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Record W1997240423 · doi:10.1080/17441692.2014.986168

Commentary: Mexico: Moving from universal health coverage towards health care for all

2015· article· en· W1997240423 on OpenAlexfundno aff
Ximena Andión Ibáñez, Alexandra Garita

Bibliographic record

VenueGlobal Public Health · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsReproductive healthEconomic growthHealth carePopulationHealth policyPolitical scienceIndigenousSocial determinants of healthMedicineBusinessEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Improving on previous social protection schemes, in 2000, policy-makers created the Mexican Social Protection System in Health (SPSS), an insurance scheme to expand financial coverage for health care, and especially to reduce and eliminate out-of-pocket health expenditures by the poorest households. Mexico has been widely applauded for achieving universal health coverage, meaning financial coverage, in 2012. However, such an achievement does not, by itself, result in adequate services for women's sexual and reproductive health and rights (SRHR; see paper by Sen & Govender, 2014). Following the reform of the National Health Law in 2004, policy-makers began to work on harmonising health standards across all states in terms of selected aspects of service quality and efficiency. By 2012, more than 52 million people were enrolled in the SPSS, and the total health expenditure increased from 5.1% to 6.3% of GDP between 2001 and 2010 (Knaul et al., 2012); these are considerable accomplishments. Nonetheless, the per cent of GDP allocated to health is low compared to other countries in the region (World Bank, n.d.), and significant gaps remain in securing SRHR, particularly for rural, poor and indigenous women and adolescents. What has SPSS contributed to meeting women's SRHR and what still needs to be done to ensure the universal access commitment of the International Conference on Population and Development (ICPD)? The SPSS is based on a paradigm shift from disease-specific treatment to provision of care across the life cycle. Thus, in theory, the SPSS covers comprehensive SRH services, including maternity care, STI and HIV prevention and treatment, safe abortion services where legal and contraception (including female condoms, emergency contraception and the sub-dermal implant, among others). Two key SPSS programmes prioritise SPSS enrollment for pregnant women and their families, and send out mobile units to work with rural midwives. SPSS also provides financial support for the treatment of cervical cancer, breast cancer and mental health, as well as other services that are critical over a woman's lifetime. While the SPSS has helped reduce the risk of crippling health costs for many of the poor, only limited information is available on the percentage of SPSS coverage that applies to SRHR services, and the impact on SRHR has been inadequate. For example, if the pace of the decline in the maternal mortality ratio, from 56.1 in 2002 to 43 in 2013, continues, Mexico will not meet its Millennium Development Goals (MDG) 5 goal of 22.2 (Government of Mexico, Office of the President, 2013). The unmet need for contraception is nearly 27% among adolescents and over 21% among indigenous women (Mexican Association for Family Planning [MEXFAM], n.d.), and adolescent pregnancy rates remain high: adolescents account for 6 out of every 10 births (National Institute on Statistics and Geography, 2013). Furthermore, one in four people living with HIV are women (UNAIDS, 2010). Particular services such as maternal health programmes do not provide the information on allocation and expenditure of resources needed for accountability, often leaving key decisions in the hands of certain decision-makers alone. These indicators demonstrate that despite ‘universal health coverage’, SRHR still lags behind. At least three factors inhibit progress and must be improved in the years ahead. First, the SPSS emphasis on financing has meant that the following key aspects of care have been neglected: improving the quality of services; strengthening and modifying the distribution of services within the health infrastructure; developing effective referral systems; increasing the number of skilled health workers, especially midwives and other primary- and mid-level providers; and ensuring access to translation for indigenous women in order to facilitate their effective use of services (CIDE & CONAPRED, 2012; GIRE, 2013; Zamarron, 2012). Areas of focus must be changed. Second, Mexico is a Federal Republic, and meeting the right to health for all is the responsibility of 32 states, posing some major challenges for quality assurance and the efficiency of resource spending, among other areas. Third, many women face other significant barriers to accessing SRHR services, including low levels of education; subordination within families and communities; lack of transportation; violence; as well as stigma and discrimination based on ethnicity, sexuality, race and age; all of which require stronger multi-sector policies and programmes to overcome. It is critical for the SPSS, and similar initiatives in other countries, to reduce and eliminate profound inequalities affecting women's access to health services, especially those that are based on income, age, ethnic origin and geographical residence. This requires allocating the maximum available resources, a human rights standard, to public health financing, particularly for SRHR. Finally, the international right-to-health standards for the availability, accessibility, acceptability and quality of goods, services and facilities need to be central to the next stages of health services development. This requires transparency in health sector budgeting and expenditures, effective accountability mechanisms and data collection and monitoring systems that, among other things, enable programme managers, policy-makers and others to track quality of care and individual health outcomes, not just services provided. It further requires mechanisms through which redress can be sought when human rights are abused or the government does not live up to its obligations under international human rights law.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.343
Teacher spread0.298 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2015
Admission routes1
Has abstractyes

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