Training Health Care and Human Services Personnel in Perinatal Substance Abuse

1995 ◽  
pp. 13-35 ◽  
Author(s):  
Kathleen J. Farkas
Keyword(s):  
Health Care ◽  
Substance Abuse ◽  
Human Services

scholarly journals Comparison of data sources on alcohol problems: An exploratory exercise using surveys vs. national healthcare registers

2020 ◽  
pp. 145507252096802
Author(s):  
Pia Mäkelä ◽  
Kristiina Kuussaari ◽  
Airi Partanen ◽  
Elina Rautiainen
Keyword(s):  
Health Care ◽  
Substance Abuse ◽  
Healthcare Service ◽  
Service Use ◽  
Basic Education ◽  
Alcohol Problems ◽  
Register Data ◽  
Data Sources ◽  
National Healthcare ◽  
Healthcare Service Use

Aims: Both survey and healthcare register data struggle as data sources to capture the phenomenon of alcohol problems. We study a large group of people for whom survey data and two types of register data are available, and examine the overlaps of similar or related measures in the different data sources to learn about potential weaknesses in each. We also examine how register-based data on the prevalence of alcohol problems change depending on which register data are used. Design: We use data from the Regional Health and Wellbeing Study (ATH) of the adult Finnish population collected in 2013 and 2014 ( n = 69,441), individually linked with data on two national healthcare registers (Care Register for Health Care; Register of Primary Health Care visits) for the survey year and previous year. Results: The prevalence of substance-abuse-related healthcare was almost two-fold if data on outpatient primary care visits were included in addition to hospitalisations. Forty-six per cent of the survey respondents self-reporting substance-abuse-related healthcare service use were identified in the registers, and 22% of all respondents with such service use according to registers reported this in the survey. Records of substance-abuse-related healthcare service use, controlled for self-reported alcohol use and self-reported substance-abuse-related service use, were found more often for men, the middle-aged, people with basic education only, and the non-employed. Conclusions: The results are suggestive of underreporting in both data sources. There is an evident need to develop recording practices in the healthcare registers regarding substance use disorders.

scholarly journals Health Care Coalitions as Response Organizations: Houston After Hurricane Harvey

2017 ◽  
Vol 11 (6) ◽  
pp. 637-639 ◽  
Author(s):  
Lori Upton ◽  
Thomas D. Kirsch ◽  
Melissa Harvey ◽  
Dan Hanfling
Keyword(s):  
Health Care ◽  
Human Services ◽  
Large Scale ◽  
The United States ◽  
Operational Planning ◽  
Advisory Council ◽  
East Texas ◽  
Health And Human Services ◽  
Department Of Health ◽  
Response Information

AbstractHealth care coalitions play an increasingly important role in both preparedness for, response to, and recovery from large scale disaster events occurring across the United States. The actions taken by the South East Texas Regional Advisory Council (SETRAC) in response to the landfall of Hurricane Harvey, and the consequential flooding that ensued, serve as an excellent example of how health care coalitions are increasingly needed to play a unifying role in response. This paper highlights a number of the strategic planning, operational planning and response, information sharing, and resource coordination and management activities that were undertaken for the response to Hurricane Harvey. The successful response to this devastating storm in the Houston, Texas area serves as an example to other regions across the country as they work to implement the 2017-2022 health care capabilities articulated by the Department of Health and Human Services Office of the Assistant Secretary for Preparedness and Response. (Disaster Med Public Health Preparedness. 2017;11:637–639)

Clinical Predictors for Unsafe Direct Discharge Home Patients From Intensive Care Units

2018 ◽  
Vol 35 (10) ◽  
pp. 1067-1073
Author(s):  
Vincent Issac Lau ◽  
Fran Priestap ◽  
Joyce N. H. Lam ◽  
John Basmaji ◽  
Ian M. Ball
Keyword(s):  
Health Care ◽  
Substance Abuse ◽  
Intensive Care ◽  
Adverse Events ◽  
Intensive Care Units ◽  
Care Facility ◽  
Health Care Facility ◽  
Surgical Trauma ◽  
Home Discharge ◽  
Return Visits

Purpose: To describe factors (demographics and clinical characteristics) that predict patients who are at an increased risk of adverse events or unplanned return visits to a health-care facility following discharge direct to home (DDH) from intensive care units (ICUs). Methods: Prospective cohort study of all adult patients who survived their stay in our medical–surgical–trauma ICU between February 2016 and 2017 and were discharged directly home. Patients were followed for 8 weeks postdischarge. Univariable and multivariable logistic regression analyses were performed to identify factors associated with adverse events or unplanned return visits to a health-care facility following DDH from ICU. Results: A total of 129 DDH patients were enrolled and completed the 8-week follow-up. We identified 39 unplanned return visits (URVs). There was 0% mortality at 8 weeks postdischarge. Eight potential predictors of hospital URVs ( P < .2) were identified in the univariable analysis: prior substance abuse (odds ratio [OR] of URV of 2.50 [95% confidence interval: 1.08-5.80], hepatitis (OR: 6.92 [1.68-28.48]), sepsis (OR: 11.03 [1.19-102.29]), admission nine equivalents of nursing manpower score (NEMS) <24 (OR: 2.28 [1.03-5.04], no fixed address (OR: 22.9 [1.2-437.3]), ICU length of stay (LOS) <2 days (OR: 2.95 [1.28-6.78]), home discharge within London, Ontario (OR: 2.44 [1.00-5.92]), and left against medical advice (AMA; OR: 6.06 [2.04-17.98]). Conclusions: Our study identified 8 covariates that were potential predictors of URV: prior substance abuse, hepatitis, sepsis, admission NEMS <24, no fixed address, ICU LOS <2 days, home discharge within London, Ontario, and left AMA. The practice of direct discharges home from the ICU would benefit from adequately powered multicenter study in order to construct a clinical prediction model (that would require further testing and validation).

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