Background

Social capital is a concept now widely used in sociology, economics, education, and more recently in epidemiology [1]. It includes real or potential resources, social structures, and regulated interactions between them [2, 3]. There is no agreed definition, but a common definition is “the set of characteristics of social organisation, such as confidence, norms, and networks that may improve the effectiveness of society by facilitating coordinated actions” [4]. Social capital represents the social connections and benefits generated by them and is associated with values that reinforce social cohesion, such as tolerance, solidarity, and confidence [5]. Most definitions focus on social relationships that have productive benefits. Many instruments have been used to measure social capital [6,7,8]. The components of social capital measured by these instruments include personal relationships, social support networks, participation and confidence of citizens, and rules for cooperation [5,6,7, 9].

Social capital may be important in the health of a population. Although mechanisms are unclear, authors have reported positive associations with physical and mental health [10,11,12]. High social capital has been linked to reduced crime, drug use, and alcoholism [13, 14]. In communities with low social capital, inhabitants report increased levels of stress [15], the well-being of children and elderly people is lower [16, 17], and the ability to respond to environmental health risks is reduced [18]. All this suggests that measuring social capital could be useful in epidemiological studies of population health [19,20,21].

The World Health Organization recommends control of the Aedes aegypti vector as the mainstay of efforts to prevent yellow fever [22], dengue [23], zika, and chikungunya [24], despite an existing vaccine for yellow fever [25] and recent advances in developing a vaccine for dengue [26, 27]. Several trials have measured the impact of community mobilisation for control of the dengue vector [28,29,30,31,32,33]. A recent systematic review concluded that community mobilisation was effective in reducing vector indices [34]. Community mobilisation strategies for dengue prevention may work, at least in part, by strengthening existing bonds between members of the community and increasing their level of social capital [35]. One small study examined the correlation between some social capital elements and vector indices [36], but we have found no other published reports of attempts to relate measures of social capital to vector indices. We used data from the impact survey of the Camino Verde cluster randomised control trial of evidence-based community mobilisation for dengue prevention in Mexico and Nicaragua [33] to examine the association of a measure of social capital with vector indices. We also explored how social capital and its association with vector indices were affected by the trial community mobilisation intervention, to examine the possibility that the lower vector indices in intervention sites might be mediated, at least in part, through an interaction between the intervention and social capital.

Methods

Details of the methods and findings of the Camino Verde trial are described in detail elsewhere [33]. In brief, the trial involved using evidence-based discussions to stimulate communities to design their own strategies for non-chemical control of the Aedes aegypti vector in their communities. The Mexican arm of the trial included 45 intervention clusters and 45 control clusters. The trial impact survey took place in all 90 clusters in late 2012 and included a household questionnaire survey, administered by trained interviewers to one member per household, and a household entomological survey. We used data from the household survey to construct a social capital index and data from the entomological survey to calculate vector indices for each household.

Indicator of social capital

Our social capital index was based on 21 questions from the household survey, initially categorised according to the four domains of social capital proposed by Siegler [5]: personal relationships, social network support, civic engagement, and trust and cooperative norms (Table 1). The response to each question was dichotomous, mostly Yes or No. The way we categorised the possible responses for other questions is shown in Additional file 1: Table S1.

Table 1 Questions and labels included in the factor analysis for the social capital index
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Factor analysis requires that there be no missing values for responses to any of the included questions. Among the 21 questions, the highest proportion of missing data was 1.9%. We used Amelia II [37] to impute the values of the missing data with an expectation-maximisation algorithm for the binary variables, conciliating the data from 10 series of imputed data using the Rubin method [38].

We undertook a factor analysis to determine the weights of the individual variables in an overall social capital index and the domains (constructs) within the index, using the “psych” package in R [39, 40]. We created a scree plot of eigenvalues, used an eigenvalue of 1.15 as the cut-off for inclusion in the final index, and carried out a parallel analysis with 100 simulations to decide which factors should remain in the final index. We assumed that all the variables would be correlated and we used oblique rotation to group the retained elements. We then dichotomised the social capital score of each household as high or low, based on the frequency distribution of the social capital scores.

Vector indices

For this analysis, we calculated four Aedes aegypti indices at household level:

  1. 1.

    Household positivity for larvae or pupae. We categorised a household as positive for larvae and/or pupae if any of the containers inspected in the household contained any larvae or pupae.

  2. 2.

    Household positivity for pupae. We categorised a household as positive for pupae if any container inspected contained any pupae. Pupal indices may be better predictors of the adult population of mosquitoes [41, 42].

  3. 3.

    Pupae per household index (PHI). Number of pupae found per household

  4. 4.

