Problem behavior in a community sample of 14- and 19-month-old children

Few studies have examined the presence, structure, and stability of behavior problems in a community sample of 14- and 19-month-old infants. A questionnaire with items on emotional, attentional, and impulsive behavior and social communication was completed by the parents of 6,491 infants aged 14 months and 1,803 infants aged 19 months. Particularly externalizing behavior problems were reported to present sometimes or often for 50% of more of the sample and could be considered as common. In contrast, social communication problems were reported to be present in less than 10% of the sample. Overall, boys showed more problem behaviors than girls. Principal component analysis at the 14 months data revealed seven factors, which could all be replicated in the 19 months data. Pearson correlations between scores at 14 months and 19 months were highest for oppositional and attention factors (0.68 and 0.63) and lowest for the inhibiton factor (0.38). More than 50% of those scoring in the top 10% for total problem score at 14 months were in the top 10% at 19 months. These results will facilitate the recognition of psychopathology at very early age and the study of its development over time.
Introduction
Psychopathological research has only recently started to focus on the expression of problematic development at a very young age, i.e., younger than 18 months, and few data are available on the expression of problem behavior in somewhat older children between 1- and 3-year-old [5, 9, 23]. Indeed, the lack of normative data for normal and deviant development in children younger than 18 months may lead to inadequate early recognition of serious behavior problems [8, 33, 34].
The differentiation of normal behavior from problem behavior is particularly challenging in young children [19]. Many symptoms of psychiatric disorders in childhood and adolescence are considered to be relatively normal in preschool-age children. For example, during the preschool years, aggressive, hyperactive, and non-compliance behaviors and separation problems are relatively common and can be viewed as relatively normal within a developmental perspective [6]. Still, these behaviors can be quite stressing for parents and are generally referred to as problem behaviors. Children younger than 18 months are learning to control themselves, to become attached to people and objects, and to explore, and are only beginning to form stable behavior patterns. Earlier studies reported that problem behavior affected between 7.3% and 24% of 2- and 3-year-old children [5, 12, 14, 18, 20, 22, 24, 26, 27, 31, 32], but there are few data for children younger than this age. One developmental screening study reported that about 10% of infants had emotional and behavioral problems in the first year of life [15], and another study reported that about 6% of the 1-year-old children had high scores on the Difficult Child domain of the Parenting Stress Index [5].
An important reason to study behavior at a young age is that it may predict problem behavior at a later age. For example, indices of temperament at 3 years were found to predict the presence and the type of psychiatric disorder in young adulthood [10], and behavior problems at 2 years, such as aggression, non-compliance, and difficult temperament, were valuable predictors of lasting problems at a later stage of development [17]. Approximately 50% of the 2- and 3-year-old children with externalizing psychiatric disorders continued to have these problems later in life [6, 21]. Such problems appear to be stable from an age of 18 months onward [23], but to our knowledge stability has not been evaluated in younger children.
More knowledge on the expression and stability of problem behaviors before the age of 18 months would be advantageous, especially for doctors and nurses working in well baby clinics. In the Netherlands, infants visit well baby clinics according to a fixed scheme. At 14 months there is a relatively extensive check up. Since this is a moment for vaccination the attendance rate for this check up is high. Therefore, more knowledge on problem behaviors at especially 14-month-old was desirable. The current study was designed to investigate behaviors at this young age and had the following aims.
The first aim was to examine the occurrence of problem behaviors or lack of competences at younger age that are considered to be psychopathological in later childhood. A second aim was to examine whether the constructs of problem behaviors and competences usually found in older children are already present at 14 and 19 months of age. A third aim was to examine the short-term stability of parent-reported problem behavior and lack of competences between age 14 and 19 months. To achieve these aims, we used a questionnaire with 55 items on mainly externalizing and social communication behaviors.
Method
Sampling
The sample was a total population birth cohort of children born between August 2000 and August 2001 (N = 12,297) in the province of Utrecht in the Netherlands. The province has a population of about 1.117.997 and includes rural and highly urbanized areas. Parents of 14-month-old children received a request from the provincial inoculation center to complete our questionnaire concerning infant problem behavior. This request was accompanied by an information brochure that explained the purpose of this study (at time t1). Parents of 6,491 infants (i.e., 53% of the 12,297 infants eligible) returned the questionnaire and consented to participate. About equal numbers of boys and girls participated. Unfortunately, we were not allowed access to information on non-responders, and so we investigated potential selection bias by comparing the data of responders to demographic data for the province of Utrecht [25]. Responders lived somewhat less often in urban areas (responders 46.6%, province of Utrecht 48.1%) and had a higher educational level, which was defined as parents with at least a high school or college degree (responders sample: mothers 43.4%; fathers 44.2%; province of Utrecht: mothers 38.9%; and fathers 36.0%). Chi-square test for differences confirmed significant differences with respect to urbanization grade (χ2 = 86.437, df 4, P < 0.000) and educational level of the mother (χ2 = 328.56, df 2, P < 0.000) and of the father (χ2 = 313.18, df 2, P < 0.000). Further, our sample contained significantly fewer children (responder sample: 5.3%, province of Utrecht: 17.94%) of non-Dutch descent (χ2 = 695.785, df 5, P < 0.000).
To parents of children born in June, July, and August 2001 (N = 4,051) who had also received the questionnaire at 14 months and had returned the questionnaire the first time, the questionnaire was sent again (at time t2) when the children were 19 months of age. This time 1,803 subjects (44%, 919 boys, 889 girls) participated. There were no significant differences in demographic characteristics between the 19-month-old group and the 14-month-old group. For further analyses of the 14- and 19-month-old data, we used the 14-month-old data (N = 6,491), and the 19-month-old sample (N = 1,803), respectively. When both samples were compared we used the combined sample (N = 1,803).
Description of participants
Approximately 88% of the parents were Dutch, 1.3% were Turkish, 1.8% were Moroccan, 1.3% were from the Caribbean (Surinam or the Dutch Antilles), 0.4% were from other Western countries, and 0.5% were of non-Western origin. Questionnaires were completed by mothers (80.5%), fathers (10.7%), and by both (0.6%). Mean child age was 14.80 months (SD = 1.84). The mean number of children in the family was 1.80 (SD = 1.03). Mean birth weight was 3,537.5 g (SD = 823.9). Of all infants, 1% had a physical or mental disability, 4.1% had a physical disease, and 6.3% used medication. Parental education level varied: 43.8% had a high level of education, 38.1% had a medium level of education, and 15.5% had a low level of education.
Measures
Behavior problem questionnaire
We used a combination of 55 items on a 3-point Likert scale (0 ‘‘never,’’ 1 ‘‘sometimes,’’ 2 ‘‘often’’) focusing primarily on externalizing behavior, social communication problems, and internalizing items. In selecting items, we first composed a large pool of potentially interesting items concerning children younger than 18 months by reviewing instruments for older children, namely The Infant-Toddler Social and Emotional Assessment; The Child Behavior Checklist for 1.5–5; and the Vineland Social-Emotional Early Childhood Scales [1, 7, 29, 30] and based on our clinical experience with very young children. Then a multidisciplinary panel of experts with clinical and research experience with infants and toddlers made a final selection of items based on two criteria, namely, the item had to be specific for externalizing, social communication or internalizing problem behaviors and to be usable for infants younger than 18 months. The final selection included items that covered attentional problems, hyperactivity, oppositional behavior, aggressive behavior, verbal and non-verbal aspects of social communication (such as initiating and responding) and internalizing behaviors such as problems with eating or sleeping.
