Trends in Undiagnosed Chlamydia Prevalence in Baltimore, 1997-1998 and 2006-2009 (ICPSR 35064)

Time Period(s) View help for Time Period(s)

Date of Collection View help for Date of Collection

Data Collection Notes View help for Data Collection Notes

Study Purpose View help for Study Purpose

To provide a more accurate understanding of the hidden epidemics of untreated infection with N. gonorrhoeae and C. trachomatis.

Study Design View help for Study Design

Please see the Original P.I. Documentation in the ICPSR User Guide for more information about the study design.

Sample View help for Sample

The sample for the Baltimore STD and Behavior Survey was drawn from households residing within the municipal boundaries of the city of Baltimore (1998 population: 645,664). Households were selected using a stratified probability sampling design that selected residences from the Baltimore Real Estate Property Registry. This registry includes all properties -- both taxable and tax-exempt -- within the city of Baltimore. Two sample strata were disproportionately sampled to ensure adequate representation of (1) young black men and (2) young adults living in predominantly white US Census tracts with elevated levels of STDs (based on Baltimore City Health Department [BCHD] STD surveillance statistics). Operationally, these two strata comprised (1) households with an age-eligible man drawn from census tracts with 95 percent to 100 percent black residents according to the 1990 US Census; and (2) adults aged 18 to 35 years residing in 13 census tracts that had the highest rates of reported gonococcal infection among tracts with 0 percent to 9 percent black residents. A third crosscutting sample stratum was created to accommodate the refielding of a 50 percent random subsample of cases for which our quality control procedures could not verify the integrity of the interview data. This sample design oversampled segments of the population that are known to have higher rates of STDs (i.e., young black men and whites living in US Census tracts with high rates of reported STDs). BSBS prevalence estimation used sampling weights to adjust for the unequal probabilities of selection across sample strata. The MSSP collected telephone survey data and biospecimens for continuous monitoring of three STIs -- gonorrhea, chlamydial infection, and trichomoniasis among probability samples of adolescents and young adults residing in Baltimore, MD. Recruitment for the MSSP began in September 2006 and ended in June 2009. A stratified, list-assisted, probability sampling design was used to maximize sample efficiency in identifying our target population of English-speaking males and females between 15 and 35 years of age residing in Baltimore households with landline telephones. The probability sample included four strata. The first three strata were sampled using commercially-available, regularly updated information on Baltimore households.

Universe View help for Universe

Population of Baltimore MD, ages 18 to 35 years.

Unit(s) of Observation View help for Unit(s) of Observation

Data Type(s) View help for Data Type(s)

Mode of Data Collection View help for Mode of Data Collection

Response Rates View help for Response Rates

BSBS:85.7 percent screened.82.8 percent interviewed. 79.5 percent provided a urine specimen adequate for testing; 16.3 percent refused to provide a urine specimen and 4.1 percent were not tested because of inadequate urine volume, interviewer error, or other logistical problems. MSSP: 69.5 percent were screened, 58.7 percent interviewed, 72.8 percent mailed in specimens for testing.

Original Release Date View help for Original Release Date

2014-09-10

Version History View help for Version History

Weight View help for Weight

The data are not weighted; however, this collection contains the weight variable BIOSPECIMEN_WT, which should be used in analysis. Two sets of BSBS sample weights were constructed to project (1) survey questionnaire results and (2) urine assay results to the reference population of Baltimore. The general approach to BSBS weight calculation involved the following steps: (1) compute housing unit level weights for the base sample, the black male supplement, and white-high STD supplement \x{2014} including adjustments for exceptions from planned size measures and planned within-property sample sizes; (2) add original identification number (based on sample from which originally drawn) to each sample record and compute rework sample weight adjustment; (3) identify base sample records that would also have been eligible for the stratum 1 high-STD sample; (4) impute any data essential for weight calculation or adjustment, compute multiplicity indicators and adjustment factors, compute rework sample weight factor, and derive design-based weights at the person level; (5) post-stratify the design-based weights to match external controls by race, gender, and age and then normalize weights to sum to 1 within mode; (6) trim extreme design weights, re-stratify, and re-normalize; (7) run extensive check tabulations to confirm general correctness of approach. Two sets of MSSP sample weights were constructed to adjust for the unequal probabilities of selection based on our stratified sample design and survey nonresponse. Initial weights were developed reflecting the inverse probability of selection within each stratum with adjustments for the differing probabilities of selection within households, the number of landline telephones within the household, and survey nonresponse. A post-stratification adjustment was applied to align the sample distribution with the 2006-2009 United States Census estimates of the Baltimore City population by age, gender, and race/ethnicity [26]. A second set of weights was constructed to compensate for additional differences in the provision of a biospecimen for STI testing among respondents who completed the survey interview. Analysis of the combined BSBS and MSSP chlamydia data should use the weight variable \"biospecimen_wt\" which merges weights from the two surveys and should be done in conjunction with the composite strata variable (\"strata\"). Stata users can use the SYVSET command: svyset [pweight=biospecimen_wt], strata (strata) singleunit (scaled).