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The importance of socio-economic status and individual characteristics on the prevalence of head lice in schoolchildren


European Journal of Dermatology. Volume 15, Numéro 5, 387-92, September-October 2005, Clinical report


Summary  

Auteur(s) : Sara WILLEMS *, Hilde LAPEERE , Nele HAEDENS, Inge PASTEELS, Jean-Marie NAEYAERT, Jan De MAESENEER , Ghent University, Department of General Practice and Primary Health Care, Campus UZ- 1K3, De Pintelaan 185, B-9000 Gent, Belgium, Ghent University, Department of Dermatology, Campus UZ-1P6, De Pintelaan 185, 9000 Ghent, Belgium.

Illustrations

ARTICLE

Auteur(s) : Sara WILLEMS*1, Hilde LAPEERE*2, Nele HAEDENS1, Inge PASTEELS1, Jean-Marie NAEYAERT2, Jan De MAESENEER1

1Ghent University, Department of General Practice and Primary Health Care, Campus UZ- 1K3, De Pintelaan 185, B-9000 Gent, Belgium
2Ghent University, Department of Dermatology, Campus UZ-1P6, De Pintelaan 185, 9000 Ghent, Belgium

accepté le 10 Juin 2005

Pediculosis, which is defined as an infestation with head, body or crab lice, is a frequent occurring skin infection [1, 2]. Especially, head lice (Pediculus humanus capitis) infestations are a common health problem mainly affecting schoolchildren aged between 3 and 12 [1]. Head lice infestation prevalence rates of 5.8% to 35% have been reported [3-8]. In Belgium, the only information concerning the prevalence of head lice in schoolchildren comes from a recent but small survey in two primary schools in the city of Ghent (an industrialised city in the northern part of Belgium, 226,083 inhabitants), revealing prevalence rates of 13.0% and 19.5% [9].The origin of a head lice infestation is unclear. Most prevalence studies examine the relationship between personal characteristics of the child (i.e. sex, age, hair length, hair type) and infestation [3, 4, 7, 10], but there are few studies that also consider the family’s socio-economic status (SES) [6-8, 11]. All these studies have conflicting results that can be partly explained by the use of different diagnostic criteria, screening methods, the presence of confounding factors, and limited statistical techniques. In some studies, children are considered to be infested if either lice or nits are present [6, 8]. However, an active infestation should only be diagnosed when a living, moving louse is detected [12]. Furthermore, there are three methods of detecting head lice: visual inspection, inspection with a detection comb and wet combing. It has been demonstrated that the latter two are more accurate than the first [10, 13]. Wet combing is probably more accurate then dry combing because lice get stuck in the water and conditioner and are therefore more easily found. Although wet combing is time-consuming and laborious in comparison to other detection methods, it has been illustrated elsewhere that it is feasible to screen large groups of children with this method [9]. Apart from the differences in diagnostic methodology, there is also a large variation in the statistical methods applied. When analysing this kind of data, statistical tests controlling associations between the different factors should be used.Therefore, the complex mechanisms of interaction between the multiple determinants of head lice are still unexplained.The objective of this study is to determine the prevalence of head lice in schoolchildren in Ghent, using the wet combing technique, and to investigate the independent association between individual characteristics of the child, socio-economic status and head lice.

Materials and method

Population and sample

The three School Health Departments (SHD) screen all schoolchildren yearly for head lice using dry inspection. In 2001, the SHD started a pilot project in which children were screened using the wet combing method. All kindergartens and primary schools in Ghent (174) were invited to participate in this pilot project, of which 30 agreed. Between January and June 2001, all children from the participating schools present on the day of the screening were included in the project. A total of 6,169 children were screened, representing 30% of the town’s total population of children aged between 3 and 12.

