MétaCan
Menu
Retour à la cohorte
Enregistrement W1997240423 · doi:10.1080/17441692.2014.986168

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

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

Notice bibliographique

RevueGlobal Public Health · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensnon disponible
Organismes subventionnairesInternational Development Research Centre
Mots-clésReproductive healthEconomic growthHealth carePopulationHealth policyPolitical scienceIndigenousSocial determinants of healthMedicineBusinessEnvironmental healthEconomics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,451
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,343
Écart entre enseignants0,298 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueGlobal Public HealthMême sujetGlobal Maternal and Child HealthTravaux en français237 207