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Evaluation of the economic impact of California's Tobacco Control Program: a dynamic model approach
Leonard S Miller1,
Wendy Max2,
Hai-Yen Sung2,
Dorothy Rice2,
Malcolm Zaretsky3
1School of Social Welfare, University of California, Berkeley, California, USA
2Institute for Health & Aging, Department of Social and Behavioral Sciences, University of California, San Francisco, California, USA
3Department of Molecular and Cell Biology, University of California, Berkeley, California, USA
Correspondence to Wendy Max, Institute for Health & Aging, University of California, San Francisco, 3333 California Street, Suite 340, San Francisco, CA 94118, USA; wendy.max{at}ucsf.edu
Objective To evaluate the long-term net economic impact of the California Tobacco Control Program.
Methods This study developed a series of dynamic models of smoking-caused mortality, morbidity, health status and healthcare expenditures. The models were used to evaluate the impact of the tobacco control programme. Outcomes of interest in the evaluation include net healthcare expenditures saved, years of life saved, years of treating smoking-related diseases averted and the total economic value of net healthcare savings and life saved by the programme. These outcomes are evaluated to 2079. Due to data limitations, the evaluations are conducted only for men.
Results The California Tobacco Control Program resulted in over 700 000 person-years of life saved and over 150 000 person-years of treatment averted for the 14.7 million male California residents alive in 1990. The value of net healthcare savings and years of life saved resulting from the programme was $22 billion or $107 billion in 1990 dollars, depending on how a year of life is discounted. If women were included, the impact would likely be much greater.
Conclusions The benefits of California's Tobacco Control Program are substantial and will continue to accrue for many years. Although the programme has resulted in increased longevity and additional healthcare resources for some, this impact is more than outweighed by the value of the additional years of life. Modelling the programme's impact in a dynamic framework makes it possible to evaluate the multiple impacts that the programme has on life, health and medical expenditures.
Cost of smoking
healthcare expenditures
California Tobacco Control Program
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.
http://dx.doi.org/10.1136/tc.2008.029421
The California Tobacco Control Program (CTCP) was established in 19891–3 using a portion of the tax revenues generated as a result of the Tobacco Tax and Health Protection Act, Proposition 99. With an annual budget of roughly $100 million, the CTCP became the largest comprehensive tobacco control programme in the world.1–3 The $0.25/pack increase in tobacco taxes, which funded the programme went into effect in January 1989. Several other components were launched in spring 1990: a statewide anti-tobacco media campaign, community-based interventions and school-based prevention programmes. From the beginning, the CTCP has emphasised a strategy of changing social norms to make tobacco use less desirable, less acceptable and less accessible.4 5 The ultimate goal is to reduce tobacco-related diseases, poor health and deaths in California.6
A number of studies have been undertaken to evaluate the impact of the CTCP.7–15 Most of them focused on process indicators such as amount of funding and the scope of programme implementation, smoking outcome measures such as per capita cigarette consumption and smoking prevalence, and percentage of population protected by smoke-free homes or workplaces. A few studies examined the health benefit of the CTCP. Fichtenberg and Glantz16 found that the CTCP was associated with an immediate reduction in deaths from heart disease. Another study reported that from 1989 to 1999, the CTCP was associated with a 6% reduction in lung cancer incidence.17 Only one study evaluated the economic effect of the CTCP, estimating that the programme saved $86 billion in 2004 dollars of healthcare expenditures between 1989 and 2004.18 However, the long-term economic effect of the CTCP, including reduced smoking-related diseases (SRDs) and reduced smoking-related deaths, has not been documented.
To the extent that the CTCP successfully reduces the incidence of SRDs, it would save smoking-attributable healthcare expenditures (SAEs). The SAEs in California were estimated at $8.6 billion for 199919 and $8.7 billion for 1993.20 These estimates, referred to as 'gross' SAEs,21 were based on an annual cost of smoking approach.22 The reduction in number of premature deaths may impose additional healthcare expenditures during the prolonged years of life for people with avoided premature death. The tobacco industry refers to the potential saving from premature death as the 'death benefit'.23 24 Cost of smoking estimates which take into consideration the expenditures net of the death benefit are referred to as 'net' SAEs.21
The issue of the 'gross' versus 'net' SAEs was first raised by Leu and Schaub.25 They estimated the lifetime cost of smoking by simulating the medical cost history of Swiss men with and without cigarette smoking. They concluded that the extra years of costs experienced by the longer-lived non-smoking cohort approximately balanced out the higher costs during each year of the smokers' shortened lives. Barendregt et al26 used a dynamic method to estimate the effects of smoking cessation on healthcare costs over time in Finland and found that if all smokers quit, healthcare costs would be lower at first but after 15 years there would be a net increase in healthcare costs. Hodgson27 used a life cycle approach to estimate the lifetime cost of smoking in the US and found contradictory evidence. His results showed that ever smokers incurred higher lifetime medical expenditures than never smokers even after adjusting for never smokers' additional years of life. In the debate over whether to use the 'gross' or 'net' SAEs, Warner et al21 suggested that the net measure is the logically correct one if the question of interest is how much greater a nation's healthcare expenditures are with smoking compared with the absence of smoking.
It is important that evaluations of public health programmes such as the CTCP consider the impact of the programme over time and capture the impact on mortality or longevity. For example, smoking cessation even late in life has been shown to increase life expectancy.28–30 Excluding this aspect of the programme from an evaluation is implicitly placing a value of 0 on life. Thus the value of lives saved and prolonged must be taken into account, in addition to changes in healthcare expenditures. The objective of this study is to evaluate the long-term net economic impact of the CTCP using models designed to capture both these effects.
Because it is virtually impossible to separate the impact of the tobacco tax increase from the impact of other tobacco control activities undertaken by the CTCP, we consider them together. Four outcome measures are considered: (1) years of life saved, (2) years of treating SRDs averted, (3) net healthcare expenditures saved after adjusting for additional healthcare expenditures for people who live longer due to not smoking and (4) total economic value of net healthcare expenditures saved and years of life saved. The evaluation is conducted on a cohort consisting of all men who resided in California in 1990. Since those who did not take up smoking or who quit smoking due to the CTCP would enjoy health benefits long into the future, we used an evaluation period from 1990 through 2079, the year when the youngest in 1990 would turn age 90.
This study relies on four data sources.
National Academy of Sciences–National Research Council (NAS-NRC) Twin Registry
This is the largest national twin registry in the US. It consists of adult male twins born between 1917 and 1927 both of whom served in the military, mostly during World War II. Two questionnaires were mailed to registry members in 1967–1969 and 1983–1985 to collect information on registrants' smoking habits at the time of survey. The registrants' mortality status was periodically obtained from the computerised records of the US Department of Veterans Affairs (DVA),31 32 which was notified of the death of approximately 98% of World War II veterans by relatives or morticians who sought to claim a burial allowance. The Twin Registry data with mortality status followed-up through November 1999 was used to estimate the dynamic smoking-attributable mortality model. We did not use the cause of death information.
National Medical Expenditure Survey (NMES-2)
This is a national household survey conducted in 1987 which contains detailed data for 34 459 individuals on smoking history, healthcare utilisation and expenditures, reasons for service use (diagnosis), source of payment, health status and history of certain diseases.33 The NMES-2 data were used to estimate the dynamic smoking-attributable morbidity, health status and healthcare expenditures models. We adjusted the expenditures to 1990 dollars using the medical care component of the Consumer Price Index (CPI).35
Tobacco Use Supplement to the Current Population Survey (TUS-CPS)
This is a national survey targeting adults aged 15 and older. It is sponsored by the National Cancer Institute and administered as part of the CPS, the US Census Bureau's continuing labour force survey.34 It contains detailed information cigarette smoking history and other tobacco use. The sampling design allows producing state-specific and national estimates.36 37 The 1992/93, 1995/96, 1998/99 and 2001/02 TUS-CPS data were used to estimate population smoking initiation and cessation rates for California and other states in the evaluation analysis.
California Tobacco Survey (CTS)
This is a telephone survey of California residents that collects information about tobacco use behaviour and tobacco-related beliefs, attitudes and knowledge.38 39 The 1990 CTS Adult File (ages 18+), Youth File (ages 12–17) and the child sample (ages 0–11) from the Screener file were used to construct a cohort of all California male residents aged 0 and older for the evaluation analysis. The study cohort consisted of a weighted total of 14 711 966 males of age 0 and older.
Analyses in this study were conducted using several statistical software packages. Mathematica40 was used to derive and estimate the dynamic smoking-attributable mortality model and to predict the four outcome measures. LIMDEP V.8.041 was used to estimate the dynamic smoking-attributable morbidity, health status and healthcare expenditures models. SAS/STAT V.8.242 was used to estimate the population smoking initiation and cessation rates.
Dynamic models of smoking
We developed a series of dynamic models to describe the impact of smoking on mortality, morbidity, health status and healthcare expenditures for men aged 40 and older. The lower boundary of age 40 was chosen because most SRDs begin to appear at this age. Figure 1 contains a flowchart showing the estimation process for these models.
Flowchart of the estimation process for the dynamic models of smoking. Mortality model: the input includes two key variables for each respondent, (a) smoking history and (b) mortality status (including the date of death). From the mortality model, the parameters of the expected tobacco exposure index and the hazard rate are estimated. Given these parameters, the expected tobacco exposure index is derived. Morbidity models (including two models, one for high risk smoking-related diseases (SRDs) and another for low risk SRDs): the two key input variables are (a) the expected tobacco exposure index and (b) the SRDs treatment status. Health status model: the input is the same as that for the morbidity models. The output is the expected health status. Healthcare expenditure models (including three models separately for individuals with high risk SRDs, individual with low risk SRDs and individuals without SRDs): the input is the same as that for the morbidity models and for health status model plus two additional variables, (a) expected health status and (b) smoking history.
The smoking-attributable mortality model
This describes the dynamic relationship between an individual's smoking history and his annual probability of death. It is at the core of all the other models because it yields an estimated index for an individual's expected tobacco exposure, given his smoking history (age started smoking, cigarettes smoked per day, age quit). Subsequent morbidity, health status and healthcare expenditures models are all functions of this tobacco exposure index. These models are dynamic in the sense that the tobacco exposure index changes as an individual's smoking behaviour changes over time.
The dynamic smoking-attributable mortality model begins by deriving a theoretical distribution of the tobacco exposure index, which is the solution to a two-equation system of stochastic differential equations describing the body's ability to accumulate and purge tobacco toxins in relationship to smoking behaviour and ageing over time.
d[toxc(t)]/dt=δp−νc(t)(1)d[νc(t)]/dt=−γ0−γ1toxc(t)+σcdWt(2)
The first equation is an instantaneous accounting identity stating that the time rate of change of cumulative tobacco exposure for a current smoker (denoted by subscript c) at time t is equal to the difference between a smoker's momentary intake of tobacco exposure at time t (ie, the product of tobacco dosage per pack, δ, and packs of cigarettes smoked per day, p) and his momentary body purging of tobacco toxin at time t, νc(t). The second equation describes the time rate of change of a current smoker's body purging of tobacco toxin. It is specified as a function of: (1) a constant, which represents the reduction in purge ability due to ageing γ0, (2) the cumulative tobacco exposure, γ1 toxc(t), with the assumption that the body's purging ability declines with more tobacco exposure and (3) an instantaneous white noise term, Wt. To simplify the estimation process, we assume tobacco dosage per pack equals 1 and tobacco exposure at time 0 is 0.
