Master Thesis : Is neighborhood socioeconomic status associated with health outcomes? An empirical study of a Dutch surgical cohort

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Master Thesis : Is neighborhood socioeconomic status associated with health outcomes? An empirical study of a Dutch surgical cohort

Ramesh Marapin (2317001) MSc Business Administration - Health

Faculty of Economics and Business, University of Groningen First supervisor: prof. dr. J.O. Mierau

Second supervisor: prof. dr. M.J. Postma Word count: 10611

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Abstract

Socioeconomic status (SES) has been empirically associated with myriad health outcomes.

Namely, in low SES populations health outcomes are relatively worse. While the effect of individual SES on health outcomes is well known, the effect of SES at the neighborhood level (NSES) remains relatively unexplored. This thesis investigates the association between NSES and health outcomes to garner new insights for policymakers in their mission to ameliorate health disparities. Using data from 585 patients, I analyze the association between NSES and both the odds of a favorable postsurgical (textbook) outcome, and pre-existent comorbidity.

The results of this thesis do not provide evidence of a significant association between NSES and the studied health outcomes. Moreover, I discover higher age to be significantly associated with lower odds of a textbook outcome, while I discover both higher age and the male gender to be significantly associated with higher pre-existent comorbidity. The findings in this thesis are in contrast to previous literature in this field, which report an inverse association between NSES and health outcomes. This discrepancy could be due to hospital-level factors (i.e., hospital volume), which potentially modify this association. Strategies to reduce health disparities should be tailored to the individuals’ socioeconomic context and characteristics.

These strategies should focus on lifestyle-related risk factors, health literacy and healthcare engagement, as well as the neighborhood context, such as educational and recreational facilities. Ultimately, concerted efforts addressing these issues are required in order to structurally mitigate the socioeconomic gradient in health outcomes in the Netherlands.

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2 Abstract

Socioeconomic status (SES) has been empirically associated with myriad health outcomes.

Namely, in low SES populations health outcomes are relatively worse. While the effect of individual SES on health outcomes is well known, the effect of SES at the neighborhood level (NSES) remains relatively unexplored. This thesis investigates the association between NSES and health outcomes to garner new insights for policymakers in their mission to ameliorate health disparities. Using data from 585 patients, I analyze the association between NSES and both the odds of a favorable postsurgical (textbook) outcome, and pre-existent comorbidity.

The results of this thesis do not provide evidence of a significant association between NSES and the studied health outcomes. Moreover, I discover higher age to be significantly associated with lower odds of a textbook outcome, while I discover both higher age and the male gender to be significantly associated with higher pre-existent comorbidity. The findings in this thesis are in contrast to previous literature in this field, which report an inverse association between NSES and health outcomes. This discrepancy could be due to hospital-level factors (i.e., hospital volume), which potentially modify this association. Strategies to reduce health disparities should be tailored to the individuals’ socioeconomic context and characteristics.

These strategies should focus on lifestyle-related risk factors, health literacy and healthcare engagement, as well as the neighborhood context, such as educational and recreational facilities. Ultimately, concerted efforts addressing these issues are required in order to structurally mitigate the socioeconomic gradient in health outcomes in the Netherlands.

Keywords: neighborhood socioeconomic status, postoperative outcomes, surgery, Dutch cohort JEL classification: I10, I14

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3 Currently, an empirically supported association exists between socioeconomic status (SES) and several health outcomes and health behaviors (Adler et al., 1993; Duncan et al., 2002; Feldman et al., 1989; Venkataramani et al., 2016). While no formal definition exists, SES can be considered as a composite construct which commonly includes measures such as occupation, educational status, household income, and so on (Oakes & Rossi, 2003). In low SES contexts, health outcomes commonly manifest as higher morbidity and mortality, and reduced access to health care (Ancona et al., 2004; Picciotto et al., 2006; Van Lenthe & Mackenbach, 2006). For example, cardiac related morbidity and mortality figures are systematically higher among people with lower SES, which is the result of a higher prevalence of cardiac risk factors in this population (Schröder et al., 2016). Therefore, it is evident that socioeconomic disparities are fundamental causes of societal health inequalities and are items which rate high on the political agenda of policymakers.

Despite considerable attention given to the problem of health inequalities since the 1980s (Murray et al., 1999), striking differences in health unfortunately still exist between and within countries today (Braveman et al., 2000). Interestingly, in the past few decades there has been growing interest in the adverse health effects of living in neighborhoods characterized by poor socioeconomic conditions, i.e., low neighborhood socioeconomic status (NSES; Anderson et al., 1997). There is an increasing body of evidence supporting the hypothesis that living in such areas has adverse effects on health outcomes, regardless of individual SES levels (Curtis &

Jones, 1998; Diez-Roux, 1998; Michael G. Marmot, 1998). This may have important implications for public health policymakers. Namely, this evidence suggests that health policies should not only focus on individuals, but to also consider the socioeconomic environment in which they live as a key determinant influencing the health of the population.

The economic ramifications associated with poor health outcomes are salient through multiple

channels, including increased healthcare costs and productive time losses resulting from illness

(McIntyre et al., 2006; Muka et al., 2015). To this end, the economic costs of health inequalities

in the European Union have been estimated at 1 trillion € per year, which is 9.5% of its GDP

(Mackenbach et al., 2011). In addition to this societal impact, other important reasons to

address the socioeconomic gradient in population’s health include moral concerns and

undermining notions of justice and fairness (Whitehead, 1992). Particularly, health disparities

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5 are morally and ethically problematic as all individuals should have equal capability to be healthy, as health is crucial for individual agency – the ability to live a life we value. Taking this multifaceted impact into account, the association between NSES and health outcomes therefore warrants further investigation to better understand the underlying pathways.