Data collection

Several SHD staff screening teams were involved in the pilot project, led by a staff member trained in the wet combing method by experts on this subject. Where necessary, screening teams were reinforced with parents and teachers, who were trained using an educational package including a video of the wet combing method. The team leader double-checked positive results found by the team members. An ordinary shampoo, conditioner and a fine-toothed plastic comb (as in a Bug Buster kit®) were used. First, the hair of the child was washed with an ordinary shampoo and plenty of conditioner was applied to the wet hair after straightening with a grooming comb. Then the scalp was systematically combed with a fine-toothed comb, first from back to front and then vice versa. Combing was started at one side of the head and ended at the other side. When conditioner is used, lice get caught in the moisture and are unable to move. The fine-toothed comb lifts out lice from the hair, even the smaller nymphal stages that sometimes remain unnoticed by the naked eye.

A child was found positive if a living louse was found. The presence of nits was also recorded, but no distinction was made between viable or dead eggs or empty eggshells. The positive children were given a letter for their parents with treatment advice and were screened again two weeks later. For every child, the SHD staff collected data about demographic characteristics (sex, date of birth, class and school), properties of the hair (length, colour and type) and characteristics of the family (number of children in the family and socio-economical status). Hair length was defined as very short (< 2 cm), short (≥ 2 cm but less than shoulder length), medium (= shoulder length) or long (> shoulder length). Hair colour was divided into 4 categories (black, brown, red or fair) (( figure 1 )) and hair type into 3 categories (straight, curly or frizzy).

The socio-economic status of the family was based on the person with the highest occupational status in the household and retrieved from the children’s school file. Occupation was classified according to the Standard Occupational Classification, published by the Office of Population Censuses and Surveys, using 4 categories: unemployed, manual worker, non-manual worker and professional [14].

The nationality of the child was also recorded but not included in the analysis since there were too many different nationalities and recording this information into a dichotomous variable (Belgian versus other) led to an important loss of data. All parents of positive children were given treatment advice and the treatment chosen was recorded for 25% of the positive children. No information was obtained on whether the treatment was applied correctly. Positive children were screened again 14 days after baseline screening. Results from the second screening are available for 87% of the children found to have lice at the baseline.

Statistical analysis

The data was analysed using SAS® release 8.02. First, two-dimensional crosstabs with χ2-test statistics and correlation matrices were built in order to study the bivariate relationships between the prevalence of head lice and the independent variables. Secondly, to determine the relative importance of the different variables in explaining or predicting the presence of head lice, the Glimmix macro written by Russ Wolfinger from SAS was used to construct a logistical model with random effects [15]. Since the sample design was typically hierarchical, this type of multilevel model was most appropriate for a dichotomous outcome variable because it incorporated the fact that observations from same subpopulations were more equivalent than observations from other ones. P < 0.05 was set as the level of statistical significance.

Results

The prevalence of head lice infestations in children from kindergartens and primary schools in Ghent was 8.9%. Another 4.6% had nits without lice, a sign of a past infestation.

Bivariate analysis demonstrated a significant association between head lice and the child’s sex, educational level, hair length and colour, number of children in the family and SES (table 1)( Table 1 ).

Multilevel analysis showed that the variance at school level is 1.93 (P = 0.011). The same parameter at class level within schools is 2.58 (P < 0.001) and the residual level (level of the different children within classes) is 0.59 (P < 0.001).

This shows that a child’s school and in particular his or her class have a greater impact on the risk of head lice than individual characteristics. The impact at child level can be attributed to SES (P = 0.017), the number of children in the family (P < 0.001), the length of the child’s hair (P = 0.028) and the hair colour (P = 0.021) (table 2)( Table 2 ).

Being from a family with a lower SES, more children and having longer hair tend to result in a higher risk of getting head lice (respectively OR: 0.80,95% CI: 0.68-0.96; OR: 1.2,95% CI: 1.10-1.32; OR: 1.2, 95% CI: 1.02-1.43). For example, for every child that a family has, the probability ratio of getting head lice increases by 1.2 times. Having black hair entails a lower risk than having brown hair (OR: 0.6,95% CI: 0.43-0.89).

