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Ethnic disparities in acquiring 2009 pandemic H1N1 influenza: a case–control study

  • Debeka Navaranjan1, 2,
  • Laura C Rosella1, 2, 3Email author,
  • Jeffrey C Kwong1, 2, 3, 4,
  • Michael Campitelli3 and
  • Natasha Crowcroft1, 2
BMC Public Health201414:214

DOI: 10.1186/1471-2458-14-214

Received: 2 September 2013

Accepted: 20 February 2014

Published: 1 March 2014

Abstract

Background

Novel risk factors were associated with the 2009 pandemic A/H1N1 virus (pH1N1). Ethnicity was among these risk factors. Ethnic disparities in hospitalization and death due to pH1N1 were noted. The purpose of this study is to determine whether there are ethnic disparities in acquiring the 2009 pandemic H1N1.

Methods

We conducted a test-negative case–control study of the risk of pH1N1 infection using data from Ontario, Canada. Cases were laboratory confirmed to have influenza using reverse-transcriptase polymerase chain reaction (RT-PCR), and controls were obtained from the same population and were RT-PCR negative. Multivariate logistic regression was used to determine the association between ethnicity and pH1N1 infection, while adjusting for demographic, clinical and ecological covariates.

Results

Adult cases were more likely than controls to be self-classified as East/Southeast Asian (OR = 2.59, 95% CI 1.02-6.57), South Asian (OR = 6.22, 95% CI 2.01-19.24) and Black (OR = 9.72, 95% CI 2.29-41.27). Pediatric cases were more likely to be self-identified as Black (OR = 6.43, 95% CI 1.83-22.59). However, pediatric cases without risk factors for severe influenza infection were more likely to be South Asian (OR 2.92, 95% CI 1.11-7.68), Black (OR 16.02, 95% CI 2.85-89.92), and West Asian/Arab, Latin American or Multi-racial groups (OR 3.09 95% CI 1.06-9.00).

Conclusions

pH1N1 cases were more likely to come from certain ethnic groups compared to test-negative controls. Insights into whether these disparities arise due to social or biological factors are needed in order to understand what approaches can be taken to reduce the burden of a future influenza pandemic.

Keywords

Influenza Ethnicity Epidemiology

Background

Canada’s proportion of visible minorities has been increasing [1]. In 2006, 16.2% of Canada’s population was comprised of visible minorities, a rise from 13.4% in 2001 and 11.2% in 1996. Ontario has the largest proportion of visible ethnic minorities in Canada. Given the rise in visible minorities, it is imperative to consider ethnic differences in health in order to address potential disparities. In Canada, ethnic disparities have been identified for chronic diseases such as cardiovascular disease, diabetes, and obesity [24]. However, the relationship with infectious diseases, including influenza, is less clear.

There have been socio-economic disparities described for influenza. A study from the United States found that pediatric influenza-associated hospitalization was three times higher among high-poverty and high-crowding census tracts than in low-poverty and low-crowding census tracts [5]. Following the 2009 H1N1 pandemic, disparities associated with acquisition and severity of the 2009 pandemic A/H1N1 virus (pH1N1) were explored. A Canadian study exploring the relationship between social determinants of health and severity of pH1N1 found that hospitalization due to pH1N1 illness was associated with low education and living in neighbourhoods with high total or material deprivation [6]. Similarly, a study from England found that individuals from the most deprived quintile of the population had mortality rates due to pH1N1 three times greater than individuals from the least deprived quintile [7]. Studies from the United States have demonstrated ethnic disparities in incidence of influenza-like illness (ILI) during the 2009 H1N1 pandemic [8] and risk of hospitalization due to pH1N1 [911]. In the Canadian context, the frequency and severity of illness from pH1N1 were higher among First Nations individuals compared to the rest of the population [1214]. However, few studies in Canada have examined the risk of acquiring pH1N1 among other ethnic minorities, where the distribution of ethnic minorities in Canada is different compared to other countries.

Clinically, groups at risk for infections, complications, hospitalization, and/or death due to influenza have been identified as children, elderly, and individuals with certain chronic medical conditions [15]. Social factors could also play a role in shaping risk groups for influenza. For example, ease of access to medical care, ability to limit risk of acquiring illness through social distancing practices, and acceptance of protective practices such as immunization can shape the groups at greater risk of acquiring influenza [16, 17]. Currently, no ethnic groups are recognized as being at high risk for infections or subsequent complications due to seasonal influenza.

In this study we used a laboratory confirmed test-negative case–control study conducted during the pandemic to investigate the association between ethnicity and risk of acquiring pandemic H1N1.

Methods

Study design and population

We conducted a test-negative case–control study, which bases case status on laboratory confirmation of pH1N1 and controls on the same test being negative [18]. Our population was a clinic-based sample, consisting of individuals presenting to clinics for medical care. We collected information on symptoms at the time of testing to restrict our sample to those presenting with influenza-like illness, defined as an acute onset of respiratory illness with fever, cough and with one or more of the following: sore throat, arthralgia, myalgia, or prostration which is likely due to influenza. Ontario residents of all ages who had nasopharyngeal swabs for pH1N1 by real-time reverse-transcriptase polymerase chain reaction (RT-PCR) were eligible for inclusion. We identified study participants as having confirmed pH1N1 through testing at the Public Health Ontario (PHO) Laboratory or Mount Sinai Hospital/University Health Network. The PHO laboratory conducted 100% of the testing in the first months of the pandemic. Later on, a few other laboratories in major centres, the largest being Mount Sinai Hospital/University Health Network, conducted testing as well.

