1. Introduction

Sociodemographic microsimulation can be used to model and understand the development of populations, their behaviors, and outcomes. Microsimulations play an increasingly important role in modeling the complex dynamics of public health phenomena as well as in the investigation of causal mechanisms and intervention effects (Jackson and Arah, 2020; Lee et al., 2019; Monteiro et al., 2016; Stephen and Barnett, 2017). Synthetic populations are datasets that have been reweighted to represent geographical areas that can represent populations at the individual level (Tanton, 2014). This approach allows for the investigation of behaviors and outcomes at an individual-level and breakdowns by demographic categories, and also allows for the updating of populations over time, i.e. using mortality or migration rates (Ballas et al., 2007).

An estimated 72,558 annual deaths in the USA can be attributed to alcohol use, with liver disease and alcohol overdose or poisoning accounting for 30.7% and 17.9% of these deaths, respectively (White et al., 2020). Indeed globally, alcohol is a major cause of the burden of disease (Griswold et al., 2018). Alcohol use in the US varies substantially by age, gender and socio-demographics, (Delker et al., 2016) and the National Survey on Drug Use and Health (NSDUH) provides a detailed individual level dataset with which to examine these patterns. In 2018, 24.5% of the population aged 12+ were estimated to drink 5+ standard drinks (technically 14g of pure ethanol, which is roughly equivalent to a 12 fluid ounce can of 5% strength beer) during the previous month Substance Abuse and Mental Health Services Administration (2019).

Our context here is a US National Institute on Alcohol Abuse and Alcoholism funded project called “Calibrated Agent Simulations for Combined Analysis of Drinking Etiologies (CASCADE)”. CASCADE aims to: (1) develop new computer models of alcohol use which draw on existing theories for why people drink and seek novel combinations of these theories in order to better explain the changes in alcohol use we observe in society; (2) provide policymakers with insight into how alcohol-related harms, particularly alcohol poisoning and liver disease, have developed over the last 35 years; (3) guide development of new policies by providing projections for how levels of harm might change under different future intervention scenarios. The microsimulation model we present in this paper forms part of a wider software architecture for modelling social systems, for more details see (Vu et al., 2020b). Our model is intended to provide a demographically representative population over time, which can be used for individual-level and agent-based simulation models and supports the adding of mechanisms to generate individual level behavior.

Several microsimulation studies around alcohol exist, and have studied aspects of treatment for alcohol dependence using microsimulation (Millier et al., 2017). Others have used microsimulation to analyse screening and brief interventions for alcohol problems (Zur and Zaric, 2016). (Brennan et al., 2015; Holmes et al., 2014) have developed a hybrid modelling approach with part individual, part cohort level analysis of alcohol use and resulting harms for alcohol policy analysis. However, to date there is not large scale, long term (30+ years) microsimulation of populations and their alcohol use.

Some sociodemographic microsimulations already exist. In the US, we reviewed the Framework for Reconstructing Epidemic Dynamics (FRED) microsimulation for infectious diseases (Grefenstette et al., 2013) which developed a synthetic population based on the US Census Bureau’s Public Use Microdata Sample and aggregated data from the 2005-2009 American Community Survey (ACS) (Wheaton et al., 2009). The FRED microsimulation provided insights on methods/data sources. In particular, we require that simulated individuals have characteristics that are representative of known features of the population e.g. the proportion of males and females, age distribution, and proportion employed/unemployed are representative. The standard approach to ensuring a simulated dataset fits these representativeness criteria is iterative proportional fitting (IPF) (Lovelace et al., 2015; Lovelace and Dumont, 2016). However, the micro-synthetic population and microsimulation implemented in FRED has a number of limitations. Firstly, they cover only a short timeframe (2005-2009), which is not far enough historically to explain long-run public health. Secondly, they are static and do not account for core demographic developments such as births, deaths, and migration. Thirdly, the model focusses on communicable diseases and does not include risk factors, such as alcohol, for non-communicable diseases.

The objective of our study was to develop a more comprehensive sociodemographic micro-synthesis and microsimulation - CASCADEPOP version 1.0, assess its validity on sociodemographic outputs and provide transparent open-source code for the research community.

2. Methods

2.1. CASCADEPOP requirements

CASCADEPOP was required to develop a simulated set of individuals with US state representative sociodemographic characteristics, starting on 01/01/1980 and to simulate individuals’ characteristics and baseline health behaviors (e.g. alcohol use) over time to 31/12/2015, also allowing for births, migrations, and deaths. This 35-year timeframe allows investigation of long-term changes e.g., in total alcohol per capita consumption, decreasing gender differences in alcohol use, and recent decreases in young people’s alcohol use (Keyes et al., 2008; Martinez et al., 2019; Patrick and Schulenberg, 2014).