    Pupae per person index (PPI). Number of pupae per household divided by the number of household members.

Analysis of associations

Analysis of associations between social capital, intervention status, and vector indices relied on CIETmap, an open source interface with the R programming language [43]. We used the Mantel-Haenszel procedure [44] to examine associations, and the Zelen test for heterogeneity to test for the significance of interactions between variables [45], conducting first bivariate and then multivariate analysis of variables associated with larvae/pupae and with pupae alone. Initial saturated multivariate models included those variables significantly associated with the outcome in bivariate analysis. We express associations as odds ratios (OR) and 95% confidence intervals, adjusted for clustering (95% CIca) by the Lamothe method [46].

Results

A total of 10,112 households (5181 in intervention clusters and 4931 in control clusters) provided responses to the household questionnaire and entomological data. The average percentage of missing data for the 21 variables to be included in the factor analysis for the social capital index was 0.42% among households in the intervention sites and 0.39% among households in control sites.

Social capital score

The factor analysis produced a social capital scale from four constructs that we interpreted as involvement, participation, investment, and communication. Figure 1 shows the combinations of the individual dichotomous variables into the four constructs. As shown in Fig. 1, some of the original variables did not have significant weight in any of the four constructs. Table 2 shows the weights of each variable in the overall social capital index and in the four constructs. Each construct groups variables with a weight of 0.3 or above for that construct. Half (50%) of the entire variance of the social capital index was explained by the four constructs, with 35% being explained by two of the constructs: participation and involvement.

Fig. 1
figure 1

Path plot of the oblique rotation of the constructs in the social capital index. Root mean square residual (RMSR) = 0.04, root mean square error of approximation (RMSEA) = 0.067, 90% CI 0.065–0.068

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Table 2 Weights of the individual variables in each of the four constructs
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The social capital index, calculated as a weighted average of the four constructs, had a minimum value of − 0.77 and a maximum value of 0.84. Figure 2 shows the frequency distribution of the social capital index among all households; the bimodal distribution was similar between intervention and control households. For the analysis of social capital in relation to other variables, we dichotomised the scores as below 0 (low social capital) and 0 or above (high social capital).

Fig. 2
figure 2

Frequency distribution of household social capital scores

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Households in rural areas were much more likely to have a high social capital score than households in urban areas (58% vs 23%, OR 4.51, 95% CIca 3.26–6.26). Households in intervention clusters were more likely to have a high social capitals score than those in control clusters, although this difference was not significant at the 5% level (OR 1.31, 95% CIca 0.87–1.98).

Social capital and vector indices

Tables 3 and 4 show bivariate associations with presence of larvae and/or pupae and with presence of pupae specifically. Households with a high social capital score were significantly more likely to be negative for larvae and/or pupae, and more likely to be negative for pupae specifically. The association between social capital and vector indices was stable across four different constructions of the social capital index and categorisation into high and low social capital. Details of these sensitivity analyses are in Additional file 1.

Table 3 Bivariate associations with absence of larvae or pupae in households
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Table 4 Bivariate associations with absence of pupae only in households
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Other variables significantly associated with the vector indices were location (urban or rural), type of house construction (permanent vs non-permanent), education of the household head, coverage with a government social programme, and presence of temephos in household water containers. There was an interaction between social capital and intervention status in their association with vector indices, and a composite variable (1 = with intervention and with high social capital, 2 = either no intervention or low social capital) was strongly associated with absence of the larvae/pupae and absence of pupae alone (Tables 3 and 4).

The mean PHI and the mean PPI were lower among households with a high social capital score. This difference was only significant among households in rural areas (Fig. 3).

Fig. 3
figure 3

Mean pupal indices with high and low social capital in rural and urban areas. Mean and 95% confidence interval for each index in rural and urban areas for different levels of social capital

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Tables 5 and 6 show the final models of multivariate analyses using the Mantel-Haenszel procedure. When the composite variable of intervention and social capital was included, the independent association between social capital and vector indices did not remain in the final models. The strongest associations in the final models were with the combined intervention/social capital variable and with intervention status.

Table 5 Final model of multivariate analysis of associations with absence of larvae or pupae in households
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Table 6 Final model of multivariate analysis of associations with absence of pupae in household
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Discussion

The social capital index and vector indices

We found a significant association between a high level of social capital and low dengue vector indices. Apart from one small study from Indonesia reporting on correlations between elements of social capital and vector indices [36], we believe this is the first study to examine the association between a measure of social capital and Aedes aegypti entomological indices. From this cross-sectional study, we do not know if this is a causal association. If it is causal, a possible mechanism is that a high level of social capital facilitates the transfer of knowledge between community members about how to control Aedes aegypti breeding sites. If community members feel that investment of time in community activities, such as mosquito control, is worthwhile, then perhaps they are more likely to participate in such activities. Households that are more involved in their communities may also be more active in eliminating mosquito breeding sites in and around their own living areas.