We included individuals with a test date between April 13, 2009 and July 20, 2009 (pandemic wave 1). The study was approved by the Research Ethics Board of University of Toronto, Toronto, Ontario, Canada.

Study variables

Trained interviewers from the Institute for Social Research at York University, Toronto conducted standardized telephone interviews between August and September 2009 to obtain information on demographics, health status, symptoms, and treatment, including hospitalization. For children 14 and under, interviews were completed by a parental proxy.

Individuals were asked to self-identify with an ethnicity/ethnicities. Ethnicity was based on the question, “People in Canada come from many different ethnic and racial backgrounds. I would like you to identify which ethnicity best describes you. You may choose more than one category.” Ethnicities were then read aloud to the participants. The ethnic groupings were based on the categories similar to Statistics Canada definitions: White, East/Southeast Asian, South Asian, Black, West Asian/Arab, Latin American, and Other. Individuals who identified with multiple ethnic groups were classified as Multi-Racial person. West Asian/Arab, Latin American and Multi-racial were combined with Other because of small sample sizes for these ethnic groups. Aboriginals could not be included in this analysis because of lack of data (<1% of the sample).

Conceptual models of the association between ethnicity and acquisition of pH1N1 were developed a priori to guide the model building. This enabled the consideration of potential confounders and effect modifiers. The variables considered for adults were age, gender, current smoking status, number of visits to their regular primary care doctor in the prior 12 months (low, ≤1; moderate, 2–6; high, >6), completion of post-secondary education, immigrant status (non-immigrant, 0–5 years in Canada, >5 years in Canada), whether there were children in the household, household density (the ratio of the number of individuals in a household to the number of sleeping rooms), previous seasonal influenza vaccination, residence in Toronto (the capital and largest metropolitan city in Ontario), healthcare worker status, and testing date before or after June 11th, 2009 (the date when clinicians in Ontario were instructed to switch from submitting samples from all patients presenting with influenza-like illness to only submitting samples from patients at high risk of complications or for clinical management of hospitalized cases) [19]. In addition to the individual level variables collected through the survey, we examined the impact of deprivation captured through the Deprivation Index (DI) developed by Institut national de santé publique du Québec (INSPQ) [6, 20, 21]. The DI includes social deprivation, material deprivation, total deprivation, employment rate, proportion of the population with less than high school education, and prevalence with low income, where individuals were classified as living in an area with a high prevalence of low income if they resided in dissemination areas that were in the top quintile for low income of the population study. We also considered if individuals were tested in Toronto since this represents the major urban centre of our study population as well as contains the highest proportion of visible minorities and other ethnic groups. Underlying medical conditions were analyzed as a binary variable (≥1 underlying medical conditions, which included diabetes, cancer, heart disease, asthma, chronic obstructive pulmonary disease (COPD), immune disorder, liver disease, kidney or renal disease, splenectomy, sickle cell anemia, seizure disorder, leukemia, cystic fibrosis, cerebral palsy), and obesity was considered separately. In children, all the same variables were considered, with underlying medical conditions restricted to those commonly observed in children (≥ 1 of either asthma or immune disorder).

Statistical analysis

We first tested the interaction between ethnicity and being a child (< 18 years of age) in order to determine if appropriate to pool adults and children. Given the significant interaction and the different confounders, we then had to stratify all analysis according <18 years of age versus ≥ 18 years of age. Descriptive statistics (means and proportions) were calculated for all variables. In order to develop the multivariable model, the Hosmer and Lemeshow forward model building strategy was used [22]. Briefly, odds ratios were obtained from univariate logistic regression to identify risk factors for acquisition of pH1N1. Variables were then entered into the model based on their effect size and p-value. Variables that had p-values <0.05 were included in the final model, as well as variables that had a significant (>10%) effect on any of the ethnic group’s OR. Age and gender were forced into the multivariate model, as these were deemed important variables to consider for the association. Gender was a priori identified as an important effect modifier and therefore the interaction was tested. At the end of this iterative model building process, a verification step was performed by adding any variable not selected for the original multivariate model one at a time to identify variables that, by themselves, were not influential but may have made an important contribution in the presence of other variables.

In addition, the following sensitivity analyses were performed: including individuals who were tested because of recent travel to Mexico; including individuals who did not present with symptoms at testing; no restrictions implemented (no exclusions); stratifying by presence of risk factors; stratifying by testing prior to June 11th, 2009; and according to Toronto residence.