CASCADEPOP has been developed for any US state and here we operationalize micro-synthesis and microsimulation for five states, California (CA), Minnesota (MN), New York (NY), Tennessee (TN), Texas (TX) and the US. These states were chosen because they reflect heterogeneous patterns of population dynamics (different age and sex distributions, socioeconomics, race/ethnicity composition, and extent of migration over time) and because they have substantially different levels and trends in aggregate population per capita sales litres of alcohol (Martinez et al., 2019).

We focus on ages 12 through 80, though the methods can be utilized for other age categories. The sociodemographic characteristics implemented in the micro-synthesis were: Sex (male, female), age (continuous), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), level of education (high school or less, some college, college degree or higher), household annual income (8 groups: $0-6999, $7-$9,999, $10-14,999 $15-19,999, $20-24,999, $25-29,999, $30-34,999, $35,000+) employment status (employed, unemployed), marital status (married, unmarried), and parental status (no children living at home, at least one child aged below 18 living at home). These characteristics were considered key requirements for CASCADEPOP, because they are related to alcohol use and to many other health behaviors (Rehm et al., 2009; 2010; 2014). The microsimulation also needed to allow dynamic changes in sociodemographic characteristics as each individual progresses through his/her life while also being representative of the US population.

Our exemplar health behavior of alcohol use was implemented as follows. We categorize ‘drinking status’ into current drinkers and abstainers. We track average grams of alcohol per day consumed, frequency of drinking and of ‘heavy episodic drinking’ for each drinker, defined below.

2.2. Data sources

Generating a micro-synthetic base population and implementing dynamic changes in the population over time required the integration of a series of data sources described below.

2.2.1. US Census data

US Census data for 1980 were obtained from the National Geographic Information service (Manson et al., 2019) and used to inform sociodemographic characteristics of the base population in terms of the joint distribution of age (12-17, 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-59, 60-80), sex, race/ethnicity, marital status, employment status, level of education, and income. Census data for years 1990, 2000, and 2010 were used to inform the addition of new individuals entering the microsimulation at age 12 (instead of at birth).

2.2.2. Panel Study of Income Dynamics

The Panel Study of Income Dynamics (PSID) is a large, nationally representative US longitudinal survey, designed to measure the dynamics of income, wealth, and expenditures (University of Michigan. Survey Research Center, 2018). PSID was available annually from 1968-1997, and biennially from 1997-2015. The 1979 survey contains the required variables including sex, age (continuous), race/ethnicity, level of education, income, employment status, marital status, and parental status. Census data contain information on household composition but do not provide individual-level parental status. Therefore, we used 1980 PSID data to inform the distribution of parental status in the micro-synthesis, and used the longitudinal data to estimate social role transition probabilities (see Statistical Procedures).

2.2.3. American Community Surveys

Data from the American Community Survey (ACS) in 1980, 1990 and for individual years for 2000-2015 were used to inform immigration and emigration at state and national levels (Ruggles et al., 2019). ACS is a sub-sample (1%) of households in the US Census and contains individual-level demographic information (age, sex, race/ethnicity) and details on whether an individual has migrated in the previous five years (1980, 1990, 2000) or one year (2000-2015).

2.2.4. Compressed Mortality Files

Mortality rates before age 80 were based on the National Center for Health Statistics National Death Index database. Mortality rates were extracted using data from the Center for Disease Control and Prevention online databases for 1979-1998 (Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File, 1979) and 1999-2017 (Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death, 1999). These provide a record of total deaths (from all causes) per year, aggregated by state, by age category, and sex.

2.2.5. National Survey on Drug Use and Health

To inform alcohol use behavior in the micro-synthetic population, we required an individual-level dataset containing sociodemographic variables alongside information on each individual’s pattern of alcohol use. For version 1.0 of CASCADEPOP, we selected the National Survey on Drug Use and Health (NSDUH) (U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2019).

NSDUH was selected because it provides individual-level nationally representative repeated cross-sections. NSDUH includes individuals aged 12+ across the US (excluding Alaska and Hawaii) based on a national area probability sample. NSDUH data were available for consecutive years 1979-2016 (excluding 1980-1981, 1983-1984 and 1989). The survey contains age in individual years (1979-1999) and in narrow category bands (1999-2016). Information on sex, race/ethnicity, level of education, income (in eight bands in earlier years, and as a continuous $ amount in later years), employment status, marital status, and parental status, are available in all survey years. For constructing the baseline population, income was expressed in 1980 U.S. dollars to correspond to the Census data. For the microsimulation, incomes for all years were converted 2015 dollars (US Bureau of Labor Statistics: Consumer Price Index. Washington, 2019).

NSDUH contains information on four alcohol use variables. ‘Drinking status’ is a binary variable defined as having used alcohol at least once in the past 12 months. Average grams per day of alcohol consumed was calculated based on the number of drinking days in the past 30 days and the number of drinks usually consumed per occasion (standard drink = 14 grams of alcohol e.g. a regular 12-ounce bottle of beer). Alcohol consumption frequency is the number of days where alcohol was consumed in the past 30 days (continuous). Frequency of ‘heavy episodic drinking’ is defined as the number of days in the past 30 days when over 5 standard drinks were consumed.