This two-equation system is the same for former smokers except that, in equation 1, the momentary exposure term, δ p, is absent and the initial value of momentary body purging at the time when a former smoker quits has the same expected value as a current smoker with an identical smoking history. This specification implies that the tobacco exposure level accumulated in the body of former smokers would be diminished over time after they quit smoking.
The solution to equations 1 and 2 describes the expressions of the expected value and variance of the tobacco exposure index in the population with the same smoking history.43 Supplementary appendix 1 derives the full theoretical distribution of this exposure index in detail.
The third equation in this analysis is a dynamic normal survival model specified as:
Die*(t)=g(t)+ζ(t)(3)
This equation states that the propensity to die by age t, Die*(t), is the sum of the expected propensity to die by age t, g(t), and a normally distributed random error term. The term g(t) is a function of an individual's age and the expressions of his expected tobacco exposure index at age t. Based on equation 3, and the expressions of the expected value and variance of the tobacco exposure index solved from equations 2 and 2, we derived the expressions of the hazard rate. Supplementary appendix 2 contains detailed description for the specification of equation 3 and the hazard rate formulation. We then estimated the parameters of the expected tobacco exposure index and the hazard rates with the maximum likelihood methods using the NAS-NRC Twin Registry data. Supplementary appendix 3 presents the detailed estimated parameters.
The smoking-attributable morbidity model
This includes two equations describing the propensity of being 'currently treated' for two groups of SRDs in a year. The first equation is for the group of high relative risk SRDs including lung cancer, laryngeal cancer and chronic obstructive pulmonary disease.44 The second equation is for the group of low relative risk SRDs such as coronary heart disease, stroke and all other SRDs.44 Both equations are specified as a function of individual's age and the expected tobacco exposure index. We estimated the morbidity model with a Probit model45 using the NMES-2 data. See supplementary appendix 3 for the estimated parameters.
The smoking-attributable health status model
This describes the probability distribution of individual's self-reported health status (excellent, good, fair, poor) for individuals who are not currently treated for any SRDs. It is specified as a function of an individual's age and the expected tobacco exposure index. We estimated the health status model with an ordered Probit model using the NMES-2 data. See supplementary appendix 3 for the estimated parameters.
The smoking-attributable healthcare expenditures model
This describes the total healthcare expenditures of an individual in a year, and is estimated using the NMES-2 data for three groups of individuals stratified by disease status. For those currently treated for high relative risk SRDs, an individual's expected total expenditures are estimated as the average total expenditures of all individuals who have the same smoking status in this group. For those currently treated for low relative risk SRDs, an individual's annual total expenditures are modelled as a function of ever smoker status and his expected poor health status. This model was estimated using the ordinary least squares (OLS) method. For those not currently treated for any SRDs, a two-part model46 is used to describe the propensity of having healthcare expenditures (first-part model) and the logarithm of the magnitude of annual expenditures for those with positive expenditures (second-part model). The first-part and second-part models are specified as functions of an individual's age, the expected tobacco exposure index, smoking status and expected poor health status. We estimated the first-part model with a Probit model and the second-part model with the OLS method. See supplementary appendix 3 for the model specification and estimated parameters.
Evaluation of the economic impact of the CTCP
We evaluated the economic impact of the CTCP over the full life of a cohort of all 1990 California male residents obtained from the 1990 CTS data. For each year, we estimated two sets of predictions for each outcome measure. The first set was estimated under the CTCP (the factual situation). The second set was estimated under the assumption that the CTCP did not exist (the counterfactual situation). The effects of the CTCP were measured as the difference between these two sets of predictions.25 Specifically, this evaluation consists of three steps.
Simulate smoking initiation and cessation rates
In order to estimate the two sets of predictions, the population smoking behaviour of the cohort under the factual and counterfactual situations from 1990 to 2079 was simulated. We focused on two measures of smoking behaviour: smoking initiation and successful cessation.
We calculated the yearly smoking initiation and cessation rates during the period of 1981–1999 using the TUS-CPS data. Never smokers were defined as those who answered 'no' to the question: 'Have you smoked at least 100 cigarettes in your entire life?' Those who answer 'yes' were ever smokers. Ever smokers were asked, 'How old were you when you started smoking cigarettes fairly regularly?' Ever smokers were also asked whether they currently smoked. If not, they were defined as former smokers and were further asked: 'About how long has it been since you last smoked cigarettes fairly regularly?' We adopted previously developed techniques47–52 to calculate yearly smoking initiation rates and cessation rates. First, we reconstructed each respondent's smoking status retrospectively for each year before the year of the survey, from 1981 to 1999. Consistent with another recent study, we assumed respondents' state of residence did not change over time during this reconstruction period.53 Second, we calculated smoking initiation rates for three age groups (11–15, 16–18 and 19–22) separately for California and for all other states by dividing the weighted number of respondents who started smoking in a given year by the weighted number of non-smokers in the beginning of that year. Third, cessation rates were calculated by dividing the weighted number of long-time quitters with at least 6 months of abstinence who quit smoking in a given year by the weighted number of respondents who were current smokers in the beginning of that year. The cessation rates were calculated for four age groups (20–34, 35–44, 45–54 and 55+) for California and all other states. Finally, these crude rates were smoothed using a 3-year moving average.
For each age group, a time series model of California's smoothed initiation (or cessation) rates during 1981–1999 was specified as a function of all other states' smoothed initiation (or cessation) rates, a dummy variable measuring the effect of the CTCP (value of 1 since 1989; 0 otherwise), and a time trend, using a method similar to that employed by Fichtenberg and Glantz.16 By including the rate for all other states in the model, we controlled for changes in the California rate due to national changes in risk factors. The simulated initiation (or cessation) rates under the factual situation were given by the predicted values from this model, and the simulated initiation (or cessation) rates under the counterfactual situation were also given by the predicted values from this model except that the dummy variable for the CTCP was assumed to be 0 in 1989 and later years. After 1999, age-specific factual and counterfactual initiation and cessation rates were assumed to be at their respective 1999 levels.
Simulate mortality, morbidity, health status and healthcare expenditures
The simulated smoking initiation and cessation rates and the estimated parameters from the dynamic models of smoking were applied to the California cohort to simulate their lifetime outcomes under the factual and counterfactual situations. For each year from 1990 to 2079, we began to simulate who dies or survives for individuals aged 40 and older. If an individual survives or is not yet 40 years old, we simulated who takes up or quits smoking and who remains at their previous year's smoking status, and estimated the expected tobacco exposure index. For individuals aged 40 and older who survives, we simulated who is currently treated for high or low relative risk SRDs and who is not, and for those not treated, what each individual's expected health status is. We then predicted each individual's healthcare expenditures. All of these simulations were performed under the factual and counterfactual situations. Supplementary appendix 4 contains details of the design of the simulations.
Estimate the effects of the CTCP on four outcome measures
Given the above simulation results, we used four different algorithms to estimate the effects of the CTCP on four outcome measures: (1) years of life saved, (2) years of treating SRDs averted, (3) net healthcare expenditures saved after adjusting for additional healthcare expenditures for people who live longer due to not smoking and (4) total economic value of net healthcare expenditures saved and years of life saved.
In the first algorithm, an individual is dropped from the factual and the counterfactual simulations when he dies in either simulation. Therefore, this algorithm derives 'gross' healthcare savings without considering the impact of potential prolonged years of life due to the CTCP. This is similar to what is assumed in the annual cost of smoking studies of national and state estimates of smoking-attributable expenditures.19 20 44 54 55
In the second algorithm, individuals who die in the factual or counterfactual simulation are still included in the other simulation until they die or reach age 90. Because more individuals live longer due to the health benefit of the CTCP, those additional years of life lead to additional healthcare expenditures. Therefore, this algorithm derives 'net' healthcare savings due to the CTCP, analogous to the lifetime cost of smoking studies,25–27 by taking into account the reduced smoking-attributable healthcare expenditures during the years while people are alive and the additional non-smoking-related healthcare expenditures during the prolonged years of life.
The third and fourth algorithms consider the value of lives saved by the CTCP in addition to net healthcare savings. Because premature deaths from smoking usually occur among older people who have relatively low market earnings, we valued years of life using a willingness-to-pay (WTP) approach. While early WTP studies implied the value of life ranging from $3 million to $7 million,56 Sloan et al29 used a conservative value of $100 000 per life year to estimate the economic losses from smoking-related mortality. We adopted $100 000 per year to value the life year in 1990 with adjustments depending on each person's disease treatment and health status: $100 000 for excellent health, $80 000 for good health, $50 000 for fair health and $25 000 for poor health or being treated for SRDs. The only difference between the third and fourth algorithms is the discount rate used to calculate the present value of expected life years saved (see below).
An alternative approach for considering the value of life is to calculate disability adjusted life years (DALYs) or quality adjusted life years (QALYs). While formally calculating DALYs or QALYs was beyond the scope of our study due to the lack of data availability, we have taken into account the dimensions of the quantity and quality of life that DALYs and QALYs capture by assigning different values for a year of life based on disease and health status in our third and fourth algorithms.
In all four algorithms, the present value of healthcare expenditures saved by the CTCP was estimated by taking into account discounting as performed in the lifetime costs of smoking literature.27 29 57 58 First, considering the potential growth in future healthcare expenditures, we expressed each person's future stream of annual healthcare expenditures during his expected 'lifetime' from age 40 to age 90 by inflating the 1990 value of the predicted healthcare expenditure by 2% per year. The growth rate of 2% is approximately the difference in average annual growth rate between the CPI for medical care and the CPI for all urban consumers during 1990–1999.35 Second, this future stream of predicted expenditures was discounted by the rate of time preference at 3% per year to derive the present value of the lifetime expenditures. For any person, the healthcare expenditures saved by the CTCP equalled the present value of the lifetime expenditures under the counterfactual situation minus the present value of the lifetime expenditures under the factual situation. Total healthcare expenditures saved for all male Californians were obtained by summing savings across individuals, taking into account sampling weights in the 1990 CTS data.
In the third algorithm, we discounted the value of future life years by the rate of time preference using 3% per year.59 In the fourth algorithm, we discounted the value of future life years by discount rates that approximated the differential probabilities of death among individuals of different smoking statuses. As an approximation, we discounted a year of a current smoker's life by 2%, a year of a former smoker's life by 1.5% and a year of a never smoker's life by 1%.
