Hypothesis and research design

This study hypothesized that the risk of spousal concordance of diabetes is associated with the individual and shared characteristics of spouses. The individual characteristics of spouses may exert different effects on spousal concordance of diabetes. Moreover, the study hypothesized the existence of a disparity in the risk of concordance between couples and noncouples. The two hypotheses were tested in a longitudinal population-based cohort by using a case–control design. The study was approved by the Research Ethics Committee of China Medical University Hospital, Taiwan.

Data source and study sample

The National Health Insurance (NHI) program, established in 1995, provides comprehensive health care benefits to more than 99.7% of the residents of Taiwan (N = 23.50 million). All the medical claims from this universal program are managed by the National Health Research Institutes (NHRI), which releases the population-based National Health Insurance Research Database (NHIRD). This retrospective study retrieved longitudinal data from the 2002–2013 registry of the NHIRD, which contains the reimbursement claims of 1 million randomly sampled beneficiaries. The NHRI has indicated that this NHIRD subset can completely represent all the enrollees. The claim diagnoses in the NHIRD were coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).

This study used the data fields “relation” and encrypted individual identifiers to match married spouses from the NHIRD registry. Only two individuals having a relationship status of being insured and dependent spouses were identified as a couple by using “spouse” in the data field “relation” and the prerequisite of the encrypted identifiers mutually matched between the two spouses. Furthermore, to obtain an initial diagnosis of diabetes throughout the observation period, individuals diagnosed as having diabetes mellitus (ICD-9-CM 250.x) in 2002 were excluded from the study. Patients younger than 16 years were also excluded. Initially, data of 5,680 couples were obtained. However, 43 patients were excluded because of inadequate or missing data (0.76%). Consequently, the current study identified a cohort of 5,643 married couples, comprising 11,286 individuals (5,643 insured and 5,643 dependent spouses).

To ascertain the similarity between the case (couples) and control (noncouples) groups, except for the couple status, the case group was matched with the control group in terms of the same single value of sex, age, and comorbidities through 1:1 propensity score matching (PSM) to reduce selection bias [20]. This procedure was repeated twice for each member of a couple to obtain two randomly selected noncouple counterparts (total four individuals in the matching: dual PSM). Thus, the three matched variables were tested twice for any significant differences between the two groups. The results indicated high similarity with no differences in sex, age, or comorbidities (all P = 1, Table 1), thus confirming that the couples and noncouples qualified for the comparison. PSM provides an alternative to adjust for covariates at the level of multivariate analysis [21]. Consequently, 5,643 couples and 5,643 noncouples (N = 22,572 individuals) were included in the subsequent analysis.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Comparisons of characteristics after 1:1 dual propensity score matching for couples and noncouples (N = 11,286 pairs).

https://doi.org/10.1371/journal.pone.0183413.t001

Variables

The concordance of diabetes was determined using a dichotomous outcome variable. Concordance was reported if both spouses or counterparts were diagnosed using ICD-9-CM codes (250.x) for diabetes mellitus; otherwise, discordance was reported.

The present study included two categories of independent variables that are possibly associated with diabetes: 1. characteristics of the insured spouse, comprising sex, age, premium-based monthly salary, occupation, urbanization level, region, catastrophic illness or injury, and comorbidities; and 2. characteristics of the dependent spouse, comprising age, catastrophic illness or injury, and comorbidities. The urbanization level and region were considered common environmental characteristics of the couples. The remaining variable was the characteristics of the individual spouses. Legally, the Taiwan government allows only heterosexual marriage; thus, one sex, that of the insured spouse, was used to eliminate collinearity. Age did not pass the normality test, including skewness and kurtosis, and was therefore classified into five ordinal levels, according to the frequency distribution. Furthermore, premium-based monthly salary, occupation, region, and catastrophic illness or injury were defined on the basis of the official NHI classifications. The National Health Insurance Administration issues the Catastrophic Illness and Injury card to patients with severe illness or injury. Patients with numerous catastrophic illness and injury conditions, such as regular dialysis or permanent disability, can apply for the card after the severity reaches the official criteria of the NHI program and is verified by a board-certified physician. Comorbidities were assessed using the Charlson comorbidity index (CCI) [22], a frequently used measure in clinical research. After original scoring from 0 to 6 conducted by weighting ICD-9-CM codes for each spouse, this study classified comorbidities into 0 (no comorbidities) and 1–3 (high comorbidities) because of the low-frequency distribution of CCI scores exceeding 3. The urbanization level was graded using a 5-point scale, with 1 and 5 indicating the highest and lowest urbanization levels, respectively. All the 11 independent variables were measured on a categorical or an ordinal level. All the variables in the case–control design were defined at the pair level (couples versus noncouples).