Data analyses
Items were recoded in such way that a score of two represented the complete presence of an unwanted behavior or the complete absence of a wanted behavior. Common and uncommon problems were identified using the approach of Koot and Verhulst [18]. Common problems were defined as all items that scored 1 (present sometimes) or 2 (present often) for 50% or more of the sample. Uncommon problems were defined as all items that scored 1 or 2 for less than 10% of the sample.
The structure of problem behavior was examined by entering all items in a principal component analysis (PCA) with varimax rotation. The number of factors was based on the scree test [13]. Items with a minimum factor loading of ≥0.30 were included [2, 4]. To validate the findings, PCA was performed on two split half samples Pearson correlations were calculated to examine the interrelationship between the factor sumscores. Finally, in order to examine possible two higher-order factors, PCA was performed requiring all items to load on two factors and Pearson correlations were calculated to examine their interrelationship.
Stability was examined parametrically by calculating Pearson correlations between scores at 14 and 19 months. Further, stability was examined non-parametrically by examining how many children with high scores (above the 75th or 90th percentile) on Total Problem scores and single factor scores at 14 months still had high scores at 19 months. ANOVA was performed to assess the effect of sex on the scores of each problem item and for the factor sum scores. Cohen’s d value is reported as an index of effect size (small, d = 0.2; medium, d = 0.5; or large, d > 0.8) [11]. All analyses were two-tailed and a 0.05 level of significance was used. These analyses were performed using the Statistical Package for Social Sciences (SPSS for Windows, version 11.5).
Results
Frequencies
Common and uncommon problem behaviors and their frequency of occurrence are shown in Table 1. Almost all common problem behaviors were externalizing items, whereas uncommon problems were social communication and internalizing items. Seven of 13 common behavior problems and two of eight uncommon behavior problems were more common among boys than among girls.
Table 1Common and uncommon problem behavior at 14 months% With score 1 (sometimes)% With score 2 (often)% With score 1 or 2Common problem behavior  Quickly shifts from one thing to othera61.926.087.9  Angry moodsa63.59.673.1  Demands must be met56.814.170.9  Can’t stand waiting, wants everything now51.116.267.3  Uses toys for fantasy playa35.229.865.0  Wants a lot of attention50.314.464.7  Accident prone56.46.462.8  Stays close to parent54.04.558.5  Easily upset46.37.553.8  Doesn’t sit still during storya39.414.053.4  Won’t share toys or other thingsa46.15.952.0  Doesn’t keep trying46.75.151.8  Can’t sit still, restless, hyperactivea40.011.051.0Uncommon problem behavior  Fails to follow with eyes8.01.49.4  Doesn’t show interest in new objects7.10.67.7  Doesn’t make happy noises6.70.87.5  Doesn’t make noises spontaneously5.81.16.9  Has less fun than others4.51.56.0  Doesn’t react at calling namea5.30.65.9  Seems unhappy without good reason4.90.75.6  Doesn’t imitate simple gesturesa4.30.95.2Note: Common is defined as >50% with score 1 or 2. Uncommon is defined as <10% with score 1 or 2. Please note further that items covering lack of competences were recoded in such way that a score of 2 represented the complete presence of an unwanted behavior or the complete absence of a wanted behavior. This explains why all items have the same-sign factor loadingsa Significantly more boys (P < 0.001)
Factor structure of problem behavior
The principal component analysis with varimax rotation with eigenvalues >1.5 for two equally sized split half samples resulted in two very similar solutions. The scree test suggested a seven-factor PCA solution (eigenvalue >1.5) with a percentage explained variance of 34.6% (see Appendix to this paper for details). The PCA for the 19-month-old children showed the same factors as the analysis at 14 months, with 37.7% of explained variance. Five of the 55 items had higher loadings at 19 months on other factors than at 14 months.
Pearson correlations between the factor scores (Table 2a) suggested a higher-order division into two problem domains, one reflecting total problem behaviors and one reflecting competences. Oppositional problem behavior at 14 months of age was mostly related to the factors Attention, Inhibition, and Dysregulation, with r values varying between 0.37 and 0.52 and the factor Language Development was mostly related to the factors Explorative Behavior and Communicative Intent (r values of 0.41 and 0.42, respectively). Indeed, principal component analysis confirmed the underlying structure of the higher-order factors when all items were forced to load on two factors and were named Total Problem Behavior (eigenvalue 6.9, explained variance 11.5%) and Competences (eigenvalue 4.5, explained variance 7.5%). Pearson correlation of these higher order constructs revealed a low correlation (0.12).
Table 2Pearson correlation matrix of factor sumscores of (a) 14-month-old and (b) 14- and 19-month-oldQuestionnaire domainsOppositional behaviorLanguage developmentAttentionExplorative behaviorCommunicative intentDysregulationInhibition14 monthsaOppositional behavior1Language development−0.0011Attention0.522**0.106**1Explorative behavior0.0050.412**0.167**1Communicative intent0.102**0.422**0.142**0.351**1Dysregulation0.436**0.0220.3150.060**0.124**1Inhibition0.379**0.098**0.235**0.076**0.308**0.308**114 months19 monthsbOppositional behavior0.678Language development0.0290.476**Attention0.498**0.112**0.625**Explorative behavior0.0890.264**0.188**0.452**Communicative intent0.057**0.255**0.131**0.206**0.466**Dysregulation0.353**0.069**0.300**0.080**0.127**0.445**Inhibition0.306**0.100**0.247**0.086**0.120**0.226**0.380Note. *Correlation is significant at the 0.05 level (2-tailed)**Correlation is significant at the 0.01 level (2-tailed)
A significant sex difference (boys had higher scores than girls) was found on several factors, namely Oppositional Behavior (F = 15.20, df 1, P < 0.000), Language Development (F = 108.88, df 1, P < 0.000), Attention (F = 22.56, df 1, P < 0.000), Explorative Behavior (F = 18.05, df 1, P < 0.000), and Communicative Intent (F = 15.76, df 1, P < 0.000). The effect sizes were small (Oppositional Behavior, d = 0.09; Language Development, d = 0.26; Attention, d = 0.11; Communicative Intent, d = 0.10). No significant sex differences were found for Dysregulation and Inhibition. At 19 months, the same sex differences were found, except for Communicative Intent.
Stability over 5 months
Pearson correlations between the factor sum scores at 14 and 19 months varied between 0.38 and 0.68, with the highest correlation being found for the Oppositional (0.68) and the Attention factors (0.63) and the lowest for the Inhibition factor (0.38). This indicates moderate stability of the constructs of problem behavior over the 5-month period (See Table 2b).
