RESEARCH DISCIPLINE
Mental Health Services Research Statistics
Service Use and Prevalence Data
Psychiatric epidemiology analysis of admissions, visits and diagnoses from clinic records, registries and population surveys, as rates, counts and prevalence, with the denominators, the clustering of patients within services and the gaps in the records taken into account.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What does psychiatric epidemiology analysis cover?
Mental health services research statistics describe how people use care and how conditions are distributed in populations: admissions, outpatient visits, medication fills and diagnoses recorded in clinics, registries and surveys. Psychiatric epidemiology analysis estimates prevalence and incidence with the right denominators and compares groups with rate or risk measures. Mental health service use data are counts that are skewed, clustered by provider and limited by what the system recorded. PhD-level statisticians working in-house at OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, prepare each rate and model and document how every measure was defined. Clinical and policy interpretation rests with the research team.
WHEN TO ENGAGE
Service and Registry Data Awaiting Analysis
The question may concern the use of care, the frequency of disorders or the gap between the two, and each leads to a different first step.
Counting the Use of Care Admissions, visits or lengths of stay are the outcomes, and the plan must choose between count, rate and time-based models and decide how to treat patients with no use at all.
Prevalence From a Survey A population survey with a diagnostic interview supplies prevalence estimates, and the weights, the recall period and the intervals have to be handled in line with the design.
Survey and Records Disagree A population survey and clinic records give different pictures of the same condition, and the analysis must explain the gap through who reaches care, how cases are counted and the period each source covers.
High Users and Change A group chosen for heavy past use is followed to see whether use falls after a program, and the analysis must allow for the way extreme values tend to be followed by lower ones.
RATES AND PREVALENCE
Rates, Prevalence and Clustered Service Records
Each measure is defined by its numerator, its denominator and its period before any model is fitted, because a rate without its denominator cannot be compared.
Counts of Use
Admissions and visits are modeled as counts with exposure time, using a negative binomial count model when variance exceeds the mean, and zero-truncated models for length of stay among those admitted.
Prevalence Across Recall Periods
Psychiatric disorder prevalence analysis keeps lifetime, 12-month and point prevalence apart, since they are not interchangeable, so the recall period is stated and intervals follow the survey design, with the reporting conventions of Epidemiology.
Episodes of Care
Contacts are grouped into episodes of care with a stated gap rule, so a transfer or a same-week return is not counted as a new admission and frequent use is not inflated.
Clustered Records
Patients are grouped within clinics and providers, so models include provider effects or use methods for clustered data, and the number of providers is reported.
Rare Outcomes
Self-harm or suicide counts are small, so rates use exact or model-based intervals with person-time denominators and are not compared by percentage alone.
Reporting With Context
Tables follow Counseling & Mental Health Psychology and Clinical Psychology practice, with the population at risk named beside each rate.
RECORD AND RATE FLAWS
Service Use Findings Distorted by the Data Source
The distortions below come from how care is delivered and recorded rather than from the statistics, and definitions agreed before modeling are the remedy.
Access Mistaken for Need Areas with more clinics record more visits and diagnoses, and the higher use is read as greater illness, though the supply of services drives recorded use as well as need.
Heavy Users Followed Forward Patients chosen for high use in one year tend to show lower use in the next with or without the program, which makes an expected decline look like a benefit of the program.
Counts Without Time at Risk Admissions are compared across patients observed for different lengths of time, with no exposure term, so people with longer follow-up appear to use more care.
Treated Prevalence Stretched A treated prevalence from clinic records is described as the prevalence of a disorder in the community, though many people with a disorder never reach care.
Clinics Treated as Interchangeable Differences between clinics are attributed to patients or to a program with no allowance for case mix or for the few patients treated at some clinics.
Length of Stay Averaged Naively Mean length of stay is compared between units though a few very long admissions dominate it, so one patient can shift a unit's average more than a change in practice would.
DELIVERABLES
Rate Tables, Denominators and Provider Models
Every rate in the tables can be traced to the people counted in it and the time they were observed.
