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Record W2600849668 · doi:10.3390/ijerph14030315

Monitoring of Selected Health Indicators in Children Living in a Copper Mine Development Area in Northwestern Zambia

2017· article· en· W2600849668 on OpenAlexfundno aff
Astrid M. Knoblauch, Mark J. Divall, Milka Owuor, Colleen Archer, Kennedy Nduna, Harrison Ng’uni, Gertrude Musunka, Anna Pascall, Jürg Utzinger, Mirko S. Winkler

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
FundersFirst Quantum Minerals
KeywordsMalariaEnvironmental healthPublic healthCross-sectional studyMalnutritionEpidemiologyGeographyOdds ratioHealth impact assessmentMedicineNursing

Abstract

fetched live from OpenAlex

The epidemiology of malaria, anaemia and malnutrition in children is potentially altered in mining development areas. In a copper extraction project in northwestern Zambia, a health impact assessment (HIA) was commissioned to predict, manage and monitor health impacts. Two cross-sectional surveys were conducted: at baseline prior to project development (2011) and at four years into development (2015). Prevalence of Plasmodium falciparum, anaemia and stunting were assessed in under-five-year-old children, while hookworm infection was assessed in children aged 9–14 years in communities impacted and comparison communities not impacted by the project. P. falciparum prevalence was significantly higher in 2015 compared to 2011 in both impacted and comparison communities (odds ratio (OR) = 2.51 and OR = 6.97, respectively). Stunting was significantly lower in 2015 in impacted communities only (OR = 0.63). Anaemia was slightly lower in 2015 compared to baseline in both impacted and comparison communities. Resettlement due to the project and migration background (i.e., moving into the area within the past five years) were generally associated with better health outcomes in 2015. We conclude that repeated cross-sectional surveys to monitor health in communities impacted by projects should become an integral part of HIA to deepen the understanding of changing patterns of health and support implementation of setting-specific public health measures.

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)0.003
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 score0.056
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.051
GPT teacher head0.384
Teacher spread0.333 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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