Are U.S. lung cancer mortality rates converging?

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Are U.S. lung cancer mortality rates converging? Sediq Sameem Department of Accounting & Finance, Alabama A&M University, Normal, AL 35762, USA

a r t i c l e

i n f o

Article history: Received 22 September 2018 Received in revised form 17 April 2019 Accepted 16 June 2019 Available online xxx JEL classification: I12 I18 J11

a b s t r a c t This study considers whether lung cancer death rates are converging in the United States. Using annual data for the contiguous U.S. states during 1968–2016, the findings indicate the occurrence of ˇ -convergence in lung cancer deaths among various demographic groups. The convergence is more prevalent in the Midwest, non-Black Belt region, and non-coastal states. Given the declining trends in lung cancer incidence and death rates, such a convergence implies an improvement in lung cancer prevention strategies of the states that were not doing quite well in the past. The findings have important policy implications regarding the dissemination of lung cancer prevention strategies across specific groups and regions. © 2019 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

Keywords: Convergence Smoking Lung cancer

1. Introduction Lung cancer is becoming increasingly ubiquitous in the United States. It is the most common type of cancer among males and the second most common cancer (preceded by breast cancer) among females (American Cancer Society, 2018). It is one of the deadliest cancers with a five-year survival rate of only up to 18% (American Lung Association, 2016; Mannino, Ford, Giovino, & Thun, 1998). Smoking contributes to 80% and 90% of lung cancer deaths in women and men, respectively, making lung cancer not only one of the leading causes of death but perhaps the leading preventable cause of cancer deaths (Ziebarth, 2018; CDC, 2017). In the United States, more than 480,000 deaths have been attributed to cigarette smoking each year, which is equivalent to nearly one in every five deaths. Actually, smoking causes more deaths each year than firearm-related incidents, motor vehicle injuries, human immunodeficiency virus (HIV), alcohol use, and illegal drug use combined (CDC, 2017). Voluminous literature looms around the impact of smoking on birth outcomes (Bharadwaj, Johnsen, & Løken, 2014; Cooper & Pesko, 2017), the impact of public smoking bans on smoking behavior (e.g. Abadie, Diamond, & Hainmueller, 2010; Adams, Cotti, & Fuhrmann, 2013; Adda & Cornaglia, 2010; Anger, Kvasnicka, & Siedler, 2011; Evans, Farrelly, & Montgomery, 1999;

E-mail address: [email protected]

Friedman, 2015; Lanoie & Leclair, 1998; Sargent, Shepard, & Glantz, 2004; Shetty, DeLeire, White, & Bhattacharya, 2011; Wildman & Hollingsworth, 2013), the impact of such bans on the revenue of hospitality sector (Marti & Schläpfer, 2014; Pieroni, Daddi, & Salmasi, 2013; Scollo, Lal, Hyland, & Glantz, 2003), and on some unforeseen negative impacts such as increase in obesity (Courtemanche, Tchernis, & Ukert, 2018; Liu, Zhang, Cheng, & Wang, 2010; Wildman & Hollingsworth, 2013).1 Yet, very little attention has been given to lung cancer in economic literature (Coccia, 2015; Hendryx, O’Donnell, & Horn, 2008; Jemal, Travis, Tarone, Travis, & Devesa, 2003; Lewis, Check, Caporaso, Travis, & Devesa, 2014; Park et al., 2010; Torre et al., 2016; Ziebarth, 2018). Contributions of this study to the literature are threefold. First, this is the first study that explores whether lung cancer mortality is converging in the United States using state level data from 1968 through 2016. Though the idea of convergence stems from the neoclassical growth theory which predicts convergence of incomes across all developed and developing regions (Barro, 1991, Barro & Sala-i-Martin, 1992; Baumol, 1986; Kuznets, 1955; Mankiw, Romer, & Weil, 1992), one can also find applications of its methodology in areas other than income. For instance, Parsley and Wei (1996) look

1 There is also plenty of research on the impact of cigarette advertising on smoking behavior (Goldberg, 2003; Kelly, Slater, & Karan, 2002; Pollay et al., 1996; Pierce & Gilpin, 1995; Taylor & Bonner, 2003). On the other hand, see Saffer and Chaloupka (2000) for the literature on the impact of advertisement ban of smoking on health outcomes.

