , Almacenamiento y Recuperación de la Información , Modelos Logísticos , Masculino , Maryland/epidemiología , Persona de Mediana Edad , Estudios Retrospectivos , Factores de Riesgo
7.
Am J Prev Med ; 57(6): e211-e217, 2019 12.
Artículo en Inglés | MEDLINE | ID: mdl-31753274

RESUMEN

INTRODUCTION: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. METHODS: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. RESULTS: Predictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). CONCLUSIONS: A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.


Asunto(s)
Analgésicos Opioides/efectos adversos , Sobredosis de Droga/mortalidad , Epidemia de Opioides/prevención & control , Programas de Monitoreo de Medicamentos Recetados/estadística & datos numéricos , Medicamentos bajo Prescripción/efectos adversos , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Estudios de Cohortes , Prescripciones de Medicamentos/estadística & datos numéricos , Femenino , Humanos , Masculino , Maryland/epidemiología , Persona de Mediana Edad , Modelos Estadísticos , Estudios Retrospectivos , Medición de Riesgo/métodos , Factores de Riesgo , Factores Sexuales , Adulto Joven
8.
Drug Alcohol Depend ; 201: 127-133, 2019 08 01.
Artículo en Inglés | MEDLINE | ID: mdl-31207453

RESUMEN

BACKGROUND: Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences. METHODS: We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphine patients (N = 25,487). We estimated a series of models that included demographics plus opioid, buprenorphine and benzodiazepine prescription variables. We applied logistic regression to generate performance measures. RESULTS: About 3.24% of the study cohort had ≥1 nonfatal opioid overdoses. In the model with all predictors, odds of nonfatal overdoses among buprenorphine patients were higher among males (OR = 1.39, 95% CI:1.21-1.62) and those with more buprenorphine pharmacies (OR = 1.19, 95% CI:1.11-1.28), 1+ buprenorphine prescription paid by Medicaid (OR = 1.21, 95% CI:1.02-1.48), Medicare (OR = 1.93, 95% CI:1.63-2.43), or a commercial plan (OR = 1.98, 95% CI:1.30-2.89), 1+ opioid prescription paid by Medicare (OR = 1.30, 95% CI:1.03-1.68), and more benzodiazepine prescriptions (OR = 1.04, 95% CI:1.02-1.05). The odds were lower among those with longer days of buprenorphine (OR = 0.64, 95% CI:0.60-0.69) or opioid (OR = 0.79, 95% CI:0.65-0.95) supply. The model had moderate predictive ability (c-statistic = 0.69). CONCLUSIONS: Several modifiable risk factors, such as length of buprenorphine treatment, may be targets for interventions to improve clinical care and reduce harms. This model could be practically implemented with common prescription-related information and allow payers and clinical systems to better target overdose risk reduction interventions, such as naloxone distribution.


Asunto(s)
Analgésicos Opioides/uso terapéutico , Buprenorfina/uso terapéutico , Sobredosis de Droga/epidemiología , Alcaloides Opiáceos/envenenamiento , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Benzodiazepinas/envenenamiento , Estudios de Cohortes , Prescripciones de Medicamentos/estadística & datos numéricos , Femenino , Predicción , Humanos , Masculino , Maryland/epidemiología , Medicaid , Medicare , Persona de Mediana Edad , Modelos Estadísticos , Tratamiento de Sustitución de Opiáceos , Estudios Retrospectivos , Factores de Riesgo , Factores Sexuales , Factores Socioeconómicos , Estados Unidos , Adulto Joven
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...