The usefulness of small-area-based socioeconomic characteristics in assessing the treatment outcomes of type 2 diabetes patients: a register-based mixed-effect study

Abstract

BackgroundAssessment of the differences in the outcomes of care by socioeconomic status (SES) is beneficial for both the efficient targeting of health care services and to decrease health inequalities. This study compares the effects of three patient-based SES predictors (earned income, educational attainment, employment status) with three small-area-based SES predictors (median income, educational attainment, proportion of the unemployed) on the treatment outcomes of type 2 diabetes patients.MethodsMixed-effect modeling was applied to analyse how SES factors affect the treatment outcomes of type 2 diabetes patients. The treatment outcomes were assessed by the patients’ latest available glycated hemoglobin A1C (HbA1c) value. We used electronic health records of type 2 diabetes patients from the regional electronic patient database, the patients’ individual register-based SES information from Statistics Finland, and the SES information about the population of the postal code area of the patients from Statistics Finland.ResultsThe effects of attained education on the treatment outcomes, both at the patient-level and the small-area-level are quite similar. Age and male gender were associated with higher HbA1c values and lower education indicated higher HbA1c values. Unemployment was not associated with HbA1c values at either the patient-level or the area-level. Income gave divergent results: high values of HbA1c were associated with low patient incomes but the median income of the postal code area did not predict the treatment outcomes of patients.ConclusionsOur comparative study of three SES factors shows that the effects of attained education on the treatment outcomes are rather similar, regardless of whether patient-based or small-area-based predictors are used. Small-area-based SES variables can be a good way to overcome the absence of individual SES information, but further research is needed to find the valid small-area factors by disease. This possibility of using more small-area-based data would be valuable in health service research and first-hand planning of health care services.

Association between area‐ and individual‐level socio‐economic factors with glycated haemoglobin—Evidence from a Portuguese population‐based study

“…We observed that lower individual‐level education increased the mean levels of HbA 1c by 1.9% (95% CI 1.1, 2.8), independently of sex, age group, other individual‐level SES variables and area‐level deprivation (social and material), which is in agreement with the few previous works 13,17,18 …”

“…We observed that lower individual-level education increased the mean levels of HbA 1c by 1.9% (95% CI 1.1, 2.8), independently of sex, age group, other individuallevel SES variables and area-level deprivation (social and material), which is in agreement with the few previous works. 13,17,18 To the best of our knowledge, there is a lack of research testing the competing models 'collective resources' versus 'local social inequality' for diabetes. Our study supports the collective resources model as we found that low municipality material deprivation is associated with better levels of HbA 1c after accounting for individual SES.…”

“…[13][14][15][16] However, very little research has considered objective measures of diabetes, such as the glycated haemoglobin (HbA 1c ), therefore leading to misclassification bias. 17,18 The levels of HbA 1c are a proxy for the average blood glucose levels and are a stable indicator of glycaemic status, recently recommended for the diagnosis and monitoring of type 2 diabetes. 19 Additionally, none of the studies concerning this subject and including the levels of HbA 1c as the outcome is population based.…”

Comparison of individual and neighbourhood socioeconomic status in case mix adjustment of hospital performance in primary total hip replacement in Sweden: a register-based study

“…Furthermore, these results are in line with previous studies showing that aggregated measures of socioeconomic status do improve the understanding of health outcomes compared to no socioeconomic information at all [11,12]. Using a similar methodological approach as the one used here, combining individual-level and neighbourhood-level official statistics, a Finnish study of patients with Diabetes type 2 found small-area based SES to be a relatively good substitute to individual-level information [23]. They found small-area based educational attainment to exhibit comparable predictive ability as individual educational attainment, while income did not have as consistent results between area-based and individual information.…”

Fast travel-distance estimation using overhead graph

“…The selected data were originally used for other research purposes and includes mainly patients with alcohol-use disorder (Rautiainen, Ryynänen, and Laatikainen 2018), type 2 diabetes (Toivakka et al 2018), coronary heart disease (Repo et al 2018) and anticoagulation (Leminen et al 2019). The data is therefore not an exact representation of the entire population, but its geographic distribution is rather similar, and it is useful for our study.…”