Data analysis

In this study, data were analyzed through statistical analysis and data mining. Statistical methods included the chi-squared test and logistic regression. The chi-squared test determined the prevalence rates of diabetes concordance at the bivariate level. Logistic regression was used mainly for predicting diabetes concordance at the multivariate level, with the adjusted odds ratio (OR) and corresponding 95% confidence interval (CI). Because the members of the couples and noncouples were matched for the three variables, conditional logistic regression was used to analyze the matched pair data without the matching factors in the regression model [23, 24]. The conditional likelihood was estimated within the same matched set for binary diabetes concordance [25]. Moreover, collinearity diagnostics were computed using indices including variance inflation and tolerance. For data mining, C&RT and Apriori, two methods under the no hypothesis paradigm, were used to explore hidden patterns that statistics might fail to detect [26, 27]. The application of data mining techniques in longitudinal study analysis of a large clinical data source may discover useful information on disease prediction and health care delivery [28–30]. C&RT, a decision tree, was used for classification [31]. The Apriori algorithm of association rules was used to mine for potential associations in the extracted research data [32]. Data mining largely served as a supplement to statistics. In contrast to theory-based statistical analysis, data mining is substantially more data-driven. Research that analyzes the individual level factors associated with the couple concordance of diabetes is still lacking. Therefore, this study used statistics and data mining for the optimization of pioneering modeling for the concordance factors. The joint findings engendered by the two approaches should increase the strength of evidence on diabetes concordance. Data were analyzed using SAS 9.4 and IBM SPSS Modeler 13.

High concordance in couples versus low concordance in noncouples

To our knowledge, this study is the first that investigated spousal concordance of diabetes in a matched case-control design. A contrast of high and low concordance rates of diabetes in couples and noncouples, respectively, was identified. The dual PSM analysis revealed this phenomenon in both prevalence rates and ORs. The determined prevalence rate of spousal concordance was 5.19% (293/5,643) in couples, strongly higher than in noncouples (0.09%). The OR of 61.743 represents the marked effect of a common family environment on the development of diabetes in couples and deserves emphasis.

Both couples and noncouples were matched by sex, age, and comorbidities; therefore, the high contrast in the concordance is not attributable to old-age vulnerability and is closely related only to the coupled status. Assortative mating and similarities between both members of a married couple in a common environment may explain the high concordance of diabetes in couples [35]. Studies have indicated resemblances between spouses [36, 37], particularly in long-standing couples. Notably, collectivism in Taiwanese culture [38] may reinforce behavioral resemblances in couples. Furthermore, through cohabitation in the same family environment, concordant health behaviors, including exercise and dietary habits, and shared lifestyles in couples can be shaped [39–42] and might thus lead to a shared exposure, such as concordant obesity [43], to diabetes [44]. Hence, family-based intervention for modifiable health behaviors is a priority in clinical practice.

Individual-level characteristics predicting couple-level concordances

Statistical analysis and data mining yielded the combined results regarding factors associated with spousal concordance of diabetes. In addition to the couple status, nine factors, including personal and shared characteristics, of spousal concordance warrant attention. Most insured spouses were men who could have a higher risk of diabetes than their female counterparts [9, 23]. The prevalence rate of diabetes was higher in insured spouses (18.41% in insured spouses versus 16.64% in dependent spouses), thus explicating the finding that insured men were more likely to experience spousal concordance of diabetes than were insured women (Table 4). Old age was markedly associated with high risks of concordant diabetes, particularly in spouses aged ≥65 years (both ORs > 13, accuracy = 85.7%–90.9%); this observation is in accordance with the findings of previous studies [45, 46]. The urbanization level and region, which are the shared geographical characteristics of couples, were identified as the determinants of spousal concordance. Levels 2 and 3 of urbanization were associated with higher odds of spousal concordance, whereas residence in the northern region was associated with a lower risk. The geographical disparities in concordant diabetes warrant further research and require the attention of health policy-makers. The findings regarding comorbidities are similar to those previously reported [47, 48] and indicate that medical conditions of individual spouses contribute to concordant diabetes in couples.

Overall, diabetes in a spouse may indicate the risk of diabetes in the partner. A previous study indicated that spousal diabetes is associated with a 26% increase in the risk of diabetes in the partner [49], echoing the present findings. The phenomenon of spousal concordance of diabetes is evident. Therefore, the clinical prevention of diabetes should target spouses whose married partners were diagnosed as having diabetes by applying the individual-level and shared geographical risk factors identified in this study, including old age, mid-range urbanization, and chronic morbidities.

Couple-oriented health insurance: couplitation

Health insurance schemes might adjust medical payments by sex, age, and morbidities, such as capitation reimbursement [50]. A family history of certain chronic and catastrophic illnesses among genetically related family members is considered for determining premiums. Nevertheless, the spouse history of diabetes is typically not involved in the risk rating of individual-level health insurance plans. Therefore, the present study proposes a novel yet reasonable direction of a couple-oriented insurance scheme, couplitation, that is aimed at developing comprehensive coverage and reimbursements for spouse-vulnerable chronic diseases [51–53], particularly diabetes. Couplitation may improve early detection through examination in a manner paralleling capitation. This spouse-related risk rating of an insurance scheme requires feasibility analysis in future studies.

The limitations of the present study are mainly related to the database used. First, the NHIRD does not include information on the educational level, health behaviors, laboratory test results, cohabitation duration, and other joint characteristics of the couples. The absence of these data weakens the statistical strength of this study. Second, the body mass index is a major risk factor for diabetes; the absence of this factor may result in residual confounding and thus bias the findings in an unknown direction. Third, high level of awareness or knowledge of symptoms of diabetes may lead to early diagnosis. Due to the lack of awareness-related data in the NHIRD, the current study failed to take this factor into consideration. Finally, all spouses retrieved from the database were limited to the insured–dependent relationship. The generalization of the study findings to all other relationships requires deliberation.