Head lice were still found in 41.4% of the children screened fourteen days after baseline screening.

According to the multilevel analysis, only the hair colour (P = 0.011) and SES (P = 0.031) were statistically significant. The lower the SES, the higher the chance of still being positive on the second screening (OR: 0.6, CI: 0.37-0.95). Children with black hair have a higher chance of being lice-free at the screening than children with brown hair (OR: 0.3, CI: 0.11-0.74).
Table 1 Sex, age, educational level, hair characteristics, number of children in the family and socio-economic status (SES) of the family: bivariate results at baseline and two weeks after baseline screening

Baseline screening

2 weeks after baseline screening

n (%)

Head lice

n (%)

Head lice

%

P

%

P

Sex

6104

< 0.001

469

0.110

Boys

2938 (48.1)

6.8

174 (37.1)

36.2

Girls

3166 (51.9)

10.7

295 (62.9)

43.7

Age (years)

5519

0.194

420

0.333

≤ 5

1297 (23.5)

9.1

95 (22.6)

33.7

6-7

1252 (22.7)

9.2

106 (25.2)

39.6

8-9

1413 (25.6)

9.5

124 (29.5)

41.9

10-11

1136 (20.6)

7.0

66 (15.7)

47.0

≥ 12

421 (7.6)

8.6

29 (6.9)

51.7

Educational level

< 0.001

459

0.731

Kindergarten

1619 (26.8)

10.2

139 (30.3)

40.3

1st and 2nd year of primary school

1778 (29.4)

9.7

153 (33.3)

41.2

3rd and 4th year of primary school

1553 (25.7)

8.0

114 (24.8)

42.1

5th and 6th year of primary school

1090 (18.0)

6.1

53 (11.5)

49.1

Hair length

6124

< 0.001

470

0.078

Very short

1329 (21 .7)

5.1

60 (12.8)

26.7

Short

2218 (36 .2)

8.5

163 (34.7)

45.4

Medium

1336 (21 .8)

11.5

136 (28.9)

44.1

Long

1241 (20 .3)

10.6

111 (23.6)

42.3

Hair type

6076

464

0.459

Straight

5192 (85.5)

8.6

0.211

386 (83.2)

42.0

Curly

785 (12.9)

10.6

72 (15.5)

41.7

Frizzy

99 (1.6)

8.1

6 (1.3)

16.7

Hair colour

6128

472

0.418

Fair

2137 (34.9)

7.7

0.035

148 (31.4)

39.9

Red

121 (2.0)

9.1

10 (2.1)

30.0

Brown

2808 (45.8)

10

246 (52.1)

44.7

Black

1062 (17.3)

8.2

68 (14.4)

35.3

Number of children in family

5943

< 0.001

458

0.004

1

948 (15.9)

7.8

65 (14.2)

38.5

2

2580 (43.3)

7.6

174 (38.0)

32.8

3

1499 (25.2)

8.6

112 (24.5)

45.5

≥ 4

916 (15.6)

13.9

107 (23.4)

54.2

SES

3742

< 0.001

303

0.66

Unemployed

296 (7.9)

17.6

38 (12.5)

50.0

Manual worker

1570 (42.0)

12.4

173 (57.1)

48.0

Non-manual worker

1490 (39.8)

5.8

75 (24.8)

34.7

Professional

386 (10.3)

5.2

17 (5.6)

23.5


Table 2 Multilevel analysis at baseline screening and two weeks after baseline screening

Base line screening

14 days after baseline screening

Estimate

P-value

Estimate

P-value

School-level

1.933

0.011

1.745

0.044

Class-level

2.581

< 0.001

1.316

0.014

Child-level

0.594

< 0.001

0.713

< 0.001

Odds ratio (95%CI)

P-value

Odds ratio (95%CI)