The most stable problems were problems of Communicative Intent, Oppositional Behavior, and Attention (see Table 3). At least half of the group with scores at or above the 75th percentile range at 14-month-old had high scores on problem behaviors at 19 months. With regard to the Total Problem sum scores, 75.5% of the group with scores at and above the 90th percentile of the Total Problem score at 14 months still had problem behaviors at 19 months within the75th percentile range. Also, 53.1% of the children at or above the 90th percentile still had high scores within the highest range 5 months later. On the other hand, about 88.1% of the children with scores below the 75th percentile at 14 months remained below the 75th percentile at 19 months. However, 3.4% of the infants with the least problems on the Total Problem score at 14 months scored at or above the 90th percentile at 19 months (see Fig. 1).
Table 3Mean factor scores at t1 (14 months) and t2 (19 months) and percentage of children with scores above 90th and 75th percentile on both 14 months t1 and 19 months t2 (N = 1,803)Behavioral dimensionMean factor scores at t1 and t2% Of children scoring >90th percentile at both t1 and t2% Of children scoring >75th percentile at both t1 and t2Oppositional behavior0.370.4244.860.3Language development0.470.4431.355.8Explorative behavior0.420.4434.250.3Attention0.420.4245.056.7Inhibition0.640.6529.043.7Communicative intent0.500.9246.851.3Dysregulation0.730.3527.551.1Note. The columns represent the percentage, of those children scoring more than 90th/75th percentile at t1, who were also above 90th/75th percentile at t2Fig. 1Transitions (expressed as percentages of children) between 14 months and 19 months for the 90th and 75th percentile cutoff of the total problem behavior score
Discussion
Recently, more interest has been paid to the manifestation of early problem behavior in infants and toddlers. As expected, we found externalizing behaviors to be among the most common problem behaviors in 14-month-old infants. Indeed, more than 70% of the parents reported that their children often or very often showed behavioral items such as “quickly shifts activities,” “angry moods,” and “demands must be met immediately.” These rates are fairly comparable with the prevalence rates reported earlier for somewhat older children [18]. Although these and other common oppositional behaviors are symptoms of DSM-IV axis-I disorders such as Attention Deficit Hyperactivity Disorder, Oppositional Defiant Disorder, and Conduct Disorder at older ages, these high scores for 14-month-old infants are not necessarily psychopathological [6, 18]. Instead, the high occurrence of parent-reported problem behaviors at this early age can best be interpreted as “relatively normal” [18, 23]. While the symptoms and frequency of externalizing problem behaviors change with age, it is of interest to categorize these changes by their severity and to study their developmental trajectories in order to classify expected abnormal outcomes [3]. Further work should therefore focus on longitudinal studies to decide where the cut-off point is and then start to index a risk for psychopathology.
Lack of competences in social communication and internalizing problem behaviors were relatively rare, occurring in about 5–9% of the community sample. These results confirm that internalizing symptoms with possible underlying constructs of withdrawal and depression are less prevalent at this early age [4]. This is not to say that these behaviors are unimportant or should be neglected. For example, other social communication items such as “failure to follow with the eyes,” “does not react to calling name,” and “does not imitate simple gestures” may indicate the presence of more serious psychopathology, such as autistic spectrum disorder. It would be interesting to determine whether the risk of later psychopathology is significantly higher for a combination of problem behaviors and for which combination this accounts. Also, the frequency or severity of these social communication problems is associated with a serious risk of problems later in development.
Principal component analysis of the data for the 14-month-old children indicated that seven factors could identify important behavioral dimensions. The observation that PCA revealed the same factor solutions for the original as well as for both split half samples is strong evidence for the presence of these distinct behavioral dimensions at 14 months. While the CBCL 1.5–5 [1] can detect externalizing problem behavior in 18-month-old children, our study indicates that these behaviors are present at an even younger age, 14 months. The two behavioral dimensions with the highest explained variance were dimensions describing problems in oppositional behaviors and language development. The dimension Language Development was correlated with the behavioral dimension Communicative Intent, which covers items of more nonverbal-related behaviors, and with Explorative Behavior. Ever since the work of Piaget, explorative behavior has been considered crucial for cognitive development. Explorative behavior is also an important construct in the field of attachment theory, where the infant is thought to use the attachment figure as a secure base for exploring its environment. Interestingly, the finding of a separate attentional factor refers to the emergence of attentional systems of higher-level control at 12–36 months, which contribute to the development of more goal-oriented behaviors and the ability to inhibit actions [28]. The behavioral dimension termed Inhibition consisted of items consistent with the construct of passive inhibition, namely items focusing on inhibition to novelty, separation distress, and affect-relating internalizing behaviors [16]. Eating and sleeping problems as well as more anxiety-related problem behavior formed a Dysregulation dimension, as found in other studies [4]. Our findings suggest that even at this young age the structures that underlie behavior problems are becoming clearer.
These behavioral dimensions are in line with dimensions described in other questionnaires. For example, our Oppositional Behavior dimension includes many items found in the Aggressive behavior factor of the CBCL 1.5–5 [1]. Items of the Communication scale of the Vineland are found in our dimension Language Development and Communicative Intent in our analysis. The latter dimension also includes some items of the Socialization scale of the Vineland [29, 30]. Next, our dimension measuring Attention consists of items from Attention dimensions of other scales (the Attention Problems of the CBCL 1.5–5 and the Attention scale of the ITSEA) [7]. Also, our dimension Explorative behavior is partly in line with ITSEA’s Mastery motivation and partly with the Socialization scale of the Vineland. Furthermore, our dimension Dysregulation consists mainly of items of the Internalizing dimension of the CBCL 1.5–5. Finally, most of the items seen in our dimension Inhibition are found in the Other Problem dimension of the CBCL 1.5–5 and some are from the Internalizing and Competence domain of the ITSEA.
Inspection of the factorsumscores correlation matrix suggested two higher-order dimensions, one consisting of problems behaviors (Oppositional behavior, Attention, Dysregulation, and Inhibition) and a second one covering competences (Language Development, Explorative Behavior, and Communicative Intent). This higher order division was confirmed in a factor analysis forcing a two-factor solution. The two higher-order factors Total Problem Behavior and Competences correlated only very weakly with each other (0.12), suggesting a fairly amount of independence. Apparently a relative high level of problem behaviors is not necessarily accompanied by a relative low level of competences and vice versa. This finding is in accordance with studies using the ITSEA in older children. In these studies too, competences emerged as a separate factor from factors on problem behaviors [4, 5, 9]. For the clinical practice our result indicates that even at a very young age, a thorough evaluation of a child’s functioning should include both an investigation of problem behaviors and an evaluation of competences.
With regard to sex differences, boys were reported to have significantly more problem behaviors than girls on all factors, except for dysregulation and inhibition, consistent with the finding that boys show more externalizing behaviors [18]. We found no sex differences in dysregulation and inhibition, whereas other studies report girls to be more fearful and to have more sleeping problems than boys [14, 27]. Boys were reported to have more problems in social communication behaviors, which suggests that boys acquire these competencies later than girls. This could play a role in the emergence of problem behavior among boys, whereas the early acquisition of competencies could be a protective factor for girls [4]. Contradictory reports on the presence of sex differences in problem behaviors may also be due to cultural or social factors, such as social role expectancy issues, and these may well complicate the interpretation of results.
