Defining service catchment areas in low-resource settings

Macharia, Peter M.; Ray, Nicolas; Giorgi, Emanuele; Okiro, Emelda A.; Snow, Robert W.

doi:10.1136/bmjgh-2021-006381

Cited by 31 publications

(37 citation statements)

References 26 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our review of the literature indicates that very few published studies targeting DNO make use of accessibility models informed by field- or expert-based information on speed and mode of transport of either samples or patients. This may impact on the realism of the model results and is at odds with the growing literature that uses this type of modeling approach for various other health services [ 10 , 40 , 41 , 42 ]. This seemingly sub-optimal use of the recent physical accessibility models may come from the recent consideration of the importance of data analytics in DNO [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

Use of Physical Accessibility Modelling in Diagnostic Network Optimization: A Review

Chenes

Albert²,

Kao

et al. 2022

Diagnostics

Self Cite

View full text Add to dashboard Cite

Diagnostic networks are complex systems that include both laboratory-tested and community-based diagnostics, as well as a specimen referral system that links health tiers. Since diagnostics are the first step before accessing appropriate care, diagnostic network optimization (DNO) is crucial to improving the overall healthcare system. The aim of our review was to understand whether the field of DNO, and especially route optimization, has benefited from the recent advances in geospatial modeling, and notably physical accessibility modeling, that have been used in numerous health systems assessment and strengthening studies. All publications published in English between the journal’s inception and 12 August 2021 that dealt with DNO, geographical accessibility and optimization, were systematically searched for in Web of Science and PubMed, this search was complemented by a snowball search. Studies from any country were considered. Seven relevant publications were selected and charted, with a variety of geospatial approaches used for optimization. This paucity of publications calls for exploring the linkage of DNO procedures with realistic accessibility modeling framework. The potential benefits could be notably better-informed travel times of either the specimens or population, better estimates of the demand for diagnostics through realistic population catchments, and innovative ways of considering disease epidemiology to inform DNO.

show abstract

Section: Discussionmentioning

confidence: 99%

Use of Physical Accessibility Modelling in Diagnostic Network Optimization: A Review

Chenes

Albert²,

Kao

et al. 2022

Diagnostics

Self Cite

View full text Add to dashboard Cite

show abstract

“…This includes issues of population size and distribution, such as the extent of daily, seasonal and permanent migration, which makes planning for and monitoring of sufficient health system capacity difficult. Approaches from rural areas, such as delineating health facility catchment areas 51 and estimating demand for maternal health services, 52 are far more complex in cities, demanding ongoing focused effort and funding. We did not find clear differences in patterns within the three maternal health services or across the care continuum related to city size, even though the included cities varied from a population of 1 million to >20 million and the relative size of the city compared with country size ranged from 4% (Addis Ababa) to around one-quarter (Monrovia, Lomé, Luanda, Cairo).…”

Section: Discussionmentioning

confidence: 99%

A tale of 22 cities: utilisation patterns and content of maternal care in large African cities

et al. 2022

View full text Add to dashboard Cite

IntroductionGlobally, the majority of births happen in urban areas. Ensuring that women and their newborns benefit from a complete package of high-quality care during pregnancy, childbirth and the postnatal period present specific challenges in large cities. We examine health service utilisation and content of care along the maternal continuum of care (CoC) in 22 large African cities.MethodsWe analysed data from the most recent Demographic and Health Survey (DHS) since 2013 in any African country with at least one city of ≥1 million inhabitants in 2015. Women with live births from survey clusters in the most populous city per country were identified. We analysed 17 indicators capturing utilisation, sector and level of health facilities and content of three maternal care services: antenatal care (ANC), childbirth care and postnatal care (PNC), and a composite indicator capturing completion of the maternal CoC. We developed a categorisation of cities according to performance on utilisation and content within maternal CoC.ResultsThe study sample included 25 326 live births reported by 19 217 women. Heterogeneity in the performance in the three services was observed across cities and across the three services within cities. ANC utilisation was high (>85%); facility-based childbirth and PNC ranged widely, 77%–99% and 29%–94%, respectively. Most cities showed inconsistent levels of utilisation and content across the maternal CoC, Cotonou and Accra showed relatively best and Nairobi and Ndjamena worst performance.ConclusionThis exploratory analysis showed that many DHS can be analysed on the level of large African cities to provide actionable information about the utilisation and content of the three maternal health services. Our comparative analysis of 22 cities and proposed typology of best and worst-performing cities can provide a starting point for extracting lessons learnt and addressing critical gaps in maternal health in rapidly urbanising contexts.

show abstract

“…The constraints reported by researchers regarding pushing the frontier to reflect closer-to-reality travel time estimates relate to capacity to accurately parametrise a model that mimics the dynamics of the journey between the residence and service provider (29,30). Data required for improved model parameterization include residential location of service users, location of the utilized facility providing EmOC, route used, mode of transport, traffic and weather variables, travel speed and transport barriers, among other travel dynamics (31). However, collecting such data is time-consuming, expensive, and probably impractical especially in low resource settings where there are many competing needs for resources.…”

Section: Common Methods For Estimating Travel Time To Emoc In Lmicsmentioning

confidence: 99%

Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings

Banke-Thomas

Macharia

Makanga

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing these deaths associated with pregnancy and childbirth. Until recently, pregnant women residing in urban areas have been considered to have good access to care, including EmOC. However, emerging evidence shows that due to rapid urbanization, this so called “urban advantage” is shrinking and in some LMIC settings, it is almost non-existent. This poses a complex challenge for structuring an effective health service delivery system, which tend to have poor spatial planning especially in LMIC settings. To optimize access to EmOC and ultimately reduce preventable maternal deaths within the context of urbanization, it is imperative to accurately locate areas and population groups that are geographically marginalized. Underpinning such assessments is accurately estimating travel time to health facilities that provide EmOC. In this perspective, we discuss strengths and weaknesses of approaches commonly used to estimate travel times to EmOC in LMICs, broadly grouped as reported and modeled approaches, while contextualizing our discussion in urban areas. We then introduce the novel OnTIME project, which seeks to address some of the key limitations in these commonly used approaches by leveraging big data. The perspective concludes with a discussion on anticipated outcomes and potential policy applications of the OnTIME project.

show abstract

Defining service catchment areas in low-resource settings

Cited by 31 publications

References 26 publications

Use of Physical Accessibility Modelling in Diagnostic Network Optimization: A Review

Use of Physical Accessibility Modelling in Diagnostic Network Optimization: A Review

A tale of 22 cities: utilisation patterns and content of maternal care in large African cities

Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings

Contact Info

Product

Resources

About