The estimated probability of survival given age and smoking history is illustrated in figure 2. The more exposure to tobacco a person has had, as measured by number of decades smoked (or formerly smoked) and by packs per day smoked, the lower the probability that he will be alive.
Probability of survival for men with different smoking histories. Seven survival curves denote different smoking histories: n, never smoker; …, former smoker who smoked 1 pack/day for 10 years since age 17 and quit at age 27; ·–·, former smoker who smoked 1 pack/day for 20 years since age 17 and quit at age 37; – –,former smoker who smoked 1 pack/day for 30 years since age 17 and quit at age 47; 5, current smoker who smoked 0.5 pack/day since age 17; 1, current smoker who smoked 1 pack/day since age 17; 2, current smoker who smoked 2 pack/day since age 17. Age 17 was chosen because it is the mean age when male smokers began to smoke.
Figure 3 shows the observed, predicted and simulated smoking cessation rates for California males during 1981–1999 for four age groups. The young adult group (20–34) had the highest increase in cessation rates—from about 2% in 1981 to over 5% in 1999—and was most responsive to the CTCP in cessation especially after 1995, as measured by the difference between the predicted cessation rates under the factual situation and the simulated cessation rates under the counterfactual situation. Figure 4 shows the observed, predicted and simulated smoking initiation rates for three age groups. The initiation rates for California males were lower than those for all other states, especially for the group aged 16–18. For the groups aged 11–15 and 19–22, their initiation rates declined noticeably after 1995 and the reduction was related to the implementation of the CTCP.
Smoking cessation rates for men in California and all other states by age, 1981–1999. The actual rates (dots) represent the 3-year moving average of the observed cessation rates for California (CA) and all other states (OTH). The predicted CA rates mean the predicted cessation rates from the time series model under the factual situation. The simulated CA rates mean the predicted cessation rates from the time series model under the counterfactual situation.
Smoking initiation rates for men in California and all other states by age, 1981–1999. The actual rates (dots) represent the 3-year moving average of the observed initiation rates for California (CA) and all other states (OTH). The predicted CA rates mean the predicted initiation rates from the time series model under the factual situation. The simulated CA rates mean the predicted initiation rates from the time series model under the counterfactual situation.
Table 1 shows the estimated economic benefits of the California CTCP for the 1990 cohort followed until death. Almost three-quarters of a million person-years of life are saved. In addition, 141 426 person-years of treatment for the high relative risk SRDs and 16 240 person-years of treatment for the low relative risk SRDs are averted.
Estimated economic impact of the California Tobacco Control Program (CTCP) over a 90-year evaluation period from 1990 through 2079
Using our first algorithm, we estimate that the CTCP saved $1.438 billion dollars (in 1990 dollars) in healthcare costs over the period from 1990 through 2079. The estimate is statistically significant at p value <0.05, two-tailed test.
Our second algorithm yields an estimate of 'net' healthcare savings from the CTCP, including the additional healthcare expenditures associated with living longer due to the CTCP. The present value of the net savings for healthcare expenditures was estimated as −0.144 billion (in 1990 dollars), but is not statistically significant.
Based on the third and fourth algorithms, we derived two estimates for the total economic value of net healthcare savings and years of life saved due to the CTCP, valuing a year of life at $100 000 with adjustments for individual's disease treatment and health status. From the third algorithm, our estimated present value of the total net healthcare resources saved plus the value of years of life saved was $22.443 billion (in 1990 dollars). From the fourth algorithm, we estimated that the CTCP would generate $107.418 billion (in 1990 dollars) of total savings including net healthcare saving and the value of life saved. Both estimates are statistically significant at p value <0.05, two-tailed test.
Our results highlight the importance of developing a comprehensive measure for evaluating the impact of a tobacco control programme that considers the value of healthcare resources used, and also the value of years of life saved and of improved health status associated with not smoking. A comparison of the 'gross' healthcare expenditures to the 'net' healthcare expenditures shows that when the healthcare costs resulting from longer life are considered, the healthcare savings from the CTCP disappear. However, these approaches ignore the value of having people live longer and healthier. When a value for life is included, the total economic value of the benefits from the CTCP amounts to $22.4 billion in 1990 dollars. This is more than a 15-fold increase over the estimate of the 'gross' healthcare savings and a very different result from the 'net' healthcare savings, which ignore the value of life. This value is equivalent to $35.5 billion in 2007 dollars (adjusted by the CPI). When an individual's probability of death is used to discount the years of life, the CTCP would generate $107.4 billion in 1990 dollars, a 75-fold increase over the estimate of the 'gross' healthcare savings. This value is equivalent to $170.2 billion in 2007 dollars. Given that a key public health outcome is improved health, the value of life saved and improved health should be central to evaluating the destructive effects of smoking, the single most important preventable public health hazard.
During the first decade of the programme, the CTCP spent about $1.2 billion dollars (A Roeseler, California Department of Public Health, California Tobacco Control Program, personal communication, 2005). This is dwarfed by the total economic value of the net healthcare savings, lives saved and health improved due to the programme. However, it must be noted that our estimates result from the combined effect of the tobacco tax increase and other components of the CTCP including the statewide media campaigns, community-based interventions and school-based prevention programmes.
There are several limitations to this study. First, women were not included in the analysis because longitudinal data on female mortality and smoking were unavailable. However, we postulate that the economic effects of the CTCP for women would be on the order of two-thirds the size of the effects for men because the smoking prevalence rate for women was approximately 69% of the rate for men in California.19 Thus, the total economic value of the CTCP including men and women may be considerably larger than our estimates. Further research is needed to include women in the evaluation so that a fuller understanding of the importance of tobacco control programmes can be obtained. Second, for the period from 2000 to 2079, we assumed that smoking initiation and cessation rates remain at their 1999 levels. For these rates to remain constant beyond 1999, tobacco control efforts must be sustainable. Future research could explore how the economic impact of the CTCP is sensitive to post 1999 smoking rates. Third, in the analyses of yearly smoking initiation rates and cessation rates, we assumed the state of residence for the respondents of the TUS-CPS data was unchanged. A recent study which compared the cessation rates between California and a group of comparison states pointed out that a large movement of former smokers from California to other states during the study period would artificially inflate the estimated cessation rate in other states, and vice versa.53 Further research is needed to explore the smoking population's movement between states so as to determine the direction of potential bias due to such assumption. Fourth, the simple specification of a single dummy variable for the CTCP in the smoking initiation and cessation equations implies that the effect of the programme was constant over time. However, it has been reported that the impact of the CTCP on smoking prevalence rates was stronger during the early 1990s than during the late 1990s, implying that the impact of the CTCP on smoking initiation and cessation rates might not be constant over time.8 Nevertheless, even with this limitation, our results are in general consistent with the findings from a study by Messer and Pierce et al53 which showed that from 1980 to 1999, cessation rates increased most for the young (age 20–34), and this age group also showed the greatest difference between California and the comparison states. Finally, we did not include any impact of the CTCP on secondhand smoke exposure, though data have shown a substantial decrease in exposure over time.
Tobacco control programmes are costly. However, the benefits of the programmes are substantial and continue to accrue for many years. Although those who are persuaded not to smoke will live longer, have better health status and require additional healthcare resources during their additional years of life, this impact is outweighed by the value of additional years of life and better health. Public health programmes need to be evaluated with healthcare costs, additional years of life and improved health considered as important outcomes.
What this paper adds
This paper develops a series of dynamic models of smoking behavior and consequences that analyze the impact of smoking initiation and cessation on morbidity, mortality, health status, and healthcare expenditures over the lifetime of Californians.
The models are used to evaluate the impact of the first decade of the California Tobacco Control Program on males.
The findings indicate that when the value of increased longevity is included, the program saved $22–$107 billion, depending on how life is valued.
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. The California tobacco control program: a decade of progress, results for the California tobacco surveys, 1990-1999. La Jolla, CA: University of California, San Diego, 2001.
Al-Delaimy WD,
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. The California tobacco control program: can we maintain the progress? results from the California tobacco survey, 1990-2005. vol 1. La Jolla, CA: University of California, San Diego, 2008.
. Has the California tobacco control program reduced smoking? JAMA 1998;280:893–9. Erratum in: JAMA 281(1):37.
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. The effectiveness of state-level tobacco control interventions: a review of program implementation and behavioral outcomes. Annu Rev Public Health 2002;23:45–71.
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. Association of the California Tobacco Control Program with declines in cigarette consumption and mortality from heart disease. N Engl J Med 2000;343:1172–5.
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. Effect of the California Tobacco Control Program on personal healthcare expenditures. PloS Med 2008;5:e178.
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. The economic burden of smoking in California. Tob Control 2004;13:264–7.
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. State estimates of total medical expenditures attributable to cigarette smoking, 1993. Public Health Rep 1998;113:447–58.
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. Medical costs of smoking in the United States: estimates, their validity, and their implications. Tob Control 1999;8:290–300.
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. Does smoking increase medical expenditures? Soc Sci Med 1983;17:1907–14.
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. The health care costs of smoking. N Engl J Med 1997;337:1052–7.
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. Cigarette smoking and lifetime medical expenditure. Milbank Q 1992;70:81–125.
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. Benefits of smoking cessation for longevity. Am J Public Health 2002;92:990–6.
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. The price of smoking. Cambridge, Mass: MIT Press, 2004.
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. Mortality in relation to smoking: 50 years' observations on male British doctors. BMJ 2004;328:1519.
Zaretsky M
. Communication between identical twins: Health behavior and social factors are associated with longevity that is greater among identical than fraternal U.S. World War II veteran twins. J Gerontol A Biol Med Sci 2003;58A:566–72.
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. Familial factors in early deaths: twins followed 30 years to ages 51-61 in 1978. Hum Genet 1978;59:39–46.
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. The current population survey: design and methodology. Washington, DC: US Dept of Commerce, Bureau of the Census, 1995. Technical paper 40.
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. Disease cases and their medical costs attributable to smoking: an analysis of the national medical expenditure survey. J Econom 2002;112:139–45.
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. Smoking initiation by adolescent girls, 1944 through 1988. An association with targeted advertising. JAMA 1994;271:608–11.
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Web only appendix
Files in this Data Supplement:
Funding This research was funded by the California Tobacco-Related Disease Research Program under grant 9RT-0157. The grant was awarded competitively and the funding agency was not involved in any aspect of the research.
Competing interests None.
© 2010, Published by the BMJ Publishing Group Limited For permission to use, (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.


