Skip to main content

Scottish Index of Multiple Deprivation (SIMD) indicators as predictors of mortality among patients hospitalised with COVID-19 disease in the Lothian Region, Scotland during the first wave: a cohort study



Sars-CoV-2, the causative agent of COVID-19, has led to more than 226,000 deaths in the UK and multiple risk factors for mortality including age, sex and deprivation have been identified. This study aimed to identify which individual indicators of the Scottish Index of Multiple Deprivation (SIMD), an area-based deprivation index, were predictive of mortality.


This was a prospective cohort study of anonymised electronic health records of 710 consecutive patients hospitalised with Covid-19 disease between March and June 2020 in the Lothian Region of Southeast Scotland. Data sources included automatically extracted data from national electronic platforms and manually extracted data from individual admission records. Exposure variables of interest were SIMD quintiles and 12 indicators of deprivation deemed clinically relevant selected from the SIMD. Our primary outcome was mortality. Age and sex adjusted univariable and multivariable analyses were used to determine measures of association between exposures of interest and the primary outcome.


After adjusting for age and sex, we found an increased risk of mortality in the more deprived SIMD quintiles 1 and 3 (OR 1.75, CI 0.99–3.08, p = 0.053 and OR 2.17, CI 1.22–3.86, p = 0.009, respectively), but this association was not upheld in our multivariable model containing age, sex, Performance Status and clinical parameters of severity at admission. Of the 12 pre-selected indicators of deprivation, two were associated with greater mortality in our multivariable analysis: income deprivation rate categorised by quartile (Q4 (most deprived): 2.11 (1.20–3.77) p = 0.011)) and greater than expected hospitalisations due to alcohol per SIMD data zone (1.96 (1.28–3.00) p = 0.002)).


SIMD as an aggregate measure of deprivation was not predictive of mortality in our cohort when other exposure measures were accounted for. However, we identified a two-fold increased risk of mortality in patients residing in areas with greater income-deprivation and/or number of hospitalisations due to alcohol. In areas where aggregate measures fail to capture pockets of deprivation, exploring the impact of specific SIMD indicators may be helpful in targeting resources to residents at risk of poorer outcomes from Covid-19.


Coronavirus disease 2019 (Covid-19) first emerged in December 2020, in Wuhan, China, and has now contributed to more than 226,000 deaths in the UK [1, 2]. Previous studies during the first wave identified being male and older, presence of comorbidities, and greater socio-economic deprivation at diagnosis as major risk factors for death and intensive care unit (ICU) admission [3,4,5,6].

Two large population-based studies – the OpenSAFELY Collaborative in England (June 2020) and the REACT-SCOT study in Scotland (October 2020) – have produced authoritative evidence that lower socio-economic status was associated with severe disease and mortality from Covid-19 even when adjusted for age, sex, and number of co-morbidities at presentation [3, 6]. Scotland, with the unenviable sobriquet “the Sick Man of Europe”, consistently ranks among the least healthy countries in Europe. Multi-generational poverty and social exclusion, drug- and alcohol-dependence, and poor educational attainment have proved pervasively difficult to eradicate and continue to negatively impact health outcomes [7, 8].

The Scottish Index of Multiple Deprivation (SIMD) ranks geographical areas of similar population across seven standardised domains (Income, Employment, Education, Health, Access to Services, Crime and Housing) to target interventions aimed at alleviating social inequalities [9, 10]. The SIMD ranks 6,976 geographical areas, termed datazones, derived from postcodes; SIMD is therefore a relative rather than absolute indicator of deprivation reflecting in-country geographical variation [10]. Studies conducted in the first and subsequent waves of Covid-19 have focused on how aggregate SIMD quintile rankings influence outcomes: patients in the lowest SIMD quintile had a consistently greater risk of death and Covid-19 has exacerbated healthcare inequalities across Scotland [4, 11, 12]. Studies looking at separate indicators of deprivation have shown that area-specific measures of income deprivation and overcrowding were predictive of poorer outcomes among affected residents, but, to date, other potentially relevant indicators within the SIMD have not been evaluated [13].

