These population groups may face health disadvantages in Switzerland
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These population groups may face health disadvantages in Switzerland
People affected by poverty, low levels of education or discrimination linked to gender identity or sexual orientation are likely to face significant health disadvantages in Switzerland. This is one of the findings of a new report by the Swiss Health Observatory (Obsan), commissioned by the Swiss health ministry.
Fictional profiles Alma, Tobias and Kim all suffer from chest pain and shortness of breath caused by myocarditis. In principle, they should have equal access to high-quality medical care. In reality, however, their chances of receiving the same treatment are not the same. This is shown by the Obsan report on health inequalities in Switzerland.
Clear patterns of inequality
Health inequalities are clearly identifiable across the country. Numerous population groups are disadvantaged when it comes to preventing illness, maintaining good health or accessing medical care. In practice, this means that Alma and Kim are less likely than Tobias to receive the same quality of treatment.
Socio-economic status matters
Alma works in retail and earns less than Tobias, who is employed in the insurance sector. This difference in income has a measurable impact on health.
“People in financially precarious situations or with a lower level of education score worse on all health indicators, have a higher burden of disease and more limited access to healthcare,” writes sociologist Laila Burla, one of the authors of the report.
In this report, 30 health indicators were analysed. These include how healthy the survey participants are (health status), what they eat, whether they exercise, smoke or consume alcohol (health-related behaviour), whether they have social support and how emotionally exhausted or burdened they are (resources and stress), how good their access to clinics and hospitals is and how often they use them (utilisation and care).
The health status indicators deal with how they themselves assess their health and quality of life, whether they have long-term health problems and limitations and what their responses are to symptoms of depression, oral and dental health, diabetes, obesity, high blood pressure and high cholesterol, heart attack and stroke and cancer. Data on the mortality rate within a population group was also taken into account.
The people in these surveys were categorised into different population groups and compared. They were categorised and compared according to gender, education, income, financial situation, material and social deprivation, family wealth, nationality, migration background, gender, sexual orientation, household type, employment status, language skills, residence status and insurance class. This made it possible to find out which population groups had poorer health or poorer access to medical facilities than others.
The analysis examined 30 health indicators, including diabetes, dental health and cancer. One striking example: people living in poverty or with a low level of education are four times more likely to suffer from depression.
Gender identity and sexual orientation
In Kim’s case, unequal health outcomes are not linked to income but to gender identity. Kim identifies as non-binary, while Alma and Tobias are cisgender – meaning their gender identity corresponds to the sex assigned at birth.
According to the report, transgender and non-binary people have significantly poorer mental health than cisgender people. The same applies to gay, lesbian and bisexual people compared with heterosexuals, with the disparities particularly pronounced among young people.
Other affected groups
Other population groups also face health disadvantages, including refugees, people living alone, young women and the unemployed.
Female adolescents between the ages of 11 and 15 report significantly lower levels of life satisfaction than male adolescents of the same age.
People in the unemployed and economically inactive population group are affected by depression around twice as often as those in employment.
The group of people living alone has a higher risk of avoidable mortality. This increased mortality rate decreases with age.
Asylum seekers and temporarily admitted refugees have a massively increased risk of unplanned hospital readmissions. In contrast, people with private and semi-private insurance have only a low risk of this.
These are a few selected examples – the list is not exhaustive. It remains unclear in this report whether other groups are at a disadvantage, as many could not be analysed due to a lack of data.
Key findings of the report
“The report clearly shows that certain groups – especially people with a lower socio-economic status – perform worse across almost all health indicators,” says Burla.
The study also analysed the various health indicators by gender. Women are more frequently affected by chronic complaints and psychological stress, while men are more frequently affected by physical illnesses, a higher mortality risk and unfavourable health behaviour. Where possible, gender was defined in the analyses in this report on the basis of gender identity.
Some socially disadvantaged population groups could not or only insufficiently be depicted with the data used here. These include people with disabilities, clandestine migrants and people of colour.
One of the reasons for the inadequate representation is that some of the data was collected through surveys. Such surveys often miss groups that are more difficult to reach. These include, for example, the homeless, clandestine migrants or people with cognitive impairments.
She stresses that health outcomes are shaped less by individual responsibility than by living conditions. “These conditions are unevenly distributed in society,” she says, adding that disparities between population groups appear to be widening.
What needs to change
According to Burla, reducing social inequality and improving access to healthcare are key priorities. To do this effectively, however, more detailed data is needed to identify particularly vulnerable groups.
There are two important limitations. Firstly, these results do not allow any statements to be made about causality. In the words of Burla: “Is it ‘poverty makes you sick’ or ‘illness makes you poor’?”
The other limitation is overlapping characteristics. Due to the data situation, only individual characteristics could be analysed in the report.
However, combinations of different characteristics often lead to greater disadvantage than just the sum of the individual characteristics. Burla explains an example of this intersectionality: “The results on chronic high-risk alcohol consumption show no differences according to education. But if you also break it down by gender, you can see that there is definitely an educational effect for women: Women with a tertiary degree are around twice as likely to have chronic high-risk alcohol consumption as women without a post-compulsory education.
There is no such difference for men.” In other words, the overlap between very good education and female gender shows a correlation with high-risk alcohol consumption.
The author of the report emphasises: “An intersectional approach could identify the most vulnerable population groups and thus specific target groups for measures.” However, this requires further studies and larger data sets.
Treating everyone exactly the same is not sufficient. Health equity means recognising that different people require different forms of support – so that everyone, including Alma, Tobias and Kim, has equal access to healthcare, regardless of income, education or gender identity.
So that everyone, including Alma, Tobias and Kim, has the same access to healthcare services, regardless of their finances, education or gender identity.
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