Several papers discuss the adverse impact of SES on health outcomes in the context of postsurgical outcomes. While surgical interventions often serve as life-saving alternatives, the outcomes of these interventions are not necessarily equally distributed among the patient population. In fact, there is increasing recognition that a low SES leads to worse postsurgical outcomes. For example, Birkmeyer and colleagues found that patients with lower SES have higher incidences of postoperative mortality compared to patients with higher SES across a wide range of surgical interventions (Birkmeyer et al., 2008). In another study conducted in patients post-cardiovascular surgery, socioeconomically disadvantaged people seem particularly vulnerable to mortality after surgery (Agabiti et al., 2008). Finally, in a Dutch population based study investigating patients with pancreatic cancer, low SES was found to be an independent risk factor for poor survival (Van Roest et al., 2016).

Although there is ample evidence on the impact of individual socioeconomic factors on health outcomes in surgical populations, the impact of NSES in these populations is relatively unexplored, even more so in the European context. More importantly, the mechanisms underlying this association remain poorly understood. Policymakers are thus faced with the daunting task of developing appropriate strategies and policies to mitigate these societal health inequalities, essentially while lacking the necessary framework and roadmap to do so. By addressing this literature gap, with this paper I aim to contribute to the conceptualization of socioeconomic disparities in health. Currently, the literature consists mainly of studies addressing the impact of NSES on surgical populations outside of Europe, warranting further research on the impact of NSES on a European surgical context. To the best of my knowledge, this paper is one of, if not the first, to investigate the association between NSES and postoperative outcomes in a European healthcare setting.

To this end, the theoretical contributions of my paper are twofold. First, I seek to fill the gap in the theory of socioeconomic disparities in health by empirically investigating the relationship between NSES and postoperative outcomes in patients in a European context.

Second, I assess whether there is a relationship between NSES and patients’ pre-existing

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6 comorbidity scores. With this study, I thus contribute to the conceptualization of socioeconomic disparities in health with novel insights on the effects of neighborhood characteristics on health outcomes in a European context. Due to the sociocultural differences between Europe and the rest of the world, garnering insights from the European context is fundamental to policymaker efforts of devising strategies aimed at their societal needs.

Furthermore, by drawing attention to the health-related outcomes associated with the social structure and ecology of neighborhoods, innovative approaches to society level interventions can be fostered.

Based on the literature gap I identified in the Introduction and the aims of my research, I have formulated the following two research questions. First, the scant empirical evidence of the association between patients’ NSES and postoperative outcomes in a European context has led to my first research question:

RQ1: What is the relationship between patients’ neighborhood socioeconomic status and

postoperative outcomes in a Dutch surgical population?

Second, my aim is to investigate whether there is a relationship between NSES and patients’

pre-existing comorbidity scores. This leads to my second research question:

RQ2: What

is the relationship between patients’ neighborhood socioeconomic status and their pre-existing comorbidity scores in a Dutch surgical population?

2. Literature Review

In this section, I will first briefly describe the components that traditionally define socioeconomic status. Next, I will describe the pathways by which SES influences health.

These pathways contain perspectives from both the individual and neighborhood level, in order

to provide a comprehensive understanding of the role of the social environment in relation to

health. Omitting either construct will result in an incomplete and possibly biased understanding

of this pathway (Pickett & Pearl, 2001). Afterwards, I will shift the focus to the association

between NSES and disparate health outcomes. Finally, I will appraise the current evidence on

the association between SES and postoperative outcomes.

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7 2.1 Components of socioeconomic status

Before characterizing the components that make up socioeconomic status, it is necessary to provide a definition of health disparities and how this relates to socioeconomic disparities.

While a general consensus is lacking, health disparities can be conceptualized as a particular type of health difference. Namely, health disparities are differences that exist among specific population groups in the attainment of maximum health potential. These disparities can be measured by differences in incidence, prevalence, mortality, morbidity, and other adverse health conditions. In this regard it is important to consider that especially disadvantaged social groups (e.g. the poor, racial/ethnic minorities, or other groups) who have continually experienced social disadvantage or discrimination, systematically experience worse health outcomes compared to more advantaged social groups (Braveman, 2006). As such, while health disparities result from differences due to biological and social disparities, here I focus on the latter as the effect is greater but also avoidable and particularly unjust as seen from an ethical perspective.

Conventionally, SES has been defined by level of education, income, and occupation. Each of these components exhibit different relationships to various health outcomes, and thus require different appraisal strategies and policies to address underlying disparities. The first component, education, furnishes important knowledge and life skills that allow better-educated persons to gain more access to resources to promote health (Ross & Chia-Ling Wu, 1995). To this end, policies focusing on the improvement of education offer several benefits, ranging from improved population health to collateral virtues such as increased productivity and decreased healthcare costs (Adler & Newman, 2002).

The second component comprises income, where the distribution of income within countries and states has been linked to mortality rates (Kaplan et al., 1996). While still a subject of contention, one explication for this phenomenon is that underinvestment in public goods and welfare and the experience of inequality are both more pronounced in more stratified communities. In turn, this can affect health outcomes in these communities. Importantly, this phenomenon is not only restricted to incomes below the poverty level (Backlund et al., 1999).

However, this burden is particularly salient for individuals living in poverty.

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8 The final component encompasses occupation. One way to conceptualize occupation is whether or not somebody is employed. Occupation is a pertinent component to study, as employed persons have better health compared to the unemployed (Ross & Mirowsky, 1995).

