MétaCan
Menu
Back to cohort
Record W2283424085 · doi:10.1016/s2214-109x(15)00294-6

Malawi and Millennium Development Goal 4: a Countdown to 2015 country case study

2016· article· en· W2283424085 on OpenAlexaboutno aff
Mercy Kanyuka, Jameson Ndawala, Tiope Mleme, Lusungu Chisesa, Medson Makwemba, Agbessi Amouzou, Josephine Borghi, Judith Daire, Rufus Ferrabee, Elizabeth Hazel, Rebecca Heidkamp, Kenneth Hill, Melisa Martínez-Álvarez, Leslie Mgalula, Spy Munthali, Bejoy Nambiar, Humphreys Nsona, Lois Park, Neff Walker, Bernadette Daelmans, Jennifer Bryce, Tim Colbourn

Bibliographic record

VenueThe Lancet Global Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersSveriges RegeringUNICEFWorld Health Organization
KeywordsCountdownMillennium Development GoalsChild mortalityDemographyMortality ratePsychological interventionInfant mortalityMedicineDeveloping countryGeographyPopulationSocioeconomicsEnvironmental healthEconomic growthEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Several years in advance of the 2015 endpoint for the Millennium Development Goals (MDGs), Malawi was already thought to be one of the few countries in sub-Saharan Africa likely to meet the MDG 4 target of reducing under-5 mortality by two-thirds between 1990 and 2015. Countdown to 2015 therefore selected the Malawi National Statistical Office to lead an in-depth country case study, aimed mainly at explaining the country's success in improving child survival. METHODS: We estimated child and neonatal mortality for the years 2000-14 using five district-representative household surveys. The study included recalculation of coverage indicators for that period, and used the Lives Saved Tool (LiST) to attribute the child lives saved in the years from 2000 to 2013 to various interventions. We documented the adoption and implementation of policies and programmes affecting the health of women and children, and developed estimates of financing. FINDINGS: The estimated mortality rate in children younger than 5 years declined substantially in the study period, from 247 deaths (90% CI 234-262) per 1000 livebirths in 1990 to 71 deaths (58-83) in 2013, with an annual rate of decline of 5·4%. The most rapid mortality decline occurred in the 1-59 months age group; neonatal mortality declined more slowly (from 50 to 23 deaths per 1000 livebirths), representing an annual rate of decline of 3·3%. Nearly half of the coverage indicators have increased by more than 20 percentage points between 2000 and 2014. Results from the LiST analysis show that about 280,000 children's lives were saved between 2000 and 2013, attributable to interventions including treatment for diarrhoea, pneumonia, and malaria (23%), insecticide-treated bednets (20%), vaccines (17%), reductions in wasting (11%) and stunting (9%), facility birth care (7%), and prevention and treatment of HIV (7%). The amount of funding allocated to the health sector has increased substantially, particularly to child health and HIV and from external sources, but remains below internationally agreed targets. Key policies to address the major causes of child mortality and deliver high-impact interventions at scale throughout Malawi began in the late 1990s and intensified in the latter half of the 2000s and into the 2010s, backed by health-sector-wide policies to improve women's and children's health. INTERPRETATION: This case study confirmed that Malawi had achieved MDG 4 for child survival by 2013. Our findings suggest that this was achieved mainly through the scale-up of interventions that are effective against the major causes of child deaths (malaria, pneumonia, and diarrhoea), programmes to reduce child undernutrition and mother-to-child transmission of HIV, and some improvements in the quality of care provided around birth. The Government of Malawi was among the first in sub-Saharan Africa to adopt evidence-based policies and implement programmes at scale to prevent unnecessary child deaths. Much remains to be done, building on this success and extending it to higher proportions of the population and targeting continued high neonatal mortality rates. FUNDING: Bill & Melinda Gates Foundation, WHO, The World Bank, Government of Australia, Government of Canada, Government of Norway, Government of Sweden, Government of the UK, and UNICEF.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.368
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations149
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Global HealthSame topicGlobal Maternal and Child HealthFrench-language works237,207