MétaCan
Menu
Back to cohort
Record W2611901291 · doi:10.1377/hlthaff.2016.0711

Mongolia’s Public Spending On Noncommunicable Diseases Is Similar To The Spending Of Higher-Income Countries

2017· article· en· W2611901291 on OpenAlexaff
Otgontuya Dugee, Enkhtuya Munaa, Ariuntuya Sakhiya, Ajay Mahal

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsPublic spendingHealth spendingEconomicsDevelopment economicsPublic economicsEconomic growthHealth carePolitical scienceHealth insurance

Abstract

fetched live from OpenAlex

Although there is increased recognition of the global challenge posed by noncommunicable diseases (NCDs), translating that awareness into resources for action requires better data than typically available in low- and middle-income countries. One middle-income country that does have good-quality information is Mongolia. Using detailed administrative data from Mongolia and supplementary survey-based information, we estimated public spending on four NCDs in Mongolia and reached four main conclusions. First, Mongolia's public spending patterns on NCDs are similar to NCD spending observed in countries with much higher per capita incomes. Second, public spending for NCDs is low relative to the NCD disease burden in Mongolia. Third, public-sector NCD spending is dominated by inpatient care and hospital-based specialist outpatient services, which suggests inefficiency in resource use. Finally, while public spending on cardiovascular disease is evenly distributed across regions, for cancers it is heavily concentrated in the nation's capital.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.105
GPT teacher head0.470
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHealth AffairsSame topicGlobal Health Care IssuesFrench-language works237,207