Problems of communicative intent, oppositional problems, and attentional problems had the highest correlations and remained moderately stable over the 5-month period between 14 and 19 months of age. The least stable were inhibition and dysregulation problems, perhaps reflecting a more temporary nature of these behaviors, as reported earlier by Briggs-Gowan and Carter [4]. At least 53% of the infants with scores within the 90th percentile of each factor sum score at 14 months still exhibited these problem behaviors at 19 months of age. These results are in accordance with other studies that have also shown substantial stability in problem behavior [21, 23]. These studies investigated children older than 19 months of age, whereas our results suggest that these problem behaviors are already moderately stable at an even younger age, emphasizing the need for early assessment. We can only speculate on the determinants of this stability, such as the child’s temperament, family environment, or parental variables. It would be interesting to investigate these determinants in relation to the frequency, severity, and pervasiveness of behavior problems over a longer period of time, for example, at 14, 19, and 36 months of age, in order to gather information on how problem behavior evolves.
Limitations
One should note that we did not aim to present a new instrument to measure problem behaviors at this young age. Instead, we wanted to report on common and uncommon problem behaviors in this young age group. Although the study population was large and community based, it was not fully representative of the general population. Our sample contained more parents with a higher socioeconomic status than expected compared to the general population. Thus scores for problem behaviors might have been higher in the non-responder group. A second limitation is that we only used parent reports. Information from other sources can be of additional value to evaluate the level of problem behavior [8, 18]. Future work should include measures of impairment of functioning and should focus on more in-depth assessments of psychopathology and level of cognitive development. Finally, it may be informative to compare a population-based sample with a clinically referred group.
Clinical implications
This study underlines the growing need to identify problem behavior in early development. A better understanding of what is normal and abnormal in early infant-toddler development is of great value for the recognition of early psychopathology. Normative data for the prevalence and stability of behavior problems of 1- and 2-year-old children in a normal population may make it possible to provide early effective preventive intervention programs, in addition to background information for clinicians and researchers in order to develop such programs.
In summary, externalizing behaviors were the most common, and social communication the least common, problems reported in a population sample of 14-month-old children. Boys were reported to show significantly more problems of externalizing behavior and competences. These behaviors were moderately stable over a 5-month period, between the ages of 14 and 19 months.


Language Preferences on Websites and in Google Searches for Human Health and Food Information

Background While it is known that the majority of pages on the World Wide Web are in English, little is known about the preferred language of users searching for health information online.
Introduction
The World Wide Web has more than 15 billion pages [1] and has become an important source of health-related information [2-5]. In the United States, for example, it has been reported that 82% of female users and 77% of male users used the Internet to obtain medical information on a routine basis [6]. Google searches have even been shown to assist physicians in the correct diagnosis of medical ailments [7]. However, two thirds of the pages on the Web are published in English [8,9], even though the world has over 5 billion non-English speakers [10], including approximately 700 million non-English-speaking Internet users [8]. In fact, the vast majority of the world’s 6900 living linguistic groups [10] have little Web content available in their language [8,9]. Adding to this problem, search engines such as Google do not translate search terms into other languages [9]—perhaps a surprise to many users. To overcome linguistic barriers, the World Health Organization (WHO) and the Food and Agricultural Organization (FAO) now publish their websites in six and four major languages, respectively. However, other globally authoritative organizations, such as the Centers for Disease Control (CDC) in the United States, primarily publish online information in only one or two languages of domestic importance.
In spite of the significant challenges created by linguistic differences in effectively communicating health information to the world’s peoples online, we could find little quantitative data on this issue. Do the world’s online users, presumably wealthier and more educated than the general population, primarily search online for health information in their local language, or do they employ Web-prevalent languages such as English? Are current online translation efforts by the world’s health and food agencies beneficial, and should these agencies be spending more resources on these efforts? In a world of human migration, which language(s) should domestic governments and international agencies use in order to communicate online health information to target populations? To transmit information to front-line health professionals in developing nations, a group that can include international aid workers from wealthy nations, which language(s) should be employed to target a particular nation? Do indigenous peoples search for online health-related information using search terms belonging to their own language or the colonial language? Real-time, accurate communication of health information might be especially critical during a pandemic infectious disease outbreak or famine. To begin to answer these questions, we have used a case-study approach that examines linguistic preferences in Internet search engine queries.
Specifically, we measured search patterns on Google for four health- and food-related terms in seven languages in 227 nations. The four search terms we chose for our study were “avian flu,” “tuberculosis,” “schizophrenia,” and “maize” (corn). We chose “avian flu” because it is an ideal model for searching for online information concerning an emerging infectious disease pandemic; as of August 2006, avian influenza (virus subtype H5N1) had killed 141 people in 10 countries [11,12] in addition to prompting the slaughter of millions of animals [13]. We chose “tuberculosis” because it is a good model for searching an established global infectious disease, as it is a major cause of death for HIV-positive patients [14] and currently afflicts about 15 million people in 207 nations [15]. We chose “schizophrenia” because psychiatric and neurological diseases affect more than 450 million people globally [16,17] and because online mental health information has the potential to help nations that have few mental health specialists [17]. Schizophrenia afflicts over 24 million people worldwide [18]. Finally, because there are currently around 850 million chronically undernourished people in the world [19], under frequent threat of famine, and because malnutrition is a major underlying contributor to infectious disease susceptibility [20], we chose “maize” (corn) because it is an important search term for global food security agencies. Maize supplies one third of all human calories in Latin America and Sub-Saharan Africa [19] and, combined with its genetic relatives rice and wheat, supplies approximately 50% of all human calories globally, either directly or via animal feed [19].
In this paper, we quantitatively demonstrate the need for health and food website translation that not only targets the world’s major languages, but also linguistic minorities within nations, including immigrants, foreign aid workers, and indigenous groups.
Methods
Measuring the Extent to Which Health and Food Agencies Translate Websites
When an online publisher translates its Web site across languages, the same source domain name can be found in the different result lists of a search engine when entering different translated search terms. In an URL, the domain name is the identifier before the first slash (eg, for the WHO, the domain name is www.who.int). Therefore, to estimate the percentage of institutions, news agencies, and other sources that translate their health and food websites, we measured the frequency that domain names overlap (institutional sources overlap) in different results lists when Google is queried using different language translations of the same search term (eg, English “tuberculosis” versus Bahasa Indonesia “tuberkulosa”). The language translations used in this study are shown in Multimedia Appendix 1. We first extracted URLs from Google.com listed as “the most relevant” (about 500-1000 URLs) based on the Google page rank algorithm [21] on July 10, 2006. We then excluded nonunique domain names from within each query group; the remaining unique subset was then compared to the URLs retrieved using the comparison language.
Measurements of Online Search Rates and Language Choice
For each of the four terms and their translations, we measured the rate at which users from 227 different countries searched Google (Multimedia Appendix 2). The country of origin for each search was identified using the geographic locations of Internet Protocol (IP) addresses. The search rates were based on proprietary Google Inc. (Mountain View, CA) data using an algorithm that measured 20% of all first-page Web search queries from January 2004 to April 2006.
The search rate, M, for a term, T, originating from a specific country was then estimated from this large sample: the algorithm calculated the ratio of searches using a specific term (T) divided by all searches (A) from that nation during the time period, such that M = T/A. Multimedia Appendix 2 contains the standard error (SE) for each search rate. To calculate the standard error, a normal distribution for the statistic was assumed. To convert standard error to the 95% confidence interval of the estimate, the following formula was used: 95% CI = M ± (1.96 × SE). Only the search rates for selected languages are shown for each nation, not the total search rate across languages or the total number of searches, which are proprietary.