Furthermore, examining the employed shows that there are differences between occupations, such as job characteristics and qualifications. Crucially, these different indicators reflecting occupational status are linked to mortality risk (Gregorio et al., 1997). This link also holds for non-mortality related measures, such as the incidence of coronary heart disease, which has been shown in the landmark Whitehall study in British civil servants (Marmot et al., 1997).

2.2 Pathways and mechanisms

Recently, many scholars have attempted to explain the mechanisms by which socioeconomic status influences health outcomes. In their work, Adler and Newman describe pathways and mechanisms by which SES influences health (Adler & Newman, 2002). First, SES has been shown to influence the environmental exposure pathway. Specifically, exposure to noxious environmental agents (e.g., asbestos) varies with SES. Occupying a lower position on the socioeconomic ladder is associated with a higher probability of working and living in worse physical environments. Additionally, less affluent neighborhoods are disproportionately located next to highways and industrialized land, as land there is less expensive.

Furthermore, housing quality is worse for families with low SES. Consequently, inhabitans from poor households demonstrate significantly higher rates of high blood lead levels compared with their counterparts from higher income households (Adler & Newman, 2002).

While the underlying mechanisms remain unclear, possible explanations for this include a decline in housekeeping as a result of increased adult work hours, and deteriorating housing stock (Cookson & Moffatt, 1997).

Socioeconomic status also exerts its effect on the social environment pathway. Lack of social

engagement and isolation in social networks are strong predictors of health (Adler & Newman,

2002). For example, individuals living in social isolation have a relative risk of mortality

ranging between 1.9 to 5 times higher compared to individuals with better social cohesion

(Berkman & Glass, 2000). Additionally, disease risk is also influenced by patterns of social

interaction. Architectural features of communities and institutions have also been found to

potentially promote social integration, which in turn improves health.

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9 Moreover, access to health care and the quality of health care received can vary by SES. In the United States, the majority of the uninsured reflect low-income families (Monheit & Vistnes, 2000). This has important ramifications, as people who lack insurance receive less medical care, including treatment, compared to those with medical insurance (Hafner-Eaton, 1994).

This problem also extends to countries that provide universal coverage, as people with lower education and less income do not necessarily use health care services in the same way that their wealthier and better educated counterparts do. For example, in a study done in Canada it was found that citizens with lower SES were less likely to get specialty care when needed (Dunlop et al., 2000). Another example where universal coverage insufficiently offsets social disparities can be seen in England (Smith et al., 1990). Thus, this underscores the notion that higher disease incidences and toxic exposure remain dominant forces by which SES affects health.

2.3 Neighborhood socioeconomic status and health disparities

The influence of neighborhoods on the health of its inhabitants has been a popular item of research in the first decade of this century (Li & Chuang, 2009). Accordingly, it is increasingly recognized that individual health is not solely influenced by individual characteristics, but also by the neighborhood socioeconomic context to which one belongs (Berkman & Glass, 2000).

Typically, these neighborhood contextual variables are either acquired at the individual level, e.g., median household income, or are central and exclusive to the neighborhood (Diez-Roux, 1998). An example of the latter is the number of recreational and educational facilities in a neighborhood (Macintyre et al., 1993). In this regard, NSES influences health opportunities and outcomes through its effect on individuals' achieved income, education, and occupation (Diez-Roux, 2001). For example, differences within and between neighborhoods could be in terms of access to (health) resources, which includes services that promote healthy behavior, and how groups and peers within neighborhoods influence each other’s behavior.

Also, as described previously, less affluent neighborhoods are disproportionately negatively

affected by their environment (e.g., higher concentrations of harmful air pollutants and worse

housing conditions), which leads to worse health outcomes for its inhabitants (Brook et al.,

2010; Hajat et al., 2013; Roy et al., 2014). Thus, when considering how to reduce health

disparities, the context in which these risk factors originate needs to be better understood.

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10 Ample evidence has demonstrated an association between NSES and health. Importantly, however, the direction of this association remains inconsistent, with several studies reporting either an association or no association at all between NSES and health. Indeed, living in neighborhoods with lower NSES has been shown to be associated with worse health and patient reported outcomes (Al Adas et al., 2019; Ana V. Diez Roux & Mair, 2010; Jerath et al., 2020a;

Jones et al., 2019; Patrick et al., 2020; Pickett & Pearl, 2001). Furthermore, findings from a Dutch study pointed to a higher prevalence of social disintegration, poor housing conditions, adverse psychologic characteristics, and unhealthy behaviors in neighborhoods with lower NSES (Bosma et al., 2001).

On the other hand, several studies have found no association between living in socioeconomically deprived neighborhood and health outcomes (Obeng-Gyasi et al., 2020;

Powers et al., 2019; Zarzaur et al., 2010). Potential reasons cited for this discrepancy revolve around the fact that high-volume centers and standardized pathways might overcome the adverse health outcomes due to socioeconomic disparities. However, as underscored by Pickett and Pearl 2001, the evidence for neighborhood effects on health is fairly consistent. Thus, the main takeaway here is that studies in various surgical patient populations have demonstrated a significant correlation between SES and health outcomes, however, this association is not always found.

Based on what I discuss above, I expect to find a similar effect for NSES with regards to its association with health outcomes. Specifically, I hypothesize:

H1a: The relationship between neighborhood socioeconomic status and adverse postoperative

outcomes is inverse (negative correlation coefficient).

H2a: The relationship between neighborhood socioeconomic status and pre-existing

comorbidity scores is inverse (negative correlation coefficient).