In this study of 710 patients hospitalised with Covid-19 in the Lothian Region of South-East Scotland between March 1st and June 30th, 2020, we investigated the impact of 12 clinically relevant individual SIMD indicators and constructed a model to determine their relationship with mortality in this cohort.


Study setting and databases

Data sources were linked using the Community Hospital Index (CHI), a unique identifier for patients residing in Scotland. Data were automatically extracted from the following platforms: laboratory information management systems, the Scottish Morbidity Record, the Scottish Drug Dispensing Database, and the Scottish Care Information Store.

Clinical and demographic data obtained from individual hospitalisation events were manually linked by a team of researchers at the Western General Hospital (Edinburgh, UK). Primary reasons for admission to ICU and mortality were adjudicated by the clinical research team to determine if Covid-19 was the principal contributor. Prior to analysis, all data were anonymised and stored in a data repository (DataLoch, Edinburgh, United Kingdom).


This was a prospective cohort study which included any patients aged > 18 whose listed postcode was in one of East Lothian, City of Edinburgh, Midlothian, or West Lothian councils and who were admitted to hospital with a laboratory confirmed, positive polymerase chain reaction (PCR) test for SARS-CoV-2 between 01/03/20 and 30/06/2020.


Our primary outcome was mortality, defined as all-cause mortality occurring among patients admitted to hospital with a positive PCR for SARS-CoV-2 during the study period, in line with definitions described in other UK-based cohort studies [3, 6].

The primary exposures of interest were Scottish Index of Multiple Deprivation (SIMD) indicators drawn from publicly available records. The SIMD is an aggregate measure of deprivation that comprises seven domains (Income, Employment, Education, Health, Access to Services, Crime and Housing) further subdivided into 37 component indicators. All indicators are interdependent with varying levels of between-indicator association. The SIMD ranks 6,976 datazones across Scotland into a relative ranking of area-based deprivation (Scottish Index of Multiple Deprivation (SIMD) 2020, version 2, Government of Scotland) [9, 10]. The study team selected 12 indicators considered clinically relevant for final analysis (see Table 1).

Table 1 Indicators of Scottish Index of Multiple Deprivation (2020 version 2) deemed clinically relevant to analysis

Additional exposure variables were selected from the risk factors associated with mortality identified in a separate descriptive cohort study in the same group of patients [5]. These included: demographic variables of age, sex, and ethnicity; World Health Organization (WHO) Performance Status, which categorises the impact of chronic disease severity on patient activity levels (0 = able to carry out normal activity without restriction; 1 = restricted in strenuous activity but ambulatory; 2 = ambulatory for > 50% of waking hours; 3 = symptomatic in a chair or bedridden for > 50% of waking hours; and 4 = completely disabled); admission pulse, in beats per minute; admission haemoglobin, in grams per Litre; neutrophil and lymphocyte counts (cells × 105); creatinine level, in milligrams/decilitre; and SIMD quintile.

Statistical analysis

Continuous outcomes were categorised into standardised brackets with the normal reference range used as the reference variable (admission pulse, admission haemoglobin, admission neutrophil and lymphocyte counts) whilst age was categorised into age groups with age group 50–59 used as the reference variable, in line with the ISARIC4C and OpenSAFELY cohort studies [3, 6]. Continuous SIMD indicator variables were categorised into quartiles, with the lowest quartile (least deprived) used as the reference variable. For SIMD standardised ratios, the variable was categorised as greater than or less than expected occurrence (standardised ratio) in each datazone.

Detailed description of analyses and modelling are further described in Fig. 1.

