Socioeconomic inequalities in children under 15 with medical complexity: A population-based study

Background Health inequalities from the first stages of life onwards have consequences for children’s health status and for their future development. Children’s health is profoundly affected by the socioeconomic position (SEP) of their parents. The situation is even more disruptive in children with medically complex (CMC) conditions. The aim of this study is to describe the characteristics and related pathologies of CMC in Catalonia, and to assess the presence of socioeconomic inequalities. Methods Cross-sectional study and cluster analysis of the diseases in the CMC population under the age of 15 in Catalonia in 2016 using administrative data. Each pathology cluster obtained was described and its association with socioeconomic position (SEP) determined. The Adjusted Morbidity Groups (Catalan acronym GMA) classification system was used to identify the CMC. Main outcome measures: SEP, GMA score, sex, and age distribution were recorded in both populations (CMC and non-CMC) and in each of the clusters identified. Results 71% of the CMC population have at least one parent with no employment or an annual income of less than €18,000. Four comorbidity clusters were identified in CMC: respiratory (47%), neurodevelopmental (18%), hemato-oncological (12%), and perinatal disorders (23%). CMC experience higher levels of poverty and social disadvantage in all clusters. SES-cluster association results were: in the respiratory OR, 2.1 in boys and 2.1 in girls; in the neurodevelopmental disease, OR 2.0 in boys and 1.8 in girls; in the hemato-oncology OR, 1.7 in boys and 1.8 in girls; and in the perinatal OR, 1.7 in boys and 1.8 in girls. Conclusions There are socioeconomic health inequalities in childhood across a broad spectrum of health outcomes. Children with lower SEP are more likely to have CMC conditions. All clusters showed SEP inequalities and respiratory and neurodevelopment clusters are the most prevalent.


Introduction
Since the 1980s, when the Black Report drew attention to the gradient in health according to socioeconomic position (SEP), health inequalities have been a major social issue 1 . The report showed that poor health is more frequent among the most disadvantaged, even in childhood.
There is increasing scientific evidence, from both biology and the social sciences, of the importance of the first years of life (including in utero exposure) in the formation of the capacities that promote wellbeing throughout the life cycle 2 . Childhood is also a structural transmitter of inequalities 3 , in terms of both health and SEP. Naturally, children are affected by their socioeconomic environment 4,5,6 , and this impact becomes stronger as they grow older 7 .
One of the most vulnerable populations is "children with medical complexity"(CMC) 8 conditions. This denotes the profile of a child with intensive use of (and placing a high cost burden on) the health services and with special health and social needs. These children often have complex acute and chronic conditions, numerous comorbidities, a broad range of mental health and psychosocial needs, major functional limitations 9 , and a higher rate of mortality 10 . Family economic resources and social and health care support are identified as important external needs factors in CMC 1112 .
Recent data from Catalonia (2015) 13,14 shows that children with the lowest SEP are twice as likely to be CMC than those at the next socioeconomic level. Clustering these patients by their different characteristics is a valuable tool for predicting children's future health and development, and also for planning healthcare services accordingly 15,16 . The aim of this study is to describe the characteristics and patterns of pathology on CMC patients and their association with socioeconomic position.

Methods
Study population criteria: Population-based risk assessment: GMA (Adjusted Morbidity Groups) The CMC population was identified from the entire resident population of Catalonia under the age of 15 in 2016 (1,189,325, 52% boys). A morbidity, complexity, and risk score tool, Adjusted Morbidity Groups classification (Catalan acronym GMA) 17 , was used to identify the CMC population.
The GMA predicts an individual patient score according to their comorbidity and complexity. The higher the GMA score, the greater the individual's medically complex conditions. This scoring is used to stratify the population for the purposes of health planning 17,18 . It is more accurate and yields less variability than other health risk tools, such as Clinical Risk Group (CRG) 18 . Information on comorbidity and complexity to construct GMA was gathered from the Catalan Health Surveillance System (CCHS) database, for present and previous years, and so on.
Those in the top 0.5% based on GMA score (5,950 children) were selected as CMC population since: 1) this is the level of highest complexity proposed by the GMA; 2) previous studies in Catalonia found that 0.3% of the population were CMC 13 ; and 3) concordance with the prevalence of CMC in other population studies 19 . The 99.5% of the rest of the children, the non-CMC population, were used as a comparative group.