P-value

Sex

1.3 (0.89-1.82)

0.187

0.9 (0.37-2.33)

0.885

Age (years)

0.9 (0.91-1.07)

0.839

1.0 (0.89-1.20)

0.679

Hair length

1.2 (1.02-1.43)

0.028

1.3 (0.80-2.00)

0.308

Hair type

Straight

1.5 (0.51-4.30)

0.465

1.4 (0.12-16.26)

0.778

Curly

1.6 (0.55-4.84)

0.376

1.3 (0.10-15.96)

0.840

Frizzy

1.0

1.0

Hair colour

Fair

0.9 (0.75-1.27)

0.861

0.8 (0.43-1.62)

0.580

Red

0.4 (0.14-1.37)

0.155

1.9 (0.16-23.14)

0.593

Brown

1.0

1.0

Black

0.6 (0.44-0.89)

0.008

0.3 (0.11-0.74)

0.011

Number of children in family

1.2 (1.10-1.32)

< 0.001

1.1 (0.92-1.40)

0.240

SES

0.8 (0.68-0.96)

0.017

0.6 (0.37-0.95)

0.031

Intercept

0.01(0.01-0.16)

0.003

0.4 (0.01-10.2)

0.556

Discussion

The prevalence of head lice infestation in a large sample of schoolchildren in Ghent was 8.9%, lower than the previously reported 13.0% and 19.5% from a previous small study in the town. The schools in the latter study were selected because of their high motivation, which was probably related to the fact that they have encountered head lice before. The multilevel analysis shows that clustering of children in groups (in classes and schools) is the most important factor determining the risk of head lice. Head lice are transmitted by head-to-head contact and probably also by fomites [16]. Children from the same class have intense contact with each other, providing frequent opportunities to transmit head lice. However individual characteristics do have some impact: hair length, hair colour, the number of children in the family and socio-economic status of the family are statistically significant associated with the prevalence of head lice.

The strength of this study is its methodology. No similar survey applying the same diagnostic criteria and screening method and including a comprehensive set of confounding factors has ever been performed on such a large sample. Furthermore, the use of multilevel analysis allowed the unique impact of each independent variable to be determined. A source of concern in the present study could be the lack of a randomised sample. However, a good representative sample of the population was achieved with only a slight overrepresentation of larger kindergartens and primary schools. Furthermore, the prevalence might be influenced by seasonal trends in head lice infestations as this study was carried out between January and June. Some authors found a difference in prevalence according to the month when a screening was performed [17, 18]. Finally, information on SES was not always recorded in detail in the child’s school record, resulting in a loss of data.

This study confirms the results of other studies in the field, yet on some points it nuances, refutes or explains earlier findings. The clustering of head lice within classes was observed in a prevalence study on 735 pupils from one school [4]. Our study, performed on a larger sample of children from different schools, confirms the observation of the latter study. Concerning hair characteristics, as reported earlier by Mumcuoglu et al. [3], we found higher prevalence rates in children with brown hair. One can assume that brown-coloured lice contrast more in fair, red and black hair and are therefore detected earlier. Earlier treatment of these children may lead to lower prevalence at formal cross-sectional screening sessions. Hair length can probably be considered as an important transmitting factor, as head lice need specific conditions to move from one head to another [16], which occur more frequently in longer hair. It also seems that the infestation rate rises with the number of children in the family, possibly because children in large families have a higher risk of being infested by their siblings (or parents). The influence of the family’s socio-economic status on the presence of head lice has been investigated before, using indicators such as educational level or profession of the parents, family income, recourse to social security and socio-economic class of the school. A positive correlation has been found [1, 6, 11, 19] in some reports but not in others [7, 8]. However, in most surveys, only bivariate analysis has been used. This study shows that when adjusting for a range of confounding factors, SES (defined as the highest occupational class in the household) is significantly related to the prevalence of head lice. In several studies, a difference was found in prevalence rates between girls and boys [1, 4, 6, 7, 20]. It is believed that gender-related behaviour differences affect transmission rates, e.g. difference in personal grooming, close contact, hairstyle changes and the use of hair accessories [21]. Counahan et al. supported the gender difference based on a large study that used multilevel statistical techniques and included type of residence, class, gender and hair length as confounding factors [20]. However, when building a model that adjusts to a more detailed set of determinants including other hair characteristics, no difference was found. We found that the commonly assumed risk factor of age was not associated with active infestation [6, 10]. Probably, this could be explained by the high association with school class which was introduced as a level in the model. This study did not confirm earlier findings of lower infestation rates in children with curly hair [3]. Nymphal stages are easily missed in curly or long hair when using the dry inspection method, possibly leading to an underestimation of head lice infestation in children with curly or long hair [22]. When applying wet combing, even the small lice are found in straight as well as in curly hair [23].