Fig. 1
figure 1

Step-by-step representation of statistical analyses employed in the study. Legend: *Demographic and Clinical Parameters determined from Mutch et al. 2022 [5] “Performance status: A key factor in predicting mortality in the first wave of COVID-19 in South-East Scotland”. Demographic parameters = Age (in years), sex; Clinical parameters = Performance Status (WHO Standardized Categories); Admission pulse rate (in beats/minute); Haemoglobin concentration (in grams/Litre); Neutrophil count (cells × 105); Lymphocyte count (cells × 105); Creatinine (in milligrams/decilitre). SIMD score = quintile distribution; 12 relevant SIMD indicators = Income Deprivation Rate; Employment Deprivation Rate; Comparative Illness Factor; Hospital stays related to alcohol use; Hospital stays related to drug use; Standardised Mortality Ratio; Proportion of population prescribed drugs for anxiety, depression, and/or psychosis; Emergency stays in hospital; Proportion of working age population with no higher qualifications; Drive to GP (in minutes); Public Transport to GP (in minutes); Overcrowding rate (Scottish Index of Multiple Deprivation (2020, version 2)). ** Variables had P < 0.157 in the univariable analysis. P < 0.157 selected as a screening value appropriate for subsequent multivariable model selection by Akaike Information Criterion (AIC) with a study population of 710. (Timo and Ilkka 1986; Perez-Guzman et al. 2021)

All 12 SIMD indicators selected by the study team were then assessed for the strength of between-indicator association, and association with age and sex using Cramer’s V [14].

Univariable analysis using logistic regression was carried out on age and sex to confirm the previously observed association with mortality (see Supplementary data). As a result, the remaining univariable models, including the 12 selected indicators of deprivation (Table 1), Performance Status and clinical parameters at admission, were run with age and sex included. Variables from these adjusted models with a P-value of < 0.157 were carried forward into a multivariable model. A screening P-value of < 0.157 is recommended for a sample size of 710 [15,16,17]. Because of the expected strong association between income deprivation rate by quartile and number of admissions due to alcohol use per datazone (Cramer V: 0.54), they could not be included in the same model. We therefore developed three nested models: SIMD quintile (Model 1), income deprivation rate by quartile (Model 2) and number of admissions due to excess alcohol per datazone (Model 3) and the model fit was assessed using Akaike information criterion (AIC). The model with the lowest AIC was considered the best.

We carried out a post hoc sensitivity analysis rerunning Model 1, using Lothian-specific SIMD quintile distributions. Results from both Lothian-specific and nationally-derived SIMD quintile distributions were compared.


Ethical approval was granted by the Lothian NHS Board (reference number CG/DF/2087). Linkage of anonymous datasets was performed by DataLoch, a data driven initiative designed to provide a secure repository of health and social care data in Southeast Scotland (Usher Institute, University of Edinburgh). Access to the final database was restricted to the core team of researchers with specific approvals and only accessible via a secure NHS network.


Baseline characteristics, symptoms, and clinical parameters at presentation

Between March 1st and June 30th, 2020, 726 patients were admitted to one of three hospitals in the Lothian Region (East Lothian, Midlothian, City of Edinburgh, and West Lothian councils) with a positive PCR for SARS-CoV-2. We excluded 13 patients from our analysis whose residential postcode was not within the Lothian Region, and three patients who did not have a registered postcode.

Case distribution by SIMD quintile was bimodal, with peaks in the second most deprived quintile (SIMD 2: n = 183, 25.8%) and least deprived quintile (SIMD 5: n = 190, 26.8%) but differences in case numbers between quintiles was negligible (see Table 2). Median age was 73 (IQR 58–83) and men accounted for 54.4% of patients. Age, sex, and ethnicity were similarly distributed across quintiles. Performance Status recorded at admission was graded at WHO stage 3 or more in just over a third of patients (34.4%, n = 245) and was evenly distributed across SIMD quintiles. Hypertension (n = 292, 41%), Diabetes (n = 166, 23%) and unspecified cancers (n = 144, 20%) were the most commonly reported co-morbidities, and most patients had two or more co-morbidities (n = 407, 57.3%) with minimal variation across SIMD quintiles. Symptoms and clinical parameters of severity at presentation were similar in patients with and without co-morbidities.

