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Record W3029616576 · doi:10.1681/asn.2019050521

Prevalence and Risk Factors for CKD in the General Population of Southwestern Nicaragua

2020· article· en· W3029616576 on OpenAlexafffund
Ryan Ferguson, Sarah Leatherman, Madeline Fiore, Kailey Minnings, Martha Mosco, James S. Kaufman, Eric Kerns, Juan José Amador, Daniel R. Brooks, Melissa Fiore, Rulan S. Parekh, Louis D. Fiore

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity Health NetworkUniversity of Toronto
FundersUniversity of TorontoSchool of Medicine, Boston UniversityAmerican Society of Nephrology
KeywordsMedicinePopulationOdds ratioEnvironmental healthKidney diseaseDiabetes mellitusDemographyGerontologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Significance Statement Most studies of Mesoamerican nephropathy have focused on regions in El Salvador and northwest Nicaragua and on agricultural workers, but information regarding prevalence and risk factors for CKD in Nicaragua’s general population is sparse. In a study of community-dwelling individuals in southwestern Nicaragua, the authors screened 1242 participants for CKD (defined as <60 ml/min per 1.73 m 2 ). Risk factors for prevalent CKD included age, diabetes, and hypertension. Current or former workers in the sugarcane industry (but not other types of agriculture) had a twofold-increased odds of CKD. CKD prevalence in southwestern Nicaragua is about 5% among the general population but is not consistent across Nicaragua. Formal CKD surveillance programs in Nicaragua are needed to assess the overall burden of CKD nationally, with a focus on agricultural workers. Background Studies have described Mesoamerican nephropathy among agricultural workers of El Salvador and northwestern Nicaragua. Data on prevalence and risk factors for CKD beyond agricultural workers and in other regions in Nicaragua are sparse. Methods We recruited participants from 32 randomly selected communities in the Department of Rivas’s ten municipalities in two phases. In phase 1, we screened participants using a field-based capillary creatinine measuring system and collected self-reported information on lifestyle and occupational, exposure, and health histories. Two years later, in phase 2, we enrolled 222 new participants, performing serum creatinine testing in these participants and confirmatory serum creatinine testing in phase 1 participants. Results We enrolled 1242 of 1397 adults (89%) living in 533 households (median age 41 years; 43% male). We confirmed CKD (eGFR<60 ml/min per 1.73 m 2 ) in 53 of 1227 (4.3%) evaluable participants. In multivariable testing, risk factors for prevalent CKD included age (odds ratio [OR], 1.92; 95% confidence interval [95% CI], 1.89 to 1.96) and self-reported history of hypertension (OR, 1.95; 95% CI, 1.04 to 3.64), diabetes (OR, 2.88; 95% CI, 1.40 to 5.93), or current or past work in the sugarcane industry (OR 2.92; 95% CI, 1.36 to 6.27). Conclusions Adjusted CKD prevalence was about 5% with repeat confirmatory testing in southwest Nicaragua, lower than in the northwest region. Risk factors included diabetes, hypertension, and current or prior work in the sugarcane industry but not in other forms of agricultural work. Formal CKD surveillance programs in Nicaragua are needed to assess the overall burden of CKD nationally, with a focus on agricultural workers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.223
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.291
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2020
Admission routes2
Has abstractyes

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