A striking result from the screening after treatment advice is that 41.4% of the children found to be positive in the baseline screening were still positive, meaning they were not adequately treated. This is rather high, considering that every parent received treatment advice. Vander Stichele et al. also found a low treatment effectiveness of 51% [9]. Several factors could be responsible for the high rate of treatment failure. It cannot be excluded that some parents, regardless of the advice given, did not treat their children. It is more likely, however, that there was resistance to the chosen pediculicide or that a lot of children were not treated correctly. Pediculicides are often expensive and should be applied correct in order to have optimum results. The wet combing method was also discussed as a treatment option. This method is relatively cheap but requires stringent implementation and is time-consuming. Wet combing should be repeated meticulously once every 3 to 4 days over a period of 14 days to break the life cycle of the head louse [9] and is not effective if the instructions are not followed correctly.

Attention should be paid when interpreting the results of this study. A significant association was found between head lice and several factors at child level, yet this does not imply a causal relationship. Presumably, these factors lead indirectly to higher prevalence rates because they hamper the detection (e.g. hair colour) or treatment (e.g. SES) of lice.

This survey illustrated that the clustering of children in classes and schools is a more important determinant of the prevalence of head lice than personal characteristics. Head lice infection is therefore difficult to avoid, making its early detection and effective treatment all the more important. Although earlier studies indicated it was feasible to screen larger groups of children for head lice using the wet-combing technique, the feasibility of organising large screening campaigns in schools several times per year is questionable. Probably, it is more advisable to introduce screening in the daily routine of the family, e.g. as part of the weekly bathing ritual. Schools and school health departments could play an important supporting role for the parents by organising information campaigns and “practice sessions” on the screening and treatment of children.

The current study showed that children from families with a lower SES are particularly at risk of getting head lice. Previous studies showed that the lives of people from the lowest socio-economic classes are often characterised by a short-term “survival” perspective. “Compliance” is, from a socio-culturally point considered, a middle-class concept because in lower social classes, the need for an adequate follow-up of the treatment instructions could be overruled by acute social problems related to income, housing conditions… [24]. Therefore special emphasis should be put on the need to establish specific support systems (e.g. by school nurses and primary care nurses) for children from low SES families with lice infestations. As Whitehead has indicated, efforts to decrease social inequalities in health must adopt a multi-level approach and focus on empowering families, strengthening communities (by educational efforts towards caregivers) and encouraging macro-economic and cultural change [25].

Further research is needed to confirm the findings of this study using a randomised sample and more detailed measurement scales, e.g. for SES. A cost-effectiveness study should be performed in which the benefits and costs of wet combing versus detection-combing of dry hair are assessed.

Acknowledgements

We would like to thank all staff members of the School Health Departments who collected the data, and the parents and children for their participation in this study. We also would like to thank the workgroup “Luis ze erin” for their enthusiasm and substantial contribution. Finally, we thank Dr. R.H. Vander Stichele for his very useful comments on the manuscript.

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