Vital Signs: Life Expectancy – by county

VITAL SIGNS INDICATOR
Life Expectancy (EQ6)

FULL MEASURE NAME
Life Expectancy

LAST UPDATED
April 2017

DESCRIPTION
Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE
State of California, Department of Health: Death Records
(1990-2013)
No link

California Department of Finance: Population Estimates
Annual Intercensal Population Estimates (1990-2010)
Table P-2: County Population by Age (2010-2013)
http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION
[e-mail]

METHODOLOGY NOTES (across all datasets for this indicator)
Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.
For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

生命体征指标

预期寿命（EQ6）

完整测量名称
预期寿命

最后更新
2017年4月

描述
预期寿命指在死亡率模式保持不变的情况下，新生儿预期活到的平均年数。该指标反映了在特定时间点人口的整体死亡率。

数据来源
加利福尼亚州卫生部门：死亡记录（1990-2013年）
无链接
加利福尼亚州财务部：人口估计
年度人口估计（1990-2010年）
表P-2：按年龄段划分的县人口（2010-2013年）
http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

联系方式
[e-mail]

方法论说明（适用于所有此指标的数据集）
预期寿命通常被用作衡量人口健康状况的指标。预期寿命并不反映任何特定个体预期活多久；相反，它是一种人工指标，用以捕捉人口死亡率的一个方面。生命体征指标衡量的是出生时的预期寿命（而非队列预期寿命）。通过对当前生命表进行统计模型估算，可以得出湾区各县和邮政编码的预期寿命，生命表需要年龄和死亡率数据。生命表是一种表格，展示了从特定人口中每个年龄段的存活率。

当前生命表是使用死亡记录和按年龄划分的人口估计数据创建的。加利福尼亚州公共卫生部门提供了基于加利福尼亚州死亡证明信息的死亡记录。记录包括死亡年龄和居住地邮政编码。区域和县级的单一年龄人口估计来自加利福尼亚州财务部的人口估计和预测（0-100+岁）。100岁及以上的人口估计被汇总为一个年龄段。使用这些数据，计算了特定年份年龄组内的人口死亡率，以形成完整的生命表（而非简略生命表）。为了计算预期寿命，假定1岁之后在j岁和(j+1)岁生日之间死亡的概率是均匀的。特别考虑了婴儿死亡率的因素。

对于邮政编码层面的预期寿命计算，假定邮政编码与邮政编码人口普查统计区域（ZCTA）共享相同的边界。有关邮政编码和ZCTA之间关系的更多信息，请参阅https://www.census.gov/geo/reference/zctas.html。由于样本量小，邮政编码层面的数据使用三年死亡数据进行稳健估计。2013年邮政编码预期寿命估计反映了2011年至2013年的死亡记录。2013年是可用的最后一年死亡数据。对于人口为零的邮政编码（如与邮政信箱相关的那些）分配到最近的有人口居住的邮政编码。2000年的人口估计来自十年一次的人口普查。2013年的人口估计来自美国社区调查（5年移动平均）。ACS提供了按五岁年龄间隔划分的邮政编码人口。通过将年龄段内的人口分布到单一年龄，计算了单一年龄人口估计。各县被分配到邮政编码，基于多数土地面积。

湾区内的邮政编码人口从超过10,000居民到不到20居民不等。传统的预期寿命估算方法（如用于区域和县级生命体征估算的方法）不能使用，因为它们对于小人口来说高度不准确，可能会导致预期寿命的高估或低估。为了避免不准确的估计，人口少于5,000的邮政编码与相邻的邮政编码合并，直到合并区域的人口超过5,000。这样，原始的305个湾区邮政编码在2013年的估计中减少到了218个邮政编码区域。接下来，使用了一种贝叶斯随机效应分析的形式，该分析使用区域分布确定了每个年龄的死亡概率的先验分布。这个先验分布用于巩固数据稀疏时的预期寿命计算。