Evidence map›Paper›PMID 16758525›Full record

ArticleCroatian medical journal2006

Risk adjustment and primary health care in Chile.

Veronica Vargas, Juergen Wasem

Abstract read
In one paragraph

Article in Croatian medical journal, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Observational
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Veronica VargasDepartment of Economics, Alberto Hurtado and Georgetown University, Santiago, Chile. vvargas@uahurtado.cl
Juergen Wasem

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo offer a capitation formula with greater capacity for guiding resource spending on population with poorer health and lower socioeconomic status in the context of financing and equity in primary health care.

methodsWe collected two years of data on a sample of 10,000 individuals from a region in Chile, Valdivia and Temuco and evaluated three models to estimate utilization and expenditures per capita. The first model included age and sex; the second one included age, sex, and the presence of two key diagnoses; and the third model included age, sex, and the presence of seven key diagnoses. Regression results were evaluated by R2 and predictive ratios to select the best specifications.

resultsPer-capita expenditures by age and sex confirmed international trends, where children under five, women, and the elderly were the main users of primary health care services. Women sought health advice twice as much as men. Clear differences by socioeconomic status were observed for the indigent population aged > or =65 years who under-utilized primary health care services. From the three models, major improvement in the predictive power occurred from the demographic (adjusted R2, 9%) to the demographic plus two diagnoses model (adjusted R2, 27%). Improvements were modest when five other diagnoses were added (adjusted R2, 28%).

conclusionThe current formula that uses municipality's financial power and geographic location of health centers to adjust capitation payments provides little incentive to appropriate care for the indigent and people with chronic conditions. A capitation payment that adjusts for age, sex, and the presence of diabetes and hypertension will better guide resource allocation to those with poorer health and lower socioeconomic status.

Indexed as

Health ExpendituresAdolescentAdultAgedCapitation FeeChildChild, PreschoolChileFemaleHumansInfantMaleMiddle AgedModels, EconomicPrimary Health CareRisk Adjustment

Identifiers

PMID16758525
PMCPMC2080435

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.