For ease of database searching and validation, we chose languages that share a Latin-based script similar to English. For detailed analysis, the languages chosen were English, Bahasa Indonesia, Spanish, Portuguese, German, and French, as they represent the primary or secondary language of about 100-500 million people worldwide and/or are significant post-colonial languages [10]. Because Turkey has reported cases of avian influenza [11,12], Turkish was added as a language of interest. A search term may be common to multiple languages, not all of which are noted here. Multimedia Appendix 1 contains a complete list of the language translations used to retrieve search rate data from the Google database.
Health and Food Security Indicators
For the number of human avian influenza cases (subtype H5N1), we employed the WHO Epidemic and Pandemic Alert and Response Database [12], updated on August 9, 2006. For the number of poultry outbreaks of type H5 since 2003, we used the August 16, 2006 Avian Flu Update from the Organisation Mondiale de la Santé Animale (OiE) [World Organisation for Animal Health] [13]. For tuberculosis (TB), we used Millennium Development Goal (MDG) Indicator 23, the estimated TB prevalence in 2004 from the Global Tuberculosis Database of the WHO Global Health Atlas [15]. The number of psychiatrists per 100000 people in 2005 was obtained from Project Atlas: Resources for Mental Health and Neurological Disorders, from the WHO Global Health Atlas Database [17]. The most recent maize consumption data (kcal/person/day in 2004) was from the Supply Utilization Accounts (SUA) Database of the Food and Agricultural Organization Statistical Division (FAOSTAT) [19].
Results
Measuring the Extent to Which Institutions Currently Translate Online Information
We measured the extent to which the world’s institutions, including health and food agencies, news organizations, and other sources, currently publish Web content in multiple languages. To quantify this, we measured the institutional source (host-domain) overlap between search results (first 500-1000 URLs) retrieved using English-language queries versus select comparison languages (Table 1). For example, as shown in Table 1, “avian flu” resulted in 906 hits, which included 539 unique hosts. The same search in French resulted in 801 hits, which included 375 hosts. Only 7.1% of the 539 hosts found in the English search were also found in the French search, while 10.1% of the 375 hosts found with the French search were also found in the English search. This means that approximately 7.1%-10.1% of all hosts have an English/French translation.
For “avian flu,” which has afflicted 56 people in Indonesia [12], only 6.8% and 2.4% of institutional domain names overlapped between Bahasa Indonesia searches and English-language searches, respectively (Table 1). For “tuberculosis,” we discovered only about 9% overlap in the institutional domain names retrieved for English and French/Portuguese translations: French and Portuguese are widely spoken by TB-afflicted nations in Africa and Brazil [15]. The host domain overlap was typically less than 10% between European languages; we were surprised by this low rate given the presence of common governmental institutions in Europe (eg, European Union). We extrapolate that 80%-90% of health- and food-related institutions do not translate their websites into multiple languages, even when the information concerns pandemic disease. This does not exclude the possibility that other agencies, such as domestic health agencies, might be translating this information.
We then specifically screened for Web pages belonging to the WHO and the CDC, both authoritative agencies for infectious disease information. Because the first page of search results is the most viewed [22], the rank order in which search results appear is critical. When we searched for “avian flu” in English on Google.com, the CDC website was the first hit, followed second by the WHO website. In the French translation, however, no CDC-affiliated Web pages appeared in the first 100 hits. When we searched Google Indonesia using the Bahasa Indonesia translation of “avian flu,” a WHO-affiliated Web page did not appear until page three of the search results (rank 21), and the first CDC-affiliated page appeared on page six (rank 63). Most significant, by searching for “avian flu” in Turkish using Google Turkey, we were unable to retrieve the websites of either the WHO or CDC in the first 500 search results.
As a cautionary note, we found that a single accent or special character in the search term sometimes changed the rank order of health search results significantly, consistent with more detailed analysis conducted for nonmedical terms [9,23]: for example, for avian flu, we found that “gripe aviária” (accent, Portuguese) yielded 49/100 top search results of Brazilian origin (ie, “br” in domain name), whereas “gripe aviaria” (no accent, Portuguese) yielded 40/100 top search results from Spanish-speaking nations and only 4/100 search results from Brazil. This is significant, because we found that Brazil, Portugal, and other Portuguese-speaking nations (Angola, Mozambique) searched Google for this term with and without the accent at nearly equal rates (Multimedia Appendix 2).
Table 1
Overlap in the institutional domain names retrieved by Google to measure the extent to which institutions translate websites across languages
English
Search
Term
Comparison
Language
English Language Search
Comparative Language Search
URLs
Unique
Hosts
Host Overlap with
Comparison Languages
URLs
Unique
Hosts
Host Overlap with
English
Avian flu
French
906
539
7.1%
801
375
10.1%
Avian flu
Indonesian
906
539
2.4%
472
190
6.8%
Tuberculosis
FrenchPortugueseDutch
834
473
8.5%
790
426
9.4%
Tuberculosis
GermanDanishAfrikaans
834
473
0.0%
809
443
0.0%
Tuberculosis
Bahasa Indonesia
834
473
0.8%
462
273
1.5%
Schizophrenia
SpanishPortuguese
813
444
10.8%
764
518
9.3%
Schizophrenia
Bahasa IndonesiaMalay (Bahasa Malayu)
813
444
17.1%
748
463
16.4%
Maize
Spanish
828
421
7.8%
830
543
6.1%
Language-Specific Searching of Infectious Disease Information
Though we did not retrieve many WHO- and CDC-affiliated Web pages when we searched across different languages, one possibility is that Internet users, many of whom are well-educated, are supplementing their Google searches for online health information by searching in English. If true, then there would be less of a need for the WHO and other global agencies to translate online information into diverse languages.
In our case study, we found the actual results to be variable (Table 2; Multimedia Appendix 2): for “avian flu,” we often found that only 1% of searches in non-English-speaking nations were in English, whereas for “tuberculosis” or “schizophrenia,” about 4%-40% of searches in non-English countries employed English. Brazil, which had an estimated 141000 cases of TB in 2004 [15], had an 18-fold higher query rate for “tuberculose” (Portuguese) than “tuberculosis” (English). However, the comparison is more important for languages that have much less Web content, such as Bahasa Indonesia and Turkish. Indonesia, which had the highest number of reported cases of human H5N1 viral infections in 2006 [12], had a 15-fold higher query rate for “avian flu” in Bahasa Indonesia (“flu burung”) than in English. We found that Turks used Turkish to search Google for health terms at 3- to over 1000-fold higher rates than English, French, German, or Spanish (Table 2; Multimedia Appendix 2). We did, however, find sites in Turkish that had translated information from the WHO. Whether or not the WHO, CDC, and FAO wish to leave it up to others to translate their information accurately and rapidly must be decided based on their confidence level of the eHealth capabilities of each target nation. Given the Google language-based search patterns, we conclude that during times of infectious disease outbreaks, though English may be useful, global agency–affiliated Web pages translated into local languages would likely be highly accessed and would have the benefits of being viewed as authoritative and accurate and of being transmitted in real time.
