Worldwide, major mental and behavioural disorders are increasing and account for approximately 7.5 % of disability-adjusted life years (DALYs) with major depressive disorder having the highest impact with 2.5 % within this group 1. In Germany, where this study was conducted, major depressive disorder is the seventh most frequent cause for loss of healthy years of life among men and the third frequent cause for DALYs among women 12.

Environmental noise in urban areas is suggested to be one of the major risk factors for adverse health effects and several studies mainly investigated and showed the link between different noise sources and cardiovascular outcomes such as hypertension ^3–5. However, fewer is known about the relationship between traffic noise 67 or aircraft noise 8 and different mental disorders such as depression. Results from the Heinz Nixdorf Recall Study showed a relative risk (RR) of 1.29 (95 % confidence interval 1.03, 1.62) for participants classified as having high depressive symptoms for exposure to road traffic noise > 55 dB(A) compared to ≤ 55 dB(A) 6.

Furthermore, it is suggested that health access as well as social services might be associated with mental health outcomes and that there is a gradient of social and health services from urban to rural areas 910.

Likewise, positive support from family was identified as a potential protective factor for the onset or the level of depressive symptoms. A follow-up study conducted in the USA revealed that patients recovered from depression by the time of the follow-up assessment reported higher perceived emotional support from family at baseline 11.

Additionally, a lower socioeconomic background is considered to be an important risk factor for poor mental health. Many studies investigated and showed the relationship between individual, educational and occupational background or income and different mental disorders such as depression and anxiety ^12–15. Weich and Lewis 15 for instance investigated in a cohort study associations between poverty, unemployment and common mental disorders and showed that the duration of episodes of common mental disorders but not the likelihood of their onset were associated with unemployment and poverty, even after adjustment for other individual indices such as educational background or marital status 15. However, the use of single variables as potential risk indicators may lead to false conclusions because they only reflect parts of the general view.

Other studies have shown that local environmental and borough conditions may play an important role in the prevalence of psychiatric disorders as it has been investigated for instance in two studies conducted by Kirkbride et al. 16 in East London, United Kingdom and by Rejneveld and Schene 14 in the city of Amsterdam, the Netherlands.

The aim of this first part of the study was to investigate associations between road traffic noise, socioeconomic and -demographic factors, and physician density on depressive disorder on borough level in the city of Hamburg. Particularly, by using the multivariate analysis of covariance (ANCOVA) model we assessed potential associations between prevalence rates of depression per borough in all age groups (excluding the age group of 0–17 years of age) and divided by sex using health claims data (year 2011) and the variables “social deprivation” and “number of family members”, which were obtained from a previously conducted principal component analysis (PCA). Furthermore, the proportion of borough area affected by noise > 65 db(A) and physician density used as a proxy measure for health access were considered as potentially associated factors for depression and entered the final model.

The current study was conducted in order to get insight into the potential association of depression with environmental and socioeconomic and sociodemographic factors. This topic is of particular importance since there is a marked increase in depression among the German population 2. This increase has led to a significantly increasing volume of health care expenses and still rising losses of productivity, both resulting in high direct and indirect costs 22. The first results of our study indicate that factors related to the socioeconomic and sociodemographic borough background as well as road traffic noise may be associated with the occurrence of depression in the city of Hamburg. Highest prevalence for depression could be found in most deprived areas, compared to boroughs with low social deprivation. Additionally, the proportion of depression was lower in boroughs where more family members, interpreted as positive family support, were present. Another independent factor was road traffic noise, showing higher prevalence rates of depression in boroughs with higher percentage of borough areas affected by noise > 65 db(A). Physician density used as a surrogate parameter for health access did not show any significant effect on depression rates on borough level.

Several studies have shown that local environmental conditions may play an important role in the prevalence of psychiatric disorders 12141623. For instance, a study conducted in the city of Maastricht, the Netherlands, found significantly higher incidences and severity of psychiatric disorders in deprived neighbourhoods 24.

A PCA is a multivariate statistical technique used to reduce the complexity and dimensionality of correlated variables to one or more uncorrelated single indicator variables. Interpretation and comparison across settings such as urban boroughs as provided herein in this study are easier. On the other hand, the principal components are artificially constructed indices and the number of selected variables included and the number of components is arbitrary 25. However, the indicators which entered the PCA and were used in our study should be reliable and hence, should approximately reflect the socioeconomic and -demographic background of a borough.

Nevertheless, single socioeconomic indicators such as unemployment or income have also been shown to potentially be associated with mental disorder and many studies, reviews or meta-analyses have been published investigating or summarizing the socioeconomic effects on mental health such as depression or anxiety 131526.

Number of family members, used as a proxy measure for positive family support, was an independent factor on depression. These results are consistent with other studies, investigating objective and subjective aspects of support from family 11 ^27–29. However, even though we used a high number of family members as a surrogate parameter for positive family support, it could contrarily have a negative effect on depression when for instance family conflicts are present 2829. Therefore, our results have to be interpreted with caution.

