Evidence map›Paper›PMID 24467776›Full record

ArticleHuman resources for health2014

The public sector nursing workforce in Kenya: a county-level analysis.

Mabel Wakaba, Patrick Mbindyo, Jacob Ochieng, Rose Kiriinya, Jim Todd, Agnes Waudo, Abdisalan Noor, Chris Rakuom, Martha Rogers, Mike English

Abstract read
In one paragraph

Article in Human resources for health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
–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

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3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Mabel WakabaWellcome Trust Collaborative Programme, Kenya Medical Research Institute Centre for Geographic Medical Research Coast, PO Box 43640-00100 GPO, Nairobi, Kenya. mwakaba@kemri-wellcome.org.
Patrick Mbindyo
Jacob Ochieng
Rose Kiriinya
Jim Todd
Agnes Waudo
Abdisalan Noor
Chris Rakuom
Martha Rogers
Mike English

Funding

PEPFARWellcome Trust 084538Wellcome Trust 097170
6 · The paper itself

Abstract

backgroundKenya's human resources for health shortage is well documented, yet in line with the new constitution, responsibility for health service delivery will be devolved to 47 new county administrations. This work describes the public sector nursing workforce likely to be inherited by the counties, and examines the relationships between nursing workforce density and key indicators.

methodsNational nursing deployment data linked to nursing supply data were used and analyzed using statistical and geographical analysis software. Data on nurses deployed in national referral hospitals and on nurses deployed in non-public sector facilities were excluded from main analyses. The densities and characteristics of the public sector nurses across the counties were obtained and examined against an index of county remoteness, and the nursing densities were correlated with five key indicators.

resultsOf the 16,371 nurses in the public non-tertiary sector, 76% are women and 53% are registered nurses, with 35% of the nurses aged 40 to 49 years. The nursing densities across counties range from 1.2 to 0.08 per 1,000 population. There are statistically significant associations of the nursing densities with a measure of health spending per capita (P value = 0.0028) and immunization rates (P value = 0.0018). A higher county remoteness index is associated with explaining lower female to male ratio of public sector nurses across counties (P value <0.0001).

conclusionsAn overall shortage of nurses (range of 1.2 to 0.08 per 1,000) in the public sector countrywide is complicated by mal-distribution and varying workforce characteristics (for example, age profile) across counties. All stakeholders should support improvements in human resources information systems and help address personnel shortages and mal-distribution if equitable, quality health-care delivery in the counties is to be achieved.

Indexed as

Health WorkforcePublic SectorAdultFemaleHealth Care CostsHumansImmunizationInformation SystemsKenyaMaleMiddle AgedNurses

Identifiers

PMID24467776
PMCPMC3913960

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LicenceCC BY
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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.