Table 2 Summary of anthropometric and clinical characteristics of patients included in study stratified by SIMD quintile


Outcomes were available for all 710 patients included in the study (see Table 3). All-cause mortality was recorded in 28% (n = 197) of patients; deaths were proportionately higher in patients in the most deprived quintile compared to the least deprived (SIMD 1–31% vs SIMD 5–26.8%) and fewer patients received mechanical ventilator support in the most deprived quintile compared to the least deprived (SIMD 1–41.6% v SIMD 5–65.2%). Data pertaining to suitability for intensive care and/or mechanical ventilation were not available for this study. Mean length of hospital stay was similar across SIMD quintiles.

Table 3 Outcomes for patients included in analysis stratified by SIMD quintile

Correlation between clinically relevant indicators of deprivation and mortality

To account for expected association between the 12 selected SIMD indicators selected for outcome analysis, a Cramer’s V correlation matrix was created which demonstrated a high degree of association between individual SIMD indicators (see Fig. 2). Relevant to our models, no SIMD indicators had medium or high association to age or sex.

Fig. 2
figure 2

Cramer’s V Correlation Matrix of 12 selected indicators of deprivation plus age and sex. Deeper shading indicates higher degree of association. Legend: 1. Distance to nearest GP surgery per datazone, in minutes; 2. Distance to nearest GP surgery by public transport per datazone, in minutes; 3. Overcrowding rate; 4. Hospitalisations due to alcohol per datazone; 5. Hospitalisations due to drug use per datazone; 6. Emergency hospitalisations per datazone; 7. Employment rate; 8. Comparative Illness factor; 9. Income rate; 10. 16–19 year-olds without qualifications; 11. Standardised Mortality Ratio; 12. Prescriptions for anxiety, depression or psychosis per datazone; 13. Sex; 14. Age

Associations between clinically relevant indicators of deprivation and mortality

Increasing age, male sex, and poorer Performance Status at diagnosis were all associated with mortality in our unadjusted and adjusted univariable logistic regression model.

Two SIMD quintiles were associated with higher mortality in the age- and sex-adjusted, univariable model (Quintile 1 (most deprived) P = 0.102, Quintile 3 P = 0.032)).

In our univariable analysis of the 12 clinically relevant area-based indicators of deprivation, two were significantly associated with mortality: income deprivation rate per datazone, categorised into quartiles (Quartile 4 (most deprived) P = 0.007) and greater than expected hospital stays due to alcohol use per datazone (P = 0.009) (see Table 4).

Table 4 Univariable age- and sex-adjusted logistic regression analysis for the association between mortality and exposure variables

In our multivariable analysis, we tested associations between mortality and SIMD quintile (Model 1); income deprivation rate per datazone, by quartile (Model 2); and greater than expected hospital stays due to alcohol use per datazone (Model 3) – see Fig. 3.

Fig. 3
figure 3

Forest Plots describing multivariable analyses of 3 SIMD indicators associated with increased mortality. Forest plots of odds ratios and 95% confidence intervals from three nested multivariable regression models investigating the association between mortality and: SIMD quintile (model 1); Income deprivation rate by quartile (model 2); Hospital stays due to alcohol use per datazone (model 3). Models were compared for goodness of fit based on Akaike Information Criterion (AIC): SIMD quintile (model 1, AIC: 692.06), income deprivation rate by quartile (model 2, AIC: 685.83), and hospital stays due to alcohol use per datazone (model 3, AIC: 679.59). Each multivariable model also contained: Age (in years), Sex, Performance Status (WHO Standardized Categories), Admission Pulse (beats/minute), Haemoglobin concentration (grams/Litre), Neutrophil count (cells × 105), Lymphocyte count (cells × 105), Creatinine (milligrams/decilitre). These variables were identified at the time of admission as risk variables in a companion paper (Mutch et al. 2022) [5]

All three models had an area under the curve of receiver operating characteristic (AUC of ROC) of > 0.8 demonstrating excellent ability to discriminate our primary outcome [18]. Comparison of AIC scores for these nested models demonstrated that Model 2 was the best of the three multivariable models, having the lowest AIC (679.59, vs. Model 3: 685.83, vs. Model 1: 692.06). Detailed results of our multivariable models are available in our supplementary data.

