Inclusive Data Gathering: The community feedback period for this idea began on 7/22/2019 and will end on 9/20/2019

What is the problem that needs to be addressed? Please describe how it is related to mental health.
Vital development statistics for Orange County highlighted in the Orange County Community Indicators annual report (http://www.ocgov.com/about/infooc/facts/indicators) or the Center for Health Statistics and Informatics County Health Status Profiles (https://www.cdph.ca.gov/Programs/CHSI/Pages/County-Health-Status-Profiles.aspx) both are missing vital development statistics important for the homeless/mentally ill demographic.

Why is this a concern for Orange County? What can Orange County and other counties learn from this project?
The current trend in healthcare spending cannot be supported via the current process and cannot be shown to be effective via the current process.

What is currently being done to resolve this problem in our county and throughout the United States? If applicable: Is it working; why or why not?
The Eight America’s Study (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0030260) from 2006 demonstrated how variance is hidden in typical reports of life expectancy.

What is new or different about this project idea? Please describe how this differs from what is already being done (Question 6). Please list any research that was done on this topic.
It is novel for an underreported/underrepresented/vulnerable population to ask for inclusion in the planning process by asking for a change in how statistics are gathered. This project proposal shares goals with Informed Consent, Mental Health Event Reporting System and Improving Patient Care. The safe harbors provision of Informed Consent is probably a prerequisite for any of the above choices for the basic reason of minimizing future litigation costs and maximizing stakeholder buy-in. Informed Consent’s intention is to utilize currently held information for learning/improving. Part of Improving Patient Care is to stop the partitioning/fractionating of data due to the large distribution of patient care across numerous healthcare referral networks. The Mental Health Event Reporting System is to crowdsource/public source events considered significant enough to lead to a dramatic decrease in health/safety/security/life-expectancy. Finally, this project is to obtain data-driven healthcare planning that captures the hidden variance known to exist.

What is the project idea? Please describe how this project will operate.
Like the other informatics project proposals, this is a platform that is not unique in healthcare but utilized to learn about mental illness/mental health. How to capture data to remove the variance and expose the underlying meaning is the ultimate goal.

Additional Information:

Respondent skipped this question

 

July 8, 2019

9 comments

Over the past 10 years, the United Nations Development Programme has transitioned from measures of economic poverty (monetary income thresholds) to a Multidimensional Poverty Index (MPI) (http://hdr.undp.org/en/2018-MPI). The 2019 UNDP MPI report measured 1.3 billion poor people worldwide with 69% of them living in middle-income countries. MPI doesn’t measure poor people living in rich countries too much. UNDP MPI measures Nutrition, Child Mortality, Schooling, School Attendance, Cooking Fuel, Sanitation, Drinking Water, Electricity, Housing, and Assets to a very specific criteria.

Reviewing activities of daily living and advanced activities of daily living give clues on what to measure to be inclusive:

ADL’s
Hygiene
Dressing
Eating
Continence
Transfer

Advanced ADLs
Communication
Transportation
Meal Preparation
Shopping
Housework / Sanitation
Medications/Healthcare
Financial Management
Care of others, pets, children
Spiritual/Religious Observances
Safety Procedures

Translating these into a balance scorecard of inclusive data gathering would measure:
Access to:
Square Footage of Privacy
Sanitation
Clean Drinking Water
Square Footage of Secure Storage Space (enabling organization)
Food Preparation Area including heat source (UNDP would go further to measure if heat source was from dung, wood, charcoal or coal)
Electricity
Trash Service
Internet Connected Computer
Phone / Text Service
Volume of Transportation Space (i.e. can carry what fits in a backpack, car or has resources to hire movers)
Electronic Form of Payment
Banked / Un-banked
etc.

Thank you for your submission. The Innovation team will review this idea and post comments and updates when available.

The World Health Organization (WHO) has an Assessment Instrument for Mental Health Systems (AIMS) https://www.who.int/mental_health/evidence/WHO-AIMS/en/. Unfortunately, the WHO-AIMS tool is used/published by low/middle income countries (https://www.who.int/mental_health/who_aims_country_reports/en/) exclusively. The Mental Health Systems in Selected Low- and Middle-Income Countries: a WHO-AIMS Cross-National Analysis (https://apps.who.int/iris/bitstream/handle/10665/44151/9789241547741_eng.pdf;jsessionid=964670DC662DC438A546216F91834E0A?sequence=1) reports that housing and employment opportunities are poor irregardless of country income (see section 3.4.3 Links with the Housing Sector and section 3.4.2 Links with the Social Welfare and Employment Sectors). The Low and Middle Income Countries AIMS analysis also reports the scarcity of day treatment programs across all incomes. The same report highlights the concentration of capital resources in the in-patient setting and higher income jurisdictions utilizing the inpatient setting as a de-facto residential facility (low income countries have short stays in mental health hospitals while higher income countries have much, much longer stays). Rural regions are much more tolerant of mental illness than urban regions – probably why rural interventions such as the OC INN Agrotherapy and Residential Care are proposed (pg. 89).