The only noteworthy thing about 90210's third official episode is that there is nothing worth noting.
Okay, so I'm not going to end the blog post there, that seems like it would be a poor strategy. There's actually a lot of little things that the show is doing right, but for the most part it is struggling to define itself as anything even close to memorable. The show just isn't going out on any limbs here: while there is something very breezy and attractive about this universe, when the "big events" do take place they feel like plot devices and little else. The setup here is so transparent, so lifeless that it is the very definition of rote, and the only thing bringing any life to the equation is cameos and callbacks to the original series that I am purely ignorant to.
So while I actually think the cast is stepping up to the plate here, I can't say that the producers are giving them enough to do.
I don't want to be a broken record, but rewatching The O.C. has reminded me of its iconic imagery, its atmosphere that was so much about location and isolation. We were seeing a whole world through Ryan's eyes, and here it just doesn't feel that way. This is primarily because, for whatever reason, Annie and Dixon have completely bought into this new lifestyle. They were supposed to be fish out of water, but the second they stepped into their new lives they were wearing fancy clothes, having fancy friends, and completely switched into "Now we feel stifled by our parents' love" mode. It's a really quick transition, and one that I don't buy – shouldn't we be heading for Annie's triumphant fall from grace, or Dixon's descent into his former life, or some sort of drama that damages this inner circle?
If I had to prescribe a solution, I think the show needs less characters: if you look at The O.C.'s example (I swear, I'll stop soon), it started out with a small core group of characters that expanded with time, eventually to the point of weakening the show severely. 90210, though, has started out with too many threads: you have the nostalgic adults, the parental adults, the bitchy children, the good children, the nerdy children, the damaged children, the cute guy, the other cute guy, and everything else you could imagine. While I understand that a large ensemble gives them a chance to play as many soap opera cliches as possible, which is in the original tradition of its namesake, nothing is landing.
The only thing that's really clicking for me is largely because Tristan Wilds is just so damn charming – that smile, which we saw so little of on The Wire, is emerging in a way that makes his flirtations with Silver work. The problem is that it isn't a narrative, it's just an interaction: we didn't get any sense that these two were going to be interested in one another, and there certainly isn't anything outside of some base chemistry for us to presume that we should really care. Yes, Shanae Grimes is very charming, and Dustin Milligan just the right balance of kind and brooding to make the whole love triangle watchable, but whether or not it's actually something I care about is sadly a completely different issue.
The rest of the episode just felt too simple: there's Naomi's strange family situation, Jennie Garth and Ryan Eggold showing some chemistry, our cameo from Mother Silver, the struggles to maintain family night, the all too casual demonstration of "hipness" through an early screening of the newest Bond film, and the fact that they actually repurposed the same establishing shot of Beverly Hills twice within about two minutes. All of these elements are generally speaking well acted, but none of them resonate: I don't care, in other words, about any of it.
And I'm not sure if other people will: it will be curious to see how the ratings hold up this week, especially since it held up decently in its second hour. This time around, though, it has FOX's buzzed about Fringe (Which I'll be revisiting from my original preview later tonight) as competition, so it's a whole new world…in the ratings, if not in the show's plot.
Seems kind of strange to immediately completely ignore Jessica Walter's Grandmother character in only the show's third episode, especially when she's "supposed" to be the whole reason they're even in Beverly Hills. It's another example of the show's setup really being pointless – within that opening scene of exposition in the premiere, apparently, we got all the setup we're really going to get.
Just so I'm clear…Dylan is Luke Perry, right? Or am I wrong? I don't even know.
Also, I enjoy that the bowling alley has the same name as the type of cigarettes that were featured in the series premiere of Mad Men.