Comparative distribution of data zones between Lothian and the rest of Scotland

We compared the distribution of datazones by SIMD quintile, income deprivation rate, and excess hospital admissions due to alcohol in Lothian and the rest of Scotland (see Fig. 4). SIMD quintile in Lothian demonstrated a bimodal distribution with a greater preponderance of datazones in SIMD quintiles 2 and 5 than the rest of Scotland (see Fig. 4a). We noted a lower median number and smaller distribution range when comparing distribution of datazones by income deprivation rate and greater than expected hospitalisations due to alcohol in the Lothian Region compared to the rest of Scotland (see Fig. 4b and c).

Fig. 4
figure 4

Violin plots. a The shape of the distribution of SIMD rank, in Lothian compared to Scotland. SIMD rank of 1 is most deprived and 6976 is least deprived. b The shape of the distribution of Income deprivation rate in Lothian compared to Scotland. c The shape of the distribution of datazones according to number of hospitalisations related to alcohol; this is a standardised ratio where 100 (dotted line) represents the expected number

Sensitivity analysis comparing national and Lothian-specific SIMD quintile distribution

To reflect the known relative affluence of the Lothian region, where up to 50% of residents reside in datazones belonging to SIMD quintiles 1 and 2 (least deprived), we carried out sensitivity analyses using Lothian-specific quintile distributions drawn from publicly available records. We ran Model 1 as described for the SIMD above.

A greater proportion of our patient population was redistributed to more deprived Lothian-specific quintiles, with a greater preponderance of patients in Lothian specific quintiles 1, 2 and 4.

Compared to results obtained using national SIMD quintile distribution, where there were no significant associations with mortality, we found a weak association between mortality and Lothian-specific quintiles 1 and 4 (quintile 1: OR = 2.00 (1.06–3.85) P = 0.032; quintile 4: OR = 2.04 (1.06–3.99) P = 0.034). The model AIC (Lothian-specific quintile distribution: 689.21) was higher than the model with income deprivation rate per datazone by quartile (Model 2: AIC, 679.59); and greater than expected hospital stays due to alcohol use per datazone (Model 3: AIC 685.83).


In this study, we aimed to establish whether specific indicators of the Scottish Index of Multiple Deprivation (SIMD) were associated with mortality in a prospective cohort of patients admitted to hospital with Covid-19 disease in the Lothian Region between March 1st and June 30th, 2020.

Previous studies have demonstrated an increased risk of death in patients living in more deprived communities in multiple countries in the first wave of the Covid-19 pandemic [3, 4, 11,12,13, 16, 19,20,21]. We found an increased risk of death among age- and sex-adjusted patients in quintiles 1 and 3 (OR 1.75, CI 0.99–3.08, P = 0.053 and OR 2.17, CI 1.22–3.86, P = 0.009, respectively), but this association was not upheld in our fully-adjusted multivariable models containing Performance Status and clinical parameters of severity at presentation.

SIMD scores are weighted calculations of each of the seven domains; Income and Employment domain are weighted twice as heavily as Health or Education in final aggregate scores [10]. We therefore selected 12 indicators of deprivation within the SIMD that could plausibly be linked to poorer outcomes in health in our cohort. In our multivariable regression models, patients residing in datazones that were more income deprived and/or reported greater than expected numbers of alcohol-related hospital admissions had a two-fold increased risk of death.

We identified several factors that may explain the divergence in our national SIMD results and contribute to the complexity of defining how deprivation, a multi-faceted entity where environmental, biological, social, economic, and educational factors interact over time, contributes to poorer outcomes in health.

Overview – deprivation and indicators of multiple deprivation (IMDs)

Deprivation is a well-established risk factor for poorer health outcomes, but its underlying physiological mechanisms remain controversial. Some studies have proposed a biological link whereby increased inflammatory responses triggered by chronic social and environmental stress – more common in deprived communities – accelerate atherosclerosis and progression of dementia [22,23,24], but few studies have sufficiently long follow-up periods to adequately account for confounders given the multifactorial nature of deprivation [25,26,27,28].