Data
We used two main sources of data: The central registry of insured persons (Catalan acronym RCA) was used to obtain the reference population (as of January 1, 2016), their income level, employment status, and Social Security benefits.
The CCHS database, for clinical purposes, includes detailed information on sociodemographic characteristics and medical diagnoses at an individual level in all contacts in primary care, emergency, mental health, long-term care services, and pharmacy prescription. It includes the total population of Catalonia, since all citizens are granted universal health coverage.

Variables
Outcome variable: The principal outcome variable was the different clusters obtained by grouping patients with similar patterns of comorbidity.
Medical diagnoses were obtained through CCHS (coded using the Agency for Healthcare Research and Quality's Clinical Classification Software (CCS)) 20 and were considered in order to determine the comorbidities registered from 2014 to 2016 in each CMC child. Only the first diagnosis of each type within each individual was included.
Exposure variable and covariates: SEP was created on the basis of the employment status, individual income, the receipt of welfare assistance of one of the child's parents or guardians from de RCA database. SEP was grouped into three categories: Low (no member of the household employed or in receipt of welfare support from the government, and an income <€18,000/year, considered poverty 21 ); Middle (employed with an income <€18,000); and High (in employment, with income >€18,000).

Statistical analysis
A descriptive analysis was carried out of both the CMC and non-CMC populations.
Afterwards, in order to determine patterns of pathology in CMC population, diagnoses related to each individual, coded through CCS, were used. Homogeneous groups of patients with similar diseases were identified in CMC population via K-means cluster analysis using the Jaccard similarity index 22 , as in similar studies 23 .
Due to the large number of CCS in each individual, only the most disabling ones were selected based on the following criteria 23 : a prevalence > 0.5%, being five times more prevalent in the CMC than non-CMC, and a median of GMA >5 for the whole population. After this, a joint correspondence analysis was performed before the K-means procedure. CCS with correlations smaller than the median in each dimension were rejected or aggregated with similar 24 ; some were recovered because of their clinical or socioeconomic relevance. In total, 93 of 279 CCS were selected (See Supplementary Table   1, Additional File 1).
The final number of clusters was determined by Calinsky-Harabasz's criterion 25 ,wich take into account minimum dispersion criteria to evaluate the optimal number of clusters. Each individual belonged to a single cluster. Finally, the clusters identified were interpreted and named under the supervision of a paediatrician. Logistic regression models, adjusted for age, were fitted for all children in Catalonia to test for association between SEP and to be allocated or not to each CMC cluster obtained by sex. Data analysis was conducted using STATA V.14/SE 2014 26 .

Characteristics of the CMC population
The main characteristics of the CMC (0.5%) and non-CMC (99.5%) populations are described in Table   1. Both populations contained a higher proportion of boys (CMC 58.5% versus non-CMC 51.1%).
More of the CMC population was in early childhood than non-CMC. Almost a quarter of CMC were in the two first years of life (25.2% boys and 24.4% girls); compared with the non-CMC population, this rate was 2.2 times higher in boys and 2.1 times higher in girls. Around 50% of children of both sexes with CMC were aged under five; compared with non-CMC, this rate was 70.4% higher in boys and 65.9% higher in girls (see Table 1).
According to SEP, more than 60% of non-CMC (70.8% of CMC) had at least one parent with an annual income of less than €18,000 (Low and Middle SEP). Low SEP had a prevalence of 13% in CMC (9% in non-CMC); 44.3% higher in boys and 46.7% higher in girls than in the non-CMC. Cluster 1 (respiratory comorbidities): this cluster included 2,815 children, representing 47% of the CMC population. Its maximum distribution was between the ages of two and 11 (67%). This cluster of comorbidities was more prevalent in girls than boys in ages 12-14 (p = 0.011). The main diseases were allergic reaction (71% boys, 73% girls), chronic obstructive pulmonary disease and bronchiectasis (58% boys and 53% girls), and asthma (52% boys and 44% girls).

Socioeconomic inequalities
SEP inequalities in the four clusters are displayed in Table 2 and Figure 3. There were socioeconomic inequalities in all clusters, for both sexes. Children in the Middle or Low SEP groups were more likely to belong to one of the disease clusters than High SEP children.