I will quote a conclusion from the Low and Middle income Countries Report (Section 6.2.3):

6.2.3 Mental health systems often are not well linked to other relevant sectors
It is crucial to connect the mental health sector to the rest of the health sector, to the welfare system and, more generally, to civil society. This is important not only for achieving a better
functioning of the mental health system, but also for reducing stigma, which is more prevalent when mental health care is isolated.

The scope of the unbanked/underbanked discussion shall be limited to its relationship to mental illness. Please refrain from discussion of other issues surrounding banking/finance or speculating as to why certain numbers are tied to certain statistics.

The Federal Deposit Insurance Corporation (FDIC) maintains its unbanked and underbanked data at http://www.economicinclusion.gov. There data can be sliced/diced by jurisdiction. The highlights for Orange County from the 2017 data-set:

Unbanked in the US: 6.5%; in California: 7.4%; in LA-LB-OC:9.0%
Underbanked in the US: 18.7%; in California: 17.6%; in LA-LB-OC: 14.9%
Unknown Banking Status: 6.3%; in California: 7.5% in LA-LB-OC: 8.7%

The banking status of 32.7% of the population in the Los Angeles-Long Beach-Anaheim region (the jurisdictional slice closest to Orange County) is either unbanked, underbanked or status unknown.

The 2017 FDIC National Survey of Unbanked and Underbanked Households (https://economicinclusion.gov/downloads/2017_FDIC_Unbanked_HH_Survey_Report.pdf) utilizes the term “working age disabled households” to describe a household owned/rented by a person aged 25-64 with a disability. Before describing the technicalities of how “working age disabled households” isn’t meaningful to this discussion it is worthwhile to note that the un-banked rate of this demographic remained about the same in the 2013, 2015 and 2017 FDIC surveys.

FDIC data comes via the U.S. Census Current Population Survey. of civilian, non-institutionalized people aged 15 years or older. To be counted in this assessment you must have been part of the sampling, indicated that you were involved in household finances, and filled out the finances portion of the sample. People experiencing serious mental illness/homelessness aren’t likely counted in the FDIC report based upon the sampling methodology described in Appendix 1: FDIC Technical Notes.

Search pubmed.gov for MeSH terms “Bankruptcy” and (“Bipolar Disorder” or “Schizophrenia”) and there are zero items returned (accessed 7/29/2019, please try yourself and see if this is just a temporary glitch). Pubmed.gov returns 865 articles for the MeSH term “Bankruptcy.” Based upon job loss, downward drift, and other financial realities of mental illness/health, the lack of bankruptcy research with mental illness is quite shocking. Anecdotally it is observed that not enough of a support system exists for a person experiencing serious mental illness to file for bankruptcy. What is more likely is the “disabled poor person’s bankruptcy” which involves getting on SSI/SSDI and stopping payments to all creditors. Since SSI/SSDI payments are spared from creditors efforts to collect the outcome is essentially the same as bankruptcy.

It is unclear what outcomes exist for serious financial choices such as bankruptcy. Anecdotal evidence suggests that pursuing bankruptcy is not well tolerated by a person experiencing serious mental illness as it does become a scavenger hunt across numerous jurisdictional processes/procedures and requires high levels of organization of large volumes of paper records. Once accounts are charged off and go into the collections market the likelihood of phantom debt/zombie debt (https://www.forbes.com/2008/10/31/debt-creditors-default-pf-education-in_af_1031investopedia_inl.html#47f4e84d53fa) is quite high. A person experiencing serious mental illness is not likely to have protections from phantom debt/zombie debt and thus the process of bankruptcy may be meaningless. When a person has trouble connecting to reality, having a reality that can change a debt from bankruptcy discharged to existing in the collections market as a valid debt isn’t likely to have good clinical outcomes.

The consequences of the “disabled poor person’s bankruptcy” aren’t well understood.