Trinity Lutheran School's early childhood program is designed to meet the developmental needs of children age six weeks to five years old. They offer program options, Monday through Friday, from 6:00 a.m. to 6:30 p.m.
At Trinity they provide daily experiences that enrich and foster each child's spiritual, cognitive, social, emotional and physical development. Each day children have opportunities to explore their creative side, develop problem solving skills and strengthen personal interaction skills through hands on experiences.


1. Since the first request I found a lot more fixes that could be made.
2. VERY IMPORTANT please read carefully.
*** Fixed tabs which could be seen as "thin content". All review pages contained tabs 30, 60 and 90 days. If a payday loan company didnt offer a loan for the specific tab (days) then each loan was given "-" as data. This could probably look like thin content as this was repeated on many pages. I have fixed these pages so they now only print out tabs for the prices they actually have. It should be much clearer now and this was actually a fix that was in the pipe since earlier.
*** Updated, fixed and relaunched the mobile app of the site. I had this since before but there was a glitch so it was down for a while. It's works correct now and is also improved. See it on home page.
*** The slider on the home page now only lists the top best regular prices (3, not a horribly long list). Instead I added a note in the end of the list that tells the visitor that if they want to check all prices they can visit the page "priser.php" which is a complete list of prices for all payday loans on the site – on it's own page.
*** Shorter listing in sidebar. I only list 5 rated payda loan companies in the sidebar instead of ~25. There is a link to a subpage if they want more information.
*** Fixed price information on review pages. Replaced "-" with "Erbjuds ej" which means it is easier to understand. (It means that it is a loan not available, previously you maybe thought that this wasn't checked at all and was empty data and therefore a thin page. It wasn't.
Improved review pages (free offers exposing): some companies have free offers for new customers. If company have a free offer it is now very clear which loans are without costs or not. Example: http://smslånonline.se/vivus.php. Better usability.
*** Updated page smslan-betalningsanmarkning.php. The text is improved quite a lot and give more in depth information. Removed old picture which was quite ugly and childish. Removed the big listing taking up all space and made a floating list to the right of the article text. It's better now as the content is more helpful while at the same time the user get the information about companies matching the article.
*** Improved design for smaller lists and summary lists. Ex: http://smslånonline.se/skef.php. Doesn't interrupt reading now.
*** Fixed review article texts for kreditkontot.php and kortlan.php that were missing.
Added link to project blog in sidebar. Should make it way more clear I am more than a thin an affiliate. I have blogged ~50 000 words about the business.
***Fixed home page, removed 7 reasons to choose SmslånOnline.se. Text moved to subpage instead.
*** Fixed daypay.php, missing text made it look like an empty duplicate page.
*** The rating summaries on review pages in the top of the pages now contains smaller texts and click buttons, avoiding pushing content to far down on the page. The intention of this div is to make it totally clear where to go if they like the price as they don't have to serach for it (as they see it the first thing they do).
*** Updated year in footer which said "2013?. Stupid miss. Updated.
*** smslanaren.php 301 redirect to expresskredit.php. The company "Smslånaren" changed and switched to "Expresskredit", I kept the page to keep traffic. Now removed.
*** Removed page Euroloan.php. Yes, it is thin and was inactive. But as they recently changed product I was thinking about launching it again as it seemed to fit the site better. Never mind. Gone.
– Fix all review pages. I use a tab for the review. I think it is good for usability. However, I guess you may see this as trying to hide content? It wasn't just thought it was useful and were playing wuth jQuery. I will trim these reviews and make them visible without tabs.
– I'll add Youtube videos on how to use the tabs and how they should interpret things.
Next planned: I had a plan to let companies blog on my site to give their view of the business and come with updates but I'll put that on hold until I know. I guess I just want to tell you that things were planned.
Please check http://simonnystrom.com/manual-action-on-payday-loan-site-reinclusion-case-study/#google Sites are actually stealing my content now. I hope this is not lowering my chances. I have worked for 1,5 years full time with this, trying to do something better of the business. Seeing that someone else is cached with MY HOME PAGE is not fun, I promise.
With that said. I hope you see my intentions with the site and that I have a chance to be reincluded. At least this made me speed up site improvements and resulted in a better site.