The social capital index we generated through factor analysis had four constructs: involvement, participation, investment, and communication. These differ somewhat from the four constructs proposed by Siegler [5]; social capital is likely to have different components in different settings. The initial grouping of our 21 questions on social capital into the categories of Siegler and their final grouping from the factor analysis is shown in Fig. 1. Differences between general measures of social capital and our measure are not surprising; as well as general questions about aspects of social capital, we included questions specific to mosquito control for dengue prevention.

The major contributing constructs in our social capital index were participation and involvement, which together explained 35% of the variance of the index. Participation reflects the extent to which people, voluntarily or because of certain persuasions or incentives, agree to collaborate on a project, often contributing their work and other resources in exchange for an expected benefit [47]. Involvement goes further and reflects the extent to which community members identify and resolve problems in the community on their own terms, in an autonomous, sustainable way [48]. Nelson and Wright define involvement as participation with a purpose, where the community or group establishes a process to control its own development [49].

The investment construct in our social capital index is specific to dengue and reflects the benefits individuals or groups expect to get from investing time and money to eliminate mosquito breeding grounds in their homes. Similarly, while communication with family, friends, and community could be a component of a general social capital index, in this study, the communication construct of the social capital index reflected communication specifically about how to avoid mosquitoes.

Our finding of higher social capital scores among households in rural sites compared with those in urban sites is consistent with earlier reports from Australia and the USA [50, 51]. The level of social connections between young people may be higher in rural than in urban settings [52].

The Camino Verde intervention

The data in this secondary analysis came from the impact survey of the Camino Verde trial. The trial demonstrated a convincing reduction in vector indices in intervention clusters compared with control clusters [33]. We intended this secondary analysis to shed light on possible mechanisms by which the intervention reduced vector indices. Our current analysis does not support the idea that the impact of the intervention was mediated through increases in social capital. Social capital was not convincingly higher in intervention sites compared with control sites in the impact survey. Further research designed to measure social capital before and after community mobilisation interventions such as Camino Verde could help to answer this question.

Our findings provide some support for the notion that the Camino Verde intervention interacted positively with existing patterns of social capital to reduce vector indices. There was a significant interaction between the intervention and social capital in the multivariate analysis. The households least likely to have immature forms of the dengue vector present were those with high social capital located in intervention communities.

In the Camino Verde intervention, each intervention community designed its own set of actions to prevent dengue; a discussion of local evidence led to a local action plan for control of the vector. The programme facilitators encouraged community members to plan their own actions to control the dengue vector and thus reduce the risk of dengue, based on the evidence about the mosquito life cycle and breeding grounds in their communities. We believe this evidence-based, participatory approach is an important reason for the success of the intervention [53,54,55]. A possible explanation for the interaction between social capital and the intervention that we demonstrated in this analysis could be that households with high social capital were more likely to participate in the evidence-based discussions about mosquito-control in the intervention communities, and these discussions then gave them the necessary knowledge to guide their activities to control Aedes aegypti breeding sites. Intervention studies that measure social capital prospectively could explore this possibility.

Our findings make a modest contribution to the wider discussion about social capital and health. Studies have reported associations between different aspects of social capital and health [56,57,58] and there is evidence that some of this association might be mediated by health behaviours [59, 60]. Most of the reported studies are observational, making it difficult to disentangle causality. There are few interventional studies that seek to change social capital and measure the impact on health behaviours and health [61]. The Camino Verde trial of community mobilisation for dengue prevention seems to have changed health behaviour (reducing mosquito breeding sites) at least partly through an interaction with social capital, even if it did not change social capital. This could be a useful starting point for future research.

Limitations

The 21 questions that we included in the factor analysis to create our social capital index might have missed aspects of social capital that are relevant to control of the dengue vector and are amenable to change in intervention sites, particularly in urban areas. We consider our factor analysis to develop the social capital index was robust and benefited from considering three criteria, rather than only a scree plot. The consistency of the index created using the three measures was reassuring. Some questions in our social capital index were specific to dengue prevention and mosquito control. Our index might be applicable in other studies of social capital and dengue prevention, but it would need to be modified for measuring social capital in other circumstances.

Conclusion

This is the first published study to demonstrate an association between a high social capital score and lower dengue vector indices. Our findings suggest interaction between a community mobilisation intervention and social capital in reducing vector indices and this merits further examination. Understanding such interactions may help to maximise the impact of future community mobilisation interventions.