2.3. Statistical procedures

2.3.1. Iterative proportional fitting for 1980 population micro-synthesis

Iterative proportional fitting is a procedure that is used to reweight individual level data to fit the known demographic constraints of populations (Lovelace and Dumont, 2016). Further information about data preparation for iterative proportional fitting is available in Appendix A and B. The goal of the micro-synthesis was to provide a synthetic base population of individuals for the selected geography. For example, to generate the base CA population on 01/01/1980 of N=18,957,712, the aim was to generate a database with over 18 million records with the sociodemographic structure that matches the demographic constraints in the Census. The method to achieve this is IPF. IPF uses an iterative algorithm to estimate the cell values of a contingency table such that the marginal totals, known as IPF constraints, remain fixed.

For CASCADEPOP v1.0, incorporated constraints from different datasets (details in Appendix A and B). The process began with the individual-level dataset (NSDUH, 2019) which contained the drinking and sociodemographic variables described above. The sample size, after removing people who have missing required attributes, is n=6,105. The ipfp package (Blocker, 2016) was used to calculate a weight for each individual in the NSDUH, 2019 dataset so that the re-weighted NSDUH had a sociodemographic structure that fits the constraints of the geography of interest (done separately for CA, MN, NY, TN, TX, and the US) (See Appendix tables B3–B5).

The constraints used for IPF were (i) a three-way cross-tabulated combination of age categories, sex, and race/ethnicity, together with cross-tabulations of (ii) employment by sex, (iii) marital status by sex, (iv) level of education by sex, and (v) income categories by sex. The data for these constraints came from the US Census 1980 and the PSID 1980 datasets. In total, we used 138 constraints: 104 constraints for 13 age categories * 2 sex * 4 race/ethnicity categories 4 for marital status * sex, 4 for employment status * sex, 16 for income categories * sex, and 6 for education * sex.

The IPF algorithm used the 138-constraint vector and the individual characteristics of the NSDUH (N=6,105 rows in our case) to estimate a weight for each NSDUH individual so that the total number of individuals in each of the 138 constraint categories matched the constraints. The algorithm followed an iterative process until a tolerance level for the error (L2 norm Euclidean distance between the vector of the reweighted population number and the constraint vector) was reached (10-10). The final set of weights indicated the number of people each NSDUH individual represented in each geography. A replication process was then undertaken to generate a database with the correct total population of CA in 1980 (N=18,957,712) and all of the required fields (Lovelace and Ballas, 2013).

2.3.2. Iterative proportional fitting for immigration and new 12-year-olds over time

Migration and births mean that additional individuals enter the microsimulation in each year 1981 to 2015 (details in Appendix C).

To account for new 12-year olds, data from the US Census in 1990 for people aged 12-22 (who would have been aged 12 in 1980-1990) were used to generate constraints for individuals entering the model aged 12 in 1980-1990. Similarly, the 2000 Census was used to generate constraints for 1990-2000 and Census 2010 was used for constraints for 2000-2010 and 2011-2015.

To account for migration, ACS data were used to generate constraints for the net number of migrants to enter into each state and year (see details in Appendix C). ACS person weights were used to determine the number of individuals in the Census represented by each ACS individual, and ensure that these constraints were representative at a population level. For each state (CA, MN, NY, TN, TX), emigration (migration out of the state) was calculated using a weighted ACS to determine the age, sex, race/ethnicity, and year of migration of all individuals who emigrated from the state of interest to another state. We also accounted for new migrants between April and December in 1980 because the US Census date was 01/04/1980.

An IPF process, similar to that for the base 1980 population, was implemented separately for each state for each year from 1981 to 2010. The process began with the NSDUH dataset for the relevant year. For some years there was no NSDUH survey and we utilized the survey from the closest year. A vector of 104 constraints was used for migrants entering the geography (13 age categories * 2 sex * 4 race/ethnicity), and 8 constraints were used for new 12-year-olds entering the population (2 sex * 4 race/ethnicity). In most years, for most states, net immigration was positive i.e., more people entered than left the state. In the case where net migration was negative, we did not require an IPF process, but instead, we quantified net emigration by age, sex, and race/ethnicity and used Monte Carlo sampling to simulate people who left (see microsimulation section).

2.3.3. Estimating social role transitions over time

Multi-state Markov models describe and estimate how an individual moves through a series of states over time, and are commonly used for estimating transitions between stages of disease (Jackson et al., 2003). We defined eight social role combinations of marital status, parental status, and employment. Using longitudinal PSID data, we applied a time homogenous Multi-State Markov Model using the msm package in R (Jackson, 2007) to calculate age- and sex-dependent annual probabilities of transitioning in and out of each social role combination. Details of the number of single transitions between social roles available in PSID data are available in Appendix D. Separate models were estimated for five time periods (1979-1983, 1984-1992, 1993-1999, 2000-2007, 2008-2015). We included sex as a dichotomous covariate and age and age squared as continuous time-variant covariates. To illustrate, we show below the transition matrix for 1993-1999, for 27-year-old females (Table 1). Here, we see that if an individual in this category holds no social roles during this period, they are likely to still hold no social roles in 1 year (P=0.649), the most likely transition for an individual with no roles is to become employed (P=0.280), The full model transition intensities with hazard ratios for age and sex are available in Appendix Table D2.