Furthermore, the proportion of borough areas affected by road traffic noise > 65 db(A) indicated associations with depression prevalence rates. Likewise, an impact of residential road traffic noise > 55 db(A) on high depressive symptoms has been suggested from the Heinz Nixdorf Recall study, conducted in three adjacent cities in western Germany 6. Same results could be found in a study conducted in the city of Tokyo showing associations between road traffic noise above a threshold of > 65 db(A) with sleep disturbance and depression 7. Nevertheless, a limitation of our study is that road traffic noise was transferred to the number of potentially affected residents per building block. Certainly this does not reflect the real environment or covers the real circumstances of noise exposure. Instead, other individual co-factors describing location of rooms, subjective noise annoyance or duration of residence should be taken into consideration 783031. Additionally, noise at workplace 3233 or other sources of noise such as aircraft or industrial noise 83134 are suggested to affect psychological disorders. Additionally, exposure to gaseous and particulate air pollutants, which is generally higher in cities than in non-urban areas, has been associated with depression 31 or anxiety 35. However, no data on further noise sources or air pollutants in the city of Hamburg were available and so far, could not be considered in our study.

Physician density applied as a surrogate parameter for health access did not show any significant association with depression. Only data from general practitioners and not from psychotherapists were used to calculate the physician density per 1,000 inhabitants. In the German health care system people potentially suffering from mental disorder first have to see a general physician or practitioner for referrals to a specialist such as psychologists. Hence, only using the physician density as a proxy measure for health access for people diagnosed with several mental diseases should mostly represent the health care for mental disorder. However, in general people do not always seek medical care adjacent to their home-address. Instead, they seek help at a general practitioner or even specialist in a borough further away or go directly to the hospital. Hence, depression prevalence rates might be underdiagnosed and skewed in some particular groups of the population due to differences in seeking health care behaviour.

Another limitation of our study is, that only data from statutory health insurance patients with at least one contact to a contract physician working in the ambulatory sector, including psychotherapists were available. Data regarding the amount and distribution of privately insured patients or data from private practice were not available. However, it is suggested that roughly 10 to 20 % of all inhabitants in Hamburg are privately insured 1821, therefore the prevalence rates used here in our analyses should cover the largest proportion of diagnosed cases. Nevertheless, due to the high costs of the private insurance, mostly people above a certain income level can afford this status. Hence, our results might be skewed in low deprived boroughs due to the lack of data from the privately insured individuals.

Furthermore, it is suggested, that the insurance status might be associated with the treatment and hence, the health outcomes of individuals. For instance, two studies in the USA investigated the influence of insurance status on the access to mental health care. Both studies found associations between the insurance status and unmet need for mental health care or the acceptance rates 3637. Additionally, a higher number of unreported cases might not be considered in our study, because not all people showing depressive symptoms do seek help at a psychotherapist. Additionally, differences in motivation for psychotherapy and illness beliefs might be one reason for underestimation of cases as shown in a study conducted among Turkish Immigrant inpatients in Germany 38. Furthermore, not all ICD-codes for depressive disorders such as F48.0 describing Neurasthenia or F92.0 describing depressive conduct disorder, were considered for our health outcome depression. Therefore, the data used in our study only capture a fraction of the estimated prevalence and hence, might have an underestimation bias in their acquisition.

Due to very low borough prevalence rates among the age group of 0 to 17 years of age in both sex (approximately 0–2 %) and hence, a possible false estimation of the respective effects, we excluded this group in the final model. Prevalence rates among the remaining age groups were approximately evenly distributed (among males range between approximately 10–20 %, among females range between approximately 15–25 %) and were chosen as one group for the analysis. Nevertheless, to check stability of the observed results, we repeated the ANCOVA model with the age group 0 to 17 years of age included. “Number of family members” as well as “social deprivation” had somewhat higher significant estimates of the coefficient B, instead “proportion of borough area affected by noise > 65 db(A)” indicated a halving of the depression prevalence (data not shown). Reversely, this result might underline the possible association of long-term effects of noise above a certain threshold and additionally the importance of duration of residence on mental disorder.

An advantage of our cross-sectional ecological study design is, that a number of large-scaled, population-based data could easily be obtained for the city of Hamburg and first theoretical associations might be analysed and discussed. Our results obtained here on borough level show similar association as demonstrated in other studies underlying the potential importance of social depravity of a borough in relation to specific disease outcomes, even though the study design used so far makes the results of this study difficult to interpret and potentially misleading.

In a next step we will conduct a case-control study with hypertensive patients as cases to get a better insight into the potential individual risk factors together with environmental living and working conditions. Participants from the Hamburg City Health Study (HCHS), the largest local follow-up health study, which just started spring 2016 and with follow-ups for the next 12 years will be matched and used as a control group. Additionally, data about noise (traffic noise, aircraft noise as well as industrial noise) and data about air pollution will be selectively collected close to the residents of participants and considered in the upcoming studies to account for the above discussed limitations and hence, to better analyse and understand potential individual and environmental co-factors of depression.

Our first results presented herein suggest that the socioeconomic and sociodemographic background of an urban borough could have a considerable effect on depressive disorder. A significantly higher prevalence for depression was detected for highly deprived boroughs. Furthermore, road traffic noise > 65 db(A) indicated a significant association with depression in the city of Hamburg. Our results suggest that large-scale socioeconomic and -demographic borough data together with noise, which could easily be obtained might be useful to pose potential associations between a borough´s socioeconomic and sociodemographic background, noise and different health outcomes such as depression. However, to regard the limitations of our study future studies will focus on collecting information on individual level and the environmental living and working conditions together with data on noise and air pollution in the near living environments of participants. These are prerequisites to assess the validity of our model and to develop strategies to reduce the increasing prevalence of depressive disorder.