Citation Type. Has PDF. Publication Type. More Filters. Complete blood count reference intervals and age- and sex-related trends of North China Han population.
Highly Influenced. View 5 excerpts, cites background. View 2 excerpts, cites background and methods. Blood counts in adult and elderly individuals: defining the norms over eight decades of life.
View 2 excerpts, cites methods. Reference intervals for RBC count, hemoglobin, hematocrit, mean cell hemoglobin, mean cell hemoglobin concentration, mean cell volume, and red cell distribution width were computed using piecewise regression, an evidence-based statistical procedure that identifies breakpoints.
The derived reference intervals were sex specific, unlike many current standards, and more precise for individuals of different ages, especially for children, adolescents, and elderly individuals, as additional breakpoints were detected for these groups.
Suggested reference values for hematocrit and hemoglobin of older adult males were substantially lower than current values. The reference intervals provided here, based on a large, nationally representative healthy population, contribute to the ongoing transition to precision medicine. RBC laboratory values are among the most important clinical laboratory tests used by health care providers for diagnosing diseases, monitoring various medical conditions, and assessing nutritional status.
They provide baseline values that are used to interpret clinical laboratory test results and for diagnosis and disease management. Various approaches have been used to determine reference intervals. Piecewise regression, also called segmented or spline regression, is a form of regression analysis that fits multiple linear models across the full range of a variable. It is an objective procedure to determine inflection points in a variable of interest and is used to find breakpoints that are values above or below which statistical differences are present.
The technique of piecewise regression was explicitly designed to detect discontinuities in data. Piecewise regression formally tests whether there is a nonlinearity in continuous data and whether an abrupt transition explains the nonlinearity. Data from seven NHANES cycles , , , , , , and were extracted for the current analysis. Volunteers were excluded from analysis if they reported the following medical conditions: anemia, coronary heart failure, coronary heart disease, angina, acute myocardial infarction, cancer, gout, emphysema, hepatic disease, celiac disease, stroke, thyroid condition, hepatitis C or HIV, or those taking daily aspirin data for aspirin use were available in NHANES for and therefore subjects who used aspirin could only be excluded from these three cycles or hematologic prescription drugs ie, erythropoiesis-stimulating agents [erythropoietin, epoetin alfa, darbepoetin alfa] and colony-stimulating agents [filgrastim].
A total of 6, children aged 1 to 19 years and 14, adults aged 20 to 79 years were excluded from the analysis for reported health conditions or use of drugs that could alter RBC levels. All participants or proxies provided written informed consent, and the Research Ethics Review Board at the National Center for Health Statistics approved the survey protocol.
Standard Beckman Coulter procedures for calibrating, counting, and sizing, including automatic diluting and mixing for sample processing, and a single beam photometer for hemoglobinometry, were used. Analyses were conducted using SAS 9. Appropriate sample weights were used to adjust for oversampling of selected groups and survey nonresponse as recommended in NHANES analytical guidelines.
Piecewise regression was then used to determine breakpoints across ages and define population subgroups. A brief description of the piecewise regression procedure follows.
In adults suppose f x is a function on [20,79] the adult age ranges , which is initially assumed constant across the 5-year intervals [20,25], [26,30], [31,35], [36,40], …, [71,75], [76,79].
Any step function on [20,79] that is constant on the subintervals [20,25], [26,30], …, [76,79] may be defined as a linear combination of the functions X1, X2, …, X12 defined above. Then to fit a step function constant on the intervals [20,25], [26,30], …, [76,79] to a dependent variable 2. This weight approximates the relative values of the inverse of the variance for each of the estimated cutoffs.
As an example,. These b1-b12 coefficients are estimated in the regression eliminating those that are not statistically significant and the significant coefficients identify the breakpoints. In children, age groups were defined as each year of age.
The particular regression fit chosen is the best least squares fit for the dependent values using stepwise regression and leaving out those x variables that are not statistically significant. The weights are used in the regression to take into account that the estimates of the dependent variable cutoffs have different standard errors at the different ages and assume the relative variances are proportional to the inverse sample sizes for the estimates at each age.
The percentages of the population below and above existing current and the piecewise regression-derived reference intervals were estimated using age- and sex- specific cutoff values.
Figure 1 displays the mean, lower reference limit LRL, 2. The piecewise regression analysis demonstrated an age-related increase in both LRL and URL values for male children with six separate reference intervals ie, , , , , , years of age.
However, for female children, both LRL and URL values decreased with age and yielded only three reference intervals ie, , , years of age Table 2 and supplemental data all supplemental materials can be found at American Journal of Clinical Pathology online in downloadable Excel spreadsheets.
Among male adults, both LRL and URL significantly decreased with age with four separate reference intervals detected ie, , , , years of age. For adult females, the reference interval did not change with age Table 3 and supplemental data. Actual and predicted computed via piecewise regression mean, lower reference limit 2.