Deprivation has also been described as a barrier to accessing healthcare and, in Lothian, this is supported by recent evidence from the Infectious Diseases Outpatient Antibiotic Treatment (OPAT) service that demonstrated that referrals were twice as likely to occur among patients belonging to the least deprived SIMD quintile [29].

Because deprivation is multifactorial, its study relies on amalgamating a range of indicators to develop a detailed picture of residents in a specific location [9, 30, 31]. Indices of multiple deprivation (IMDs) such as the SIMD have gained traction as useful tools for governments to direct funds to specific locations based on the assumption that the spatial characteristics of a geographical locality’s deprivation indicators affects the opportunities for poverty reduction for the entire population [30, 32]. The limitations of this approach are that IMDs fail to capture the key aspects of deprivation affecting any one individual. Experienced general practitioners operating in “Deep End” practices that serve the most deprived communities in Scotland have called for increased devolution of healthcare in at-risk communities as well as heightened awareness of the impact of deprivation on health and health-seeking behaviour to reduce inequities in health [33, 34].

Study strengths and limitations

We were able to analyse a rich dataset of prospectively recruited individuals benefiting from integration of healthcare data extracted from multiple digital platforms into a centralised database. Our cohort study design enabled us to carry out a detailed analysis of deprivation-related exposures in relation to our outcomes of interest. We believe this is one of the few studies examining the role that specific indicators of deprivation in an IMD may play in contributing to poorer outcomes in patients hospitalised with Covid-19 disease. Whilst we were not able to establish that deprivation by SIMD quintile was a risk factor for poorer outcomes in our cohort, we found that patients who resided in datazones with greater income deprivation and greater-than-expected admissions to hospital due to excess alcohol consumption had a two-fold increased risk of death. This suggests that a more granular analysis of deprivation indicators alongside locally representative deprivation quintile distributions may help to identify individuals or groups at risk of greater mortality in areas where deprivation may be masked by greater overall affluence. This is one of the major strengths of this study.

Our study further highlights the association between income deprivation and increased incidence and higher rates of hospitalization and mortality due to Covid-19, now well-established in both high- and low-income settings, further demonstrating the need for public health interventions to reduce barriers to testing, access to medical services, and mitigation of correlated risk factors for increased mortality such as obesity and co-morbidities [35,36,37,38].

Alcohol consumption, has, in contrast, not been found to be significantly associated with poorer outcomes, whether measured in terms of harmful intake in individuals [39] or in spatial analyses of excessive alcohol consumption [40]. In our correlation matrix of our 12 pre-selected SIMD indicators of deprivation, our variable for greater than expected admissions due to alcohol use was strongly associated with comparative illness factor – which measures how many individuals receive contributions for chronic disability – and employment, income, emergency room and drug-related admission rates per datazone. Our findings may reflect the situation in Scotland, where excess hospitalisations and mortality due to harmful alcohol consumption are potentiated by inequality in income, educational attainment, and socio-economic class and may be a useful proxy marker for deprivation not captured elsewhere in the SIMD [41].

Our study has several limitations. First, our study was restricted to hospitalised patients, and we were therefore unable to capture data on community transmission and outcomes in those not admitted to hospital. Another limitation is that a greater proportion of the Lothian region population is both more affluent and less likely to be from a minority ethnic group [42]. In our post-hoc analysis re-running our Model 1 (SIMD quintiles) using Lothian-specific quintile distributions, we found a weak association between mortality and Lothian-specific Quintiles 1 and 4, which in turn mirrored the redistribution of our patient population into locally-representative quintiles. This further highlights the weakness of relying on national SIMD quintile distribution in areas that are less representative of Scotland as a whole. Lastly, other SIMD indicators not selected for logistic regression analysis that our researchers judged less clinically relevant to health outcomes may be strongly influencing SIMD aggregate scores.