Mactaquac Headpond - Jackson Road / 13 km.
Woodstock Road - Fredericton / 17 km.
Fredericton Small Craft Aquatic Centre n - Fredericton / 21 km.
Carleton Park - Fredericton / 22 km.
Ecological Adventures - Oromocto / 32 km.
Portabello Stream - Old Church Road / 36 km.
Nashwaak River - Taymouth / 40 km.
Spednic Lake - Upper Spednic Lake / 51 km.
Spednik Lake - Water St. / 54 km.
Grand Lake - Thoroughfare / 55 km.
St. John River - Old Kirk Road / 55 km.
Eel River - Eel River Dam / 57 km.
Grand Lake - The Keyhole / 64 km.
Coldwell Whaft - Lower Cambridge / 65 km.
Mud Lake / 69 km.
Half Moon Cove / 69 km.
Public Launch - Orient / 72 km.
Southwest Miramichi River - Half Moon Pool / 73 km.
Grand Lake / 73 km.
St. John River - Evandale / 74 km.
East Grand Lake - Weston / 77 km.
South Branch Meduxnekeag River - So. Branch Meduxnekeag River / 78 km.
Brundage Point Info Center - Grand Bay-Westfield / 78 km.
Meduxnekeag River / 79 km.


text:
Biola Pressroom
Experts on "Scientology"
Kevin Alan Lewis
Associate Professor of Theology and Law
First Amendment Religion And Speech
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Moral Relativism
Para-church (faith- Based Organizations)
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Pop Culture & Christianity
Religion & American Culture
The Occult And Witchcraft
Lewis specializes in the topics of systematic and polemical theology, theology and law integration, jurisprudence and Christian apologetics. He is the Founder and Director of the Evangelical Legal Society, an organization dedicated to the task of restoring the theological foundations of law and justice in society and providing Christians with the resources necessary to do law, justice and public policy from a foundation of evangelical theology. Lewis is an active lecturer at churches, colleges and conferences and has appeared on several radio and television shows, addressing a variety of topics.
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The Westside Union School District in Southern California serves the western parts of Palmdale and Lancaster and their immediate suburbs, including Quartz Hill, Del Sur, Leona Valley, Antelope Acres, and Neenach.
It enrolls transitional kindergarten through eighth grade. High school education (ninth through 12th grades) is provided by the Antelope Valley Union High School District.


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Why do some people still need to use a prescription for painkillers?
A prescription for morphine and codeine is often the first step for treating severe pain, but it is not the only option for people who cannot tolerate painkillers, according to a new study from the University of Rochester Medical Center.
The study, published online today in the journal Pain, explored the role of opioids, a class of painkillers that include morphine, oxycodone and codeinone, in the treatment of chronic pain.
"A prescription for opioid analgesics may not be the first choice for pain patients, but if they can't afford an opioid medication, it could be the only one available to them," said senior author Dr. Matthew T. Hausman, an assistant professor of medicine in the department of medicine at the University at Buffalo.
"These drugs are often used as an alternative to opioids, which have a higher potential for addiction and overdose."
People who cannot afford an opiate medication can often find relief with a combination of pain relievers, but people who do not have access to opioids should consider alternative treatments.
"Hausman said the study examined the relationship between pain and the prevalence of opioid use among individuals with chronic pain and their families.
It found that opioid use was significantly associated with pain severity, a key indicator of severity of pain, as well as with a higher prevalence of depression, anxiety and substance abuse."
These results show that a substantial proportion of people do not receive a full, safe and effective treatment for their chronic pain.""
The use of opioids has become a significant public health problem.
The study used data from the National Longitudinal Survey of Adolescent Health (Add Health) and the National Comorbidity Survey Replication (NCS-R), two of the largest ongoing surveys of US adults conducted by the Centers for Disease Control and Prevention (CDC).
The survey collected information on the demographics of adults aged 15 and older who self-identified as having chronic pain, chronic anxiety disorder, and depression and who had received at least one prescription for opioids.
The survey also asked about other factors that may influence use of pain medications, including social, economic, and religious factors.
Hausmann and his colleagues examined data from a subset of this subset, those who used opioids in the past year.
They found that among adults aged 18 to 59 who self reported using opioids in their lifetime, the odds of using opioids for pain increased from 5.3 to 9.1 percent, a significant increase from the previous report, which found a prevalence of 6.1.
A more recent analysis of the data by the authors also showed a significant association between pain severity and opioid use, with a prevalence greater than 7 percent among people who used morphine or codeine more than once per day.
Haumans findings were echoed by other research.
"Prescription opioids are associated with increased rates of depression and anxiety and a higher rate of substance use," said Dr. Michael W. Durbin, an associate professor of health sciences at the Icahn School of Medicine at Mount Sinai, who was not involved in the study.
"These data suggest that the current opioid crisis has the potential to exacerbate chronic pain for many patients."
Durbin is a co-author of the report, and he noted that more research is needed to understand the specific mechanisms that contribute to the increased risk for opioid use.
Henslow said his organization has recently begun to investigate the use of opioid analgesic prescriptions among chronic pain patients.
"We want to know how opioids influence the patient's health and pain management, and whether opioids are used by patients with chronic medical conditions to manage chronic pain in their treatment," he said.
"It's also important to understand how opioids are marketed, whether they are prescribed in an effort to reduce the risk for overdose, and the long-term risks of misuse."###
India: India's biggest companies are moving away from big data and into real-time analytics
Why Gujarat Govt is not doing enough on EV chargers


