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Enregistrement W4409741586 · doi:10.1016/s2214-109x(24)00537-0

Improving primary health-care services in LMIC cities

2025· article· en· W4409741586 sur OpenAlexaff
Richard Lilford, Benjamin Daniels, Barbara McPake, Zulfiqar A Bhutta, Robert Mash, Frances Griffiths, Akinyinka Omigbodun, Elzo Pereira Pinto, Radhika Jain, Gershim Asiki, Eika Webb, Katie Scandrett, Peter J Chilton, Jo Sartori, Yen‐Fu Chen, Peter Waiswa, Alex Ezeh, Catherine Kyobutungi, GM Leung, Cristiani Vieira Machado, Kabir Sheikh, Sam Watson, Jishnu Das

Notice bibliographique

RevueThe Lancet Global Health · 2025
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Systems and Reforms
Établissements canadiensCentre for Global Health Research
Organismes subventionnairesMedical Research CouncilNational Institute for Health and Care Research
Mots-clésPrimary health carePrimary careHealth servicesMEDLINEMedicineEnvironmental healthPolitical scienceFamily medicinePopulation

Résumé

récupéré en direct d'OpenAlex

Urban environments are home to more than half of the people living in low-income and middle-income countries (LMICs), and this proportion will only increase over the coming decades. Much policy discussion of health services in LMICs still relies on knowledge and models derived from rural contexts, for which a single public-sector clinic is often the only option. In contrast, contemporary evidence shows that the urban health service landscapes are made up of dense networks of competing provider clinics that constitute a market. Improvement strategies that work in non-urban contexts are therefore unlikely to be sufficient in these environments. Innovative policy approaches that leverage choice and competition to re-shape markets offer great promise. The first paper1 in this Series of two describes the configuration, cost, and quality of primary-care services in LMIC cities, along with the preferences of service users for different types of service. First, we find extensive evidence that numerous facilities are available to citizens, even in low-income neighbourhoods of LMIC cities. As a result, most people can reach multiple doctor or nurse clinics within 30 min.2 With the exception of some hospital-based polyclinics, most facilities are not busy, with the result that clinical capacity is under-used.3 Second, service costs vary greatly and are substantially tied to commodities such as pharmaceuticals and diagnostics.2 Most people report low out-of-pocket costs, but the variance is wide and asymmetrical such that a minority face catastrophic expenses.4 A few LMICs at higher income levels offer freely available public services or insurance, but this is not the global norm. Third, the average quality of services is generally poor; many clinicians fail to make the correct diagnosis or implement the appropriate treatment,3 long-term conditions are poorly managed,5 antibiotic stewardship is inadequate,6 and medicine stockouts are frequent.7 Fourth, despite the complexity of this environment, patients (including those who are very financially disadvantaged) exhibit considerable agency, seeking out clinics perceived to offer a higher quality care, even if they have to travel further and pay more.8 These facts present a compelling new image of primary health services in LMIC cities. Facilities are omnipresent and easy to reach, but are very diverse in terms of cost, quality, and crowding. The geography of LMIC cities has resulted in what might best be described as a market in which a variety of private and public providers compete, at least implicitly. Most providers are low cost, low quality, and not crowded—but there are important exceptions to these characteristics. The second paper9 discusses the implications of these findings for policy aimed at the improvement of primary health services in these cities. The presence of primary health-care markets provides an opportunity to reshape the market through policies that change the mix of available providers. This opportunity is not available in rural areas for which choice and competition are rare (and public facilities often dominate). In this Series paper we therefore describe not only methods to improve the quality of existing providers, but also methods that take advantage of competition and choice to reshape the market. Thus, while recognising that there are no one-size-fits-all solutions, we discuss approaches in three categories: (1) shaping the market by changing the mix of available providers; (2) improving existing services (including quality and financial accessibility); and (3) facilitating effective demand for better service. One powerful example of shaping the market is investing in public facilities, which can stimulate improvement among facilities and crowd out those that fail to improve.10 Likewise, judicious regulation has been shown in a recent randomised controlled trial to improve quality in the public sector, while having positive knock-on effects for the private sector.11 One of the best ways to invest in improving existing services is through the formation of muti-disciplinary primary care teams integrating facility care (provided by doctors and nurses) with community care (provided by community health workers). Evidence from Brazil, an early adopter of this model, suggests that these teams provide integrated and equitable, preventive, acute, and long-term care.12 Existing services can also be improved by well designed continuing professional development, information technology (including virtual consultations), and various forms of management support. Many initiatives have tried to improve care by stimulating demand. Successful interventions include providing patients with information on available services, involving communities in shaping local services, and providing free access by removing user fees or providing vouchers. There is experimental evidence for most of the previously mentioned initiatives,13 but judging relevance and prioritisation has been difficult because most studies evaluate compound (ie, multi-component) interventions without using a factorial design, there is little evidence beyond immediate effects, and there are few cost-effectiveness or cost-benefit analyses. In addition, there is little evidence regarding people who are homeless or unregistered and for peri-urban areas and towns. Now is a propitious time for primary care. After many years there are signs that it is getting the recognition it deserves at a time when health investments are rising with economic growth and a renewed focus on universal health coverage. But for any increased investment to be efficacious, it needs to account for the context and environment in which it is introduced. Policies in cities offer multiple opportunities—but also multiple challenges as market interactions can lead to unintended consequences. The evidence and analysis offered in our Series is intended to provide a framework for this debate. RJL, FG, JS, and SIW received funding from the National Institute for Health and Care Research (NIHR) Research and Innovation for Global Health Transformation (NIHR 200132) using UK Aid from the UK Government to support global health research. RJL, JS, and SIW received funding from the NIHR Global Health Research Unit on Improving Health in Slums. RJL, EPP, KSc, and CM received funding from the NIHR Global Health Research Unit on Social and Environmental Determinants of Health Unit. RJL, JS, and SIW received funding from the NIHR Midlands Patient Safety Research Collaboration. RJL and PJC received funding from the NIHR Applied Research Collaboration West Midlands. SIW also received funding from Medical Research Council (grant MR/V038591/1). EPP received support from the Bill and Melinda Gates Foundation to attend symposiums and meetings. CM has a Research Productivity Grant from the National Council for Scientific and Technological Development of Brazil and a Distinguished Scientist Grant from Carlos Chagas Research Foundation—State of Rio de Janeiro. All other authors declare no competing interests. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,535
Score d'incertitude au seuil0,911

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 Opus0,022
Tête enseignante GPT0,294
Écart entre enseignants0,272 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations9
Publié2025
Routes d'admission1
Résumé présentoui

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