Table 1. Exemplar transition matrix for the period 1993-1999 for females aged 27.
_ _ _ _ _ P _ M _ E _ _ _ M P E M _ E _ P E M P
1993-1999 _ _ _ 0.649 0.025 0.017 0.280 0.003 0.014 0.010 0.001
1993-1999 _ _ P 0.013 0.605 <0.001 0.008 0.054 <0.001 0.298 0.021
1993-1999 _ M _ 0.025 0.001 0.537 0.012 0.118 0.261 0.001 0.045
1993-1999 E _ _ 0.087 0.003 0.004 0.816 0.001 0.059 0.026 0.005
1993-1999 _ M P <0.001 0.010 0.005 <0.001 0.677 0.003 0.007 0.297
1993-1999 E M _ 0.004 <0.001 0.079 0.035 0.013 0.757 0.002 0.110
1993-1999 E _ P 0.003 0.091 <0.001 0.030 0.006 0.001 0.817 0.051
1993-1999 E M P <0.001 0.002 0.001 0.001 0.083 0.013 0.025 0.876

The time-periods and covariates were selected due to annual data availability in the PSID, to ensure enough data was available to fit each model. These transition probabilities were applied during the microsimulation over time to simulate individuals transitioning between social roles each year.

2.4. Microsimulation over time

2.4.1. Socio-demographic microsimulation

Figure 1 provides an overview of the steps that happen to generate the baseline and migrant populations, and in each simulated year, with more details in Appendix E. The microsimulation proceeds on year by year basis. During each year, steps were implemented to account for aging, social role transitions, new 12-year-olds, deaths and migration. On January 1st of the next modelled year, each simulated person increases in age by +1 years. The probability that a simulated person moves from a particular combination of social roles e.g., married, employed, and a parent to another of the eight combinations was based on the transition matrices described above, operationalized using Monte Carlo sampling. New migrants and 12-year-olds enter the model for each year of the simulation. In the case of net emigration, we removed the estimated count of migrants to leave the state by age, sex and race/ethnicity category using Monte Carlo sampling. Individuals can also leave the simulation due to death – implemented by taking the total count of deaths by age category and sex and removing the corresponding number of individuals from the simulation, again using Monte Carlo sampling. Migration rates are adjusted according to a procedure described in detail in Appendix E to ensure correspondence with counts of the total population of each geography in 1990, 2000 and 2010. In the results presented here, we have tested the microsimulation at 10% scale due to computational constraints and all results are presented at this scale.

Figure 1. Schematic describing the steps taken to generate the baseline and migrant and 12-year-old populations over time, and the steps taken during each year of the microsimulation over time.

2.4.2. Place within the structure for incorporating mechanisms into the microsimulation

The dynamic microsimulation also enables the updating of the individual drinking behavior (and other behavior – if required) over time. These are not discussed in depth in this paper, but the reader is referred to previous examples of their implementation using this microsimulation with mechanisms from social norms theory (Probst et al., 2020) and social role theory (Vu et al., 2020a). In these papers, the updating of drinking behaviors is simulated on a daily basis and can account for related changes in not only their individual-level attributes (such as gender, age, social roles, etc.), but also macro social level factors and variables (such as social norms about alcohol consumption).

2.4.3. Software

CASCADEPOP v1.0 micro-synthesis and microsimulation were written in R, as was the collation of all base data files. Although originally coded in R, CASCADEPOP can be reprogrammed in any programming language or software. In the CASCADE project, the micro-synthesis (base synthetic population) and the microsimulation over time are passed to the C++ based agent-based modeling environment called Repast HPC (Collier and North, 2013).

2.5. Analyses

In this paper, we have not operationalized any of the agent-based models to alter drinking over time because our focus is to describe and test the sociodemographic microsynthesis and microsimulation. Three analyses were undertaken:

  1. Generate modeled micro-synthesis estimates of the numbers of individuals in each age * sex * race/ethnicity subgroup and income subgroups, education subgroups and social role subgroups in each state at the base population date of 01/01/1980 and to compare these with values in the Census data.

  2. Generate modeled micro-synthesis estimates of the prevalence of drinking, average quantity of drinking, frequency of drinking and frequency of heavy episodic drinking in each state on 01/01/1980 and to compare the US micro-synthesis with observed values in the 1979 NSDUH data.