Last week I dedicated my blog to showcasing 3 hors-d'oeuvres for the holidays. Just when I thought I was done with appetizers, I realized that I had the ingredients to make an impromptu one. Next thing I know, I'm whipping up a batch of one of my favorite appetizers, wild mushroom bruschetta on crostini with whipped goat cheese. This is a great substitute for the classic bruschetta when tomatoes are not in season. The wild mushrooms paired with creamy, whipped goat cheese on a crusty slice of baguette is a match made in heaven!
Toss sliced baguette with olive oil, salt, and pepper. Spread on a baking sheet and bake for 5-7 minutes or until golden brown. Turn crostini over and toast for an additional 3 minutes or until both sides are golden brown. Let cool. Set aside.
Combine goat cheese, cream cheese and heavy cream in a medium bowl. Whip with mixer until softened and cheeses are combined, about 3-4 minutes. Set aside.
Heat 1 tsp oil in nonstick saute pan over medium heat. Add sliced mushrooms and thyme; cook until browned, about 6-7 minutes, stirring frequently. Deglaze the pan with sherry and cook until liquid is almost all evaporated. Season with salt and pepper. Remove from heat.
Spread goat cheese spread on the toasted crostini. Top with sauteed mushrooms and garnish with micro green or minced parsley. Serve.
Bruschetta is one of the most popular appetizers and rightly so. The simple yet beautiful blend of flavors from fresh tomatoes, basil, garlic, olive oil and balsamic vinegar with a lightly toasted crostini just works. Unfortunately, tomatoes are not in season year-round. The solution? Substitute with wild mushrooms! Oyster, shiitake, chanterelle, lobster, maitake and porcini mushrooms would all work with this hors d'oeuvre; you can even have a mixture of mushrooms for an exotic range of umami.
When sauteing the mushrooms, make sure not to crowd the pan. You want the mushrooms to brown nicely and overcrowding the pan will cause them to steam rather than sear. Deglaze the pan with sherry to add a slightly nutty flavor to the bruschetta. You can also use red wine vinegar which adds a more tart note but still tastes delicious.
When I first prepared this bruschetta, I originally planned on just using goat cheese. While preparing the appetizer, I realized that the cheese was a little difficult to spread and wanted it to be lighter. Combining it with cream cheese and whipping the mixture lightened up the spread while making it easier to apply on the crostini.
Serve the bruschetta cold, at room temperature or hot. The umami flavor of the mushrooms with a hint of thyme topped on a light, slightly tart goat cheese spread on a perfectly toasted baguette slice is just divine. It's hard to stop with one serving, especially because they taste guilt-free! If you have leftover mushrooms, use them as a pizza topping or for a brisket sandwich which I will post in a couple days!
Mmm this sounds amazing! I love bruschetta, and mushrooms are amazing. Together? Perfect! Thank you!
Just had these last night and I want more. I'm drooling.


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Jimmi Simpson – Current and Past Work in the Entertainment Industry
Jimmi Simpson is an actress and motorcyclist who is most popular for her role as Samantha in the film, "Scary Movie," but she has a lot of other roles in her resume. Read on to learn about her current and past work in the entertainment industry.
Jimmi Simpson is an American actor who has starred in numerous TV shows and films. He has played a number of weird, oddball characters throughout his career. However, he has been most well known for playing the lead character on House of Cards.
Jimmi Simpson was born on November 21, 1975, in Hackettstown, New Jersey. At the age of twenty, he decided to pursue a career in acting. A student at Bloomsburg University, he earned a Bachelor of Arts in Theater.
After graduating, he attended the Williamstown Theatre Festival for four seasons. While there, he played a lead role in Aaron Sorkin's play The Farnsworth Invention. His performance received a Theatre World Award.
Jimmi Simpson made his film debut at the age of twenty-five with the romantic comedy Loser (2000). Following this, he appeared in a number of critically acclaimed films.
Along with his work in Hollywood, he founded the Safehaven Foundation, which offers traumatic brain injury patients a new beginning. The foundation provides twenty-four by seven services to non-ambulatory clients.
He also starred in a number of films, including the sci-fi series Westworld. In 2018, he was nominated for the Online Film & Television Association Award and a BAFTA for his performance in the "USS Callister" episode of Black Mirror.
Motorcycling career
A talented and exciting road racer, Jimmi Simpson is regarded as one of the most successful riders of his generation. Before the Second World War, he won over twenty Grand Prix races on the continent. He also won the first 350 cc European Championship.
Throughout his career, he was also a works rider, riding for Norton and AJS. He was also a member of the Shell petrol company racing division. In his last few years, he suffered from injuries and had to retire from competition.
Jimmi Simpson started his motorcycling career in 1922. His first race was a Senior TT on a 500cc Scott. In 1923, he set a new lap record on a 350cc Junior bike for the British Lightweight TT.
During his time on the continent, he also won races on 500cc bikes, including the Belgian Grand Prix at Spa-Francorchamps. Simpson was also the winner of the German Grand Prix at the AVUS track in August.
He was the first non-British rider to win the 350cc class. Besides his success on the Grand Prix circuits, he was also one of the few British riders who made a living on the continent.
Several years after retiring from racing, he became a racing manager for Shell-Mex. He also competed in the Sidecar TT. This was a popular series for British racers, and he was one of the fastest competitors.
Recent film work
If you are a fan of televised action and intrigue, you may have heard of actor Jimi Simpson. The actor has been working in the entertainment industry for a number of years now. He has appeared in several notable television shows and starred in a few films. In addition to his acting credits, he also voices Detective David Mills in a stop-motion animated musical series, Ultra City Smiths. AMC+ is airing the series, which is currently in its second season.
The actor has played a variety of memorable characters over the years, including a hyper-intelligent intern on the long-running TV show, 'Dave Letterman'. Also, he starred in 'It's Always Sunny in Philadelphia' and has been involved with several other cult-classic television programs. Some of his other notable'show-offs' include 'House of Cards', 'West World', 'Hap and Leonard' and 'Unsolved'.
Among his most impressive recent films, 'The Man Who Fell To Earth' has been a major highlight. Taking place in the mid-1970s, the storyline revolves around an alien who arrives on Earth at a pivotal moment in human evolution. Despite his mysterious origins, he is able to form an unbreakable bond with a human.
Another notable movie is the 'The Invention of Lying'. This film stars Jimi Simpson as Noah. Though it is a romantic comedy, it is not the first time the actor has played a snarky villain.
Tom Kenny – The Voice of SpongeBob SquarePants