After playing around with pubmed.gov some more I felt it was important to compare mental illness with a similar disease to see the differences in research coverage. Mental illness is a non-communicable disease (genetic undertones) with early onset, but diagnosis likely between ages 18-30 for the class of traditional serious mental illnesses (bipolar, schizophrenia, etc). Cancer is a great comparison as it is also a non-communicable disease (genetic undertones). The American Cancer Society cites prevalence of cancer at about 14 million (https://www.cancer.org/cancer/cancer-basics/cancer-prevalence.html). NAMI cites prevalance of mental illness at about 46 million (https://www.nami.org/learn-more/mental-health-by-the-numbers). There is some nuance to counting – ACS counts anyone alive with cancer anytime in their lifetime. NAMI counts the people who have mental illness in a given year, not counting the people that had it previously, but not currently. When comparing the numbers ACS count has an over-counting issue while NAMI has an under-counting issue.

Lets look at pubmed.gov in the context of cancer and mental illness.

MeSH Terms:
Retirement – The state of being retired from one’s position or occupation.

Neoplasm (the MeSH term for Cancer) – New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.

When it comes to serious mental illness there isn’t a good search term. Mental illness returns the mesh term Mental Disorders which includes restrooming diagnoses (enuresis, encopresis), Motor Disorders, Amnesia, Delirium, Dementia, Dyslexia, Paraphillic Disorders, Sexual Dysfunctions, etc. So its not a good match to the scope of care at Orange County Behavioral Health.

Mental Disorder- Psychiatric illness or diseases manifested by breakdowns in the adaptational process expressed primarily as abnormalities of thought, feeling, and behavior producing either distress or impairment of function.

Bipolar Disorder -A major affective disorder marked by severe mood swings (manic or major depressive episodes) and a tendency to remission and recurrence.

Schizophrenia – A severe emotional disorder of psychotic depth characteristically marked by a retreat from reality with delusion formation, HALLUCINATIONS, emotional disharmony, and regressive behavior.

Depression – Depressive states usually of moderate intensity in contrast with major depression present in neurotic and psychotic disorders.

Fun with searching:

Retirement – 9247 journal articles

Retirement and Neoplasm – 125 journal articles with some high quality studies such as:
Long-Term Economic and Employment Outcomes Among Partners of Women With Early-Stage Breast Cancer. University of Michigan, Ann Arbor VA, University of Colorado
Predictors of early retirement after cancer rehabilitation-a longitudinal study. – A German multi-center study
Non-graduation after comprehensive school, and early retirement but not unemployment are prominent in childhood cancer survivors-a Finnish registry-based study.
Financial toxicity: a potential side effect of prostate cancer treatment among Australian men.
Putting it off: family breast cancer history and women’s retirement planning.
Wage-subsidised employment as a result of permanently reduced work capacity in a nationwide cohort of patients diagnosed with haematological malignancies. – Denmark
Early retirement in cancer patients with or without comorbid mental health conditions: a prospective cohort study. – likely a German study
Early retirement and non-employment after breast cancer. – Finland
Cancer rehabilitation and prehabilitation may reduce disability and early retirement
Employment and retirement status of older cancer survivors compared to non-cancer siblings.
Cancer and the risk for taking early retirement pension: a Danish cohort study.
Employment status of Finnish cancer patients in 1997.
Breast cancer survival, work, and earnings.
And so forth…

Retirement and Mental Disorder – 488 journal articles, but recall the wide definition of Mental Disorder as a MeSH term. So lets look at some specific diagnoses.

Retirement and Bipolar Disorder – 2 (both about how other diseases resemble bipolar disorder)

Retirement and Schizophrenia – 13 (5 written in either German or Romanian. Of the English Language articles the following countries are represented: Finland, Germany (likely based upon European origin of journal and author’s country of residence), Germany, Netherlands, more Germany and an article of general retirement issues in the United States).

Retirement and Depression – 244 journal articles, mostly about how the 65 year old white male is at very high risk for depression/suicide after retirement – a classic medical board question memorized by first year medical students.

Retirement and PTSD – 16 Articles ( 3 are 9/11/2001 studies, 1 is published in the Russian language, 1 is about upper extremity amputation, 1 is about the Irish Emergency Services, 1 likely from Australia based upon employment of authors, 1 is about Canadian peacekeeping veterans, 1 is about South African Security forces)

Retirement and Personality Disorders – 6 articles (1 from Finland based upon author employment, 1 is a comment/not a clinical study of any kind, 1 is written in French, 1 is a case study, 1 is about death/dying in the geriatric population.

See WRAP Inclusion and Training for more discussion comparing and contrasting mental illness and cancer.

The World Health Organization publishes a Mental Health ATLAS every few years, 2017 is the most current (https://www.who.int/mental_health/evidence/atlasmnh/en/). This is a standardized set of metrics used to benchmark the mental health resources for a given jurisdiction. It would be very nice if each County in California could publish data to the WHO Mental Health ATLAS balanced scorecard standard (no blanks/none’s nor not reported’s allowed).

