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Record W7000709719

Gastos com medicamentos distribuídos em Atenção Primária de Saúde em Fortaleza- Ce e co-fatores influentes do ano de 2007

2012· article· en· W7000709719 on OpenAlexaboutno aff

Bibliographic record

VenueAmericanae (AECID Library) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Health in Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaEquity (law)Investment (military)Health careDescriptive statisticsPopulationConsumption (sociology)Distribution (mathematics)Quarter (Canadian coin)Work (physics)Defined daily dose
DOInot available

Abstract

fetched live from OpenAlex

The increased investment in the acquisition of drugs have shown significant improvements in health indicators and in the Pharmaceutical Assistance (P.A.). Addressing the needs of the population and ensure access to medicines with equity and efficiency has been a major challenge for health authorities. This work started from the need for a diagnosis of resource allocation for P.A. in the administrative areas of Fortaleza and its Health Units (H.U). This is a retrospective analysis of the database with features of ecological and descriptive study where we examined the secondary data acquisition and distribution of drugs between January - December 2007, the H.U. operating in the municipality. The main goal was to determine the distribution of drug costs in Primary Health Care (PHC) in Fortaleza, Brazil between the H.U. and its regional and analyze the associated factors. To this end, characterized the cost of medication by number of patients in each H.U., it was classified according to ATC and scaled up consumption in DDD have been correlated with the characteristics of the service by examining the Pearson correlation test and t - student to identify co-influencing factors. Total expenditure on essential drugs was estimated at R$9.29 million and the per capita expenditure of R$3.82 and spending per patient from R$2,41. The period of the year that more was spent on drugs was in the 2nd quarter (28,36%) and 9,57% in April and 10,46% in June. The region had the highest spending was the Region II (R$ 2.216.886,94) that has a high Human Development Index (HDI). The average cost per patient was highest in Region V (R$2.82) which is concentrated the population with lower income and low HDI. The therapeutic classes with the highest representation were systemic antibiotics (18,8% of total spending, represented mainly by beta-lactam antibiotics, penicillins), followed by antidiabetics (9.4% oral hypoglycemic agents) and with antihypertensive action on the renin-angiotensin (8,6% and 8,2% only with captopril). The most frequently consumed drugs were: captopril (17,2 DDDs / 1,000 patients seen per day), hydrochlorothiazide (11,9) and aspirin (7,9). The Antiasthmatic represented the most expensive (unit price R$20,66 for Beclometasone 250mcg, R$18,36 Beclomethasone 50mcg and Salbutamol 100mg R$8.57) though the relationship cost / DDD were the most costly: Fenoterol 0.5% (R$11,88), penicillin 600.000 UI (R$6.59) and norethisterone 0.35 mg (R$5,03). The quality of P.A. showed no statistically significant association with the cost of medication, but had an inverse correlation (r = -0,110) with a tendency to reduce spending, the presence of the Pharmacist in the H.U. showed a significant positive correlation with the quality of P.A. (p -value = 0,014) and has an economic impact in spending on drugs with potential savings of R$ 0,32 on average per patient. We conclude that despite the efforts of decentralization still there is inequity in the tip of the Public Health System, which in the upper middle class there was a higher resource allocation relating to medicinal products and in the poorest regions of the city with the highest volume of patients served. Per capita spending on essential drugs in PHC Fortaleza and spending per patient are not consistent with the values agreed upon by management levels (R$6.20). It is recommended that the presence of the Pharmacist in the H.U. aimed at rationalizing spending and consumption of drugs contributing to P.A. quality and efficient in Fortaleza.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.324
Teacher spread0.308 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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