This dataset includes two tables on tuberculosis (TB) in California: 1) TB cases and rates by place of birth, sex, age and race/ethnicity 2) TB cases by local health jurisdiction (LHJ). TB case reports are submitted to the California Department of Public Health (CDPH), TB Control Branch (TBCB), by 61 local health jurisdictions (58 counties, and the cities of Berkeley, Long Beach, and Pasadena).

Numbers, percentages and rates of TB cases by country of birth, 2017, Santa Clara County. Source: California Reportable Disease Information Exchange, 2017, data are provisional as of February 12, 2018METADATA:Notes (String): Lists table title, notes and sourcesCountry of birth (String): List of birth countriesNumber (Numeric): Number of TB diagnoses in 2017 Percentage (Numeric): Percentage of TB diagnoses in one birth country among all TB diagnoses in 2017Rate per 100,000 people (Numeric): Number of TB diagnoses per 100,000 people among people from the same birth country

Tuberculosis (TB) control programs use whole-genome sequencing (WGS) of Mycobacterium tuberculosis (Mtb) for detecting and investigating TB case clusters. Existence of few genomic differences between Mtb isolates might indicate TB cases are the result of recent transmission. However, the variable and sometimes long duration of latent infection, combined with uncertainty in the Mtb mutation rate during latency, can complicate interpretation of WGS results. To estimate the association between infection duration and single nucleotide polymorphism (SNP) accumulation in the Mtb genome, we first analyzed pairwise SNP differences among TB cases from Los Angeles County, California, with strong epidemiologic links. We found that SNP distance alone was insufficient for concluding that cases are linked through recent transmission. Second, we describe a well-characterized cluster of TB cases in California to illustrate the role of genomic data in conclusions regarding recent transmission. Longer presumed latent periods were inconsistently associated with larger SNP differences. Our analyses suggest that WGS alone cannot be used to definitively determine that a case is attributable to recent transmission. Methods for integrating clinical, epidemiologic, and genomic data can guide conclusions regarding the likelihood of recent transmission, providing local public health practitioners with better tools for monitoring and investigating TB transmission.

TB incidence rates, overall trend (2007-2017), by sex (2017), age (2017), race/ethnicity (2017), and nativity (2017), Santa Clara County. Source: Tuberculosis Information Management System, 2007-2009, California Reportable Disease Information Exchange, 2010-2017, data are provisional as of February 12, 2018; State of California, Department of Finance, E-2. California County Population Estimates and Components of Change by Year — July 1, 2010–2017. Sacramento, California, December 2017; State of California, Department of Finance, State and County Population Projections by Race/Ethnicity and Age, 2010-2060, Sacramento, California, January 2018; U.S. Census, American Community Survey 1-Year Estimate, 2016METADATA:Notes (String): Lists table title, notes and sourcesYear (Numeric): Year of TB diagnosisCategory (String): Lists of categories: Santa Clara County total for each year (2007-2017), sex (2017): male, female; race/ethnicity: African American, API, Latino, White (non-Hispanic); age group (2017): <15, 15-24, 25-44, 45-64, and 65 and older; foreign-born (2017), U.S.-born (2017)Rate per 100,000 people (Numeric): Number of TB diagnoses per 100,000 people in each cateogry

Numbers and percentages of TB cases by age, 2017, Santa Clara County. Source: California Reportable Disease Information Exchange, 2017, data are provisional as of February 12, 2018METADATA:Notes (String): Lists table title, notes and sourcesAge (String): Age group: <15, 15-24, 25-44, 45-64, 65+Number (Numeric): Number of TB diagnoses for an age group in 2017Percentage (Numeric): Percentage of TB diagnoses in an age group among all TB diagnoses in 2017