Posts Tagged by data sharing
SAMHSA’s 2015 Behavioral Health Barometer: Pennsylvania Offers Look at Substance Use, Mental Health Treatment
|April 2, 2016||Posted by M. P. under Behavorial Health, Children and Family, Drug and Alcohol||
Earlier this year The Substance Abuse and Mental Health Services Administration (SAMHSA) published the third edition of their Behavioral Health Barometer: Pennsylvania – part of series of reports at both the national and the state level that provides a “snapshot of behavioral health.” The Barometer pulls data on youth and adult behavioral health markers from the National Survey on Drug Use and Health, the National Survey of Substance Abuse Treatment Services, the Youth Risk Behavior Survey, the Monitoring the Future survey, and services used by Medicare enrollees. This free report is a great source of data for needs assessments and grant proposals, be sure to download the national and state (of your choice) report at the SAMHSA website.
Below are data from the report on aspects of youth and adolescent behavioral health and substance use. Overall, the state percentages are comparable to national percentages, with higher proportions in reported cigarette use and binge drinking.
For Pennsylvania in 2013/2013-14:
- approximately 84,000 adolescents (12 to 17 years old), just under 9 percent of all adolescents, used illegal drugs during the month prior.
- 6.6 percent of adolescents used cigarettes within the last month – this is higher than the national data point of 5.2 percent.
- 16.5 percent of adolescents binged on alcohol within the last month – again, higher than the national percentage of 14 percent.
- 198,088 youth (under 18 years of age) received services from the public mental health system, with 63.5 percent reporting improvement post-treatment, lower than the national data point of 69.5 percent.
Report Citation: Substance Abuse and Mental Health Services Administration. Behavioral Health Barometer: Pennsylvania, 2015. HHS Publication No. SMA–16–Baro–2015–PA. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2015.
|October 12, 2015||Posted by M. P. under Philanthropy, Research|
Research from the Women’s Philanthropy Institute at the Indiana University Lilly Family School of Philanthropy indicates gender income differences influence charitable giving, particularly among married couples. Where Do Men and Women Give? Gender Differences in the Motivations and Purposes for Charitable Giving and Do Women Give More? Findings from Three Unique Data Sets on Charitable Giving, both authored by Debra Mesch, Una Osili, Jacqueline Ackerman, and Elizabeth Dale, utilize data from the Philanthropy Panel Study (PPS), the Bank of America/U.S. Trust Studies of High Net Worth Philanthropy surveys (HNW), and the Million Dollar List (MDL) to examine patterns in giving level and activity. Their analysis found that single women made more charitable contributions than their male counterparts (except in the highest net worth category) but overall, marriage increased the occurrence and dollar amount of charitable contributions.
Among those married, an increase in the husband’s income was associated with increased giving in both activity and amount, specifically to charitable organizations related to religion, basic human needs, health, and education. Married couples who shared in decision making around philanthropy also tended to give more. Still, the relationship between income, gender, and charitable giving is a complicated one. For example, when women earned more than their husbands, giving activity dropped in comparison to households where the husband’s income was higher.
Sectors supported also differed by gender, as households headed by a female were more apt to donate to youth and family, health, and international causes, while those with a male decider were more likely to give to religious and education organizations. As far as social issues however, married couples with female deciders ranked animal welfare as a top priority, while those with a male decider prioritized the arts.
Examining giving at a level deeper than the “household” may help nonprofits and charities improve engagement with current and future donors. These papers, as well as a literature review on women’s charitable giving, are available at the Indiana University Lilly Family School of Philanthropy’s website.
|June 11, 2015||Posted by M. P. under Children and Family, Health, Juvenile Delinquency, Policy, Research, Youth Development||
According to 2011 data, 12.5 percent of children under the age of 18 are abused or neglected in the United States each year. A Facts on Youth brief from the Center for Health and Justice at TASC cites a study published in JAMA Pediatrics that found confirmed maltreatment for 1 in 8 youth, with nearly 6 percent of cases (just less than half of confirmed reports) involving children ages 5 and under. The brief also notes that studies of child abuse and maltreatment that rely on self-reports rather than substantiated reports indicate a rate of up to 40 percent.