  3. Generate modeled estimates from the microsimulation over time of the numbers of people in each age *sex *race/ethnicity subgroup and social role subgroups in the USA and each state at dates of January 1st 1990, 2000 and 2010 and to compare these with values in the Census. Census data beyond 2010 do not yet exist for comparison of the microsimulation to 2015.

3. Results

3.1. Micro-synthesis validation – the 1980 base population

Table 2 and Figure 2 shows a comparison of the micro-synthetic population in the USA with the observed data from the 1980 Census. The comparisons show that the modeled numbers of males and females by age, race/ethnicity, education, employment status, marital status, parental status, and income category are all within 0.01% of the observed data. Similar results were found for CA, MN, NY, TN and TX, as a whole (shown in Appendix F).

Figure 2. Validation of Micro-synthesis for the USA 1980:- modelled synthetic population compared to observed 1980 Census data for age, sex, race/ethnicity, education, social roles status and income.
Table 2. Validation of Micro-synthesis for the USA 1980: modelled synthetic population compared to observed 1980 Census data for age, sex, race/ethnicity, education, social roles status and income.
Female Male
Census Micro-synthesis Difference Census Micro-synthesis Difference
Age category
 12-13 350229 350205 -0.007% 363772 363800 0.009%
 14-17 791699 791646 -0.007% 819431 819457 0.003%
 18-19 428371 428374 0.001% 416172 416174 0.001%
 20-22 643066 643061 -0.001% 614279 614288 0.001%
 23-24 417044 417041 -0.001% 404086 404082 -0.001%
 25-28 788183 788182 0.001% 764900 764913 0.002%
 29-30 377449 377447 0.001% 366634 366636 0.001%
 31-34 698488 698489 0.001% 673779 673785 0.001%
 35-39 708491 708494 0.001% 678687 678684 -0.001%
 40-44 594513 594520 0.001% 565942 565942 -0.001%
 45-49 568258 568258 0.001% 534878 534884 0.001%
 50-59 1216357 1216358 0.001% 1101383 1101380 -0.001%
 60-80 1722769 1722765 0.001% 1328453 1328450 -0.001%
Race
 Non-hispanic black 1057394 1057385 -0.001% 884885 884883 0.001%
 Hispanic 538344 538341 0.001% 519074 519080 0.001%
 Non-hispanic other 217081 217077 -0.002% 205423 205428 0.002%
 Non-hispanic white 7492101 7492037 -0.001% 7023014 7023084 0.001%
Education
 High school graduate 6737070 6736982 -0.001% 5927517 5927574 0.001%
 Some college 1565697 1565708 0.001% 1361130 1361140 0.001%
 College + 1002151 1002150 -0.001% 1343748 1343761 0.001%
Social roles
 Employed 4172331 4172328 0.001% 5688374 5688393 0.001%
 Unemployed 5132587 5132512 -0.001% 2944021 2944082 0.002%
 Married 4998998 4999000 0.000% 5008429 5008440 0.000%
 Unmarried 4305920 4305840 -0.002% 3623966 3624035 0.002%
 Not parent 5854373 5854284 -0.002% 5510957 5511025 0.001%
 Parent 3450545 3450556 0.000% 3121438 3121450 0.000%
Income category
 $0-$6999 1162186 1162200 0.001% 595240 595247 0.001%
 $7000-$9999 728592 728572 -0.003% 525864 525871 0.001%
 $10000-$14999 1170164 1170154 -0.001% 928740 928736 0.000%
 $15000-$19999 1032560 1032568 0.001% 1045736 1045737 0.000%
 $20000-$24999 1054906 1054900 -0.001% 1100713 1100716 0.000%
 $25000-$29999 1452311 1452312 0.000% 1570514 1570508 0.000%
 $30000+ 1562268 1562283 0.001% 1682380 1682403 0.001%
 youth-no income 1141928 1141851 -0.007% 1183202 1183257 0.005%

Table 3 shows the detailed micro-synthesis breakdown by the 8 combinations of the three social roles (employment status, marital status, and parental status) for each state. For example, the micro-synthesis contains 4,300,758 individuals in CA aged 12-80 years who were employed AND married AND a parent. The available aggregate level Census does not report these specific combinations, but we can compare against the marginal totals for each role. Again, the percentage difference between modeled and observed is very small.

Table 3. Population counts of individuals holding social roles from the Census compared to the microsimulation.
California Minnesota New York Tennessee Texas US
Social Role Combination
 _ _ _ 399,495 65,449 333,353 77,874 220,355 3,864,726
 _ _ P 60,378 5,848 53,551 10,807 29,980 522,189
 _ M _ 209,202 42,456 189,861 54,541 122,314 2,439,605
 E _ _ 335,976 53,877 228,962 49,159 170,089 2,782,588
 _ M P 162,057 22,544 111,788 32,301 102,662 1,484,920
 E M _ 201,329 42,494 142,986 43,569 141,361 2,119,030
 E _ P 123,002 16,391 97,333 18,978 55,532 1,026,699
 E M P 430,076 76,084 277,627 82,297 279,061 3,999,107
% difference versus observed data
 Married 0.0001% 0.0008% 0.0003% 0.0024% 0.0009% 0.0001%
 Not married 0.0001% 0.0010% 0.0003% 0.0032% 0.0012% 0.0001%
 Employed 0.0001% 0.0013% 0.0002% 0.0016% 0.0003% 0.0000%
 Not Employed 0.0001% 0.0018% 0.0002% 0.0017% 0.0004% 0.0000%
 Parent 0.0003% 0.0017% 0.0004% 0.0044% 0.0008% 0.0001%
 Not Parent 0.0002% 0.0010% 0.0003% 0.0028% 0.0005% 0.0001%

Notes: each possible combination using the abbreviations E – employed, M – married, P – parent, _ - not

3.2. Micro-synthesis model validation – the 1980 drinking patterns

Table 4 shows the estimated drinking patterns for the 1980 micro-synthesis in all six geographies. The prevalence of current drinkers (aged 12-80) ranged from 68.9% to 72.1%. The US national estimate was 70.3%, compared to the prevalence estimate based on NSDUH data of 73.0%. The frequency of alcohol use in the national micro-synthesis was 7.0 days in the past 30 days, compared to 6.8 based on NSDUH data. The mean quantity of alcohol consumed per day was estimated at 8.2 grams per day compared to 8.4 grams per day based on NSDUH. The model for heavy episodic drinking was the least close to the observed data with the mean number of 0.97 heavy drinking days in the past 30 days in the micro-synthesis compared to 0.88 days in the NSDUH US data – a difference of 9%. Table 4 also shows that the results vary by state and that the socio-demographics alter the estimated alcohol consumption patterns. There is no observed data from NSDUH representative at state level, therefore we were unable to undertake detailed state-level comparisons at this stage.

Table 4. Implied baseline drinking prevalence, quantity, frequency and heavy drinking for each of the modelled geographies compared to US alcohol use data (NSDUH) with 95% CI.
State Alcohol use prevalence Mean alcohol use quantity (grams per day) Mean alcohol use frequency (days per month) Mean 5+ drink days per month
California 72.17% 8.22 7.04 0.95
Minnesota 72.14% 8.38 7.07 0.98
New York 70.01% 8.09 6.94 0.96
Tennessee 68.88% 8.16 6.91 0.99
Texas 71.58% 7.90 6.79 0.94
USA 70.33% 8.18 6.97 0.97
NSDUH, 2019 data 72.96% [70.58, 75.33] 8.44 [7.75, 9.13] 6.88 [6.44, 7.31] 0.88 [0.77, 0.99]

3.3. Validation of the sociodemographic microsimulation over 30 years

Figure 3 and Table 5 show the results of running the CASCADEPOP microsimulation for the USA over 30 years including the modeled dynamics for migration, births, and deaths. The population totals at the end of each decennial simulation year were compared against the corresponding Census data (1990, 2000, and 2010). The differences between the simulated population and the Census population were small for all comparisons undertaken for numbers of males and females, numbers in each of the nine age categories, and numbers of individuals in each of the four race/ethnicity groups - all of these being within 1.5% of the observed data across the whole 30-year simulation. The results for the other four states and for the US are similarly close (see Appendix G).

Figure 3. Validation over 30 years: Comparison of microsimulation population with observed US Census data by sex, age and race/ethnicity for the USA in 1980, 1990, 2000 and 2010.
Table 5. Validation over 30 years: Comparison of microsimulation population with observed US Census data by sex, age and race/ethnicity for the USA in 1990, 2000 and 2010.
1990 2000 2010
Census Microsimulation Difference Census Microsimulation Difference Census Microsimulation Difference
Non-hispanic black 2242309 2247300 0.22% 2626753 2640870 0.54% 3032982 3045380 0.41%
Hispanic 1675274 1674900 -0.02% 2635389 2652050 0.63% 4119840 4118710 -0.03%
Non-hispanic other 707779 708070 0.04% 1377643 1394650 1.23% 1861652 1865980 0.23%
Non-hispanic white 15285788 15293890 0.05% 15893199 15950070 0.36% 16287723 16328920 0.25%
Female 10204297 10217040 0.12% 11450460 11505330 0.48% 12828100 12838960 0.08%
Male 9706853 9707120 0.00% 11082530 11132310 0.45% 12474100 12520030 0.37%
12-17 2004212 2012300 0.40% 2417936 2426610 0.36% 2567835 2570450 0.10%
18-24 2673777 2676540 0.10% 2714345 2724760 0.38% 3104722 3121590 0.54%
25-29 2131305 2131710 0.02% 1938134 1948060 0.51% 2134600 2151870 0.81%
30-34 2186289 2188090 0.08% 2051039 2063780 0.62% 2021027 2031350 0.51%
35-39 1996312 1993430 -0.14% 2270666 2276720 0.27% 2042091 2051510 0.46%
40-44 1761579 1760510 -0.06% 2244186 2260780 0.74% 2113322 2118880 0.26%
45-49 1387257 1387940 0.05% 2009240 2020340 0.55% 2295657 2290950 -0.21%
50-59 2188227 2190440 0.10% 3105479 3112160 0.22% 4242635 4249330 0.16%
60-80 3582194 3583200 0.03% 3781961 3804430 0.59% 4780309 4773060 -0.15%