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11 3. Data and methods

3.1 Study design and cohort

In this investigation, I perform a retrospective cohort analysis of patients who underwent hepatopancreatobilliary (HPB) surgery. The data I use in this study was retrieved from the Managed Clinical Network, which is a database of HPB surgery from the northeastern part of the Netherlands. This network consists of five collaborating hospitals (University Medical Center Groningen [UMCG], Medisch Centrum Leeuwarden [MCL], Isala Zwolle, Medisch Spectrum Twente [MST], and Tjongerschans Hospital). This network contains data from approximately 3000 patients, who were operated between 2013-2017. Due to feasibility and ethical reasons related to accessing this data, I only use data from the UMCG for this analysis.

This dataset consists in total of 585 patients.

The protected patient electronic health records contain level 6 postal code data, which were collected in collaboration with a researcher at the UMCG who has access to these records.

These postal codes were used determine the NSES for each patient, as I explain more in depth below. Patients were included if they underwent an oncologic HPB procedure in one of the MCN affiliated centers between 2013 and 2017, and if there was sufficient data for analysis.

Patients who were under the age of 18 (on the date of the surgery), underwent diagnostic laparotomy or laparoscopy without resection, registered an objection to scientific research, and had no available postal code data were excluded from this analysis. In the case of missing data, these were obtained from the respective patients’ electronic health records.

For patients included within the UMCG, consent for medical research was checked through means of the “no objection” register. As this is a retrospective cohort study with the primary goal of improving the quality of patient care and includes a large proportion of deceased patients, the Medical Ethics committee (METc) of the UMCG deemed this to be sufficient. For this reason, I did not actively seek consent from patients to include them in this study.

3.2 Socioeconomic Analysis

For this analysis, I use a composite index measure for socioeconomic status, where I aggregate

several variables related to socioeconomic status at the neighbourhood level in order to derive

a unidimensional measure of NSES. I opted for this approach, since single SES related

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12 measures (e.g., income and level of education) may not be sufficient or relevant to adequately differentiate between different levels of household SES. Furthermore, using myriad separate single measures may lead to collinearity and cumbersome results (Pickett & Pearl, 2001).

I obtained data on the variables needed to derive the NSES from Statistics Netherlands (in Dutch: Centraal Bureau voor de Statistiek), a Dutch governmental institution that gathers statistical information about the Netherlands. Statistical Netherlands provides SES related variables at the neighborhood level, which was matched to patients using their level 6 postal code data retrieved from their electronic health records.

Using the socioeconomic status related variables, I applied the principal components analysis (PCA) technique to derive a NSES index. Principal component analysis is a dimensionality- reduction statistical method that reduces the dimensionality of large data sets, by transforming a large set of variables into a smaller, composite variables. Essentially, PCA creates non- correlated linear combinations of the variables with maximal variance, which allows for the best contrast between statistical units. Principal component analysis was run using the Rstudio statistical software package (Version 1.2.5042). In order to overcome missing data, I used the NIPALS (Nonlinear Iterative Partial Least Squares) PCA algorithm, as this allowed me to include more neighbourhoods in this analysis.

The data used for this study was collected in 2016 by Statistics Netherlands, this

Neighbourhood dataset contains data from 12,822 neighbourhoods. Of these, 1,318 have no

socioeconomic data as a result of having low numbers of inhabitants. This constituted 0.1% of

the total dataset and was removed from the data. All other neighbourhoods were included in

the analysis. The variables used for the PCA were neighbourhood income statistics (percentage

people in lower 2 quintiles of national income, percentage people in highest quintile), social

welfare reliance (percentage of people on benefits, disability benefits, or unemployment

benefits), and housing market characteristics (average estimated housing-price, percentage

owner-occupied, percentage council housing). The scores on the first principal component

(factor) defined the NSES score. Following the method of Asaria et al. (2016), I ordered the

neighborhoods by NSES score and divided them into roughly equal-sized quintiles (with

NSES1 being the most deprived neighborhoods and NSES5 the most affluent; NSES3 was

chosen as the reference category).

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13 3.3 Dependent variables of interest

In this study, I use two health indicators, namely postoperative outcomes and pre-existing comorbidity scores (CCI). First, postoperative outcomes were determined using variables registered in the Dutch Pancreatic Cancer Audit (DPCA) and the Dutch Hepatobiliary Audit (DHBA). These two databases belong to the Dutch Institute for Clinical Auditing (DICA), which acts as a national facilitating body for cross center quality assessment research. The DHBA and DPCA encompass patients undergoing oncologic liver and pancreas resections.

These postoperative outcomes were subsequently classified into “textbook” (TO) and “non- textbook” (NTO) clinical outcomes.

Textbook outcome is a theoretically grounded composite measure reflecting the “ideal”

surgical outcome, which consists of factors such as postoperative complications, no readmission to the hospital within 30 days, discharge from the hospital within two weeks, and no mortality. There is no universal definition of a “normal” postoperative course, and many classifications have been proposed which all remain subjective and significantly overlapping between other classifications. Therefore, I operationalized a “normal” postoperative course using the “textbook outcome” definition which was forged trough an international expert consensus (Van Roessel et al., 2020). Postoperative complications were scored in accordance with the Clavien Dindo classification, which classifies complications based on severity into one of five predetermined categories.

Second, patients' pre-existing comorbidities were scored in accordance with the Charlson Comorbidity Index (CCI). The CCI consists of 19 predetermined comorbidities, each assigned a score (1-3) based on their severity. Furthermore, an additional point is added for each decade above the age of 40.

3.4 Contextual factors of interest

In this study, I include five potential explanatory contextual (confounding) factors in the

analysis between NSES and postoperative outcomes. The first two variables I included were

age and body mass index (BMI), as both these variables have been shown to be independent

predictors of complications and worse outcomes in general surgery patients (Gajdos et al.,

2013; Yanquez et al., 2013). This data was collected from the electronic patient records.

