Evidence map›Paper›PMID 23170895›Full record

ArticleBMC health services research2012

Exploring the relationship between population density and maternal health coverage.

Michael Hanlon, Roy Burstein, Samuel H Masters, Raymond Zhang

Abstract readComparative Study
In one paragraph

Article in BMC health services research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  13. Health Policy Performance in 16 Caribbean States, 2010-2015.American journal of public health · 2019
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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

4 authors.

Michael HanlonInstitute for Health Metrics and Evaluation, University of Washington, 2301 5th Avenue, #600, Seattle, WA 98121, USA. hanlonm@uw.edu
Roy Burstein
Samuel H Masters
Raymond Zhang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDelivering health services to dense populations is more practical than to dispersed populations, other factors constant. This engenders the hypothesis that population density positively affects coverage rates of health services. This hypothesis has been tested indirectly for some services at a local level, but not at a national level.

methodsWe use cross-sectional data to conduct cross-country, OLS regressions at the national level to estimate the relationship between population density and maternal health coverage. We separately estimate the effect of two measures of density on three population-level coverage rates (6 tests in total). Our coverage indicators are the fraction of the maternal population completing four antenatal care visits and the utilization rates of both skilled birth attendants and in-facility delivery. The first density metric we use is the percentage of a population living in an urban area. The second metric, which we denote as a density score, is a relative ranking of countries by population density. The score's calculation discounts a nation's uninhabited territory under the assumption those areas are irrelevant to service delivery.

resultsWe find significantly positive relationships between our maternal health indicators and density measures. On average, a one-unit increase in our density score is equivalent to a 0.2% increase in coverage rates.

conclusionsCountries with dispersed populations face higher burdens to achieve multinational coverage targets such as the United Nations' Millennial Development Goals.

Indexed as

Insurance CoveragePopulation DensityAdultBirth RateCross-Sectional StudiesFemaleGeographic Information SystemsGlobal HealthHealth ExpendituresHealth Services AccessibilityHospital Bed CapacityHumansMaternal-Child Health CentersPopulation SurveillancePregnancyPrenatal Care

Identifiers

PMID23170895
PMCPMC3511226

What OpenQuestion holds

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LicenceCC BY
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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.