{"doi":"10.1080/03014460.2020.1808065","title":"A method for calculating BMI z-scores and percentiles above the 95<sup>th</sup> percentile of the CDC growth charts","abstract":"Background The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values.Aim This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population.Subjects and methods Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures.Results The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth.Conclusion This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.","journal":"Annals of Human Biology","year":2020,"id":57974,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":75,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9431,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":40906,"name":"Cynthia L. Ogden","orcid":"0000-0003-1147-7157","position":1,"is_corresponding":false},{"id":208688,"name":"Van L. Parsons","orcid":null,"position":2,"is_corresponding":false},{"id":303652,"name":"David S. Freedman","orcid":"0000-0001-8014-0289","position":3,"is_corresponding":false},{"id":303653,"name":"Craig M. Hales","orcid":"0000-0002-2355-1177","position":4,"is_corresponding":false},{"id":303651,"name":"Rong Wei","orcid":"0000-0003-0204-4982","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-18T21:07:11.345501Z","pmid":"32901504","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}