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14 Furthermore, I included gender and patient’s diabetic status as covariates in this analysis, as these have also been shown to be independently associated with worse postoperative outcomes (Browne et al., 2007; Komatsu et al., 2020; Yang et al., 2014; Zimmerman et al., 2011). Finally, I included the CCI score as a covariate in the analysis of NSES association with textbook outcome, as pre-existing comorbidity is associated with postoperative outcomes.

3.5 Statistical Analysis

Health and contextual characteristics of NSES quintiles were analyzed by descriptive statistics.

In order to understand the association between NSES and postoperative outcomes in a Dutch surgical cohort, I use a logit regression analysis. In this analysis, I aim to assess the role of the explanatory predictor and contextual variables of interest in the variation of TO. As such, the model I use for this analysis takes the following form:

Pr[TO=1|𝑥𝑖]=Λ(t)(𝛽⁰+ 𝛽¹NSES𝑖+𝛽2𝑎𝑔𝑒𝑖+𝛽³diabetes^𝑖+𝛽⁴gender^𝑖+𝛽⁵BMI^𝑖+𝛽⁶CCI^𝑖+𝜀ⁱ⁾

where i represents the individual and Λ(t)=𝑒𝑡/(1+𝑒𝑡) is the logistic function with values ranging from zero to one. “TO” is a dummy variable indicating if the individual i has a textbook outcome or not.

Furthermore, a multiple linear regression model was used to assess the role of the predictor and contextual variables in the variation of CCI. The linear model used for this analysis takes the following form:

𝑦 =

𝛽⁰+ 𝛽¹NSES^𝑖+𝛽²𝑎𝑔𝑒^𝑖+𝛽³diabetes^𝑖+𝛽⁴gender^𝑖+𝛽⁵BMI^𝑖+𝜀ⁱ

where i represents the individual and 𝑦 represents the linear outcome CCI.

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15 4. Results

4.1 Descriptive statistics

The descriptive statistics on neighborhoods characteristics by quintiles of neighborhood socioeconomic status is shown in Table 1 below. As can be observed, the majority of participants had a textbook outcome after surgery (62.05%), i.e., a “positive” postoperative outcome. This was also the case for each NSES quintile separately, where the lowest ranked quintile (Q1) had the highest rates of textbook outcomes, and the fourth quintile (Q4) had the lowest rates of textbook outcomes. The second outcome variable of interest, the Charlson Comorbidity Index (pre-existent comorbidity), had a mean score of 7.00 (± 3.69). Furthermore, the mean NSES was -0.0049 (± 0.0079). The majority of the sample is male; the first NSES quintile is the only subsample of which the majority gender was female. Regarding diabetes status, 15.73% of the sample has diabetes; the highest prevalence of diabetes is in the lowest NSES quintile (Q1), namely 20.51%. With regards to nutritional status, the average BMI of the population was 26.50, which falls under the category of “overweight”. More information on the contextual variables included in this analysis can be found below.

Table 1. Descriptive statistics on neighborhoods characteristics by neighborhood socioeconomic status quintiles.

NSES-Q1 NSES-Q2 NSES-Q3 NSES-Q4 NSES-Q5 Total/Mean

Patients

117 117 114 120 117 585

Age (SD)

65.06 (11.08)

64.36 (10.67)

64.13 (9.92)

64.06 (10.34)

61.06 (13.44)

64.00 (11.20)

Gender: % male

46.15 50.43 51.75 50.83 53.86 50.60

TO, yes (%)

65.81 64.10 58.77 58.33 63.25 62.05

CCI (SD)

6.61 (3.69)

6.92 (3.44)

7.04 (3.59)

6.81 (3.63)

7.12 (3.97)

6.90 (0.15)

NSES (SD)

-0.016

(0.0037)

-0.0089 (0.0014)

-0.0050 (0.0013)

-0.0011 (0.0013)

0.0063 (0.0040)

-0.0049 (0.0079)

Diabetes, yes (%) 20.51

16.24 16.67 14.67 11.11 15.73

BMI (SD)

26.76 (5.42)

26.93 (5.85)

26.26 (4.46)

26.43 (4.96)

25.93 (4.26)

26.50 (4.80)

Abbreviations: BMI: body mass index; CCI: Charlson Comorbidity Index; NSES: neighborhood socioeconomic status; SD: standard deviation; Q= quintile.

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16 4.2 The socioeconomic gradient in postoperative outcomes

The neighborhood socioeconomic gradient in postoperative outcomes can be conceptualized in this paper as the inverse correlation between neighborhood socioeconomic status and adverse postoperative outcomes, the latter of which is assessed using the outcome measure textbook outcome. As can be seen in Table 1, an initial descriptive analysis of the whole population data fails to confirm the hypothesis that there indeed exists an inverse relationship between NSES and adverse postoperative outcomes (H1a). Namely, the highest rate of TO (i.e., “positive postoperative outcomes) is seen in the lowest NSES quintile (Q1), whereas the lowest rate of TO is seen is the second highest NSES quintile (Q4). The data suggests that the rate of textbook outcome decreases up until Q4; the rate of textbook outcome increases from Q4 to Q5.

Fig 1. The association between NSES and textbook outcome. Textbook outcome is denoted in percentage. Abbreviations: NSES = neighborhood socioeconomic status.