Also for hemoglobin levels in male E and female G children and male F and female H adults. Figure 1 also displays sex-specific pediatric and adult reference intervals for hemoglobin.
In the piecewise regression analysis, the reference interval values increased with age for male and female children for female children the LRL increased for those up to age 14 years and decreased for individuals years and decreased with age among male adults. For female adults, the LRL decreased for 31 to 50 years of age and then increased for 51 to 79 years of age but the URL increased slightly. However, there were fewer breakpoints for male adults than for female adults and children Table 2 and Table 3 and supplemental data.
The LRL of hemoglobin for older males aged was The sex-specific pediatric and adult reference intervals for hematocrit obtained via piecewise regression analysis are provided in Figure 2 , Table 2 and Table 3 , and supplemental data. The reference interval values generally increased with age for both male and female children except for female children aged 15 to 19 years where the LRL decreased. The Jackknife weight variables have been included in this dataset. All missing values that had been coded within the data, have been recoded to "System Missing" in order to provide consistency between the other respective NHANES studies.
The user guides that are presently available are comprised of documentation from the NCHS. These user guides do not reflect the merging of each file with the demographics file, as this was done by ICPSR staff.
Every year, approximately 7, individuals, of all ages, are interviewed in their homes and of these, approximately 5, complete the health examination component of the survey. A majority of the health examinations are conducted in mobile examination centers MECs. The MECs provide an ideal setting for the collection of high quality data in a standardized environment. In addition to the MEC examinations, a small number of survey participants receive an abbreviated health examination in their homes if they are unable to come to the MEC.
The household interview component is comprised of Screener, Sample Person, and Family interviews, each of which has a separate questionnaire please refer to the data file documentation.
Trained household interviewers administer all of the questionnaires. In most cases, the interview setting was the survey participant's home. When the interviewer arrives at the home, he or she shows an official identification badge and briefly explains the purpose of the survey. If the occupant has not seen the advance letter, a copy is given to them to read. The interviewer requests that the occupant answer a brief questionnaire to determine if any household occupants are eligible to participate in NHANES.
If eligible individuals are identified, the interviewer proceeds with efforts to recruit these individuals. Initially, the interviewer explains the household questionnaires to all eligible participants 16 years of age and older, informs the potential respondents of their rights, and provides assurances about the confidentiality of the survey data reiterating what is stated in the advance letter.
A majority of the household interviews are conducted during the first contact. If this is inconvenient for the survey participant, an appointment is made to administer the household interview questionnaires later.
Household interviews for survey participants under 16 years of age are conducted with a proxy usually their parent or guardian. If there is no one living in the household who is over 16, participants under 16 years of age are permitted to self-report.
Respondents are asked to sign an Interview Consent Form agreeing to participate in the household interview portion of the survey. After the household interview is completed, the interviewer reviews a second informed consent brochure with the participant. All interviewed persons are asked to complete the health examination component.
Those who agree to participate are asked to sign additional consent forms for the health examination component. The interviewer telephones the NHANES field office from the participant's home to schedule an appointment for the examination. The interviewer informs the participants that they will receive remuneration as well as reimbursement for transportation and childcare expenses, if necessary. Data users should review the survey questionnaire codebooks thoroughly to determine the target populations for each NHANES questionnaire section and sub-section.
The participant is given a disposable paper gown, a pair of slippers, and a urine specimen cup. MEC staff direct survey participants to the rooms where the examination components are conducted. Raw data can be also queried using a drag and drop interface Fig. With the NHANES, we organized each of the variables into a multi-level hierarchy that was ordered by the module i. Variables are shown with sample sizes.
Highlighted in the screen shot are all laboratory measures of dioxins, a type of environmental exposure assayed in serum. A Usage Guide and. Rdata files are provided for download in GitHub Fig.
We have provided two additional resources for individuals to learn about the resource. We plan to assess how frequently our data descriptor and data resources are being utilized by the scientific community through traditional means e. The second file contains a data dictionary file which contains the name of the variable as represented in the data file, a human readable description of the variable, the categories that the variable belongs to , and the levels of the categories if a categorical variable Fig.
Also, to facilitate analyses using the R programming language, we have provided a 4th file that contains all the files described above as a R data object in. Rdata format. Therefore, statistical analyses need to take into account the structure of the sampling into account to provide accurate estimates of the population, such as means, standard errors, and correlations Rmd , we describe how to associate one of the top findings, serum cadmium, with all-cause mortality using survey-weighted Cox proportional hazards regression.
In our example, we show how to query the API to estimate the quartiles of serum lead in the US population of all ages and aged under The issue of reproducibility, replicability, and scalability in computational scientific research has been raised on multiple occasions 34 , How to cite : Patel, C.
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