The literature on health inequalities often uses measures of socio-economic position pragmatically to rank the population to describe inequalities in health rather than to understand social and economic relationships between groups. Theoretical considerations about the meaning of different measures, the social processes they describe, and how these might link to health are often limited. This paper builds upon Wright’s synthesis of social class theories to propose a new integrated model for understanding social class as applied to health. This model incorporates several social class mechanisms: social background and early years’ circumstances; Bourdieu’s habitus and distinction; social closure and opportunity hoarding; Marxist conflict over production (domination and exploitation); and Weberian conflict over distribution. The importance of discrimination and prejudice in determining the opportunities for groups is also explicitly recognised, as is the relationship with health behaviours. In linking the different social class processes we have created an integrated theory of how and why social class causes inequalities in health. Further work is required to test this approach, to promote greater understanding of researchers of the social processes underlying different measures, and to understand how better and more comprehensive data on the range of social class processes these might be collected in the future.

Background Non-communicable diseases (NCDs) account for an estimated 71% of all global deaths annually and nearly 80% of these deaths occur in low- and middle-income countries. This study aimed to assess the readiness of existing healthcare systems at different levels of health care in delivering NCDs management and prevention services in Kenya. Methods A cross-sectional survey of 258 facilities was conducted between June 2019 and December 2020 using multistage sampling, examining facility readiness based on the availability of indicators such as equipment, diagnostic capacity, medicines and commodities, trained staff and guidelines for NCDs management. Readiness scores were calculated as the mean availability of tracer items expressed as a percentage and a cut-off threshold of ≥ 70% was used to classify facilities as “ready” to manage NCDs. Descriptive and bivariate analyses were performed to assess the readiness of facilities by type, level, and location settings. Logistic regressions were used to identify factors associated with the readiness of facilities to provide disease-specific services. Results Of the surveyed facilities, 93.8% offered chronic respiratory disease (CRD) diagnosis and/or management services, 82.2% diabetes mellitus, 65.1% cardiovascular disease (CVD), and only 24.4% cervical cancer screening services. The mean readiness scores for diabetes mellitus (71%; 95% CI: 67–74) and CVD (69%; 95% CI: 66–72) were relatively high. Although CRD services were reportedly the most widely available, its mean readiness score was low (48%; 95% CI: 45–50). The majority of facilities offering cervical cancer services had all the necessary tracer items available to provide these services. Modeling results revealed that private facilities were more likely to be “ready” to offer NCDs services than public facilities. Similarly, hospitals were more likely “ready” to provide NCDs services than primary health facilities. These disparities in service readiness extended to the regional and urban/rural divide. Conclusions Important gaps in the current readiness of facilities to manage NCDs in Kenya at different levels of health care were revealed, showing variations by disease and healthcare facility type. A collective approach is therefore needed to bridge the gap between resource availability and population healthcare needs.