Discussion
Four different comorbidity clusters in CMC were obtained with different prevalence rates according to sex and age. All of them showed inequalities for SEP, being the most disadvantaged who had a higher probability to belong in all CMC clusters and moreover a lack of economic support within their family to have the best development and care.  characteristics of all clusters were a high prevalence of allergic reaction (>30%) and a higher proportion of boys, of around 58%. This data supports the contribution of sex to the origin and development of health and disease. Male foetuses mature slower than female; and, after birth, males have more perinatal issues. This vulnerability may persist for up to five years 28 , coinciding with the time of maximum prevalence of the respiratory and perinatal clusters (57% and 71%, respectively).
The respiratory cluster involves mainly pulmonary diseases and allergic reactions. In accordance with the natural development of most respiratory diseases, its prevalence is highest in the mid-age range.
Risk factors known to be related to social and economic inequalities are: exposure to air pollution 29 , in utero exposure to tobacco 30 , maternal stress 31 and low weight at birth and prematurity 32 . Age distribution varied according to sex. The presence of boys was more marked under the age of 11, related to adverse birth outcomes 28 , while at the age of 12 to 14 girls presented a more chronic profile 33 .
Theneurodevelopmental disorders cluster includes two related types of disease: on the one hand, nervous system and developmental disorders such as paralysis and epilepsy, and on the other hand, congenital, perinatal and degenerative anomalies. Their prevalence increases as the child grows older; they have a chronic, cumulative profile due to the difficulty of healing and they may be precursors of future complications in other systems 4 . The aetiology of nervous system anomalies may be related to social inequalities such as exposure to certain environmental factors 34 , maternal stress during pregnancy, or adverse gestational and delivery outcomes 35,36 . All these events occur in the pre-natal and perinatal period but their impact may be felt at a later stage.
The hemato-oncology cluster had the highest median GMA, since the prognosis and development of the pathology entail a high risk. This profile includes all cancers, where leukaemia is the most prevalent (30% boys, 26% girls), along with related hematological diseases. Its association with SEP is weaker than that of the other clusters. There is general agreement that oncology is influenced less by SEP 37 and more by exposure to environmental contaminants 38 .
The perinatal disease cluster comprises mainly perinatal and congenital diseases and cardiac and circulatory congenital anomalies. Its maximum prevalence was seen in the first two years of life (49%). In this short time, socioeconomic and social determinants influence the child mainly through the mother: maternal behaviour during pregnancy has been identified as a risk factor 31,39,40 . It should also be noted that advances in perinatal care have increased the likelihood of survival for extremely preterm infants, who are likely included in this cluster.
The age distribution of each cluster shows the ages of maximum expression of each of the patterns (see Figure 2). Diseases are not static, and prognosis may mean that individuals move from cluster to cluster.
All clusters showed SEP inequalities, thus corroborating the previous analyses carried out in Catalonia 13,14 and elsewhere 41 . Respiratory and neurodevelopmental clusters (see Figure 3) slightly showed a higher SEP inequality, in line with similar studies in the US 4 and the UK 5 .
Having to care for CMC may negatively affect families' economic position and health 12 . In addition, stressful factors affecting the parents, such as poverty or poor mental health, impose heavy developmental burdens on young children 42 . In contrast, families with more economic resources are able to provide more active stimulation, alternative treatments, and an environment that is safer and more conducive to maintaining good health in childhood. This phenomenon has been termed the "buffering effect of income" in chronic conditions 4,5 . The family's SEP is related to CMC, thus impacts on the development of this vulnerable population.

Strengths and limitations
The criteria for defining CMC population presented certain limitations. The GMA classification was originally created for adults. Nevertheless, CRG takes the same point of view and has been successfully used in CMC populations. 43 Because of the limited data available on income, we were unable to obtain a more detailed segmentation of the SEP variable. This was especially true of the High SEP category, which includes a wide range of income levels. A more segmented category would have given a more accurate approximation of the SEP gradient.
The health status data is based on the use of public healthcare resources, and data from private healthcare was not available. Even so, the bias is presumably very low, as CMC patients require    Prevalences of comorbidity clusters among CMC by age and sex. Catalonia, 2016.

Figure 3
Odds ratio between socioeconomic position (SEP) and each comorbidity cluster among CMC by sex. Catalonia, 2016.* *Models were adjusted by age. Odds ratio and 95% Confidence Interval. High SEP was the reference category.

Supplementary Files
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