this analysis examines the impact of the level of unemployment on levels of flu activity in a given area . i use reports of overall influenza activity provided by the centers for disease control and prevention ( cdc ) . influenza surveillance coordinators and epidemiologists in each state summarize data on influenza activity levels for cdc each week throughout the regular flu season ( october through mid may ) . their summaries are based on a number of data inputs , including virologic surveillance reports from public health laboratories , medical centers , and outpatient care sites , as well as vital statistics , and estimates derived from diffusion models . state epidemiologists cull and analyze these data then provide a summary assessment of flu activity levels within their state each week :  no activity,sporadic,local,regional, or  widespread. the  widespread classification is of particular interest , as it indicates serious levels of flu activity , which have immediate public health implications . influenza is classified as widespread when there is evidence of  outbreaks of influenza or increases in [ influenzalike illness ] , and recent laboratoryconfirmed influenza , in at least half the regions of the state. 
  the regional classification is similar to the widespread classification but indicates that influenza activity is only evident in more than two but fewer than half of the regions of a state . local refers to outbreaks of influenza a single region of the state , while sporadic activity reflects small numbers of laboratoryconfirmed influenza cases reported or a single outbreak , but no recent increase in cases of influenzalike illness . these classifications lack precision , but they are less prone to selection bias caused by differential access to health care that plague mortality and hospital data . i record these classifications for each week comprising the 19992000 through the 20102011 flu seasons . in my analysis , regional activity is not trivial and is often a precursor of widespread activity , i also conduct a parallel set of analyses in which i specify the risk of the lower threshold of widespread or regional influenza activity during a given time period . statespecific unemployment data are available from the bureau of labor statistics ( bls ) via the monthly current population surveys ( cps ) . the cps is a monthly survey of about 50  000 households conducted by the bureau of the census . an individual is classified as unemployed if s / he does not have a job , s / he has actively looked for work at some point in the past 4  weeks , and s / he is currently available for work . these cases are aggregated each month by state and expressed as a percent of the total labor force . this definition focuses on people who want to be working but are not and thereby captures the relative size of the most affected segment of the population . the bls often adjusts unemployment rates for typical seasonal unemployment ( e.g. because of seasonal fluctuation in construction , agricultural , or tourism work ) . i present analyses that use the original unadjusted rates , as seasonal unemployment is also a source of uncertainty , stress , and poverty in some areas . , 
  however , supplementary analyses that use seasonally adjusted unemployment rates yield comparable results ( available upon request ) . centers for disease control and prevention reports state influenza activity levels on a weekly basis during the regular flu season , while statelevel unemployment data are only available by month . to estimate the relationship between unemployment and influenza activity levels , i construct a set of statemonth observations for all flu seasons between october 1999 and december 2010 . i use the weekly influenza activity reports to determine the highest level of flu activity reported for each state during each month of the study period . the analysis includes all months between october and may for each flu season , yielding 91  months of observations . the primary dependent variable , y , is a dichotomous indicator of whether influenza activity was ever regional during the month in question . given that these outcomes are dichotomous and vary over time , a logistic timeseries crosssection regression model is appropriate : where p is the probability that flu is widespread ( y  =  1 ) in a given state during a given monthyear . x includes a set of 11 predictors of influenza that are measured for a given state in the month prior to the observation ( described later ) , where the state s unemployment rate is the primary predictor of interest . because influenza has a predictable seasonal pattern , t is a set of 17 fixed effects that specify the time period of the observation , including six separate parameters for the month of the observation ( e.g. january ) as well as 11 parameters for the flu season in which it occurs ( e.g. 20092010 ) . to capture unobserved unit heterogeneity , the model incorporates fixed effects for each state , s , which control for factors that could affect the overall rate of influenza spread in a given state , such as geographic size , transportation infrastructure , number of large cities , as well as differences in influenza surveillance strategies and resources . finally , to attenuate autocorrelation across time points within states and to capture the survivability of the virus , the model also includes y 
 s , t1 , which is an indicator of whether flu was widespread in the state in question in the month prior to the outcome observation . first , the seasonality of both influenza activity and the unemployment rate has been linked to climate , as the survivability of influenza viruses is greater where air is cool and dry . the peak of influenza during the winter months may also be due to behavioral changes like more frequent congregation of groups indoors . failure to take climate into account may therefore result in omitted variable bias that yields misleading estimates of the impact of unemployment on influenza . as proxies for climatic factors that may affect influenza survivability , i use data on temperature ( f ) and precipitation ( in inches ) compiled by the northeast regional climate center ( nrcc ) . i average these across all days and stations to generate mean averages for each statemonth for the entire period . including both average temperature and precipitation variables simultaneously resulted in variance inflation , but removing one or the other does not change the results . spatial autocorrelation is a potential concern , as the influenza activity level in one state could reflect broader regional factors . states are not only exposed to similar climates as adjacent states , they are also exposed to influenza viruses circulating within adjacent states because of interstate travel associated with daily commutes and commerce . , i identify all states that border a given state and calculate the average influenza activity levels ( measured ordinally : 0  =   no activity, 3  =   widespread ) of those states in the previous month . this measure , which is included in x , is an approximation of the extent to which influenza is encroaching on a given state at a given time . a number of other covariates may vary with both influenza activity and unemployment . using monthly cps data , i created measures of the size of the labor force , percent urban ( spatial concentration of the population ) , percent collegeeducated and percent impoverished , percent married , average household size , and age for the month prior to the observation in question . these are statemonthspecific covariates that are also included in x. because these measures are so sluggish and do not change much from month to month within states , they are effectively collinear with the state fixed effects and therefore give rise to major variance inflation . norming these variables within states by expressing them as zscores for the state in question ( e.g. for a given state , how many standard deviations below / above the norm was the observed percent of residents who were collegeeducated in a given month , as compared with all other months observed for that state ) eliminated this multicollinearity . this restricts outcomes to be predicted by the models to those that occur during the 7month period between november and may , which is when epidemic peaks are most evident . 