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Table 1 Lung Cancer Incidence and Death Rates in the United States.

Males Incidence Rate Death Rate Females Incidence Rate Death Rate

U.S.

Northeast

Midwest

South

West

68.1 52.0

67.6 48.3

74.1 58.1

75.9 58.5

49.8 38.2

50.8 34.7

54.4 33.9

56.2 39.0

51.6 36.2

40.9 28.7

Note: The rates are per 100,000 people. Source: CDC, 2017.

at the convergence of prices across U.S. cities.2 Li and Wang (2016) explore the convergence of U.S. obesity rates during 1996–2013. Cook and Winfield (2013) look for convergence of crime across the U.S. states during 1960-2009. A closely related study to ours is that of Rivadeneira and Noymer (2017) that analyses lung cancer mortality by age and sex in the United States during 1959-2013. Their findings suggest that log lung cancer mortality rates are quadratic by age, fitting into a quadratic-Gompertz model that shows an early exponential growth followed by slower growth rate which reaches a plateau by age 70. Based on sex, they find different patterns of lung cancer mortality over the sample period with female pattern converging to males since the 1960s. Our sample starts at 1968 and the results lend support to their findings. Others such as Jemal et al. (2003), Lewis et al. (2014) and Torre et al. (2016) find convergence in lung cancer mortality rates among different races, birth cohorts, and histologic types in the United States using national data.3 Second, this study explores convergence of lung cancer deaths across different geographic locations. Table 1 displays important statistics about lung cancer incidence (number of people who gets cancer) and death rates in the U.S. In general, both rates are higher for men than for women although there are variations across the regions. For men, both incidence and death rates are highest in the South whereas for women, they are highest in the Midwest. The rates are lowest in the West for both men and women. Therefore, this study looks at the convergence of lung cancer mortality across different regions. Following Li and Wang (2016), the regions considered here include Northeast, Midwest, South, and West. In addition, regional differences in health and consequently mortality may result not only from socioeconomic factors such as income, poverty, and educational attainment, but also from cultural norms, structural limitations, and disparities in access to healthcare (Saint Onge & Krueger, 2017). Such factors could also play important roles between the health status of whites and African Americans. Hence, the study introduces another classification of region based on the predominance of a particular race, that is, Black Belt region versus non-Black Belt region. The former includes most of the Southern states where the plantation system, with its large number of black slaves, predominated before the Civil War. Furthermore, residence in proximity to the coasts is indicated to be associated to better health and wellbeing (Wheeler, White, Stahl-Timmins, & Depledge, 2012), higher cognitive ability of infants (Lynn & Yadav, 2015), and better nutritional status of children (Shin, Aliaga-Linares, & Britton, 2017). Also, Mitchell and Popham (2008) and Maas, Verheij, Groenewegen, De Vries, and Spreeuwenberg (2006) indicate that access to good environments may play a part in reducing health inequalities. Therefore, the present study also looks at the convergence of lung cancer mortality across the states located on east and west coasts and those located elsewhere.

2 For detailed literature on price convergence and the law of one price, see Goldberg and Verboven (2005) and Yazgan and Yilmazkuday (2011). 3 Brenner et al. (2019) find an inverse relationship between alcohol consumption and the overall risk of lung cancer.