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17 4.3 The socioeconomic gradient in comorbidity

In a similar manner to the previous socioeconomic gradient described, the neighborhood socioeconomic gradient in comorbidity can be conceptualized as the inverse correlation between neighborhood socioeconomic status and pre-existing comorbidity scores; as explained previously, these comorbidity scores are assessed using the outcome measure CCI. As can be observed in Table 1, a descriptive analysis of the data at population level fails to confirm the hypothesis that there indeed exists an inverse relationship between neighborhood NSES and pre-existing comorbidity scores (H2a). Surprisingly, the CCI is seen in the highest NSES quintile (Q5), whereas the lowest CCI is seen is the lowest quintile (Q1). Based on these findings, the data suggests that there exists a positive correlation rather than an inverse correlation between NSES and CCI. Below, I look at the relationship between the outcome variables of interest (TO and CCI) and predictor and contextual variables in greater detail.

Fig 1. The association between NSES and CCI. CCI represents the pre-existing comorbidity scores.

Abbreviations: CCI = Charlson Comorbidity Index; NSES = neighborhood socioeconomic status.

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18 4.4 Statistical analysis

As previously described, in order to understand the association between neighborhood socioeconomic status and textbook outcome (favorable postoperative outcome), I conducted a logit regression analysis to further explore this association. To explore the association between NSES and CCI scores (pre-existing comorbidity), I conducted a multiple linear regression. The principal aim here is to analyze these associations while controlling for the relevant contextual variables of interest described previously.

The coefficients reported in both these analyses are in the units of log odds. Specifically, these coefficients indicate the amount of change expected in the log odds when there is a one unit change in the predictor variable with all of the other variables in the model held constant.

Model 1 depicts the results of the regression analysis while looking solely at the association between the predictor variable of interest (NSES) and the outcome variable of interest (TO or CCI), while excluding the effects of the contextual variables. Model 2 presents the results of these regressions by including the contextual variables in the analysis. In both these analyses, NSES Q3 was chosen as the reference category.

The first analysis, namely looking at the association between NSES and TO, will be discussed here. For the results of this regression analysis, I refer to Table 2. The first model shows that, with Q3 as the reference level, Q1 yields the highest (log) odds of a TO, as was also observed in the previous section and Figure 1. From the table we can observe that the predictor variable of interest, NSES, shows no significant association with TO. This is in accordance with the results presented in Figure 1, which does not seem to suggest a clear relationship between these variables.

In the second model, it can be observed that age has a significant negative correlation with TO

(-0.019; 95% CI [0.036, -0.0015]; p < 0.05). When comparing the Pseudo R

²

for both models

(0.0028 and 0.017 for model 1 and 2, respectively), which is a goodness-of-fit measure for

logistic regression models, we see that model 2 has a higher Pseudo R

²

value. By virtue of this

higher value (albeit still quite low), model 2 thus constitutes the better fit model.

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19

Table 2. Logit regression model results between textbook outcome and the predictor and contextual variables of interest.

Variables of interest Textbook Outcome Model 1 Textbook Outcome Model 2

NSES quintile=1

0.30 0.33

[-0.23, 0.83] [-0.22,0.87]

NSES quintile=2

0.23 0.21

[-0.31, 0.76] [-0.33,0.75]

NSES quintile=4

-0.019 -0.033

[-0.54, 0.50] [-0.56,0.49]

NSES quintile=5

0.19 0.16

[-0.34, 0.72] [-0.38,0.70]

Age

- -0.019

^*

[-0.036,-0.0015]

Gender

- 0.073

[-0.28,0.42]

Diabetes

- 0.19

[-0.30,0.68]

BMI

- 0.038

[-0.00019,0.077]

CCI

- 0.0145

[-0.036,0.065]

Constant

0.35 0.38

[-0.018, 0.73] [-1.15,1.92]

Observations

585 585

Textbook outcome is denoted in terms of coefficients scales in log odds. 95% confidence intervals are shown in the brackets. Gender is a binary dummy variable, where males are denoted by 0 and females by 1. Model 1 Pseudo R² = 0.0028; Model 2 Pseudo R² = 0.017.

Abbreviations: BMI = body mass index; CCI = Charlson Comorbidity index; NSES: neighborhood socioeconomic status; TO = textbook outcome.

* p < 0.05, ^** p < 0.01, ^*** p < 0.001

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20 In the second analysis, I explore the association between NSES and CCI. For the results of this regression analysis, I refer to Table 3. The first model shows that, with Q3 as the reference level, Q1 yields the highest coefficients for the association with CCI, and this correlation is negative. To explicate, this suggests that, compared to the reference level of Q3, individuals in the Q1 of NSES have a lower value of CCI (pre-existent comorbidity). This observation is in line with what has been explained in the previous section and Figure 1.

Conversely, the highest quintile Q5 is shown to have a positive association with CCI. From Table 3, we can also observe that the predictor variable of interest, NSES, shows no significant association with CCI scores. This is in accordance with the results presented in Figure 1, which does not seem to suggest a definite relationship between these variables.

In the second model, it can be observed that both the age and gender variables demonstrate

significant associations with CCI (p < 0.001). First, age is shown to be positively associated

with CCI (0.11; 95% CI [0.089, 0.14]). Second, the female gender is shown to be negatively

associated with CCI (-1.20; 95% CI [-1.75, -0.64]). When comparing the adjusted R

²

for both

models (-0.0045 and 0.16 for model 1 and 2, respectively), which is a goodness-of-fit measure

for linear regression models, we see that model 2 has a higher value adjusted R

²

value and thus

represents the better fit model.

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21

Table 3. Logit regression model results between CCI and the predictor and contextual variables of interest.