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Focus on Training » Locations » North West » Crewe » Crewe
Situated in the heart of South Cheshire The Crewe Arms Hotel offers the very best for both business users and pleasure seekers.
Conveniently situated opposite Crewe railway station close to the business parks in Crewe and just minutes away from J16 of the M6. The hotels location makes it an ideal base for visiting Nantwich, Sandbach, Stoke on Trent and even the historic city of Chester, and an ideal conference venue.
The hotel can offer your business a choice of seven meeting and conference rooms all equipped with the latest audio-visual technology.
Some of the most interesting and exciting attractions are right on our doorstep, Bridgemere Garden World, Alton Towers, Jodrell Bank, Oulton Park, and Chester Racecourse. The world famous potteries including Wedgwood, Royal Doulton and Aynsley all have their factory shops and visitor's centres located only a short drive away.
Please note, the venues are subject to change
The Crewe Arms Hotel
Nantwich Road
CW2 6DN
By Air Nearest Airport Manchester - 45 miles.
Nearest Station Crewe - 200 yards.
By Road From Birmingham Join the M6 and go North until Junction 16. Take the first exit at the Junction 16 roundabout, the A500 to Nantwich. At the first roundabout take the second exit, A5020, to Crewe. Take the first exit at the next roundabout, still the A5020, named Weston Road (following the signs for Crewe Railway Station). Proceed along this road, go straight through the traffic lights and take the second small exit at the next roundabout to the hotel.
From London Take the M1 until the Junction with the M6. Join the M6 and go North until Junction 16. Take the first exit at the Junction 16 roundabout, the A500 to Nantwich. At the first roundabout take the second exit, A5020, to Crewe. Take the first exit at the next roundabout, still the A5020, named Weston Road (following the signs for Crewe Railway Station). Proceed along this road, go straight through the traffic lights and take the second small exit at the next roundabout to the hotel.
From Manchester and Liverpool Join the M6 and head South. Leave the M6 at Junction 16. Take the third exit at the Junction 16 roundabout, the A500 to Nantwich. At the first roundabout take the second exit, A5020, to Crewe. Take the first exit at the next roundabout, still the A5020, named Weston Road (following the signs for Crewe Railway Station). Proceed along this road, go straight through the traffic lights and take the second small exit at the next roundabout to the hotel.
From Chester and North Wales Join the A51 at Chester following the signs for M6 Birmingham Southbound/Stoke-on-Trent, heading towards Crewe and Nantwich. When nearing Crewe and Nantwich follow the signs for Crewe Railway Station. This brings you onto Nantwich Road. Follow Nantwich Road until you find the railway station on your right hand side. Follow past the station and directly on your left you'll see the hotel. When you reach the roundabout take the first small exit off the roundabout to bring you into the hotel car park.


text:
#include <stdarg.h>
#include <stddef.h>
#include <setjmp.h>
#include "cmockery.h"

#include "c.h"
#include "postgres.h"

#include "../inval.c"

#if defined(__i386)
#define TRANS_INVALIDATION_INFO_SIZE 40
#define INVALIDATION_LIST_HEADER_SIZE 12
#define INVALIDATION_CHUNK_SIZE 56
#elif defined(__x86_64__)
#define TRANS_INVALIDATION_INFO_SIZE 80
#define INVALIDATION_LIST_HEADER_SIZE 24
#define INVALIDATION_CHUNK_SIZE 64
#else
#error unsupported platform: only x86 and x86_64 supported by this test
#endif

/*
 * Tests that the size of the data structures used for the transaction
 * invalidation information has not changed.
 *
 * Background: To test metadata versioning, these structures are also
 * declared in the test UDF that lives in tincrepo (mdversion_test.c).
 *
 * This test will fail if any change is made to these structs in the product
 * If that happens, two things are needed:
 * - update the sizes here to reflect the new definition
 * - update the definitions in mdversion_test.c (in tincrepo)
 *
 */
void
test__sizeof__TransInvalidationInfo(void **state)
{
	assert_true(sizeof(TransInvalidationInfo) == TRANS_INVALIDATION_INFO_SIZE);
}

void
test__sizeof__InvalidationListHeader(void **state)
{
	assert_true(sizeof(InvalidationListHeader) == INVALIDATION_LIST_HEADER_SIZE);
}

void
test__sizeof__InvalidationChunk(void **state)
{
	assert_true(sizeof(InvalidationChunk) == INVALIDATION_CHUNK_SIZE);
}

/* ==================== InvalidateSystemCaches ==================== */
/*
 * Tests that InvalidateSystemCaches resets the local MDVSN cache when
 * MD Versioning is enabled
 */
void test__InvalidateSystemCaches__resets_mdvsn_enabled(void **state)
{
	will_be_called(ResetCatalogCaches);
	will_be_called(RelationCacheInvalidate);

	/*
	 * Initialize a fake transInvalInfo and transInvalInfo->local_mdvsn.
	 * They are used by GetCurrentLocalMDVSN()
	 */
	mdver_local_mdvsn local_mdvsn_fake;
	TransInvalidationInfo transInvalInfo_fake;
	transInvalInfo_fake.local_mdvsn = &local_mdvsn_fake;
	transInvalInfo = &transInvalInfo_fake;

	will_return(mdver_enabled, true);

	expect_value(mdver_local_mdvsn_nuke, local_mdvsn, &local_mdvsn_fake);
	will_be_called(mdver_local_mdvsn_nuke);

	InvalidateSystemCaches();
}

/*
 * Tests that InvalidateSystemCaches does not reset the local MDVSN cache when
 * MD Versioning is disabled
 */
void test__InvalidateSystemCaches__resets_mdvsn_disabled(void **state)
{
	will_be_called(ResetCatalogCaches);
	will_be_called(RelationCacheInvalidate);

	/*
	 * Initialize a fake transInvalInfo and transInvalInfo->local_mdvsn.
	 * They are used by GetCurrentLocalMDVSN()
	 */
	mdver_local_mdvsn local_mdvsn_fake;
	TransInvalidationInfo transInvalInfo_fake;
	transInvalInfo_fake.local_mdvsn = &local_mdvsn_fake;
	transInvalInfo = &transInvalInfo_fake;

	will_return(mdver_enabled, false);

	InvalidateSystemCaches();

	/*
	 * Part of the test we're implicitly asserting that mdver_local_mdvsn_nuke
	 * does not get called in this scenario.
	 */
}

/*
 * Tests that InvalidateSystemCaches does not reset the local MDVSN cache when
 * there is no transaction invalidation information (not in a transaction, special
 * backend)
 */
void test__InvalidateSystemCaches__resets_mdvsn_no_xact(void **state)
{
	will_be_called(ResetCatalogCaches);
	will_be_called(RelationCacheInvalidate);

	/*
	 * Set transInvalInfo to NULL to simulate an case where we don't have
	 * a transaction invalidation information (e.g. auxiliary process)
	 */
	transInvalInfo = NULL;

	InvalidateSystemCaches();

	/*
	 * Part of the test we're implicitly asserting that mdver_local_mdvsn_nuke
	 * does not get called in this scenario.
	 */
}

int
main(int argc, char* argv[]) {
	cmockery_parse_arguments(argc, argv);

	const UnitTest tests[] = {
			unit_test(test__sizeof__TransInvalidationInfo),
			unit_test(test__sizeof__InvalidationListHeader),
			unit_test(test__sizeof__InvalidationChunk),
			unit_test(test__InvalidateSystemCaches__resets_mdvsn_enabled),
			unit_test(test__InvalidateSystemCaches__resets_mdvsn_disabled),
			unit_test(test__InvalidateSystemCaches__resets_mdvsn_no_xact)
	};
	return run_tests(tests);
}

redpajama_set_name: RedPajamaGithub