The Cost of Providing the Foundational Public Health Services in Ohio
Bibliographic record
Abstract
OBJECTIVES: To examine levels of expenditure and needed investment in public health at the local level in the state of Ohio pre-COVID-19. DESIGN: Using detailed financial reporting from fiscal year (FY) 2018 from Ohio's local health departments (LHDs), we characterize spending by Foundational Public Health Services (FPHS). We also constructed estimates of the gap in public health spending in the state using self-reported gaps in service provision and a microsimulation approach. Data were collected between January and June 2019 and analyzed between June and September 2019. PARTICIPANTS: Eighty-four of the 113 LHDs in the state of Ohio covering a population of almost 9 million Ohioans. RESULTS: In FY2018, Ohio LHDs spent an average of $37 per capita on protecting and promoting the public's health. Approximately one-third of this investment supported the Foundational Areas (communicable disease control; chronic disease and injury prevention; environmental public health; maternal, child, and family health; and access to and linkages with health care). Another third supported the Foundational Capabilities, that is, the crosscutting skills and capacities needed to support all LHD activities. The remaining third supported programs and activities that are responsive to local needs and vary from community to community. To fully meet identified LHD needs in the state pre-COVID-19, Ohio would require an additional annual investment of $20 per capita on top of the current $37 spent per capita, or approximately $240 million for the state. CONCLUSIONS: A better understanding of the cost and value of public health services can educate policy makers so that they can make informed trade-offs when balancing health care, public health, and social services investments. The current environment of COVID-19 may dramatically increase need, making understanding and growing public health investment critical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".