Language of Online Mental Health Information Searches
In terms of mental health, many developing nations have 10- to 100-fold fewer psychiatrists per capita than many developed nations [17] (Table 2). For this reason, global accessibility of online mental health information has the potential to be very beneficial to physicians and families of patients in developing nations [24]. Given ethnic taboos [24], we first asked whether or not Internet users from developing nations are searching online for mental health information—potentially useful information for global mental health experts. Indeed, we found that the search rate for “schizophrenia” was similar between developed and developing nations in the local language, demonstrating an active need for online mental health information in poor countries. We have made the full mental health dataset available (Multimedia Appendix 2). Given that Google estimates 3-fold more search results concerning schizophrenia in English versus the next 10 languages combined (data not shown), we asked whether middle- and low-income nations searched for this topic in English. We found that people from Brazil, a nation of about 180 million people, searched for “schizophrenia” in Portuguese at a 28-fold higher rate than in English (Table 2).
Table 2
Google search rates for selected health terms in local languages relative to English*
Search Term
Country
Public Health Comparison Metric
Local Language
English Searches (% of Local Language Searches)
Avian flu
Human Cases† (Poultry Outbreaks)‡
United States
0 (0)
English
100.0
France
0 (1)
French
1.6
Germany
0 (1)
German
0.8
Turkey
12 (176)
Turkish
0.9
Democratic Republic of Congo
0 (0)
French
1.1
Cote d’Ivoire
0 (3)
French
1.4
Burkina Faso
0 (4)
French
100.0
Mozambique
0 (0)
Portuguese
143.8
Mexico
0 (0)
Spanish
3.3
Brazil
0 (0)
Portuguese
1.0
Indonesia
56 (211)
Bahasa Indonesia
6.5
Tuberculosis
TB Cases
§
United States
10510
English
100.0
France
5901
French
9.8
Germany
5243
German
14.3
Turkey
32371
Turkish
29.1
Democratic Republic of Congo
307554
French
23.6
Cote d’Ivoire
116349
French
8.6
Burkina Faso
46815
French
15.9
Mozambique
123360
Portuguese
28.4
Mexico
45710
Spanish
same term
Brazil
141115
Portuguese
5.6
Indonesia
605759
Bahasa Indonesia
1779.3
Schizophrenia
Psychiatrists per 100000|| People
United States
13.70
English
100.0
France
22.00
French
6.6
Turkey
1.00
Turkish
10.5
Democratic Republic of Congo
0.04
French
43.0
Cote d’Ivoire
0.20
French
7.1
Burkina Faso
0.05
French
9.5
Mozambique
0.04
Portuguese
24.4
Mexico
2.70
Spanish
4.1
Brazil
4.80
Portuguese
3.6
Indonesia
0.21
Bahasa Indonesia
115.4
*Based on sampling 20% of all searches on Google.com from January 2004 to April 2006.
†Subtype H5N1, from the WHO Epidemic and Pandemic Alert and Response Database [12], updated August 9, 2006.
‡Type H5 outbreaks since 2003, from the OiE [13], updated August 16, 2006.
§Estimated TB prevalence in 2004 from the WHO Global Tuberculosis Database [15].
||2005 data from Project Atlas: Resources for Mental Health and Neurological Disorders, from the WHO Global Health Atlas Database [17].
In contrast, people from Indonesia, a nation of 220 million people, searched for “schizophrenia” at similar rates in Bahasa Indonesia as in English. As with infectious disease searching, our data would suggest that users from most nations tend to search Google for “schizophrenia” in their official language at up to 10-fold higher rates than other languages (Multimedia Appendix 2). Many of the world’s people might therefore benefit if the world’s most authoritative mental health agencies (eg, US National Institute of Mental Health) translated information into other languages, even though this is not part of their domestic mandate.
The Online Search Rates of Immigrant Minorities
In some developed nations, there is concern that immigrant groups might spread infectious diseases. In Europe, the TB prevalence is 43/100000 people in Turkey but lower in wealthier nations such as Germany (6/100000); in Asia, the TB prevalence is 273/100000 people in Indonesia, compared to 48/100000 in Singapore [15] (Table 2). Unlike many of their neighbors, both Turkey and Indonesia have reported human cases of avian influenza [12]. Governments may be interested to know whether their immigrant communities consult infectious disease–related websites originating from their adopted country or their native country. As a case study, we examined the search rates of Turks and Indonesians after they migrated to other nations in Europe or Asia, respectively, as determined by searches in Turkish and Bahasa Indonesia originating outside Turkey and Indonesia. The Turkish and Indonesian languages are distinct relative to many of their surrounding nations, permitting us to discern the search behavior of these populations after they have emigrated, provided they search in their native language. As shown in Table 3, we detected searches in Turkish for “avian flu” and “tuberculosis” throughout Europe, including high rates in Belgium and Austria, respectively. In Asia, we could detect searches in Bahasa Indonesia for “tuberculosis” and very high rates for “avian flu” throughout Asia and the Pacific region, including Hong Kong, Singapore, and Australia. We cannot exclude that these (presumed) Turkish and Indonesian immigrants also searched online in their adopted language(s), nor could we measure the fraction of their searches in their native language versus adopted language(s). However, based on the fraction of a country’s population that belongs to a particular immigrant group versus the fraction of searches conducted in the immigrant language compared to the adopted language, then if every person in a country searched the Internet for the same term at the same rate, but in different languages, we could then extrapolate that Turkish immigrants in Belgium searched for “avian flu,” “schizophrenia,” and “tuberculosis” 100%, 40%, and 15% of the time, respectively, in Turkish rather than French (Table 3). Using the same simplistic assumptions, in Austria, 39% of searches for “tuberculosis” by Turkish immigrants were in Turkish rather than German. In reality, immigrant groups are likely searching for a term such as “avian flu” at a higher rate than the general population when the corresponding disease affects their homeland and is in the news. However, we also found high search rates for “tuberculosis” and even “schizophrenia” in Turkish in these nations, which are less featured in the news. We conclude that it is important for health officials to be aware that if they wish to disseminate health information to susceptible immigrant groups, they should not rely on websites published in the majority language(s) of the nation. High priority domestic health-related websites should be multilingual, particularly those that concern infectious disease.
Our analysis also revealed surprises: for example, in Bangladesh, one of the world’s poorest and most populated nations, with 136 million residents, we found that the search rate for “avian flu” in Bahasa Indonesia (“flu burung”) was equivalent to the search rate in English (Table 3); the Bahasa Indonesia translation of “schizophrenia” was also high in Bangladesh. The high Bahasa Indonesia search rate may reflect the fact that many Bangladeshi citizens work in Singapore and Malaysia, nations that speak a similar language, Malay (Bahasa Malayu) [25]; it would appear that when they return to Bangladesh, Bangladeshis continue to use the terminology they learned while away, but the reason is unclear. We suggest that international health organizations aiding Bangladesh should publish or meta tag health information in Malay (Bahasa Malayu) to reach health practitioners in that nation. This result also highlights the practical value of analyzing linguistic preferences during online searching.