Results

A total of 402 adults and 352 children were included. There were 229 controls and 173 cases among adults, and 112 controls and 240 cases among children. The demographic, clinical, and ecologic-level characteristics are described in Table 1. Among cases, more than half were females and 59% of adult cases and 40% of pediatric cases were of white ethnicity.
Table 1

Characteristics of the adult and pediatric study population by case status

  

Adults

 

Children

 

Total study population

 

Tested negative

 

Tested positive

 

Total study population

 

Tested negative

 

Tested positive

 

(N = 402)

 

(N = 229)

 

(N = 173)

 

(N = 352)

 

(N = 112)

 

(N = 240)

Variable

N

%

N

%

N

%

N

%

N

%

N

%

Ethnicity*

White

266

66.2

164

71.6

102

59.0

166

47.2

70

62.5

96

40.0

East/Southeast Asian

42

10.5

23

10.0

19

11.0

47

13.4

11

9.8

36

15.0

South Asian

32

8.0

11

4.8

21

12.1

47

13.4

14

12.5

33

13.8

Black

19

4.7

5

2.2

14

8.1

35

9.9

5

4.5

30

12.5

Other

35

8.7

20

8.7

15

8.7

49

13.9

10

8.9

39

16.3

Age

<5

N/A

 

N/A

 

N/A

 

94

26.7

45

40.2

49

20.4

5-11

N/A

 

N/A

 

N/A

 

135

38.4

31

27.7

104

43.3

12-17

N/A

 

N/A

 

N/A

 

123

34.9

36

32.1

87

36.3

18-34

150

37.3

79

34.5

71

41.0

N/A

 

N/A

 

N/A

 

35-49

124

30.9

71

31.0

53

30.6

N/A

 

N/A

 

N/A

 

50-64

95

23.6

58

25.3

37

21.4

N/A

 

N/A

 

N/A

 

65+

33

8.2

21

9.2

12

6.9

N/A

 

N/A

 

N/A

 

Gender (female)

241

60.0

139

60.7

102

59.0

246

69.9

80

71.4

166

69.2

BMI class

Normal weight (18.5 ≤ BMI <25)

187

46.5

111

48.5

76

43.9

115

32.7

38

33.9

77

32.1

Overweight (25 ≤ BMI < 30)

114

28.4

62

27.1

52

30.1

32

9.1

8

7.1

24

10.0

Obese (BMI ≥ 30)

72

17.9

38

16.6

34

19.7

      

Current smoker

110

27.4

70

30.6

40

23.1

4

1.14

2

1.79

2

0.83

Missing

N/A

 

N/A

 

N/A

 

214

60.8

74

66.07

410

58.3

Number of family doctor visits in the past year

<1

73

18.2

45

19.7

28

16.2

78

22.2

20

17.9

58

24.2

2 to 6

257

63.9

152

66.4

105

60.7

200

56.8

68

60.7

132

55.0

>6

72

17.9

32

14.0

40

23.1

74

21.0

24

21.4

50

20.8

Underlying medical conditions

86

21.4

42

18.3

44

25.4

56

15.9

20

17.9

36

15.0

Material deprivation

1 (low)

95

23.6

63

27.5

32

18.5

71

20.2

26

23.2

45

18.8

2

87

21.6

46

20.1

41

23.7

74

21.0

26

23.2

48

20.0

3

65

16.2

37

16.2

28

16.2

67

19.0

23

20.5

44

18.3

4

58

14.4

28

12.2

30

17.3

70

19.9

19

17.0

51

21.3

5 (high)

73

18.2

45

19.7

28

16.2

56

15.9

18

16.1

38

15.8

Social deprivation

1 (low)

84

20.9

47

20.5

37

21.4

105

29.4

37

33.0

68

28.3

2

69

17.2

35

15.3

34

19.7

73

20.7

23

20.5

50

20.8

3

74

18.4

44

19.2

30

17.3

60

17.1

20

17.9

40

16.7

4

84

20.9

51

22.3

33

19.1

58

16.5

19

17.0

39

16.3

5 (high)

67

16.7

42

18.3

25

14.5

42

11.9

13

11.6

29

12.1

Total deprivation category

1 (low)

71

17.7

38

16.6

33

19.1

77

21.9

30

26.8

47

19.6

2

257

63.9

153

66.8

104

60.1

219

62.2

72

64.3

147

61.3

3 (high)

50

12.4

28

12.2

22

12.7

42

11.9

10

8.9

32

13.3

Individuals living in a neighbourhood with low employment rate §

88

21.9

53

23.1

35

20.2

57

16.2

15

13.4

42

17.5

Individuals residing in a neighbourhood with high proportion of low income §

100

24.9

56

24.5

44

25.4

74

21.0

13

11.6

61

25.4

High school or less education

58

14.4

35

15.3

23

13.3

53

15.1

16

14.3

37

15.4

Post-secondary education completed

261

64.9

153

66.8

108

62.4

250

71.0

77

68.8

173

72.1

Household density (median) ǁ

 

1.0

 

1.0

 

1.0

 

1.3

 

1.2

 

1.3

Children in household

0

231

57.5

144

62.9

87

50.3

N/A

 

N/A

 

N/A

 

1

170

42.3

84

36.7

86

49.7

247

70.2

79

70.5

168

70.0

2

N/A

 

N/A

 

N/A

 

79

22.4

25

22.3

54

22.5

3

N/A

 

N/A

 

N/A

 

24

6.8

7

6.3

17

7.1

Receipt of 2008 seasonal vaccine

156

38.8

83

36.2

73

42.2

108

30.7

25

22.3

83

34.6

Tested before June 11th, 2009

315

78.4

17

7.4

103

59.5

251

71.3

112

100.0

139

57.9

Healthcare provider

48

11.9

23

10.0

25

14.5

N/A

 

N/A

 

N/A

 

Resides in Toronto

121

30.1

84

36.7

37

21.4

78

22.2

24

21.4

54

22.5

Immigrant category

Non-Immigrant

251

62.4

148

64.6

103

59.5

316

89.8

106

94.6

210

87.5

Immigrant

N/A

 

N/A

 

N/A

 

36

10.2

6

5.4

30

12.5

Immigrant, settled for 0–5 years

24

6.0

9

3.9

15

8.7

N/A

 

N/A

 

N/A

 

Immigrant, settled for >5

121

30.1

67

29.3

54

31.2

N/A

 

N/A

 

N/A

 

Hospitalized

64

15.9

0

0

64

37.0

79

22.4

0

0

79

32.9

*This was based on the question “People in Canada come from many different ethnic and racial backgrounds. I would like you to identify which ethnicity best describes you. You may choose more than one category.” The ethnic groupings were based on the categories similar to Statistics Canada definitions.