Because the Lothian region is comparatively more affluent than other regions of Scotland, it is likely that using postcode-based SIMD as a marker for individual deprivation fails to account for pockets of deprivation in Lothian that are not captured in the traditional quintile distribution of SIMD. The SIMD is an imperfect tool that relies on area-specific characteristics to determine deprivation, and fails to capture non-spatial deprivation factors that contribute to poorer health outcomes among individuals [25, 28]. Further, aggregate scores are weighted according to domain and assign a greater weight to income and employment deprivation than to health. Lastly, SIMD rankings are reviewed based on ten-year census data, which fail to capture between-census demographic change that may influence a specific data-zone’s evolving deprivation ranking, for example, because of gentrification.

Our pilot study highlights interesting findings that shed light on the applicability of SIMD in determining outcomes in patients hospitalised with Covid-19. Our findings may have important policy implications for government responses to targeting public health interventions to address social inequities affecting health outcomes in emerging infectious diseases (EIDs) [43]. We further demonstrate that income deprivation rate and excess hospitalisations due to alcohol use may act as useful proxy indicators to identify areas of Scotland where these social inequities are not adequately captured by aggregate SIMD ranking. We plan to apply our model to a nationwide dataset to determine whether these SIMD indicators may be applicable at a national level and in the context of future responses to EIDs.


We present findings of a prospective cohort study of patients hospitalised with Covid-19 in the Lothian region recruited consecutively during the first wave of the Covid-19 pandemic. We performed unadjusted and age- and sex-adjusted univariable analysis and compared three multivariable models investigating the impact of aggregate and specific indicators of deprivation on mortality. We found that locally representative SIMD quintile distribution and, within specific indicators of deprivation, datazones that were more income deprived and those with greater than expected number of hospitalisations due to alcohol use were associated with an increased risk of death. In contrast to other studies, greater deprivation as measured by national SIMD quintile distribution was not associated with mortality in our cohort. We propose that our findings are divergent due to the demographic characteristics of the Lothian population, which is generally more affluent and ethnically homogenous than the wider Scottish population and where up to 50% of deprived individuals live in non-deprived datazones [32]. Lastly, we suggest that further research could investigate how individual indicators of deprivation may help target future government response to EIDs and identify population subgroups at risk of poorer health outcomes not captured by SIMD quintile.

Availability of data and materials

The data that support the findings of this study are available from DataLoch (Edinburgh, United Kingdom) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of DataLoch.


  1. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708–20.

    Article  CAS  PubMed  Google Scholar 

  2. Deaths in the UK | Coronavirus in the UK. 2023. Accessed 22 Feb 2023.

  3. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. OpenSAFELY: factors associated with COVID-19 death in 17 million patients. Nature. 2020;584:430–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. McKeigue PM, Weir A, Bishop J, McGurnaghan SJ, Kennedy S, McAllister D, et al. Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): a population-based case-control study. PLoS Med. 2020;17:e1003374.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mutch CP, Ross DA, Bularga A, Cave RNR, Chase-Topping ME, Anand A, et al. Performance status: a key factor in predicting mortality in the first wave of COVID-19 in South-East Scotland. J R Coll Physicians Edinb.

  6. Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020;369:m1985.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Whyte B, Ajetunmobi T. Still ‘the sick man of Europe’? Scottish Mortality in a European Context 1950–2010: an analysis of comparative mortality trends. Glasgow: Glasgow Centre for Population Health; 2012.

    Google Scholar 

  8. OECD Regional Well-Being - how is life in Scotland? OECD Reg. Well-Being. Accessed 22 Feb 2023.

  9. Scottish Government. Scottish index of multiple deprivation 2020. Accessed 22 Feb 2023.

  10. Scottish Government. SIMD 2020 technical notes. 2020. Accessed 22 Feb 2023.

  11. Lone NI, McPeake J, Stewart NI, Blayney MC, Seem RC, Donaldson L, et al. Influence of socioeconomic deprivation on interventions and outcomes for patients admitted with COVID-19 to critical care units in Scotland: a national cohort study. Lancet Reg Health Eur. 2021;1:100005.

    Article  PubMed  Google Scholar 

  12. Wyper GMA, Fletcher E, Grant I, