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The authors have declared that no competing interests exist.

Conceived and designed the experiments: JL SG. Analyzed the data: JL. Contributed reagents/materials/analysis tools: JL SG. Wrote the paper: JL SG.

Previous research has shown that tobacco control funding in California has reduced per capita cigarette consumption and per capita healthcare expenditures. This paper refines our earlier model by estimating the effect of California tobacco control funding on current smoking prevalence and cigarette consumption per smoker and the effect of prevalence and consumption on per capita healthcare expenditures. The results are used to calculate new estimates of the effect of the California Tobacco Program.

Using state-specific aggregate data, current smoking prevalence and cigarette consumption per smoker are modeled as functions of cumulative California and control states' per capita tobacco control funding, cigarette price, and per capita income. Per capita healthcare expenditures are modeled as a function of prevalence of current smoking, cigarette consumption per smoker, and per capita income. One additional dollar of cumulative per capita tobacco control funding is associated with reduction in current smoking prevalence of 0.0497 (SE.00347) percentage points and current smoker cigarette consumption of 1.39 (SE.132) packs per smoker per year. Reductions of one percentage point in current smoking prevalence and one pack smoked per smoker are associated with $35.4 (SE $9.85) and $3.14 (SE.786) reductions in per capita healthcare expenditure, respectively (2010 dollars), using the National Income and Product Accounts (NIPA) measure of healthcare spending.

Between FY 1989 and 2008 the California Tobacco Program cost $2.4 billion and led to cumulative NIPA healthcare expenditure savings of $134 (SE $30.5) billion.

Previous research using aggregate state level data found a relationship between per capita funding for population-based tobacco control programs and reductions in per capita cigarette consumption, which were in turn associated with reductions in per capita healthcare costs in California

The California Tobacco Control Program was established in 1989. It adopted a comprehensive approach designed to change social norms to reinforce the nonsmoking norm rather than a frontal attack on smokers that markets cessation services. The social norm change approach seeks to indirectly influence current and potential future tobacco users by creating a social milieu and legal climate in which tobacco becomes less desirable, acceptable and accessible. The Program combines an aggressive media campaign with three consistent themes (the tobacco industry lies, nicotine is addictive, and secondhand smoke kills) with public policy change, particularly in the area of promoting smokefree environments. The Program has been premised on the fact that youth smoking will decline when more adults stop smoking

Per capita cigarette consumption, which includes all the nonsmokers, is a very simple measure of smoking behavior. Tobacco control program funding may affect smoking prevalence and cigarette consumption per current smoker differently, and each, in turn, may have a different effect on healthcare expenditures. This paper refines our earlier model by replacing total per capita consumption with a two-dimensional measure of smoking behavior – prevalence of current smoking and cigarette consumption per smoker. This two dimensional measure may provide more insight into the mechanisms by which tobacco control programs work and how reductions in smoking reduces healthcare expenditures and may provide a better predictive model for use in forecasting the effect of policy changes on smoking and healthcare expenditure.

The estimates for the new model, which are based on a different sample period than the old model (due to limits on state specific data on prevalence), show that increased per capita cumulative tobacco control funding is associated with reductions in both prevalence and cigarette consumption per smoker, and reductions in both measures of smoking behavior reduce per capita healthcare expenditures in California compared to control states. Newly available data for a commonly used measure of healthcare expenditure from the Centers for Medicare and Medicaid Services allowed a true out of sample forecasting experiment; the new model using prevalence and average cigarette consumption per smoker produces better forecasts than the previously published per capita cigarette consumption model

As in our earlier work

The new model consists of three equations: one equation for the relationship between cumulative per capita tobacco control funding and current smoking prevalence; one for the relationship between tobacco control funding and cigarette consumption per smoker; and one for the relationship between smoking behavior (prevalence of smoking and cigarette consumption per smoker) and per capita healthcare expenditures.

All monetary values are expressed in year 2010 dollars using the Medical Care (healthcare expenditures) and All-Item (tobacco control funding, cigarette price and personal income) Consumer Price Index for Urban Consumers (CPI-U)

From published research on per capita cigarette consumption, we expect that cigarette consumption per current smoker (

Consumption per smoker was calculated by dividing per capita cigarette consumption for the respective populations by current smoking prevalence. The definition of tobacco control funding used for the main analysis included state and federal funding; private funding was omitted, though including it makes almost no difference in the results. Cumulative real per capita tobacco control funding was constructed by summing annual real per capita funding.

The main results use the National Income and Product Account (NIPA) measure of per capita healthcare expenditure. Sensitivity analyses used an alternative measure of healthcare expenditure from the Centers for Medicare and Medicaid Services (CMS)

Per capita healthcare expenditures were calculated by dividing totals by the state resident populations. For sensitivity analysis the population was adjusted for race (African-American, white and other) and ethnicity (Hispanic and non-Hispanic).

The sample for the model connecting per capita tobacco control funding to smoking behavior consists of 24 annual observations from 1985 to 2008 (The 1984 observation was lost due to lagging the explanatory variables one period).

The 38 control states are Alabama, Arkansas, Colorado, Connecticut, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin, and Wyoming.

Estimates of smoking prevalence are not available for all of the 38 control states starting in 1985; data from 13 states were available as of 1984 and all were available by 1994. As a result, each of the 38 control states contributed to the control population as annual estimates of state smoking prevalence became available.

See the online

The variables were tested to determine whether they were stationary or nonstationary. The main statistical analysis used a regression specification called a reduced form vector autoregression (VAR) in which the explanatory variables are expressed as a function of the lagged explanatory variables. The reduced form VAR can be used for unbiased estimates regardless of whether the data are stationary or nonstationary

See the online

Oracle Crystal Ball

The effect of the California Tobacco Control Program was estimated using model predictions of the historical time series and predictions of a counterfactual history with all California tobacco control funding set to zero from FY1989 through FY2008. Monte Carlo simulations, using the regression results, estimated the effect of the California Tobacco Control Program. Predictions for prevalence (

Several sensitivity analyses were conducted to check the robustness of the methods and estimation results. See the Online

It may be difficult to determine the best specification of a regression with a relatively small sample (up to 24 annual observations in this study). Therefore an automatic model selection algorithm, the Autometrics module in Oxmetrics

If the data are nonstationary, then the estimates using the VAR specification should be consistent with those from a static regression (called a “cointegrating regression”)

The prevalence (

The model was estimated using different groups of control states to explore the sensitivity of the results to control states that would reflect different regional trends in the explanatory variables, particularly healthcare expenditure and smoking behavior.

The automatic selection procedure, Autometrics, used to check the specifications of

The model was re-estimated with variables for racial and ethnic composition of California and control populations, using estimates of the proportion of Hispanic, Black and All Other races from BRFSS survey data, added to the

The model was estimated with alternate measures of tobacco control funding that included private nonprofit funding.

The CMS provides a commonly used measure of healthcare expenditure for the U.S. and individual states, though state specific estimates are not released at regular intervals. CMS healthcare expenditure data were used to estimate

CMS healthcare expenditure data (

The unit root tests indicated that all the variables except for prevalence of current smoking were nonstationary with autoregressive unit roots; the results for prevalence were unstable and difficult to interpret. Smoking prevalence may be stationary, so estimation using cointegrating regressions (which were used in previous research) may be inappropriate. These results imply that that the reduced form VAR specification is more robust than the cointegrating regression estimates (used in earlier research

The reduced form VAR estimates of

Eq. | Sample Period | Dependent Variable | Statistic | Estimate | dimension |

1 | 1985–2008, 24 obs | (_{c, t} – prev_{CA, t} |
_{0} |
6.30 (0.610) | |

_{1} |
0.0497 (0.00347) | /$ per capita | |||

_{2} |
−1.00 (0.477) | /$ per pack | |||

_{3} |
0.416 (0.0730) | /$1000 per capita | |||

^{2} |
77 | ||||

_{1} |
0.154 | ||||

2 | 1985–2008, 24 obs | (_{c, t} – cps_{CA, t} |
_{0} |
67.9 (10.2) | |

_{1} |
1.39 (0.132) | /$ per capita | |||

_{2} |
−26.6 (6.80) | /$ per pack | |||

_{3} |
2.97 (1.21) | /$1000 per capita | |||

^{2} |
81 | ||||

_{1} |
0.148 | ||||

3 | 1985–2008, 24 obs |
_{CA, t} |
_{0} |
−550 (433) | $ |

_{1} |
1.15 (0.180) | ||||

_{2} |
−35.4 (9.85) | $/%point | |||

_{3} |
−3.14 (0.786) | $ pack per smoker | |||

_{4} |
−108 (6.79) | $/$1000 per capita | |||

^{2} |
80 | ||||

_{1} |
0.262 | ||||

3 |
1985–2008, 24 obs |
_{CA, t} |
_{0} |
1056 (112) | $ |

_{1} |
0.847 (0.0542) | ||||

_{2} |
−67.8 (7.31) | $/%point | |||

_{3} |
−5.48 (0.928) | $ pack per smoker | |||

_{4} |
−107 (22.3) | $/$1000 per capita | |||

^{2} |
89 | ||||

_{1} |
0.486 |
||||

3 |
1985–2004, 20 obs |
_{CA, t} |
_{0} |
1001 (967) | $ |

_{1} |
0.856 (0.227) | ||||

_{2} |
−69.8 (12.6) | $/%point | |||

_{3} |
−5.59 (1.77) | $ pack per smoker | |||

_{4} |
−112 (17.5) | $/$1000 per capita | |||

^{2} |
78 | ||||

_{1} |
0.483 |

_{CA, t}_{CA, t}_{c, t}_{c, t}

significant at the 5% level.

All of the other explanatory variables are statistically significant at the one percent level except the price of cigarettes (_{2}) in _{3}) (P = 0.023) in

The in-sample predictions for prevalence (

Top panel: Difference between California and control state current smoking prevalence (

The dynamic simulation of the time paths of prevalence of smoking, consumption per smoker and per capita healthcare expenditures (

California current smoking prevalence, middle panel: California cigarette consumption per smoker, bottom panel: California per capita healthcare expenditures using the NIPA measure. Black circles: observed, black line: predictions with California tobacco control program (using historical data on tobacco control funding), gray line: predictions without California tobacco control program (California tobacco control funding set to zero).

In fiscal year 2008, 19 years after the Program started, smoking prevalence was 3.46 (SE 0.242) percentage points and cigarette consumption per smoker was 96.3 (SE 13.7) packs/year, and per capita healthcare expenditures were $411 (SE $92.0) below what is predicted in the absence of the California Tobacco Control Program.

From FY1989 to FY2008, the Program is associated with a cumulative reduction in 8.79 (SE 0.616) million person-years of smoking, 6.79 (SE 0.605) billion packs of cigarettes worth $28.5 (SE $2.55) billion in pre-tax sales to the cigarette companies. The cumulative savings in the NIPA measure of healthcare expenditures is $134 (SE $30.5) billion for the years 1989 to 2008.

The reduction in prevalence is responsible for 36.4% (SE 4.06%) of the reduction in cumulative total cigarette consumption per smoker and 31.2% (SE 3.48%) of the reduction in NIPA healthcare expenditures, respectively. The rest of the reductions are due to reductions in consumption per smoker.

See online

Autometrics selected regression specifications are similar to those for prevalence (

Autometrics did select a regression specification for

The results of the OLS and robust regression estimates of the VAR and cointegrating regressions are consistent with those of the reduced form VAR estimates and the residuals are stationary. This result provides more evidence that data are nonstationary and that the results are robust to different regression specifications.

Models that estimated an exponential decay in the effect of tobacco control did not produce statistically significant regression relationships and the residuals showed significant autocorrelation.

The estimates for

The estimated coefficients of the alternative model chosen by Autometrics are −2.96 (SE 0.232) for the difference California and control state tobacco control funding and −15.46 (SE 5.00) for the price of cigarettes in California. Tobacco control funding has a statistically significant effect on cigarette consumption per smoker in California in the alternative model.

The variables for proportion of the population that African-American or Hispanic do not enter the regressions (all P values>0.10) and their inclusion do not change the values of the other coefficients substantially. The variable for Other Race (neither White nor African-American) enter the regressions for prevalence (

Estimates of healthcare expenditure using the CMS measure of healthcare expenditure (rather than the NIPA measure) from 1989 to 2004 show a reduction of one percentage point in prevalence of current smoking and consumption of one pack per year per smoker in California reducing per capita healthcare expenditures by $69.8 (SE $12.6) and $5.59 (SE $1.77), respectively (

The out-of-sample forecasts using the model estimated in this paper that uses current smoking prevalence and cigarette consumption per smoker as the measure of smoking behavior performs better than the previously estimated model that used per capita cigarette consumption. The new model performs better on all forecast performance measures, particularly for per capita cigarette consumption. (See

The results show that the California Tobacco Control Program had a substantial effect on both smoking prevalence and cigarette consumption per smoker, and both in turn had a substantial effect on per capital healthcare expenditure. The out-of-sample forecasts of the model (using the CMS measure of healthcare expenditure) presented in this study using prevalence and cigarette consumption per smoker are superior to the previously published model that uses per capita cigarette consumption.

From 1989 to 2008, the California Tobacco Control Program cost $2.4 billion and resulted in $243 billion (SE $38.5 billion) in CMS health expenditure savings by reducing total cigarette consumption by a total of 6.79 billion (SE 0.605 billion) packs of cigarettes worth $28.5 billion (SE $2.55 billion) in pre-tax sales to the tobacco industry. 36.4% (SE 4.06%) of this effect was due to reductions in prevalence and 63.6% (SE 4.06%) was due to reductions in consumption among continuing smokers. The fact that such a large fraction of the total effect was due to reductions in consumption points to the importance of considering per smoker consumption in addition to changes in prevalence when evaluating the effects of tobacco control programs. The California Tobacco Control Program has been shown in other research to reduce the prevalence of heavy (>20 cigarettes per day) and moderate smoking (10 to 19 cigarettes per day), and increase the prevalence of light (<10 cigarettes per day) smoking

The estimated NIPA healthcare expenditures attributable to smoking using the new model are $548 (SE $27.8) per capita and between $2,262 (SE $121) and $2,904 (SE $184) per smoker. About one third of the smoking-related cost is due to smoking prevalence and the rest due to consumption per smoker.

The estimated annual per capita excess per capita healthcare expenditure (using the CMS measure) attributable to differences in per capita cigarette consumption in our earlier paper

The cumulative reduction in packs sold attributable to the California Tobacco Control Program (between 1989 and 2004) is 4.2 (95% CI 3.4, 4.9) million packs, which is not significantly higher than the 3.6 (95% CI 1.5, 5.9) million packs estimated in using our previous model

The average price elasticity over the sample period of prevalence is −0.198 (SE 0.0951) and of cigarette consumption per smoker is −0.352 (SE 0.164). The total elasticity of cigarette demand is −0.474 (SE 0.164). The results are more consistent with existing price elasticity estimates for cigarette demand

The VAR regression approach used in this study is consistent with the cointegrating regression estimates in previous research, and produces a similar long run relationship as the cointegrating regression approach. The prevalence of smoking may be stationary with high autocorrelation, or nonstationary with a unit root. If the data are nonstationary, then the dynamic VAR equations can be solved estimate the combined cointegrating equation and error correction model that should equal the static cointegrating regressions. If the data are stationary, but with high autocorrelation, the VAR estimates are still consistent; the consistency of the static cointegrating regressions can be questioned. Thus, the VAR are more robust if the data are really stationary, and will give the same result for the long relationships as the cointegrating regressions if the data are nonstationary.

This analysis uses aggregate measures of population characteristics to estimate the relationships between per capita tobacco control funding, smoking and per capita healthcare expenditures. The estimated relationship between smoking and healthcare expenditures reflects differences in smoking behavior and healthcare expenditures in different state populations with different histories of aggregate population measures of smoking and resulting cost estimates should not be interpreted as healthcare costs arising in, or due to, an individual smoker. These estimates reflect all the healthcare expenditures associated with smoking that will arise in a population: short and long term direct effects on the smoker, and short and long term effects of second- and third-hand

The results of this study are subject to the limitations of analysis of aggregate observations using observational data. A study of this nature that used aggregate data and a relatively small sample size cannot, by itself, establish a causal connection between tobacco control programs, smoking behavior and healthcare costs, and is not the goal of this study. Rather, it should be evaluated in the context of the existing body of research that has already established that this relationship is causal using a variety of study designs

The best regression specification for cigarette consumption per smoker (

Data were not available to conduct a detailed analysis of the possible independent effect of regional variations in local smokefree policies or sales regulations for tobacco on smoking behavior. However, existing research has shown that these factors should be considered intermediating variables for the effects of large scale state tobacco control programs, which operate, in part, through such changes in state tobacco control policy

Omission of exogenous trends that play no intermediating role in determining smoking behavior or healthcare expenditures could produce bias in the estimated regression coefficients. Examples are prevalence of obesity, abusive alcohol consumption, diabetes, prevalence of racial and ethnic populations, regional capacity of healthcare providers, and penetration of managed care organizations. An extensive sensitivity analysis of the possible effect of these factors, reported in previous research for California

The results extend previous results for California

Because of the study design, the coefficients for prevalence and consumption per smoker for the health expenditure (

The results suggest that tobacco control is very effective at reducing consumption in smokers in addition to reducing prevalence, and that reduction in consumption in continuing current smokers also makes an important contribution to reducing healthcare expenditure for the overall population. Tobacco control programs should evaluate their effectiveness using both changes in prevalence and consumption in current smokers. At the same time, since even low levels of cigarette consumption substantially increase the risk of some diseases, particularly cardiovascular disease

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_{1}: results from the Lung Health Study. Eur Respir J: 1011–1027.