Underlying medical conditions for adults was having at least one of the following: diabetes, cancer, heart disease, asthma, chronic obstructive pulmonary disease (COPD), immune disorder, liver disease, kidney or renal disease, splenectomy, sickle cell anemia, seizure disorder, leukemia, cystic fibrosis, or cerebral palsy. In children, underlying medical conditions was having either asthma or immune disorder.

Material Deprivation was based on employment to population ratio, average income and proportion without high school diploma. Social deprivation was based on proportion of persons living alone; proportion separated, divorced or widowed; and proportion of single-parent families. Quintiles of material and social deprivation were created based on dissemination areas in Ontario, with a score of 1 representing low deprivation and 5 indicating high deprivation. The material and social deprivation were combined to form a total deprivation score, which was categorized as high, middle or low deprivation.

§Low employment rate and High Proportion of Low Income are based on individuals residing in a dissemination area in Ontario. Individuals living in a neighbourhood in the bottom quintile of all dissemination areas for low employment/with high proportion of low income, based on our study population, were classified as being in the low employment rate/high proportion of low income categories.

ǁCalculated as the number of people in the household divided by the number of sleeping rooms in the household.

Healthcare provider is defined as a doctor, nurse or another healthcare professional.

N/A = Not applicable.

In adults, cases were more likely than controls to be South Asian (OR 3.07, 95% CI 1.42-6.63) or Black (OR 4.50, 95% CI 1.57-12.87), have greater than moderate material deprivation (4th quintile) (OR 2.11, 95% CI 1.08-4.11), have children in the household (OR 1.70, 95% CI 1.13-2.53), or be a recent immigrant (2.40, 95% CI 1.01-5.68) (Table 2).
Table 2

Unadjusted odds ratios for pH1N1 influenza acquisition for various potential risk factors in adults and children

  

Adults

 

Children

Variable

 

OR (95% CI)

 

OR (95% CI)

Ethnicity*

White

ref

 

ref

 

East/Southeast Asian

1.33 (0.69-2.56)

 

2.39 (1.14-5.01)

 

South Asian

3.07 (1.42-6.63)

 

1.72 (0.86-3.45)

 

Black

4.50 (1.57-12.87)

 

4.38 (1.62-11.84)

 

Other

1.21 (0.59-2.46)

 

2.84 (1.33-6.08)

Age

18-34

ref

<5

0.45 (0.26-0.79)

 

35-49

0.83 (0.52-1.34)

5-11

1.39 (0.79-2.43)

 

50-64

0.71 (0.42-1.20)

12-17

ref

 

65+

0.64 (0.29-1.39)

  

Gender (Female)

 

0.93 (0.62-1.39)

 

0.90 (0.55-1.47)

BMI class

II (18.5 ≤ BMI <25)

ref

 

ref

 

III (25 ≤ BMI < 30)

1.23 (0.77-1.96)

 

1.48 (0.61-3.60)

 

IV (BMI ≥ 30)

1.31 (0.76-2.26)

 

Current smoker

 

0.68 (0.43-1.07)

 

0.96 (0.91-1.02)

Number of visits to family doctor in the past year

Low (<=1)

0.90 (0.53-1.54)

 

1.49 (0.83-2.69)

Moderate (2–6)

ref

 

ref

 

High (>6)

1.81 (1.07-3.07)

 

1.07 (0.61-1.90)

Underlying medical conditions

 

1.51 (0.93-2.44)

 

0.81 (0.44-1.48)

Material deprivation

1 (low)

ref

 

ref

 

2

1.76 (0.96-3.19)

 

1.07 (0.54-2.10)

 

3

1.49 (0.78-2.85)

 

1.11 (0.55-2.22)

 

4

2.11 (1.08-4.11)

 

1.55 (0.76-3.17)

 

5 (high)

1.23 (0.65-2.31)

 

1.22 (0.58-2.56)

Social deprivation

1 (low)

ref

 

ref

 

2

1.23 (0.65-2.34)

 

1.18 (0.63-2.23)

 

3

0.87 (0.46-1.63)

 

1.09 (0.56-2.13)

 

4

0.82 (0.45-1.52)

 

1.12 (0.57-2.20)

 

5 (high)

0.76 (0.39-1.46)

 

1.21 (0.56-2.61)

Total deprivation category

1 (low)

ref

 

ref

 

2

0.78 (0.46-1.33)

 

1.30 (0.76-2.23)

 

3 (high)

0.91 (0.44-1.87)

 

2.04 (0.88-4.75)

Individuals living in a neighbourhood with low employment rate §

 

0.84 (0.52-1.36)

 

1.37 (0.73-2.60)

Individuals residing in a neighbourhood with a high proportion of low income §

 

1.05 (0.67-1.66)

 

2.60 (1.36-4.96)

High school or less education

 