This is quite important for a variety of reasons.

Kaiser Family Foundation (KFF) came out with a poll earlier this year (https://www.kff.org/other/press-release/california-poll-access-to-mental-health-care-insurance-coverage-affordability-rank-among-californians-top-health-care-priorities-for-new-governor-legislature/) about mental health access. Nowhere in the report does KFF benchmark doctors per capita, psychiatrists per capita, utilization metrics, mental health diagnoses per capita, etc. How does one interpret the KFF poll/study? Do we have enough mental health resources, but they are just poorly organized, have poor outcomes, highly error prone (see Mental Health Event Reporting System), or some other inefficiency? Are we shoveling the cash into inpatient settings that keep patients for highly extended periods of time?

A few years back a different mental health advocacy organization took a reductionist approach to measure mental healthcare quality/accessibility/etc by annual funding. Annual funding and any financial/monetary measure of resources is a poor metric for healthcare in California because the financing/monetary measures are partitioned across numerous payers and referral networks. The general message was “State X spends only Y dollars per capita and it should be more.” Y ranged from unknown to about $450. Of course, whatever Y was, it wasn’t enough according to this mental health advocacy organization. Regressing per capita mental health spending with per capita income by state was quite interesting and a chaotic graph. The $343.83 per capita cited in the USA Mental Health ATLAS 2017 seems quite high compared to previous data I have encountered. Also of note, the USA Mental Health ATLAS 2017 reports the percent of government healthcare spending on mental health is less than 0.05% of all government healthcare spending (Australia is 7.81%, France is 15%, Saudi Arabia is 4%). The dollars always seem to mislead as a metric.

Standards for minimum space or area per person tend to be associated with institutional settings like prisons and hospitals. The California Board of State and Community Corrections(http://www.bscc.ca.gov/wp-content/uploads/Adult-Title-24-Min-Standards-for-Local-Detention-Facilities-2013.pdf) has standards for space that range between about 10 square feet per person and 70 square feet per person depending on the setting. European standards are similar (https://rm.coe.int/16806cc449). The Centers for Medicare and Medicaid Services (CMS) cite about 60-80 square feet per person (https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/som107c02.pdf). These standards are for warehousing people to sleep and possibly restroom/hand wash. These standards don’t likely include other activities of daily living and not likely to be met based upon overcrowding conditions.

Other standards for per person minimum space probably do exist. It is up to the reader to find the right standards for their purposes.

The Auditor of the State of California released the report “Department of Healthcare Services – Millions of Children in Medi-Cal Are Not Receiving Preventative Health Services” in March 2019 (https://www.auditor.ca.gov/pdfs/reports/2018-111.pdf). Scroll to pages 16, 19 21, and 22. In the informal ranking of who gets care Children, Medicare beneficiaries and people with resources who speak English rank towards the top and can serve as a proxy metric maximum for what mental health care looks like. If there is someone out there that knows about a similar report for mental health then that would be great to share with the community. Unfortunately, agreement on what constitutes quality mental health care tends not to exist. This report uses the American Academy of Pediatrics Bright Futures standard to measure quality and utilization. There is not an equivalent standard known to this submitter for Psychiatry, Psychology, Social Work, Homeless Intervention Services, etc. Thus an equivalent report is not expected to be found.

This report highlights success in Orange County, but also shows that there aren’t many alternatives in California that have a functioning healthcare system. If this submitter had children and read this report then I would likely get in my car and relocate to Orange County or San Francisco. If this submitter was in Inyo County then relocation to just about anywhere else would be better for a healthcare consumer. Having such a supply-demand pressure isn’t ideal for community stability in either the originating county nor the receiving county.

However, 60% utilization for the Bright Futures standard (screenings for hearing issues, vision issues, autism, developmental issues, behavior issues, drug use, depression, TB screening, lead poisoning screening, immunizations, dental issues) is concerning and suggests that 40% of Children in Orange County Medi-Cal are not getting care consistent with the American Academy of Pediatrics. Or, they are getting such care, but it is not being captured by the bioinformatics systems currently in use.
These children will grow into the mental health system – especially those with lead poisoning, hearing issues, vision issues, autism, developmental issues, behavioral issues, drug use, depression, etc. This submitter doesn’t know what 40% of Medi-Cal children calculates out to be as an absolute number of Children, but it sounds like a high number. Having quality healthcare records and quality healthcare is very important for knowing where to allocate resources.

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