  with 48 contiguous states and an observation for each of 7  months between the 19992000 flu season , and for the first 2  months of the 20102011 flu season , this equals 79 time periods , and a total of 3792 possible statemonths of observation . there are 80 observations missing because of missing reports of influenza activity during a given month in a given state ( or in adjoining states ) , yielding a total of 3712 valid statemonth observations . statespecific unemployment data are available from the bureau of labor statistics ( bls ) via the monthly current population surveys ( cps ) . the cps is a monthly survey of about 50  000 households conducted by the bureau of the census . an individual is classified as unemployed if s / he does not have a job , s / he has actively looked for work at some point in the past 4  weeks , and s / he is currently available for work . these cases are aggregated each month by state and expressed as a percent of the total labor force . this definition focuses on people who want to be working but are not and thereby captures the relative size of the most affected segment of the population . the bls often adjusts unemployment rates for typical seasonal unemployment ( e.g. because of seasonal fluctuation in construction , agricultural , or tourism work ) . i present analyses that use the original unadjusted rates , as seasonal unemployment is also a source of uncertainty , stress , and poverty in some areas . , 
  however , supplementary analyses that use seasonally adjusted unemployment rates yield comparable results ( available upon request ) . centers for disease control and prevention reports state influenza activity levels on a weekly basis during the regular flu season , while statelevel unemployment data are only available by month . to estimate the relationship between unemployment and influenza activity levels , i construct a set of statemonth observations for all flu seasons between october 1999 and december 2010 . i use the weekly influenza activity reports to determine the highest level of flu activity reported for each state during each month of the study period . the analysis includes all months between october and may for each flu season , yielding 91  months of observations . the primary dependent variable , y , is a dichotomous indicator of whether influenza activity was ever regional during the month in question . given that these outcomes are dichotomous and vary over time , a logistic timeseries crosssection regression model is appropriate : where p is the probability that flu is widespread ( y  =  1 ) in a given state during a given monthyear . x includes a set of 11 predictors of influenza that are measured for a given state in the month prior to the observation ( described later ) , where the state s unemployment rate is the primary predictor of interest . because influenza has a predictable seasonal pattern , t is a set of 17 fixed effects that specify the time period of the observation , including six separate parameters for the month of the observation ( e.g. january ) as well as 11 parameters for the flu season in which it occurs ( e.g. 20092010 ) . to capture unobserved unit heterogeneity , the model incorporates fixed effects for each state , s , which control for factors that could affect the overall rate of influenza spread in a given state , such as geographic size , transportation infrastructure , number of large cities , as well as differences in influenza surveillance strategies and resources . finally , to attenuate autocorrelation across time points within states and to capture the survivability of the virus , the model also includes y 
 s , t1 , which is an indicator of whether flu was widespread in the state in question in the month prior to the outcome observation . first , the seasonality of both influenza activity and the unemployment rate has been linked to climate , as the survivability of influenza viruses is greater where air is cool and dry . the peak of influenza during the winter months may also be due to behavioral changes like more frequent congregation of groups indoors . failure to take climate into account may therefore result in omitted variable bias that yields misleading estimates of the impact of unemployment on influenza . as proxies for climatic factors that may affect influenza survivability , i use data on temperature ( f ) and precipitation ( in inches ) compiled by the northeast regional climate center ( nrcc ) . i average these across all days and stations to generate mean averages for each statemonth for the entire period . including both average temperature and precipitation variables simultaneously resulted in variance inflation , but removing one or the other does not change the results . spatial autocorrelation is a potential concern , as the influenza activity level in one state could reflect broader regional factors . states are not only exposed to similar climates as adjacent states , they are also exposed to influenza viruses circulating within adjacent states because of interstate travel associated with daily commutes and commerce . , i identify all states that border a given state and calculate the average influenza activity levels ( measured ordinally : 0  =   no activity, 3  =   widespread ) of those states in the previous month . this measure , which is included in x , is an approximation of the extent to which influenza is encroaching on a given state at a given time . a number of other covariates may vary with both influenza activity and unemployment . using monthly cps data , i created measures of the size of the labor force , percent urban ( spatial concentration of the population ) , percent collegeeducated and percent impoverished , percent married , average household size , and age for the month prior to the observation in question . these are statemonthspecific covariates that are also included in x. because these measures are so sluggish and do not change much from month to month within states , they are effectively collinear with the state fixed effects and therefore give rise to major variance inflation . norming these variables within states by expressing them as zscores for the state in question ( e.g. for a given state , how many standard deviations below / above the norm was the observed percent of residents who were collegeeducated in a given month , as compared with all other months observed for that state ) eliminated this multicollinearity . because influenza activity surveillance is not reported for september , there are no data on previous time points to predict the october observations . this restricts outcomes to be predicted by the models to those that occur during the 7month period between november and may , which is when epidemic peaks are most evident . 
  with 48 contiguous states and an observation for each of 7  months between the 19992000 flu season , and for the first 2  months of the 20102011 flu season , this equals 79 time periods , and a total of 3792 possible statemonths of observation . there are 80 observations missing because of missing reports of influenza activity during a given month in a given state ( or in adjoining states ) , yielding a total of 3712 valid statemonth observations . table  1 displays the distribution of influenza activity levels across states , by month , from october 1999 to december 2010 . widespread influenza activity designations are least common in november , april , and may and are most common in february , with 431% of states reaching that level of influenza activity at least once during that month . a similar pattern is evident when the lower threshold of  regional or widespread activity is used . a seasonal connection between influenza activity and the unemployment rate becomes evident when comparing the distribution of influenza activity levels to the distribution of the average unadjusted unemployment rate across states , by month , during the study period ( figure  1 ) . unemployment peaks in january , 1  month prior to the peak in the prevalence of widespread and regional influenza activity . estimates of average monthly statelevel unemployment rates and influenza activity levels during the regular flu season , november 1999december 2010 ( n  =  3712 ) 
 
 estimates are based on observations that are included in the multivariate regression analysis . average proportion of states reporting regional and/or widespread influenza and average statelevel unemployment rate during the regular flu season , october 1999december 2010 ( n  =  4367 statemonth observations ) . table  2 displays trends in influenza activity for the flu seasons that comprise the study period , from 1999 to 2010 . in general the beginning of that season was the most active on record for this study period ( 938% of states reported regional or widespread activity in november 2009 ) , while the typical peak of flu season ( february ) was one of the two weakest peaks on record for the study period ( 146% of states reported regional or widespread activity in february 2010 ) . estimates of average monthly statelevel unemployment rates and influenza activity levels for ten consecutive regular flu seasons , november 1999may 2010 ( n  =  3618 ) 
 
 estimates are based on observations that are included in the multivariate regression analysis . does not include observations for the 20102011 flu season because of incomplete data . table  3 presents odds ratios from the pooled timeseries crosssection logistic regression analysis predicting influenza activity for statemonths spanning the study period . the first two models predict whether influenza activity was widespread . from the second column ( which removes nonsignificant statemonth predictors ) , the odds of widespread influenza activity in a given statemonth are greater when influenza was widespread in the previous month ( or  =  266 , 95% ci 198 , 359 ) . the odds of widespread influenza are also greater for a given state when adjacent states had elevated influenza activity levels in the previous month ( or  =  440 , 95% ci 349 , 555 ) , suggesting a significant degree of betweenstate influenza spread and/or larger regional influences that affect adjacent states influenza levels in similar ways . however , closer inspection reveals that both temperature and precipitation are significantly negatively associated with widespread influenza activity if the fixed effects for month are removed ( or  =  093 , 95% ci 092 , 094 ; or  =  080 , 95% ci 068 , 094 ; respectively ) . odds ratios from pooled timeseries crosssection logistic regression analyses predicting high monthly influenza activity levels in american states during the regular flu season , november1999december 2010 ( n  =  3712 statemonth observations ) 
  ci , confidence interval ; d.f . , degress of freedom . net of these factors , the odds that influenza is widespread in a given state were higher under conditions of high unemployment ( or  =  121 , 95% ci 107 , 137 ) . the magnitude of the association is shown in figure  2 , which plots predicted probabilities of widespread flu under different labor market conditions . the predicted probability of widespread influenza activity when unadjusted unemployment is at 36% ( which was the overall average unemployment rate in 1999 ) is 005 , compared with a predicted probability of 013 when unemployment is at 88% ( the overall average unemployment rate in 2010 ) . a supplementary analysis shows similar results when using seasonally adjusted unemployment rates instead of the original unadjusted rates ( or  =  118 , 95% ci 103 , 134 ) .  predicted probabilities of regional and/or widespread influenza activity levels during the regular flu season , given different levels of unemployment . predicted probabilities are calculated from log(odds ) for the models presented in columns 2 and 4 on table  3 . it is worth noting that the effect of unemployment is slightly more pronounced at the  regional or widespread threshold of influenza activity , as shown in columns 3 and 4 of table  3 . from the final model , a onepercentagepoint increment in the unemployment rate is associated with a 302% increment in the odds of regional or widespread influenza ( 95% ci 117 , 145 ) . as shown in figure  2 , the predicted probability that a state has at least regional influenza activity when the unadjusted unemployment rate is at 36% is 021 , compared with a predicted probability of 051 when unemployment is at 88% . results are similar when using seasonally adjusted unemployment rates ( or  =  128 , 95% ci 115 , 143 ) . this paper examined the relationship between the statelevel unemployment rate and influenza activity within the 48 contiguous united states during the 19992010 flu seasons . the primary finding is that high statelevel unemployment increases the risk of both regional and widespread influenza activity during the subsequent month . these results persist despite the inclusion of a number of timevarying statelevel characteristics , including proximity to influenza activity in adjacent states , temperature , and precipitation . it is worth noting that neither temperature nor precipitation is significantly associated with the likelihood of widespread influenza activity within states . these measures are significant when month fixed effects are removed , however , suggesting that the month indicators are jointly correlated with climate . it is unclear to what extent this correlation reflects social factors such as the prevalence of indoor group activities related to holiday celebrations , or unobserved heterogeneity with respect to climate itself , like humidity . finally , it is beyond the scope of this paper to specify the mechanisms that account for the link between unemployment and influenza activity . previous work suggests that this association partly reflects the consequences of unemployment for access to health care , as well as stress and suppression of immune function in both employed and unemployed individuals . , 
  models of statelevel influenza like the ones presented here can not capture factors that increase individuallevel risk of contracting influenza . additional work is needed to determine the role of the macroeconomic climate as an independent predictor alongside individuallevel factors in multilevel models of influenza risk . knowledge of the particular vulnerability of disadvantaged populations has in the past motivated tailored interventions to increase access to influenza vaccinations in innercity areas . , 
  tests of interventions that target areas with high local unemployment rates or where mass layoff have occurred may provide useful tests of the general hypothesis that unemployment facilitates influenza spread .
please cite this paper as : cornwell b. ( 2012 ) unemployment and widespread influenza in america , 19992010 . 
 influenza and other respiratory viruses 6(1 ) , 6370 . 
 background   research shows that unemployment reduces access to health care and vaccines and increases financial difficulty , family conflict , and other sources of stress that are known to suppress immune function . in addition , 
 seasonal unemployment rates parallel seasonal influenza activity . following a theory that argues that macroeconomic conditions affect population health 
 , this paper examines whether there is an association between monthly unemployment rates and influenza activity . 
 methods   data from influenza activity surveillance reports from the centers for disease control and prevention are combined with information from the bureau of labor statistics on statelevel unemployment rates in the u.s . for the flu seasons between 1999 and 2010 . 
 pooled timeseries crosssection logistic regression analyses are conducted to examine the effect of the unemployment rate on the likelihood of widespread and/or regional influenza activity in the 48 contiguous states throughout this period . 
 a total of 3712 statemonth observations are examined . 
 results   net of other factors included in the multivariate regression analysis , a onepercentagepoint increment in the unemployment rate is associated with between a 71% and 370% increment in the odds of widespread influenza ( odds ratio  =  121 ) . 
 likewise , a onepercentagepoint increment in the unemployment rate is associated with between a 171% and 447% increment in the odds of at least regional influenza ( odds ratio  =  130 ) . 
 results hold regardless of whether timevarying statelevel characteristics are included . 
 conclusions   higher statelevel unemployment increases the likelihood of regional and widespread influenza activity .