0.85 (0.48-1.50)

 

1.09 (0.58-2.06)

Post-secondary school completion

0.82 (0.54-1.24)

 

1.21 (0.74-1.98)

Household density ǁ

 

1.26 (0.84-1.87)

 

1.81 (0.95-3.44)

Children in household

 

1.70 (1.13-2.53)

1

ref

   

2

1.02 (0.59-1.75)

   

>3

1.14 (0.46-2.87)

Receipt of 2008 seasonal vaccine

 

1.25 (0.84-1.88)

 

1.96 (1.16-3.31)

Tested prior to June 11th, 2009

 

0.12 (0.07-0.21)

 

N/A

Healthcare provider

 

1.51 (0.83-2.77)

 

N/A

Toronto

 

0.47 (0.30-0.74)

 

1.07 (0.62-1.83)

Immigrant category

    

Non-Immigrant

 

ref

 

ref

Immigrant

 

N/A

 

2.52 (1.02-6.25)

Immigrant, Settled for 0–5 years

 

2.40 (1.01-5.68)

 

N/A

Immigrant, Settled for >5

 

1.16 (0.75-1.80)

 

N/A

*This was based on the question “People in Canada come from many different ethnic and racial backgrounds. I would like you to identify which ethnicity best describes you. You may choose more than one category.” The ethnic groupings were based on the categories similar to Statistics Canada definitions.

Underlying medical conditions for adults was having at least one of the following: diabetes, cancer, heart disease, asthma, chronic obstructive pulmonary disease (COPD), immune disorder, liver disease, kidney or renal disease, splenectomy, sickle cell anemia, seizure disorder, leukemia, cystic fibrosis, or cerebral palsy. In children, underlying medical conditions was having either asthma or immune disorder.

Material Deprivation was based on employment to population ratio, average income and proportion without high school diploma. Social deprivation was based on proportion of persons living alone; proportion separated, divorced or widowed; and proportion of single-parent families. Quintiles of material and social deprivation were created based on dissemination areas in Ontario, with a score of 1 representing low deprivation and 5 indicating high deprivation. The material and social deprivation were combined to form a total deprivation score, which was categorized as high, middle or low deprivation.

§Low employment rate and High Proportion of Low Income are based on individuals residing in a dissemination area in Ontario. Individuals living in a neighbourhood in the bottom quintile of all dissemination areas for low employment/with high proportion of low income, based on our study population, were classified as being in the low employment rate/high proportion of low income categories.

ǁCalculated as the number of people in the household divided by the number of sleeping rooms in the household.

Healthcare provider is defined as a doctor, nurse or another healthcare professional.

N/A = Not applicable.

Among children, cases were more likely than controls to be East/Southeast Asian (OR 2.39, 95% CI 1.14-5.01), Black (OR 4.38, 95% CI 1.62-11.84) and either Latin American, West Asian/Arab or Multi-racial ethnicity (OR 2.84, 95% CI 1.33-6.08), be from a neighbourhood with a high proportion of individuals with low income (OR 2.60, 95% 1.36-4.96), have obtained the 2008 seasonal vaccine (OR 1.96, 95% 1.16-3.31), or be an immigrant (OR 2.52, 95% CI 1.02-6.25).

In multivariate analysis, adjusting for all significant confounders identified through our model building strategy increased the observed ORs for all ethnic groups (Table 3). After adjustment, for adults, cases were more likely to be East/Southeast Asian (OR 2.59, 95% CI 1.02-6.57), South Asian (OR 6.22, 95% CI 2.01-19.24), and Black (OR 9.72, 95% CI 2.29-41.27). The sensitivity analyses indicate that including individuals who had travelled to Mexico increased the estimates for all ethnic groups. In addition, the analysis was conducted without any restrictions and this resulted in a slight increase in ORs for East/Southeast Asian (OR 2.96, 95% CI 1.24-7.05), South Asian (OR 7.42, 95% CI 2.79-19.74), and Black (OR 11.28, 95% CI 2.85-44.60). Among those who did not have risk factors, the point estimates for the odds ratio after adjustment increased for South Asians; the confidence intervals for these estimates were wider given the reduced samples size after stratifying. As noted, the risk of diabetes and heart disease differs by ethnicity, hence, these medical conditions were considered separately as a group in the same analysis, and they did not appreciably change the effect sizes.
Table 3

Odds ratios from multivariate logistic regression models for pH1N1 illness acquisition and ethnicity under various sensitivity analyses

 

Adults

Children

Ethnic group*

Adjusted OR (95% CI)

Adjusted OR (95% CI)

Restricted to those with symptoms and those tested for reasons other than travel to Mexico (primary analysis)

 

White

ref

ref

East/Southeast Asian

2.59 (1.02-6.57)

1.96 (0.82-4.66)

South Asian

6.22 (2.01-19.24)

1.88 (0.80-4.41)

Black

9.72 (2.29-41.27)

6.44 (1.83-22.59)

Other

2.00 (0.74-5.42)

2.06 (0.83-5.11)

Individuals tested because of travel to Mexico added

  

White

ref

ref

East/Southeast Asian

2.99 (1.20-7.45)

2.01 (0.85-4.73)

South Asian

7.34 (2.42-22.28)

1.84 (0.80-4.24)

Black

10.93 (2.61-45.73)

6.45 (1.86-22.44)

Other

2.24 (0.88-5.70)

1.70 (0.73-3.97)