Finally, we were surprised to find that online searches for “avian flu,” “schizophrenia,” and “maize/corn” in Spanish in the United States occurred at less than 1% of the English search rate (Table 3). The US online Hispanic population (legal and illegal) is estimated to be 12% of the total US online population, of which nearly half use Spanish for some (28%) or all (21%) of their Internet usage [8,26]. Therefore, our data suggest that Latin American immigrant groups in the United States search for health information to a lesser degree in Spanish than might be predicted, although these data could also be a result of the digital divide between the groups.
Table 3
Online search rates of immigrant minorities
Search Term
Country
Immigrant Language
Comparative Major Language* of Adopted Country
Immigrant Language Searches per 10000 Major Language Searches
Relevant Immigrants per 10000 Total Population†
Turkish immigrants in Europe
Avian flu
Belgium
Turkish
French
56.1
39.7‡
Switzerland
Turkish
German
9.2
N/A
United Kingdom
Turkish
English
31.2
N/A
Tuberculosis
Austria
Turkish
German
60.2
155.4§
Belgium
Turkish
French
5.8
39.7‡
Germany
Turkish
German
11.8
212.2||
Switzerland
Turkish
German
10.7
N/A
United Kingdom
Turkish
English
2.8
N/A
Schizophrenia
Belgium
Turkish
French
15.9
39.7‡
Switzerland
Turkish
French
62.2
N/A
United Kingdom
Turkish
English
2.9
N/A
Indonesian/Malaysian immigrants in Asia/Pacific
Avian flu
Australia
Bahasa Indonesia
English
234.7
625.2¶
Bangladesh
Bahasa Indonesia
English
10474.0
N/A
Hong Kong
Bahasa Indonesia
English
3424.4
N/A
India
Bahasa Indonesia
English
154.4
N/A
Singapore
Bahasa Indonesia
English
2268.0
N/A
Tuberculosis
Hong Kong
Bahasa Indonesia
English
23.3
N/A
Singapore
Bahasa Indonesia
English
17.0
N/A
Schizophrenia
Australia
Bahasa Indonesia
English
6.0
625.2¶
Bangladesh
Bahasa Indonesia
English
687.4
N/A
Hong Kong
Bahasa Indonesia
English
295.1
N/A
India
Bahasa Indonesia
English
16.0
N/A
Spanish immigrants in English-Speaking North America#
Avian flu
Unites States
Spanish
English
102.8
> 503.0**
Schizophrenia
Unites States
SpanishPortuguese
English
91.8
> 503.0**
Corn (maize)‡‡
Unites States
SpanishIndigenous (“choclo”/”elote”)
English
37.9
> 503.0**
*a major language for which Google search rates were accessible, not necessarily the largest linguistic group of the nation.
†Total population data are from United Nations Population Division, 2005 data. URL: http://www.un.org/esa/population/unpop.htm.
‡Data are from Institut National De Statistique, 2003 data. Population et Ménages: Mouvement de la population et migrations, "Immigrations extérieures par nationalité et groupe d'âges – Belgique." URL: http://www.statbel.fgov.be.
§Data are from Statistics Austria, Volkszählung. Hauptergebnisse I – Österreich, 2001 census data. URL: statistik.at/neuerscheinungen/vzaustria.shtml.
||Data are from Statistisches Bundesamt (Federal Statistical Office, Germany), 2006 data. URL: http://www.destatis.de/themen/e/thm_bevoelk.htm.
¶Data are from Australia Bureau of Statistics, Cultural and Language Diversity, 2001 Census data. URL: http://www.abs.gov.au/.
#“Tuberculosis” is not included as it is the same term in both English and Spanish.
**Data are from Department of Homeland Security Yearbook of Immigration Statistics. Legal immigrants, 2005 data. URL: uscis.gov/graphics/shared/aboutus/statistics/ybpage.htm.
‡‡The search term used was “corn.”
Table 4
Search rates in European languages in Sub-Saharan Africa
Country
Colonial
Language*
Minority Language
Minority Language Searches per 10000 Colonial Language Searches
Search Term: Avian Flu
Angola
Portuguese
English
6629
Cameroon
French
German
323
Ghana
English
French
2024
Ghana
English
German
1744
Ghana
English
Dutch
498
Kenya
English
French
2941
Mozambique
Portuguese
English
14286
Mozambique
Portuguese
French
4444
Nigeria
English
French
4235
Nigeria
English
German
897
Rwanda
French
English
1481
Senegal
French
English
732
Search Term: Tuberculosis
Angola
Portuguese French
EnglishSpanish
1504
Cameroon
French Portuguese
English
2405
Democratic Republic of Congo
French Portuguese
EnglishSpanish
2364
Ghana
English
FrenchPortuguese
836
Kenya
English
FrenchPortuguese
1128
Mozambique
Portuguese French
EnglishSpanish
2842
Nigeria
English
FrenchPortuguese
506
Rwanda
French Portuguese
EnglishSpanish
2808
South Africa
English
Afrikaans
1547
Search Term: Schizophrenia
Angola
Portuguese Spanish
English
1547
Cameroon
French
English
1922
Democratic Republic of Congo
French
English
4304
Ghana
English
French
488
Mozambique
Portuguese Spanish
English
2441
Rwanda
French
English
18453
Senegal
French
English
3287
*When two languages are noted, the term is the same in both languages.
Search Rates in European Languages in Sub-Saharan Africa
Sub-Saharan Africa suffers from high rates of infectious disease, including TB and HIV [18], and high rates of malnutrition [19]. As demonstrated in Table 4, our search results suggest that the dissemination of online health or food security information to this region by international agencies should not be limited to the colonial language(s) of the target nations. For example, in English-speaking Ghana, 20% of searches for avian flu were in French (“grippe aviaire”), 17% in German (“vogelgrippe”), and 5% in Dutch (“vogelgriep”) relative to English (Table 4). In Mozambique, a former Portuguese colony, we found 1.4 times more searches for “avian flu” in English relative to Portuguese, and a high rate in French. In the Democratic Republic of Congo (DRC), a former French colony with more than 300000 TB infections in 2004 [15], 24% of the searches for tuberculosis were in English relative to French. Because Internet use is only 1.9% is Ghana, 0.7% in Mozambique, and 0.2% in DRC [8], it is plausible that these high rates of non-colonial language searches using Google may reflect searches by health professionals trained in other nations, including workers from international agencies who would be expected to have better Internet access than the general population.
The Effect of Region-Specific Cultural and Indigenous Terminology
Finally, we measured the effects of cultural bias within the same linguistic group, using a term important for human nutrition and food security, “maize.” Maize is eaten directly, but it is also a major source of animal feed worldwide. Maize is known as “corn” in the United States and the United Kingdom, but as “maize” in many other English-speaking nations. We found that the US searches for “corn” were at a 28-fold higher rate than for “maize,” while other English-speaking nations such as Nigeria and Zimbabwe queried “maize” at a 1.5- to 4-fold higher rate than “corn,” respectively (Table 5); in the latter nations, “corn” may also refer to any large cereal grain (eg, wheat). This search behavior has consequences, as we found only three domain names that overlapped between searches for “maize” versus “corn” out of the first 50 unique Google search results.
