Health literacy strengths and challenges among residents of a resource-poor village in rural India: Epidemiological and cluster analyses

Abstract

Cluster analysis can complement and extend the information learned through epidemiological analysis. The aim of this study was to determine the relative merits of these two data analysis methods for describing the multidimensional health literacy strengths and challenges in a resource poor rural community in northern India. A cross-sectional survey (N = 510) using the Health Literacy Questionnaire (HLQ) was undertaken. Descriptive epidemiology included mean scores and effect sizes among sociodemographic characteristics. Cluster analysis was based on the nine HLQ scales to determine different health literacy profiles within the population. Participants reported highest mean scores for Scale 4. Social support for health (2.88) and Scale 6. Ability to actively engage with healthcare professionals (3.66). Lower scores were reported for Scale 3. Actively managing my health (1.81) and Scale 8. Ability to find good health information (2.65). Younger people (<35 years) had much higher scores than older people (ES >1.0) for social support. Eight clusters were identified. In Cluster A, educated younger men (mean age 27 years) reported higher scores on all scales except one (Scale 1. Feeling understood and supported by a healthcare professional) and were the cluster with the highest number (43%) of new hypertension diagnoses. In contrast, Cluster H also had young participants (mean age 30 years) but with low education (72% illiterate) who scored lowest across all nine scales. While epidemiological analysis provided overall health literacy scores and associations between health literacy and other characteristics, cluster analysis provided nuanced health literacy profiles with the potential to inform development of solutions tailored to the needs of specific population subgroups.

Health literacy of Brazilian undergraduate students: a cluster analysis

“…Although the descriptive analysis demonstrated the challenges of this sample in achieving adequate HL, the cluster analysis revealed subgroups of participants with similar socio-demographic data, which exhibit characteristic patterns of strengths and limitations. 37 This statistical method has the potential to signal the speci c needs of strategically delimited population subgroups, especially groups facing social disadvantage or marginalization, which can be masked by using descriptive data analysis alone to explain the characteristics of a population. 37 The cluster analysis applied to this study showed that all the groups were made up mostly of female participants, thus representing the general characteristic of this sample (84.3% female participants), which corroborates the scenario of health professions nationwide.…”

“…37 This statistical method has the potential to signal the speci c needs of strategically delimited population subgroups, especially groups facing social disadvantage or marginalization, which can be masked by using descriptive data analysis alone to explain the characteristics of a population. 37 The cluster analysis applied to this study showed that all the groups were made up mostly of female participants, thus representing the general characteristic of this sample (84.3% female participants), which corroborates the scenario of health professions nationwide. 38 In Brazil, women represent 65% of the total workforce in the health sector, and this share is even higher in some professions, such as nutrition (90%), nursing (85.1%) and psychology (80%).…”

Health literacy strengths and challenges of people in New South Wales prisons: a cross-sectional survey using the Health Literacy Questionnaire (HLQ)

“…The specificity of these measurement tools limits their application in large non-homogenous populations as found within prisons [ 28 ]. Further, most health literacy measurement tools produce an overall and “cut-off” scores [ 31 ], minimising and reducing the complexity of health literacy to a singular number [ 32 ]. The singular number is then utilised to classify individuals, using “cut-off” scores, as having low, medium or high health literacy, which does not reflect the real world experience of an individual [ 31 ].…”