- PhD Sociology/Demography at UW-Madison

- Consultancy within and outside the academia

- Data Science projects

- Applied Population Lab, UW-Madison
**acsr**: R package to extract and compute statistics from the ACS and US Census (https://github.com/sdaza/acsr)

- Center for Applied Social Studies, Catholic University
- Sampling design, weighting, non-response, multiple imputation
**sampler**: R package to design samples adjusting for DEFF (https://github.com/sdaza/sampler)

- Data Incubator Fellow
- Tracking congress member tweets

- What is the long-term association between imprisonment and mortality in the US?

**Panel Study of Income Dynamic (PSID)**Since 1968, the survey has followed the same families

The first wave included roughly 5,000 families (18,000 individuals)

Recent waves have about 9,000 families (22,000 individuals)

**National Longitudinal Survey of Youth 1979 (NLSY)**- 12,686 respondents ages 14-22 when first interviewed in 1979

**Mortality**Year of death, National Death Index (NDI) and non-response records

6,457 deaths

**Incarceration**Reports of whether a member of a household was incarcerated (n=630)

1995 wave: have ever served time in jail or prison (n=836)

**Incarceration PSID**

**Covariates**- Age, gender, race, education attainment, household income, and health

**Survival Parametric Models (Gompertz)**- Validate set up of the data using the underlying US population

**Survival Semi-parametric Models (Cox)**Heterogeneity (gamma, family identifier)

Non-proportional hazard adjustments

Marginal Structural Models (MSM)

- Attrition (non-independent censoring)
- Time-varying confounders

- Specifications

- Missing data
- Last observation carried forward (LOCF) and backwards
- Multiple imputation (100)

- Sampling weights

$$ D_{(x)} = MU_{(x)} - mu_{(x)} $$

$$ MU_{(x)} = MUo_{(x)} \times (P_{(x)} \times (E-1) + 1) = MUo_{(x)} \times H_{(x)}$$

$$ mu_{(x)} = muo_{(x)} \times (p_{(x)} \times(E-1) + 1) = muo_{(x)} \times h_{(x)}$$

$$D_{(x)} = A_{(x)} +B_{(x)}$$

- $A_{(x)}$ is the contribution of the population who has not experienced prison

- $B_(x)$, is the contribution of the population who has been in prison

- $\frac{B_{(x)}}{D_{x}}$ is the fraction of the difference attributable to imprisonment

Incarceration is associated with a moderate risk of mortality

- Losses of life expectancy at age 50 of about 4 years or 12% of current U.S. life expectancy

- The fraction of the mortality gap between the US and the UK that can be attributed to imprisonment experience ranges from
**2%**to**10%**

- Heterogeneity / Interactions / Sample size

- Causality

Bayesian approach

Previous knowledge (priors)

Model Uncertainty: BMA or stacking

Measurement of incarceration

Underestimation?

Clear treatment

- The UK better data