Variables of interest CCI Model 1 CCI Model 2

NSES quintile=1

-0.42 -0.46

[-1.37, 0.53] [-1.34, 0.41]

NSES quintile=2

-0.11 -0.15

[-1.06, 0.84] [-1.02, 0.72]

NSES quintile=4

-0.23 -0.22

[-1.17, 0.72] [-1.08, 0.65]

NSES quintile=5

0.085 0.42

[-0.86, 1.03] [-0.45, 1.29]

Age

- 0.11***

[0.089, 0.14]

Gender

- -1.20***

[-1.75, -0.64]

Diabetes

- -0.072

[-0.85, 0.71]

BMI

- -0.044

[-0.015, 0.10]

Constant

7.04 [6.36, 7.71]

-0.82 [-3.22, 1.58]

Observations

585 585

CCI is denoted in terms of coefficients scales in log odds. 95% confidence intervals are shown in the brackets. Gender is a binary dummy variable, where males are denoted by 0 and females by 1.

Model 1 adjusted R² = -0.0045; Model 2 adjusted R² = 0.16

Abbreviations: BMI = Body Mass Index; CCI = Charlson Comorbidity Index; NSES: neighborhood socioeconomic status.

* p < 0.05, ^** p < 0.01, ^*** p < 0.001

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22 5. Discussion

Socioeconomic status has been empirically established to be associated with myriad health outcomes and health behaviors. Particularly, health outcomes are relatively worse in low SES contexts, which can be conceptualized as the socioeconomic gradient in population health. This evidence also exists at the neighborhood level, i.e., living in neighborhood areas with lower neighborhood socioeconomic status has adverse effects on health outcomes, regardless of individual SES (Curtis & Jones, 1998; Diez-Roux, 1998; Michael G. Marmot, 1998). Against this background, the relationship between NSES and health outcomes therefore merits further investigation to better understand the drivers underlying this association. Therefore, in this thesis I investigate the association between NSES and health outcomes in a European healthcare setting. Specifically, I explored the association between NSES and health outcomes in a Dutch surgical context. In addition to this association, I aimed to understand the role of contextual variables known to be associated with health outcomes, namely age, gender, BMI, and diabetes status.

The results of my analyses show that there is no significant association between NSES and health outcomes in the studied Dutch surgical cohort. The first health outcome I studied was

“textbook outcome”, which represents a favorable outcome after surgery. The second health outcome I studied was pre-existent comorbidity (e.g., heart failure) proxied using the variable Charlson Comorbidity Index. Regarding the contextual variables, I found higher age to be significantly associated with lower odds of a textbook outcome, while I found both higher age and the male gender to be significantly associated with higher pre-existent comorbidity scores.

The results reported in this thesis are both surprising and interesting, as they are in contrast to findings reported in previous studies investigating the relationship between (neighborhood) socioeconomic status and postsurgical outcomes (Agabiti et al., 2008; Birkmeyer et al., 2008;

Bosma et al., 2001; Bucholz et al., 2018; Jerath et al., 2020b; Moaven et al., 2019; Van Roest

et al., 2016; Zell et al., 2007). Zooming in more closely, e.g., Agabiti et al. (2008) report that

people with a lower SES were more likely to die after a cardiac procedure. They reasoned that

this could be due to higher levels of comorbidities and baseline risks, which is suggested to be

the case in lower SES contexts (Picciotto et al., 2006). It is important to note, however, that in

this study I include pre-existent comorbidity as a contextual variable to control for this effect,

whereas Agabiti et al. (2008) do not consider this controlling variable in their analysis.

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23 Additionally, in my literature review, I refer to several studies which report statistically significant associations between NSES and health outcomes in general, which makes the results in thesis somewhat surprising. Finally, drawing on Poulton et al.'s (2018) systematic review where they identified 58 studies investigating the association between SES and postsurgical outcomes, the overall findings this review demonstrated evidence for higher mortality in more deprived socioeconomic groups, contrasting with the findings of my thesis.

However, in my literature review I mention that several studies have also failed to find evidence for a neighborhood socioeconomic gradient in health (Obeng-Gyasi et al., 2020; Powers et al., 2019; Zarzaur et al., 2010). As described previously, the reasons cited for this discrepancy are based on the fact that medical centers which conduct high volumes of interventions and consist of standardized pathways might overcome the adverse health outcomes resulting from socioeconomic disparities. In this regard, Agabiti et al. (2008) point to hospital factors (hospital volume of procedures) as potential modifying factors in the association between NSES and health outcomes. Particularly, they found the disparities in outcomes after cardiac surgery to be the highest in low-volume hospitals.

Importantly, low volume of hospital procedures is a well-known risk factor for poor outcomes in several surgical procedures (Finks et al., 2011). For my analysis, I included a surgical cohort which consisted of patients who were operated in a high volume academic medical center (UMCG), which is vastly different from the cohort studied by Agabiti et al. (2008).

Specifically, Agabiti et al. (2008) studied a cohort of patients who were operated at several medical centers, each with different volumes of hospital procedures. As a result of the high volume of surgeries performed at the UMCG, this hospital could potentially overcome the obstacles which are associated with low NSES populations, and thus yield no differences in postsurgical outcomes across NSES levels. Altogether, while the evidence from the literature for neighborhood effects on health is fairly robust, the aforementioned reasons may well explain why I found no significant association between NSES and postsurgical outcomes.

Another important reason for the observed discrepant results which should be highlighted is

the relatively small sample size (n = 585) I used for my analysis, which means that the current

study was potentially underpowered. The sample sizes of most of the studies I referred to are

significantly higher, with many studies consisting of sample sizes higher than 3000 patients.

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24 Moreover, an additional reason why a significant correlation between SES and health outcomes was not observed in this study is possibly a result of the poor survival in this surgical cohort who underwent oncologic hepatopancreatobilliary surgery, even after surgical intervention.