3.4. Validation of the social roles microsimulation over 30 years

Figure 4 and Table 6 shows the results of running the CASCADEPOP social roles simulation for the USA between 1980 and 2010, applying the transition probabilities derived from PSID to each individual in each year of the simulation. The percentage of individuals employed and married were compared with Census data from 1980, 1990, 2000 and 2010, and parenting was compared with PSID data. Across all years of the simulation, the mean difference between modeled employment and Census data was 3.3% for women and 3.6% for men, the difference between modeled marriage and Census data was 5.8% for women and 6% for men. For parenting, the difference between the simulated and PSID data was 2.9% for women and 0.3% for men.

Figure 4. Validation over 30 years. Comparison of microsimulation population social roles with observed US Census data (employment and marriage) and PSID data (parenting) for the United States 1980-2010
Table 6. Validation over 30 years. Comparison of microsimulation population social roles with observed US Census data (employment and marriage) and PSID data (parenting) for the United States 1980-2010
Female Male
% 1990 2000 2010 1990 2000 2010
E only 16.10 20.86 22.52 21.07 21.69 23.91
E + M 14.71 16.78 17.21 20.93 23.40 22.63
E + P 5.09 5.89 5.04 6.71 6.03 5.37
E + M + P 20.03 18.05 15.26 25.81 23.08 19.01
Total Employed 55.93 61.57 60.02 74.52 74.20 70.92
Employed Census 53.27 63.06 54.89 69.79 76.43 65.16
Employed PSID 55.51 57.57 55.22 71.64 73.14 63.89
Difference (Microsimulation and Census) 2.66 -1.49 5.13 4.73 -2.24 5.76
M only 15.37 13.83 13.64 13.45 11.47 12.11
M + E 14.71 16.78 17.21 20.93 23.40 22.63
M + P 7.60 5.86 5.11 3.66 3.81 3.43
M + E + P 20.03 18.05 15.26 25.81 23.08 19.01
Total Married 57.71 54.51 51.22 63.85 61.76 57.17
Married Census 51.18 47.58 46.45 58.89 53.23 50.59
Married PSID 62.51 59.97 56.67 68.55 66.75 61.61
Difference (Microsimulation and census) 6.53 6.93 4.77 4.97 8.52 6.58
P only 3.36 2.20 2.55 1.69 1.25 1.12
P + E 5.09 5.89 5.04 6.71 6.03 5.37
P + M 7.60 5.86 5.11 3.66 3.81 3.43
P + E + M 20.03 18.05 15.26 25.81 23.08 19.01
Total Parent 36.08 31.99 27.96 37.87 34.17 28.93
Parent PSID 36.24 33.87 28.16 34.29 33.27 27.07
Difference (Microsimulation and PSID) -0.16 -1.88 -0.20 3.58 0.90 1.86

4. Discussion

This study describes the methodology used to develop a US sociodemographic micro-synthesis and microsimulation over a 35-year period from 1980 to 2015 which can be used for a broad range of research questions in the fields of public health, epidemiology, demography, and policy analyses. This study demonstrated that CASCADEPOP v1.0 was able to generate a micro-synthesis (i.e., a synthetic baseline population) that accurately represents the sociodemographic structure of the 1980 populations of five different states and the US as a whole with regard to the joint distribution of age, sex, race/ethnicity, social roles (employment status, marital status, and parental status), education, and income. The 1980 drinking patterns simulated at baseline were also accurate. When the microsimulation was run forward in time, it reproduced demographic developments with regard to the age-sex-race/ethnicity structure of the US population over time.

Our work builds on the ideas of previous sociodemographic microsimulation approaches, in particular, the FRED (Grefenstette et al., 2013). However, with a much broader timeframe, rich sociodemographic characteristics and accurate sociodemographic developments over time CASCADEPOP can be used in numerous contexts with an expansive range of applications. We have developed methods to account for new entrants aged 12, deaths, and migration over an extended time period. To be able to accurately model sociodemographic changes over such a long period, as successfully tested in this operationalization of our approach, provides a platform for further modeling exercises. Our further work is now implementing agent-based models informed by several theories of what drives drinking decisions.

However, the CASCADEPOP platform and the microsimulation itself are not tied to simulations regarding alcohol use. Any population-representative survey with data on a behavior or risk factor, or combinations of behaviors and risk factors, could be utilized through the IPF process. This could include, for example, tobacco smoking, dietary behaviors, and physical activity measures. It could also include data on biomedical measures such as blood pressure, cholesterol, and blood sugars e.g. HbA1c as a measure of diabetes. Furthermore, if data sets are available to inform transition probabilities, dynamic changes in any of the implemented characteristics and behaviors can be modelled. In our application, we are using the microsimulation model to populate individuals into agent-based models and apply mechanisms to update drinking behavior. However, these mechanisms are not limited to this approach, and could be based on other methods including regression-based equations to update behavior of interest over time.