Those not presenting with symptoms are included

  

White

ref

ref

East/Southeast Asian

2.57 (1.06-6.23)

1.92 (0.84-4.38)

South Asian

6.03 (2.21-16.44)

1.83 (0.81-4.14)

Black

10.34 (2.58-41.46)

5.44 (1.72-17.24)

Other

1.67 (0.70-3.99)

2.28 (0.95-5.45)

No restrictions

  

White

ref

ref

East/Southeast Asian

2.96 (1.24-7.05)

1.99 (0.88-4.50)

South Asian

7.42 (2.79-19.74)

1.82 (0.82-4.06)

Black

11.28 (2.85-44.60)

5.62 (1.78-17.71)

Other

1.79 (0.78-4.08)

1.80 (0.81-4.01)

Heart disease and diabetes added independently of other underlying medical conditions

 

White

ref

 

East/Southeast Asian

2.56 (0.99-6.64)

 

South Asian

6.55 (1.99-21.57)

 

Black

10.19 (2.35-44.22)

 

Other

1.34 (0.46-3.90)

 

Without risk factors

  

White

ref

ref

East/Southeast Asian

2.63 (0.91-7.59)

2.48 (0.92-6.64)

South Asian

10.34 (2.39-44.80)

2.92 (1.11-7.68)

Black

11.90 (2.30-61.65)

16.02 (2.85-89.92)

Other

1.69 (0.54-5.30)

3.09 (1.06-9.00)

Tested prior to June 11th, 2009

  

White

ref

ref

East/Southeast Asian

2.66 (1.01-7.02)

2.90 (1.10-7.64)

South Asian

6.97 (2.07-23.50)

1.82 (0.68-4.87)

Black

8.18 (1.85-36.22)

7.21 (1.72-30.21)

Other

2.05 (0.70-6.05)

2.34 (0.82-6.71)

*This was based on the question “People in Canada come from many different ethnic and racial backgrounds. I would like you to identify which ethnicity best describes you. You may choose more than one category.” The ethnic groupings were based on the categories similar to Statistics Canada definitions.

For adults, all models were adjusted for age, sex, children in household, material deprivation, chronic conditions, receipt of 2008 seasonal vaccine, tested prior to June 11th, 2009, residing in Toronto, length of stay in Canada.

For children, all models were adjusted for age, sex, total deprivation, receipt of 2008 seasonal vaccine, high proportion of neighbourhood with low income, material deprivation, post-secondary education, chronic condition, residence in Toronto.

In multivariate analysis for children, cases were more likely to be Black (OR 6.43, 95% CI 1.83-22.59) (Table 3). Adjusting for confounders increased the observed OR for Black ethnicity. When the analysis included individuals who did not have symptoms at time of testing, the estimates did not change for any ethnicity except for the Black group (OR 5.44, 95% CI 1.71-17.24). For the analysis conducted without any restrictions (no exclusions), the estimates did not change appreciably. When stratifying by presence of risk factors, among those without risk factors, the estimates increased for all ethnic groups, but the greatest change was observed for the Black group. Cases were more likely to be South Asian (OR 2.92, 95% CI 1.11-7.68), Black (OR 16.02, 95% CI 2.85-89.92), or West Asian/Arab, Latin American, or Multi-racial (OR 3.09 95% CI 1.06-9.00). The ORs for ethnic groups were higher for adults who resided outside Toronto compared to those who resided in Toronto (Table 4). This effect was seen for all ethnic groups, except East/Southeast Asian. This trend was also observed in children but both failed to reach statistical significance (results not shown). Testing date was controlled for in the multivariate model, however, since many of the controls were tested after June 11, we ran a sensitivity analyses restricting to only controls tested prior to June 11, and aside from increasing confidence intervals the parameter estimates for ethnicity were relatively unchanged.
Table 4

A multivariate logistic regression model for pH1N1 illness acquisition in adults and ethnicity, stratified by Toronto residence

 

Wave 1

Ethnic group*

OR (95% CI)

Not Residing in Toronto

 

White

ref

East/Southeast Asian

1.86 (0.57-6.08)

South Asian

9.95 (2.17-45.59)

Black

14.61 (1.49-143.36)

Other

2.26 (0.68-7.53)

Residing in Toronto

 

White

ref

East/Southeast Asian

6.80 (1.02-45.51)

South Asian

2.32 (0.29-18.79)

Black

4.20 (0.38-46.46)

Other

1.50 (0.17-13.48)

*This was based on the question “People in Canada come from many different ethnic and racial backgrounds. I would like you to identify which ethnicity best describes you. You may choose more than one category.” The ethnic groupings were based on the categories similar to Statistics Canada definitions.

For adults, all models were adjusted for age, sex, children in household, material deprivation, chronic conditions, receipt of 2008 seasonal vaccine, tested prior to June 11th, 2009, length of stay in Canada

Discussion

In this case–control study we found evidence of ethnic disparities in acquiring the 2009 pandemic H1N1 such that among adults lab-confirmed pH1N1 cases were more likely to be of East/Southeast Asian, South Asian and Black ethnicity compared to test-negative controls. This association persisted after controlling for several important confounders, including individual and ecological socioeconomic measures and clinical risk factors, and under a variety of restrictions imposed as part of a sensitivity analysis. Among children, not all ethnic disparities seen in adults were detected, however consistent with adults, pediatric cases were more likely to be classified as Black compared to other ethnic groups after controlling for confounders.

Previous studies have found ethnic discrepancies when looking at the distribution of pH1N1 illnesses. In Utah, U.S., it was reported that 29.5% of pH1N1 hospitalized cases were minorities (those not classified as non-Hispanic Whites), significantly greater than the mean of 22% of hospitalized cases that were minorities during the previous three influenza seasons [9]. The majority of studies have looked at hospitalization rates due to pH1N1 and ethnic disparities. For example, in Chicago, U.S., Asian/Pacific Islanders, the non-Hispanic Black group, and the Hispanic group had higher rates of hospitalization due to pH1N1 compared to the non-Hispanic White group [10]. Also, in Wisconsin, U.S., it was reported that the groups classified as Black, Hispanic, and Asian had several-fold higher hospitalization than the non-Hispanic White group due to pH1N1 [11]. Similar findings have also been reported in the United Kingdom [23]. Social factors may explain the ethnic disparities seen in this and other studies, which may operate through a variety of mechanisms. Social factors such as crowding, use of public transit, and ability to keep children away from other groups of children during school or day-care centre closures during a pandemic can play a role in increasing exposure risk [16]. A study in the U.S. compared social risk factors, including social distancing across ethnicities, and found that those identifying as Black and Hispanic (both Spanish and English speaking) found it more difficult to avoid public transportation than non-Hispanic White [17]. Also, those identifying as Black and Spanish-speaking Hispanic reported greater challenges to finding childcare that separated their children from other children, compared to those identifying as non-Hispanic White. There may also be differential access to information regarding the risks associated with influenza as well as preventive measures. For example, we have evidence from seasonal vaccination programs that certain ethnic groups are less likely to receive seasonal influenza vaccines [24, 25].

This study does have a few limitations. First, we were somewhat limited in sample size for certain ethnic groups, resulting in wide confidence intervals observed for adults and children who identified their ethnicity as Black and South Asian. Including all variables in the multivariate model did slightly increase the width of the confidence intervals, however, the variables included were all important confounders and thus failing to control for these variables would produce confounded results. In addition, most of the variables are based on self-report and thus are susceptible to misclassification. Healthcare seeking behaviour was represented by number of primary care physician visits in the last 12 months. However, healthcare seeking behaviour could also be measured by the resources and services that are available to ethnic groups within their own communities. It is important to note that all individuals in this study sought care, thus minimizing the bias that access would have on the results. Another limitation is that a few of the variables were not available at the individual level, such as household income and employment, which are important confounders to consider for studying this relationship. Ecological-level variables were used to assess impact of income based on prevalence of low income among total persons in household within a dissemination area. It should also be noted that these findings were specific to our study, and they may be different for other studies with different ethnic compositions.

Conclusions

The purpose of this study was to determine whether there are ethnic disparities in acquiring the 2009 pandemic H1N1, and this study did find evidence for such disparities. Identifying ethnic differences in infectious disease risk is critical to understanding how best to reduce any excess burden experienced by these populations. To minimize risk in these ethnic groups, further insight needs to be gained as to whether these findings are a result of social or biological factors, or both. The understanding of the origin of these disparities in acquiring pH1N1 can help us to promote and plan interventions accordingly to reduce the burden of illness among ethnic groups.

Declarations

Acknowledgements

The study was funded by the Ontario Ministry of Health and Long-Term Care. The views expressed here are those of the authors and not necessarily those of the funding agency. The funding agency had no role in the data collection or in the writing of this paper. The guarantors accept full responsibility for the conduct of the study, had access to the data, and controlled the decision to publish.

Authors’ Affiliations

(1)
Department of Public Health Sciences, Public Health Ontario
(2)
Dalla Lana School of Public Health, University of Toronto
(3)
Institute for Clinical Evaluate Sciences
(4)
Department of Family and Community Medicine, University of Toronto

References

  1. Statistics Canada: Canada’s Ethnocultural Mosaic, 2006 Census. http://www12.statcan.ca/census-recensement/2006/as-sa/97-562/pdf/97-562-XIE2006001.pdf,
  2. Anand SS, Yusuf S, Vuksan V, Devanesen S, Teo KK, Montague PA, Kelemen L, Yi C, Lonn E, Gerstein H, Hegele RA, McQueen M: Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE). Lancet. 2000, 356 (9226): 279-284. 10.1016/S0140-6736(00)02502-2.View ArticlePubMedGoogle Scholar
  3. Manuel DG, Schultz SE: Diabetes Health Status and Risk Factors. Diabetes in Ontario: An ICES Practice Atlas. Edited by: Hux J, Booth G, Laupacis A. 2002, Toronto, Ontario: Institute for Clinical Evaluative Sciences, 4.77-4.93.Google Scholar
  4. Tremblay MS, Pérez CS, Ardern CI, Bryan SN, Katzmarzyk PT: Obesity, overweight and ethnicity. Health Rep. 2005, 16 (4): 23-34.PubMedGoogle Scholar
  5. Yousey-Hindes KM, Hadler JL: Neighborhood Socioeconomic Status and Influenza Hospitalizations Among Children: New Haven County, Connecticut, 2003–2010. Am J Public Health. 2011, 101 (9): 1785-1789. 10.2105/AJPH.2011.300224.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Lowcock EC, Rosella LC, Foisy J, McGeer AJ, Crowcroft NS: The Social Determinants of Health and Pandemic H1N1 2009 Influenza Severity. Am J Public Health. 2012, 102 (8): e51-e58. 10.2105/AJPH.2012.300814.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Rutter PD, Mytton OT, Mak M, Donaldson LJ: Socio-economic disparities in mortality due to pandemic influenza in England. Int J Public Health. 2012, 57 (4): 745-750. 10.1007/s00038-012-0337-1.View ArticlePubMedGoogle Scholar
  8. Kumar S, Quinn SC, Kim KH, Daniel LH, Freimuth VS: The impact of workplace policies and other social factors on self-reported influenza-like illness incidence during the 2009 H1N1 pandmic. Am J Public Health. 2012, 102 (1): 134-140. 10.2105/AJPH.2011.300307.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Centres for Disease Control and Prevention: Severe Illness from 2009 Pandemic Influenza A (H1N1) -Utah, 2009—10 Influenza Season. Morb Mortal Wkly Rep. 2011, 60 (38): 1310-1314.Google Scholar
  10. Centers for Disease Control and Prevention: 2009 Pandemic influenza a (H1N1) virus infections - Chicago, Illinois, April-July 2009. MMWR Morb Mortal Wkly Rep. 2009, 58 (33): 913-918.Google Scholar
  11. Chitnis AS, Truelove SA, Druckenmiller JK, Heffernan RT, Davis JP: Epidemiologic and clinical features among patients hospitalized in Wisconsin with 2009 H1N1 influenza A virus infections, April to August 2009. Wis Med J. 2010, 109 (4): 201-208.Google Scholar
  12. Zarychanski R, Stuart TL, Kumar A, Doucette S, Elliott L, Kettner J, Plummer F: Correlates of severe disease in patients with 2009 pandemic influenza (H1N1) virus infection. CMAJ. 2010, 182 (3): 257-264. 10.1503/cmaj.091884.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Helferty M, Vachon J, Tarasuk J, Rodin R, Spika J, Pelletier L: Incidence of hospital admissions and severe outcomes during the first and second waves of pandemic (H1N1) 2009. CMAJ. 2010, 182 (18): 1981-1987. 10.1503/cmaj.100746.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Campbell A, Rodin R, Kropp R, Mao Y, Hong Z, Vachon J, Spika J, Pelletier L: Risk of severe outcomes among patients admitted to hospital with pandemic (H1N1) influenza. CMAJ. 2010, 182 (4): 349-355. 10.1503/cmaj.091823.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Public Health Agency of Canada: Frequently Asked Questions - Influenza. http://www.phac-aspc.gc.ca/influenza/influenza-faq-eng.php#h,
  16. Blumenshine P, Reingold A, Egerter S, Mockenhaupt R, Braveman P, Marks J: Pandemic influenza planning in the united states from a health disparities perspective. Emerg Infect Dis. 2008, 14 (5): 709-715. 10.3201/eid1405.071301.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Quinn S, Kumar S, Freimuth V, Musa D, Casteneda-Angarita N, Kidwell K: Racial disparities in exposure, susceptibility, and access to health care in the US H1N1 influenza pandemic. Am J Public Health. 2011, 101 (2): 285-293. 10.2105/AJPH.2009.188029.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Orenstein WA, Bernier RH, Dondero TJ, Hinman AR, Marks JS, Bart KJ, Sirotkin B: Field evaluation of vaccine efficacy. Bull WHO. 1985, 63: 1055-1068.PubMedPubMed CentralGoogle Scholar
  19. Ontario Ministry of Health and Long-Term Care: Important health notice – June 11, 2009. http://www.health.gov.on.ca/en/pro/programs/emb/health_notices/ihn_20090611.pdf,
  20. Pampalon R, Hamel D, Gamache P, Raymond G: A deprivation index for health planning in Canada. Chronic Dis Can. 2009, 29 (4): 178-181.PubMedGoogle Scholar
  21. Institut national de santé publique du Québec: Indice de défavorisation, Canada. 2006, http://www.inspq.qc.ca/santescope/indicedefavo.asp?NoIndD=9,Google Scholar
  22. Hosmer D, Lemeshow S: Chapter 4: Model Building Strategies for Logistic Regression. In Applied Logistic Regression. 2000, New York: John Wiley, 2View ArticleGoogle Scholar
  23. Nguyen-Van-Tam JS, Openshaw PJ, Hashim A, Gadd EM, Lim WS, Semple MG, Read RC, Taylor BL, Brett SJ, McMenamin J, Enstone JE, Armstrong C, Nicholson KG, Influenza Clinical Information Network (FLU-CIN): Risk factors for hospitalisation and poor outcome with pandemic A/H1N1 influenza: United Kingdom first wave (May-September 2009). Thorax. 2010, 65 (7): 645-651. 10.1136/thx.2010.135210.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Quach S, Hamid JS, Pereira JA, Heidebrecht CL, Deeks SL, Crowcroft NS, Quan SD, Brien S, Kwong JC: Ethnic disparities in influenza immunization in Canada. CMAJ. 2012, 184 (15): 1673-1681. 10.1503/cmaj.111628.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Uscher-Pines L, Maurer J, Harris KM: Racial and ethnic disparities in uptake and location of vaccination for 2009-H1N1 and seasonal influenza. Am J Public Health. 2011, 101 (7): 1252-1255. 10.2105/AJPH.2011.300133.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/14/214/prepub

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.