Rate and Prevalence Sheet Each count, rate and prevalence with its numerator, denominator, period and source, so readers can see exactly what every figure in the tables measures.
Denominator and Exposure File Population or person-time at risk for each rate, with its source and the treatment of people who died, moved or left the service.
Service Use Models Count or rate models for each utilization outcome, with overdispersion checked and results as rate ratios with 95% confidence intervals.
Prevalence Tables Prevalence by group with intervals suited to the design, the recall period stated and the weights documented where a population survey is used.
Provider Variation Summary Differences between clinics adjusted for case mix, shown with their uncertainty and the number of patients at each, so small units are not over-read.
Denominator-Aware Code Code in which each rate is computed from a single, documented denominator rule, so populations at risk cannot drift between tables prepared months apart.
ILLUSTRATIVE EXAMPLE
Following Frequent Crisis Service Users
An illustration of how change in service use was estimated for a group selected because of heavy use.
ILLUSTRATIVE EXAMPLE · CRISIS SERVICE UTILIZATION RESEARCH · UNITED STATES
A psychiatric nursing researcher used county records to ask whether emergency visits for a mental health crisis fell after a group of frequent users was enrolled in a case management program.
Patients had been enrolled because they had used the service heavily in the previous year, so a fall in visits was expected even without the program, as extreme values tend to be followed by lower ones. The plan therefore added a comparison group of patients with similar prior use who were not enrolled, and compared the change in visit counts between the groups with a negative binomial count model using person-time as the exposure. Deaths and moves out of the county during follow-up were recorded, and the analysis allowed for the shorter follow-up of those patients. Because enrollment had not been randomized, the comparison was reported as an association between enrollment and later visits.
Outcome: The researcher received the rate ratio comparing change between groups with its 95% confidence interval, the case and denominator definitions, the tables and the code.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Analysis Tools for Records and Surveys
Records held in a health system's own files and public-use surveys call for different tools, and the analysis uses the one that matches the data source.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
Funding and Care Decisions Rest on Clinical Judgment
Patterns of service use carry consequences for funding and care, and turning them into recommendations calls for clinical and policy judgment that sits with the research team.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your research. Authorship of the service evaluation, the thesis or the article remains yours.
Your Research Decisions The research question, the clinical reading of the diagnoses, the interpretation of service use, the discussion and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Records, Rates and Prevalence in Mental Health Research
Small units make rates jumpy, frequent users drift toward the average, and clinic counts miss people who never seek care.
What do mental health services research statistics cover?
Psychiatric epidemiology analysis at OnlineSPSS covers service use, case definitions, prevalence and incidence, using rates, counts and comparisons that respect the denominators and the clustering of patients within services. The work draws on clinic, registry, claims and survey sources and sits within Medical, Health & Clinical Research.
Can self-harm rates be compared between clinics or small areas?
Only with care. With few events, the rate in a small unit swings widely from year to year, so the highest and lowest rates usually belong to the smallest units. Exact intervals, funnel plots against the overall rate or model-based smoothing show which differences exceed what chance would produce, and units are not ranked on raw rates.
How should a program for frequent users be evaluated?
Patients selected for heavy past use tend to show lower use afterward even if the program did nothing, which is regression to the mean. A comparison group chosen on the same prior-use criterion allows change in the program group to be set against change without it. The comparison remains observational unless enrollment was randomized, so the result is reported as an association.
Why does treated prevalence differ from community prevalence?
Treated prevalence counts the people who reached care, and many people with a disorder never do, so it depends on access, stigma and local services as well as on how common the disorder is. Community prevalence comes from a survey that interviews a sample of the population whether or not they use services. The two answer different questions and should not be used interchangeably.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the study protocol, the case definitions and the dataset, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Rates Built on Denominators
You Can Defend
Service records count what a system saw, not everything that happened, so the definition of a case and of the population at risk shapes every rate long before any model does.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014