Here our model accounts for deaths from all-causes. In future work we intend to use our model to investigate mortality from specific causes that could be ameliorated by policy. To do this, we intend to calculate deaths from specific causes (i.e. liver cirrhosis, ICD-10 code 74) and subtract these from all-cause deaths to partition mortality into policy-modifiable and other causes. Other researchers can use our simulation to appraise a wide variety of policies from a number of diseases.

While modeling some of these additional risk factors may require additional sociodemographic variables to be included in the CASCADEPOP micro-synthesis and microsimulation. However, in developing our approach we were cognizant of many projects we have undertaken on epidemiological and health economic modeling in which the key variables have been age, sex, race/ethnicity, social roles, education, and income. The inclusion of all of these provides a strong basis for generalizable use of the platform. Furthermore, with all code being written in R and available as open-source, CASCADEPOP can be easily adjusted and modified. We will be seeking research funding to extend the tool across other nations, starting with the UK.

Limitations of our analysis are related to the datasets currently available and the requirements of IPF procedures. We are unable to examine the eight combinations of social roles against published census data because the census does not produce a report of a 3-way combination of employment status * marital status * parental status. We have compared, where possible, against marginal totals. A further challenge we have not been able to address is that religion is an important factor in explaining differences in drinking, but we have not found variables on religion in our key datasets that have been fit for the purpose, and so in version 1.0 of CASCADEPOP religion has been excluded. The IPF process does involve its own assumptions and produces a synthetic dataset by replicating records from the original dataset of interest used – in our case NSDUH. The NSDUH dataset utilized here is limited to data from 6105 respondents, containing some missing data points. As this paper is intended as a methodological description of the simulation, we have not imputed the missing data points, as the differences in alcohol consumption between missing and non-missing data were small, so would give a marginal benefit. As noted above, our microsimulation model is not tied to alcohol use, and others using other datasets may wish to explore imputation methods for missing data before the IPF procedure.

The social roles transition probabilities are only dependent on the age and sex of agents, and therefore cannot be used to generate more nuanced breakdowns of social role holding in society (i.e. differences by race/ethnicity or by education level). It was not possible for additional covariates to be added, as using 3 covariates for an 8-way transition matrix is already computationally intensive. Future work requiring a more nuanced description of role holding may utilize other approaches, such as regression modelling to develop transition rates dependent on several covariates.

We plan two substantial developments for CASCADEPOP version 2.0. The first and most important is that we plan to utilize a different exemplar dataset – the Behavioral Risk Factor Surveillance System (BRFSS). This contains data on health-related risk behaviors, chronic health conditions, and use of preventive services and was set up in 1984 with a large sample size, growing over time, with strong assessments of validity (Pierannunzi et al., 2013) and which has been used to study alcohol consumption behavior patterns (Delnevo et al., 2008). One limitation of self-reported alcohol consumption is that respondents often underestimate their consumption (Nelson et al., 2010). As part of this effort, we will also be relating reported levels of drinking in the survey to aggregate levels of sales data of alcohol, using methods to adjust individuals’ alcohol consumption so that the resulting synthetic population’s alcohol use is aligned with the reported sales (Meier et al., 2013; Rehm et al., 2010). Secondly, we will be further developing the dynamic sociodemographic variables to include income and education transitions.

The CASCADEPOP platform provides the capability to incorporate agent-based models that seek to explain and predict behaviors. We have already the first iteration of an agent-based model in which alcohol use is related to a theory of social norms (Probst et al., 2020). A similar model has also been developed which links alcohol use to the three social roles in a theory partly related to time available to drink given other responsibilities and partly to the stresses of having these roles (Bai et al., 2019; Vu et al., 2020a; Vu et al., 2020b). In each case, parameters theorized to be important in predicting drinking are estimated via a Bayesian calibration process (van der Vaart et al., 2015). This approach adjusted the parameters of the agent-based model so that the drinking behavior of individuals in the models matches historically observed alcohol consumption (i.e. from alcohol sales data). In future work, we also aim to calibrate our models to levels of alcohol related harms, namely liver cirrhosis and alcohol poisoning morbidity and mortality. A further key component of methodological development will be using genetic programming to alter features of the agent-based models and produce new model variants that could contain hybrid components of different theories to test whether they fit the observed data better than researcher defined theories and provide insights (Vu et al., 2019).

In summary, we have developed and validated a new sociodemographic microsimulation population model. The CASCADEPOP model can be used at State- and US-levels to simulate the evolution of populations and, when linked to data on behaviors and risk factors, can be used to analyze behaviors of public health significance.