Therefore, when African nations attempt to retrieve information about growing maize in English, they may unknowingly be excluding authoritative information from the United States and other Western English-speaking nations, such as practical information from the US National Corn Growers’ Association, whose website does not appear in the first 100 Google hits for “maize,” but ranks sixth when “corn” is searched. Similarly, when international organizations such as the FAO wish to transmit knowledge, for example, using Spanish or Portuguese in Latin America, indigenous terminology usage may make some of this information inaccessible. Maize originated from Southern Mexico and fed indigenous Latin American civilizations [27]. In Mexico and Guatemala, we found that an indigenous synonym for maize, “elote,” was searched at a similar rate to the Spanish term, “maíz” (Table 5). In Peru, however, we found that a different indigenous term, “choclo,” was searched at a 2-fold higher rate than “maíz.” We conclude that cultural and indigenous linguistic divisions may be preventing large numbers of food security and nutrition websites from reaching those people aiding 800 malnourished people or 1.2 billion agricultural workers that live in developing countries [19]. Cultural and indigenous bias may be particularly prevalent for terms related to crops, diseases, or pathogens that have pre-colonial origins.
Table 5
The effect of region-specific cultural and indigenous terminology
Country
Calories from Maize (kcal/person/day)*
Search Term Comparison
Search Rate Ratio
Canada
N/A†
corn:maize
36:1
United States
512
corn:maize
28:1
United Kingdom
115
corn:maize
7:1
India
38
corn:maize
2:1
Nigeria
179
corn:maize
1:2
Kenya
775
corn:maize
1:3
Tanzania
646
corn:maize
1:4
Zimbabwe
720
corn:maize
1:4
Spain
N/A†
maíz:elote:choclo
10:1:4
Venezuela
467
maíz:elote:choclo
16:1:3
Colombia
312
maíz:elote:choclo
20:1:5
Mexico
1081
maíz:elote:choclo
14:16:1
Guatemala
869
maíz:elote:choclo
12:10:1
Peru
145
maíz:elote:choclo
11:1:25
Argentina
132
maíz:elote:choclo
41:1:51
*Data from FAOSTAT [19].
†N/A: Data not available.
Discussion
Principal Results
In a world where infectious disease pandemics and threats of famine are always present, and in spite of the fact that the World Wide Web offers great hope for rapid and accurate sharing of information between peoples, we have demonstrated that one linguistic group does not or cannot access the health and food security websites of a different linguistic group. Our data suggest at least three reasons for this.
The first reason is that the websites of most institutions are not published in more than one or two languages. When we sampled Web pages found by the English-language queries “avian flu,” “tuberculosis,” “schizophrenia,” and “maize/corn” and their counterpart queries in other languages, the Google search results typically overlapped by only less than 10% in terms of the domain names retrieved, indicating that 90% of the relevant Web pages had not been translated into at least two languages (see Table 1). For example, when Turkish or Bahasa Indonesia was used as the search language, Web pages from very authoritative sources, such as the CDC or WHO, were not retrieved by Google. We also found that a single linguistic accent or special character in the search query could significantly alter the number and content of health-related search results retrieved by Google. Therefore, one reason for the linguistic digital divide is that the majority of health and food Web pages are not translated into multiple languages and/or that their cross-language retrieval by search engines is poor.
The first problem would not be important if the world’s online community, better educated than the general public, searched in English, since the majority of the world’s Web pages are published in English [8,9]. However, we found that there was a 2- to 100-fold higher Google search rate for health and food terms in the native language of a country compared to English (see Table 2).
Finally, within a nation, it might be assumed that language would not be a problem if a domestic agency only published their health websites in the majority language of their own people. However, we found that in Asia and Western Europe, a subset of immigrants from Indonesia and Turkey, respectively, searched Google for health and food information in their native language, not the language(s) of their adopted countries (see Table 3). In Sub-Saharan Africa, we detected unexpectedly high search rates for health information in non-colonial European languages (see Table 4), perhaps reflecting the presence of international aid workers. Finally, in Latin America, we found that indigenous words were used to search Google for information about food, rather than the colonial language of Spanish (see Table 5). Therefore, domestic agencies, in addition to global agencies, face a linguistic challenge when publishing information online: their target audiences still require information to be published in different languages, even though Internet users are presumably more educated and thus more multilingual than the general population.
Recommendations
Given our observation that the world’s peoples appear to be searching for health and food terms in their local language or mother tongue, not in English, previous online language translation efforts by the WHO and FAO have no doubt been worthwhile. This is also revealed by the high page rank (first page results) of WHO-affiliated search results when Google is searched for infectious disease information in one of the WHO’s six online languages. We recommend that these efforts be continued and further expanded to include more languages; this recommendation applies to global agencies, but also to domestic agencies, in order to meet immigrant or indigenous needs and/or to make information accessible to other nations. To achieve such translation goals, better health-specific translation software must be developed and more translators are needed who specialize in human health and food security terminology. For example, improved cross-language search retrieval [23] of health information by online search engines would be beneficial. These investments should then be used to initially target health and food security information to the world’s most important linguistic groups, which include speakers of Chinese (1080 million people), Hindi (about 500 million), English (350-500 million), Spanish (390 million), Arabic (255 million), Portuguese (190 million), Bengali (215 million), Russian (255 million), Bahasa Indonesia (200 million), Japanese (127 million), Punjabi (104 million), German (123 million), and French (119 million) [10]. In a world that is primarily non-English speaking, such attempts will help to reduce the linguistic digital divide in health and food information on the World Wide Web.
Furthermore, as we did in this study (see Table 2 to Table 5), we recommend that when global or domestic health and food security organizations wish to use the Internet to disseminate information to other nations [28] or to their own immigrant or indigenous communities, they should first consult search engine query rates for different translations of possible search terms in order to determine which online languages are most needed. Multimedia Appendix 2 contains extensive linguistic online search pattern data to help health and food agencies better select languages for targeted website publishing. In order to measure search rates for other subjects of interest, we note that free online tools exist, such as Google Trends. In some situations, such as targeting indigenous groups, who often speak the majority language of a nation, all that may be needed is to imbed translated keywords into a majority-language website (eg, Spanish) so that search engines such as Google can cross-retrieve relevant information.
Future Studies
Though this study examined the extent to which agencies such as the WHO are publishing information in multiple languages, we did not systematically address the quality of health and food information available in different languages. Quality analysis would especially be important for minor languages that have little content available on the Web: for example, we estimate that there are more than 4000-fold fewer search results in Bahasa Indonesia than in English for “tuberculosis,” more than 200-fold fewer search results for “avian flu” in Arabic or Japanese than in English, and about 500-fold fewer search results for “schizophrenia” in Arabic than in English. Though these precise numbers are considered to be unreliable [29], they do illustrate the point that most of the world’s linguistic groups, even major ones, have much less available online health information relative to English. As to the quality of the websites that are available, this will require systematic analysis, which poses significant methodological problems [30]. One could, however, perform a subjective survey-based evaluation by multilingual physicians, as has been performed to evaluate disease-specific websites published in English [31]. Such a survey should include quantifying to what extent online information from the WHO, CDC, and FAO is being translated into the world’s minor languages in order to help these agencies determine where they need to target their online translation efforts.

language
google
health
internet
linguistic
indigenous
avian flu
tuberculosis
schizophrenia
maize
nutrition
food security
immigrant