The Child Trends brief Preventing Violence: Understanding and addressing determinants of youth violence in the United States reviewed relevant research on interventions and policy approaches to reducing youth violence, with an emphasis on individual, family and school/community factors. This review identified several predictors of violence, including domestic violence, dysfunctional parenting, gun availability, low self-control, and lack of connectedness to school. Child maltreatment, however, was a strong predictor of nearly every type of violence. The prevention of child abuse and provision of interventions to address the impact of such trauma appear to be critical actions in reducing the potential of future violence. That said, although child maltreatment is a risk factor for criminal behavior, the longer term negative effects of that experience may be offset or amplified by other life events. Completing high school/getting a GED and getting married were two factors identified by a research team at the Social Development Research Group at the University of Washington as having a positive impact on a person’s life, thus reducing the power of the relationship between the maltreatment and future high risk behaviors. A history of maltreatment combined with additional risk factors, such as poverty, increases the likelihood of criminal behavior.
As safety and health are essential factors in optimal child development, and may affect a multitude of life outcomes, new strategies have emerged to better identify and “triage” high-risk situations. States are turning to the big data playbook to assist in investigations of abuse and maltreatment, using predictive analysis to help prioritize reports and better provide preventive services. Information such as family history, school reports and other administrative data, plus case officer knowledge, gives child welfare decision-makers more (if not necessarily better) data to guide the use of resources for the protection of children. Along with Connecticut, Florida, and Los Angeles County, Allegheny County here in western Pennsylvania is utilizing predictive analytics in an effort to reduce child maltreatment, abuse, and fatalities. For more information on how predictive analysis is being used in child welfare, see Who will Seize the Child Abuse Prediction Market by Darian Woods and Checklists, Big Data and the Virtues of Human Judgement by Holden Slattery, both in The Chronicle of Social Change.
|June 19, 2013||Posted by M. P. under Federal Government, News, Policy, Research||
An April 2013 briefing to Congress on surveys and statistics focused on the problematic trend of declining response rates for federal surveys, including the American Community Survey and the National Survey of Child Health. The briefing, Policy Makers & Businesses Need Reliable Information and Data: The Impact of Falling Response Rates to Social Surveys and What Can Be Done, organized by The American Academy of Political and Social Science (AAPSS), outlined the risks to research and the impact on policy-making if response rates to surveys on health, employment and household continue to subside. The largest risk is that of biased results. Other issues:
- Nonresponse rates currently range from 30 to more than 60 percent. This is an all-time high.
- Over 60 percent of nonresponses were refusals, while approximately another 1/4 were due to the inability to contact the intended recipient.
- Young single-person households, minorities, renters and the poor were less likely to respond.
- One-time surveys have higher nonresponse rates than more complex longitudinal studies that follow the same group of respondents over period of time.
While incentives (such as a gift card or a small amount of money) for completing and returning a survey would boost response rates, it would also increase costs – a risky proposition in an atmosphere of austerity. The authors of a related paper, Where Do We Go from Here? Nonresponse and Social Measurement, published in the January 2013 volume of AAPSS’s The Annals, suggest that a solution to this growing problem is a strategic outreach plan to inform both politicians and the public of the purpose of national surveys. Clear explanation of what the data is used for, as well as the regulations and protocols in place to protect it from being presented other than in aggregate form could have a favorable impact on perception. Unfortunately for these and other large-scale surveys, the recent news of metadata collected absent suspicion may have even the most tech-savvy survey-loving among us rethinking issues of privacy, transparency and information storage and retrieval.
Perhaps in the future these surveys that, by the way, inform funding decisions on infrastructure, education, and transportation to name a few, will be deemed too intrusive and/or obsolete and left behind. Funding and other governing decisions can then be made based on variables extracted from all that we have uploaded onto the digital data heap. So, will big data replace big surveys? Will traditional statistical methods be successful in tracking, analyzing and accurately reporting big data to inform policies at the federal, state and local level?