Last but not the least, cancer deaths are one of the common exceptions in the literature on the pro-cyclical pattern of mortality, that is, many major types of mortality rates have been found to be declining during recessionary periods, but cancer deaths appear to be unrelated to transitory cyclical fluctuations in the economy (Ariizumi & Schirle, 2012; Gonzalez & Quast, 2010; Gerdtham & Ruhm, 2006; Granados, 2005; Heutel & Ruhm, 2016; Neumayer, 2004; Ruhm, 2015, 2000; Sameem & Sylwester, 2017). This study looks at the long term trends of lung cancer mortality which is a major share of all cancer deaths, hence, it could possibly fill in the gap in this literature. The results indicate the presence of ˇ -convergence in lung cancer mortality rates in the United States among various demographic groups such as males, females, whites, African Americans, 20–44 year-olds, 45–64 year-olds, and 65+ in the United States. The findings suggest that mortalities due to lung cancer are reverting to their cross sectional mean, which is used as a benchmark in this analysis. Since the lung cancer incidence and death rates are declining in the United States over most of the sample period, such a convergence would imply that some states that were not as successful in the prevention of lung cancer in the past are actually catching up to those states that are doing better now. The results are stronger for females, African Americans, and those aged 20–44 year-olds. Regionally, the evidence of convergence is more prevalent in Midwest. Comparison of non-Black Belt with the Black Belt region suggests faster convergence in the former whereas comparison of coastal states with non-coastal ones suggests faster convergence in the latter. The reductions in smoking patterns and pollutions in the United States could provide an explanation to these findings. For policy relevance, a significant implication of the study is that due to convergence, the same policy can be applied more successfully for the entire population because the presence of convergence also implies the presence of some internal mechanisms among states/regions that could signal lower costs of implementation/dissemination of strategies that tackle the issue of lung cancer. At the same time, the regional variations in convergence could also provide an opportunity for efficient allocation of resources across locations. The remainder of the paper is organized as follows: Section II presents data. Section III explains empirical methodology. Section IV explains results and policy implications. Section V provides conclusions and limitations of the study.

2. Data The study sample spans nearly five decades (49 years) from 1968 through 2016, and includes all contiguous states, excluding Alaska, Hawaii and Washington D.C. Data come from five sources: (a) the Compact Mortality Files (CMF) of the Centers for Disease Control and Prevention (CDC), (b) National Cancer Institute (NCI), (c) Environmental Protection Agency (EPA), (d) the Bureau of Labor Statistics (BLS), and (e) the Bureau of Economic Analysis (BEA). The data on lung cancer mortality and population demographics are

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Fig. 1. Trends of Lung Cancer.Mortality Rates in the US for Total Population during 1968–2016.

Fig. 2. Trends of Lung Cancer Mortality Rates in the US by Gender during 1968–2016.

obtained from the CMF.4 Lung cancer incidence rate data is obtained from Surveillance, Epidemiology, and End Results (SEER) program of the NCI and it is available from 1975 to 2015.5 Unless otherwise stated, all rates are calculated as the number of deaths or incidents per 100,000 people in the United States. It is worth mentioning that CDC marks death rates “unreliable” when death count is less than 20, and “suppressed” when death count is less than 10.6 Such rates are dropped altogether in this analysis.7 We also use emissions data which is obtained from the EPA and it is available from 1980 to 2015.8 For the purpose of conditional convergence, data for some control variables such as state personal income and unemployment rate are obtained from the remaining two sources.9 ,10 Fig. 1 illustrates the pattern of growth of lung cancer mortality rates for the total population. The growth rates appear to be declining over the sample period, implying the existence of ˇ convergence. Similarly, Figs. 2 and 3, respectively, show declining growth rates of lung cancer mortality rates by gender and race. The size of marker (circle, diamond, square, triangle) is scaled according to the size of the weights of state-average death rates. Table 2 reports average lung cancer mortality rates. The upper panel of the table displays average rates for the United States as a whole as well as for the major four regions. Consistent with Siegel, Miller, and Jemal (2018) and CDC (2017), average mortality rates are higher for males than those for females throughout the regions.

4

Data link: http://wonder.cdc.gov/mortsql.html https://seer.cancer.gov/faststats/selections.php?#Output 6 CDC, 2017. https://wonder.cdc.gov/wonder/help/cmf.html#Age Group 7 As opposed to Coccia (2014), the data used here is based on site-specific classification of lung cancers rather than the histologic classification into non-small cell and small cell lung cancers. 8 Data Link: https://www.epa.gov/air-emissions-inventories/air-pollutantemissions-trends-data 9 Data link: http://www.bls.gov/lau/ 10 Data link: https://www.bea.gov/itable/ 5

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Fig. 3. Trends of Lung Cancer Mortality Rates in the US by Race during 1968–2016.

Consistent with Rivadeneira and Noymer (2017), most of the deaths due to lung cancer are among the elderly. The lower panel of table indicates that lung cancer deaths are higher, on average, in the Black Belt region as compared to non-Black Belt region. The Black Belt region covers most of the southern states including Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia. The table also provides comparison of means between coastal states and non-coastal ones. “Coasts” include states that are located in east and west coasts, that is, California, Connecticut, Delaware, Florida, Georgia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Rhode Island, South Carolina, Virginia, and Washington. In general, average lung cancer deaths appear to be higher in coastal states, except for young age group and African Americans. Given such disparate statistics, it is plausible that other characteristics, including the convergence rates, could also differ across such geographical locations. Hence, further examination is warranted. 3. Methodology The concept of convergence refers to merging a collection of series over time. The most common types of convergence in the literature are ˇ -convergence and  -convergence. When a series with a lower initial value experiences faster growth than a series with a higher initial value, there is ˇ -convergence. When the dispersion of a series, as measured by the coefficient of variation or more simply standard deviation, falls over time, there is  -convergence. The former is considered a necessary but not a sufficient condition for the latter (Young, Higgins, & Levy, 2008). The formal model of analysis here is ˇ -convergence. To examine convergence for lung cancer mortality rate in the United States, two models have been used. First, consistent with Barro and Sala-i-Martin (1990) and Li and Wang (2016), the following simplistic model of convergence is applied.  LCM it = ˛ + ˇ LCM i0 + εit

(1)

The model in (1) relates changes in the natural logarithm of lung cancer mortality (LCM) rate in state i over sample period t, or simply the growth rate (LCM it ), to the natural logarithm of the initial level of mortality rate in state i (LCM i0 ), and an error term (εit ). LCM it is defined as the percentage difference in the rate of mortality at time t between state i (LCMit ) and the benchmark – cross sectional ¯ t ).11 One could also use median average mortality rate at time t (LCM or a particular cross sectional unit (state) as a benchmark. A neg-

11

Mathematically, LCM it = ln

 LCMit  ¯ t LCM

where LCM it the lung cancer mortality rate

¯ t is the cross sectional average mortality rate at time t. in state i at time t and LCM

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Table 2 Average Lung Cancer Mortality Rate in the United States.

All Males Females Whites Afr.Americans Young Age Middle Age Old Age

All Males Females Whites Afr.Americans Young Age Middle Age Old Age

United States

Northeast

Midwest

South

West

49.3 64.7 34.6 52.2 40.9 3.1 75.6 258.7

52.2 67.0 38.5 54.8 36.7 3.0 74.0 261.6

48.6 64.0 33.8 50.1 43.3 2.8 72.3 248.9

55.4 75.4 36.5 60.0 43.5 3.4 90.3 283.3

38.7 48.2 29.5 40.8 32.7 2.3 59.0 231.0

United States

Blackbelt

Nonblackbelt

Coasts

Noncoasts

49.3 64.7 34.6 52.2 40.9 3.1 75.6 258.7

53.3 73.8 34.0 58.4 40.7 3.3 88.7 275.0

48.1 62.0 34.8 50.3 41.0 2.9 71.7 253.8

51.1 66.3 36.8 54.8 37.9 2.9 77.2 265.7

48.2 63.8 33.3 50.6 43.2 3.2 74.6 254.4

Note: The mortality rates are the number of deaths per 100,000 people.

ative and statistically significant ˇ with a value between 0 and 1 manifests unconditional ˇ -convergence. Some of the caveats of model (1) include, among others, (a) lack of cross sectional and time fixed effects to account for spatial heterogeneities and seasonal effects, (b) reliance of inference on the sample starting value only instead of including all the previous values, and (c) possibility of serial correlation. Therefore, following Barro (1991); Barro and Sala-i-Martin (1992); Parsley and Wei (1996), and Enamorado, López-Calva, and Rodríguez-Castelán (2014), a modified version of model (1) is employed here as the main model of analysis. This model is commonly used in the analysis of price convergence and the law of one price.  LCM it = ˛i + ıt + ˇ LCM it−1 +

s(k) i=1

  LCM it−1 + εit

Males Females Whites Afr.Americans Young Age

Akaike Information Criterion (AIC) is used to determine the number of lags. When dealing with panel data, model errors in different time periods for a given cluster (state here) may be correlated, while model errors for different clusters are assumed to be uncorrelated, and failure to control for within-cluster error correlation can lead to misleading small standard errors, large t-statistics, and consequently misleading inferences (Cameron & Miller, 2015). 13

All

(2)

The model in (2) is more inclusive in a sense that it replaces the initial level of mortality rate in state i (LCM i0 ) by incorporating all the previous levels of state i mortality rates (LCM it−1 ) in addition to the inclusion of state (˛i ) and time (ıt ) fixed effects and the lag values of the dependent variable (LCM it−1 ).  is the first difference operator and s(k) represents the number of lags included in our model to control for serial correlation.12 The main coefficient of interest is. Again, a negative and statistically significant ˇ with a value between 0 and 1 indicates the presence of unconditional ˇ -convergence in lung cancer mortality rates across the sample. The various demographic groups considered for analysis here include all people, males, females, whites, African Americans, 20–44 year-olds, 45–64 year-olds, and 65 and above. Model (2) is first estimated for all these groups across the United States, and then, for each of the four regions separately. It is also estimated for the Black Belt region and coastal states and for their counterparts, respectively. All specifications are estimated using wild cluster bootstrap standard errors in order to control for any possible geographical correlation between mortality rates as well as for smaller cluster sizes when dealing with different regions (Cameron & Miller, 2015; Cameron, Gelbach, & Miller, 2008; Enamorado et al., 2014).13

12

Table 3 Simple ␤-Convergence of Lung Cancer Mortality Rate.

Middle Age Old Age

(1) ␤-Coefficient

(2) # Observations

(3) R Squared

−0.009** (0.004) −0.010** (0.005) −0.018* (0.010) −0.013*** (0.004) −0.016* (0.010) −0.023 (0.022) −0.002 (0.009) −0.017** (0.008)

2,437

0.002

2,437

0.002

2,252

0.001

2,437

0.004

1,438

0.002

1,368

0.001

2,400

0.000

2,400

0.002

Notes: Sample period is 1968–2016. *** p < 0.01. ** p < 0.05. * p < 0.1.

There are two main streams of research in ˇ -convergence literature; (1) unconditional convergence and (2) conditional convergence. The results obtained from models (1) and (2) indicate the former. The convergence in lung cancer could vary due to differences in income levels or population demographics. Therefore, we also have results for convergence when it is conditional upon certain variables such as the percentages of whites, African Americans, age 20–44, age 45–64, age 65+, state unemployment rate, and the natural logarithm of personal income. 4. Results The results are presented in accordance to the order of the convergence models. Table 3 displays results that are based on model (1). As most of the ˇ coefficients are negative, statistically significant, and between 0 and 1, they are consistent with the model specification, hence, indicating the occurrence of ˇ -convergence in the lung cancer mortality rates across the United States for the population as a whole, for males and females, for whites and African Americans, and for the senior citizens, during the sample period 1968-2016. Given the declining trend of lung cancer, the results suggest that states with higher (lower) lung cancer mortality rates

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Table 4 ␤-Convergence for Different Demographic Groups in Different Regions.

All Males Females Whites Afr.Americans Young Age Middle Age Old Age

(1) United States

(2) Northeast

(3) Midwest

(4) South

(5) West

−0.137*** (0.014) −0.175*** (0.017) −0.265*** (0.032) −0.150*** (0.016) −0.340*** (0.032) −0.481*** (0.035) −0.222*** (0.018) −0.243*** (0.022)

−0.116*** (0.036) −0.176*** (0.055) −0.275*** (0.046) −0.174*** (0.040) −0.645*** (0.101) −0.729*** (0.115) −0.396*** (0.081) −0.484*** (0.070)

−0.301*** (0.055) −0.301*** (0.053) −0.579*** (0.061) −0.307*** (0.050) −0.531*** (0.085) −0.576*** (0.070) −0.605*** (0.088) −0.493*** (0.066)

−0.068*** (0.014) −0.095*** (0.013) −0.118*** (0.021) −0.092*** (0.017) −0.202*** (0.037) −0.537*** (0.053) −0.105*** (0.022) −0.137*** (0.025)

−0.177*** (0.029) −0.267*** (0.043) −0.366*** (0.061) −0.177*** (0.036) −0.584*** (0.132) −0.506* (0.209) −0.456*** (0.055) −0.481*** (0.065)

Notes: Average rate of mortality for each sub-group is used as the benchmark. All regressions contain state and year fixed effects. Sample period is 1968-2016. Wild cluster bootstrap standard errors are in parentheses. ***p < 0.01. **p < 0.05. *p < 0.1.

Table 5 ␤-Convergence for Different Demographic Groups in Black Belt and Coastal States.

All Males Females Whites Afr.Americans Young Age Middle Age Old Age

(1) United States

(2) Blackbelt

(3) Nonblackbelt

(4) Coasts

(5) Noncoasts

−0.137*** (0.014) −0.175*** (0.017) −0.265*** (0.032) −0.150*** (0.016) −0.340*** (0.032) −0.481*** (0.035) −0.222*** (0.018) −0.243*** (0.022)

−0.050*** (0.015) −0.074*** (0.015) −0.082*** (0.026) −0.068*** (0.018) −0.101*** (0.034) −0.552*** (0.055) −0.082*** (0.028) −0.113*** (0.030)

−0.161*** (0.017) −0.209*** (0.021) −0.324*** (0.033) −0.173*** (0.021) −0.519*** (0.043) −0.446*** (0.038) −0.267*** (0.027) −0.285*** (0.027)

−0.083*** (0.018) −0.114*** (0.020) −0.130*** (0.023) −0.091*** (0.020) −0.277*** (0.041) −0.558*** (0.058) −0.179*** (0.026) −0.207*** (0.031)

−0.213*** (0.025) −0.264*** (0.026) −0.434*** (0.047) −0.214*** (0.026) −0.422*** (0.042) −0.564*** (0.038) −0.292*** (0.031) −0.306*** (0.029)

Notes: Average rate of mortality for each sub-group is used as the benchmark. All regressions contain state and year fixed effects. Sample period is 1968-2016. Wild cluster bootstrap standard errors are in parentheses. Blackbelt includes Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia. Coasts include states located in east and west coasts, that is, California, Connecticut, Delaware, Florida, Georgia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Rhode Island, South Carolina, Virginia, and Washington. *** p < 0.01. ** p < 0.05. * p < 0.1.

in the past are catching up with those having lower (higher) rates recently. Tables 4 and 5 report results based on model (2), the main model of the study. The first column of Table 4 indicates the occurrence of ˇ -convergence in lung cancer mortality rates in the U.S. for all demographic groups considered. This implies that such mortalities are reverting to their cross sectional means. In other words, on average and in the entire United States (see Table 4, column 1), the gap between lung cancer mortality rates and their cross sectional means has been decreasing at 13.7%, 17.5%, 26.5%, 15%, and 34% for total population, males, and females, whites, and African Americans, respectively. Other coefficients could be interpreted in similar fashion. The more prevalent convergence among females and African Americans is inversely related to the higher incidence rate among these groups as reported by the American Lung Association, 2016; Mannino et al., 1998 and Patel, Cheng, and Gomez

(2015).14 Based on age, the findings show stronger convergence among the young age group (20–44 year-olds) as compared to the middle aged (45–64 year-olds) and the elderly (65+).15 The remaining columns (2–5) of Table 4 display the results for different regions of the United States. The convergence rate is highest in the Midwest for all the demographic categories considered, except for young age population whose convergence is highest in the Northeast followed by the Midwest. Table 5 reports results for ˇ -convergence while comparing Black Belt versus non-Black Belt region as well as coastal versus

14 American Lung Association, 2016. Lung cancer fact sheet. http://www.lung.org/ lung-health-and-diseases/lung-disease-lookup/lung-cancer/resource-library/lungcancer-fact-sheet.html 15 Similar classification of age groups is used in Sameem and Sylwester (2017) and Ruhm (2000).

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non-coastal states. Column 1 is repeated from Table 4 mainly for comparison. Columns 2 and 3 show the results of convergence for lung cancer death rates between Black Belt and non-Black Belt regions; convergence rate is lower in the former. Columns 4 and 5 show results of convergence between coastal and non-coastal states; convergence rate is higher in the latter, which is consistent with the findings of Wheeler et al. (2012) that residence in proximity to the coasts is associated with better health and wellbeing. The results are consistent to the inclusion of several control variables such as the percentages of whites, African Americans, 20–44 yearolds, 45–64 year-olds, 65+ year-olds, state unemployment rate, and the natural logarithm of personal income. The results are available upon request. The findings from these convergence models could shed light on how states are doing regarding the prevention of lung cancer compared to their previous levels and/or other states. Depending on whether the cross sectional means (benchmark) are rising or falling, the results could be interpreted in different ways. For instance, the presence of convergence in lung cancer mortality rates may imply that the previously successful states in prevention of lung cancer deaths are not doing as good as before if the cross sectional means are trending downward or the states that were not as successful before are improving in terms of preventing lung cancer deaths if the cross sectional means show an upward trend or a combination of the two. Furthermore, faster speed of convergence of lung cancer mortality rates within a given group or a region will reflect faster dissemination of information due to some internal mechanisms that drive the results. Therefore, faster diffusion of implemented policies represents an opportunity for policymakers to save resources to be used when the diffusion of information is not so smooth. Although the focus of the paper is the convergence of lung cancer mortality rates in the United States it is important to explore some probable causes of such convergence. Since, on average in the US, the lung cancer incidence and death rates are declining particularly since early 1990s, the presence of convergence would imply that some states which were not as successful in lung cancer prevention in the past (states with lung cancer deaths higher the benchmark) are getting closer to those states that are doing better on average (states with lung cancer deaths lower than the benchmark). One explanation for this could be the reduction in smoking prevalence among persons aged greater than or equal to 18 years from 42.2% in 1965 to 15.5% in 2016 (CDC).16 Contributing factors to the reduction in smoking include increased taxation on tobacco products, restrictions on cigarette advertising, legislations restricting smoking in public places, etc. The decline in smoking, a leading cause of lung cancer, can also suggest a reduction in lung cancer incidence and death rates. Alonso et al. (2018) provide similar explanation for reduction in lung cancers in Uruguay. Another explanation could come from the decline in emission levels in the United States. Coccia (2015) suggests that the higher level of technological innovations and industrialization in OECD countries tends to spread several environmental genotoxic carcinogens that appear to support the growing incidence of a variety of cancers in such societies. If increases in mutagens and genotoxic carcinogens that are generated by pollution from industrialization are considered to have caused increase in cancers, then a reduction in the emission levels would imply decrease in such diseases as well. We explore this discussion by looking at the trends of different measures of pollutants and their association with lung cancer incidence. The different emissions reported by EPA include Carbon Monoxide (CO), Nitrogen Oxide (NOx), Particulate Matter with

16

https://www.cdc.gov/mmwr/preview/mmwrhtml/mm4843a2.htm

Fig. 4. Trends of Emissions in the US during 1980–2015.

diameters that are generally 10 (PM10) and 2.5 (PM2.5) micrometers and smaller, Sulfur Dioxide (SO2), Volatile Organic Compound (VOC), and Ammonia Emissions (NH3).17 Table 6 displays positive correlation among these emissions and lung cancer incidence rate. Also, Fig. 4 shows a declining trend in these emissions. The combination of these two results would also indicate declining pattern of lung cancer incidence and death rates. In sum, the declining patterns of smoking and emission levels in the United States might have potentially contributed to the reduction in lung cancer mortality rates that could have driven our results. 5. Conclusion Applying the analysis of convergence to the trends of lung cancer mortality rates, this study finds the occurrence of ˇ -convergence among various demographic groups such as males, females, whites, African Americans, 20–44 year-olds, 45–64 year-olds, and 65+ in the United States. Using state level annual data on mortality rates during 1968–2016, the results suggest that mortalities due to lung cancer are reverting to their cross sectional means (benchmark). Since the lung cancer incidence and death rates are trending downward in the United States, the presence of such a convergence would imply that some states that were not as successful in prevention of lung cancer in the past are actually converging to those states that are doing better now. The results are stronger for females, African Americans, and those aged 20–44 year-olds. Regionally, the evidence of convergence is more prevalent in Midwest. Comparison of non-Black Belt with the Black Belt region suggests faster convergence in the former whereas comparison of coastal states with non-coastal ones suggests faster convergence in the latter. The findings of this study provide important policy implications for lung cancer intervention. First, the presence of convergence indicates the ease in implementation of policies at the Federal level because the same policy could be applied more efficiently and effectively to the entire nation due to inherent mechanisms that drive the convergence results. For instance, if two groups have different rates of convergence, then a single policy might be more appropriate for a group which has higher convergence rate. Since convergence rates are higher for females, African Americans, and the 20–44 year-olds, a policy targeting such demographic groups might be applied in a cost effective way. Second, although there is an evidence of convergence in lung cancer deaths in the entire nation, the rates vary across the regions. For example, convergence rates are more prevalent in Midwest, non-Black Belt region, and non-coastal states, which would imply faster diffusion or implementation of policies that are targeted towards lung cancer prevention in these locations. Hence, this would provide an oppor-

17 Similar pattern is shown by the association between lung cancer incidence rate and carbon dioxide.

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tunity for policy makers to save resources to be used where the diffusion of information is not so smooth. Third, except for the West, there in an inverse relationship between a region’s lung cancer death rates and the convergence rates in that region. For instance, lung cancer mortality rate per 100,000 people is lowest in the Midwest whereas in the same region the convergence rate is highest. This association would imply that where there is an opportunity for a single policy to work smoothly, the lung cancer mortality rates are low. Last but not the least, public policies should not ignore the negative impacts of pollutants on environment and health status of the people. This study is not free from limitations. First, it uses a much coarse data at the state level of the United States. Future work using a more refined data such as at county level could provide more nuanced results because the degree of within state-state variation is likely to be larger than within-county variation allowing for more heterogeneity within the unit of analysis. Second, this study is not considering the effect of a specific public policy such as cigarette taxes, smoking bans, minimum wages, etc. As mentioned in the outset, plenty of research has been done on such dimensions. This does not, however, reduce from the importance of the topic of convergence itself as the mere existence of convergence can provide vital information regarding efficient allocation of resources for the policymakers. Third, the benchmark for comparison in this study is the cross sectional means. Supported by theoretical explanation, one can also use other benchmarks such as median or a particular state or region, which could lead to potentially more nuances. Lastly, the study is based on the analysis of the U.S. data. Similar methodology could be applied to other countries as well. For instance, comparative studies for Uruguay (Alonso et al., 2018), Australia (Blizzard & Dwyer, 2001; Luo et al., 2018; Wilson et al., 2018), England (Peto et al., 2000), and United Kingdom (Brown et al., 2018) have looked at the convergence, trends, and risk factors of lung cancer.18 Alonso et al. (2018) analyze the impact of anti-smoking measures in Uruguay and their impact on the reduction in lung cancers in Uruguay. Similarly, Luo et al. (2018) provide explanation for predicted decline in lung cancer mortality rates in Australia based on models incorporating tobacco consumption. Overall, these studies lend support to our findings for the United States. Declaration of interest None. References Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505. Adams, S., Cotti, C., & Fuhrmann, D. (2013). The short-term impact of smoke-free workplace laws on fatal heart attacks. Applied Economics, 45(11), 1381–1393. Adda, J., & Cornaglia, F. (2010). The effect of bans and taxes on passive smoking. American Economic Journal Applied Economics, 2(1), 1–32. ˜ Alonso, R., Pineros, M., Laversanne, M., Musetti, C., Garau, M., Barrios, E., . . . & Bray, F. (2018). Lung cancer incidence trends in Uruguay 1990–2014: An age-period-cohort analysis. Cancer Epidemiology, 55, 17–22. American Cancer Society, 2018. Lifetime risk of developing or dying from cancer. https://www.cancer.org/cancer/cancer-basics/lifetime-probability-ofdeveloping-or-dying-from-cancer.html American Lung Association, 2016. Lung cancer fact sheet. http://www.lung.org/ lung-health-and-diseases/lung-disease-lookup/lung-cancer/resource-library/ lung-cancer-fact-sheet.html Anger, S., Kvasnicka, M., & Siedler, T. (2011). One last puff? Public smoking bans and smoking behavior. Journal of Health Economics, 30(3), 591–601. Ariizumi, H., & Schirle, T. (2012). Are recessions really good for your health? Evidence from Canada. Social Science & Medicine, 74(8), 1224–1231.

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