While I included pre-existent comorbidity scores as a controlling variable in my analysis, I did not include other relevant factors such as tumor stage and differentiation as controlling variables. This is an important consideration as these factors could potentially modify the association between NSES and health outcomes.

With regards to the contextual variables I included in my analysis, it was surprising to perceive that, while age and gender were found to be significant predictors of health outcomes, this was not found to be the case for BMI and diabetes status, despite the well-established links described in the literature (Browne et al., 2007; Gajdos et al., 2013). Furthermore, it was also surprising to observe that CCI (pre-existent comorbidity) was not associated with the odds of a textbook outcome, while comorbidity is a known risk factor for worse postsurgical outcomes (Dias-Santos et al., 2015). This discrepancy can similarly be explained as a result of the relatively small sample size of this study.

Overall, there are several methodological concerns of this thesis which need to be addressed.

First of all, not all relevant variables were included in this analysis. For example, lack of data on surgery characteristics (e.g., tumor stage) and smoking status are likely to be important factors in this regard. Second, while patients were operated between 2013-2017, I used CBS data from 2016 to operationalize the NSES variable. Third, as already described above, the relatively small sample size means that this study is likely underpowered. Fourth, because of a longer exposure to worse socioeconomic neighborhood circumstances, socioeconomic effects may be larger in people who lived for a longer period in particular neighborhoods (Waitzman

& Smith, 1998). Unfortunately, we could not explore this effect specifically because there was no information available on how long people lived in their respective neighborhoods.

Finally, this study lacks individual level data on socioeconomic status. Ideally, I would have included both individual and neighborhood level socioeconomic status as separate predictor variables of interest, as these types of measures are not considered interchangeable.

Furthermore, both these variables have their strengths and limitations strengths in predicting

health outcomes.

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25 6. Conclusion and implications

Socioeconomic disadvantage is associated with worse health outcomes for myriad health conditions. With this thesis, I add to a growing body of literature on the relationship between neighborhood socioeconomic status and health outcomes. Importantly, the results in my analysis contradict the overall evidence currently reported in the literature. Namely, whereas most studies find evidence of a socioeconomic gradient in health, this study failed to find such an effect. A minority of studies have results similar to this thesis and point to hospital-level factors such as standardized pathways and hospital volume as important modifying factors.

Therefore, this topic warrants further research, while also accounting for both individual-level factors (e.g., comorbidity) as well as hospital-level factors (e.g., hospital volume).

Despite the fact that I did not find evidence for the association between NSES and health outcomes due to reasons outlined above, I nevertheless operate on the assumption that NSES is an important predictor for health outcomes. Having said this, I now discuss the implications of my thesis for policymakers moving forward. From a public health policy perspective, strategies should be tailored to the individuals’ socioeconomic context and characteristics in order to be maximally fruitful in endeavors aimed at improving population health. Examples of cogent public health initiatives include lifestyle-related risk factors, health literacy and engagement with healthcare services.

As an example, the lifestyle factor diet can be targeted, as diet is important to maintain a healthy

BMI. Namely, BMI has been shown to be an important independent predictor of post-surgical

outcomes (Gajdos et al., 2013; Yanquez et al., 2013). Furthermore, there needs to be attention

to the components of health literacy and healthcare service engagement. Drawing on the

literature review of this thesis, policymakers should consider the fact that people with lower

education and less income do not necessarily use health care services in the same way that their

counterparts from higher SES contexts do, despite the universal healthcare coverage in the

Netherlands. The implication of this is that Dutch policymakers should carefully monitor

healthcare seeking behaviors in low NSES contexts, as delayed healthcare seeking can lead to

suboptimal health outcomes (i.e., worse prognosis due to relatively more advanced disease

stages for lower SES patients).

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26 Tackling the socioeconomic context at the neighborhood level, policymakers should devise coordinated national strategies in deprived socioeconomic areas which focus on building and improving important facilities for its inhabitants, such as educational, recreational, and physical activity facilities. The focus on neighborhood educational facilities is important, as several studies have shown that the neighborhood’s educational climate improves the educational outcomes of individuals living in that neighborhood (Nieuwenhuis & Hooimeijer, 2016).

Moreover, strong links have been found between access to recreation and physical activity facilities and the amount of physical activity among inhabitants of that neighborhood (Hume et al., 2005). Finally, as mentioned in the literature review, lack of social engagement is a strong predictor of health (Adler & Newman, 2002). In this regard, improved (access to) the facilities mentioned can foster social engagement and integration, which can in turn improve health in low NSES contexts and thus reduce the socioeconomic gradient in health outcomes.

Lastly, another important implication of this study is the fact that at high volume centers, the effect of NSES on health outcomes is potentially mitigated. Therefore, it is crucial that there are no differences in referral patterns for low and high NSES populations, in order to avoid scenarios where low NSES populations are treated at low volume centers and vice versa.

Hospitals that disproportionately treat patients of lower SES can and should be assisted with cost-effective and tailored interventions in order to prevent disparate health outcomes.

Furthermore, it is pivotal for both policymakers and healthcare providers to be wary of potentially greater risks of morbidity and complex care needs after surgery in lower NSES patients. Namely, complex care needs often continue following hospital discharge, such as the need for specialized rehabilitation. As such, extra attention should be given into the monitoring of lower NSES patients following hospital discharge to timely intercept these obstacles.

In conclusion, the implications I describe above call for explicit policymaker efforts to identify the factors that amplify these inequalities, providing future avenues of strategies and research.

Ultimately, national concerted efforts addressing these issues are required in order to

structurally mitigate and prevent the existence of a socioeconomic gradient in health outcomes

in the Netherlands.

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Master Thesis : Is neighborhood socioeconomic status associated with health outcomes? An empirical study of a Dutch surgical cohort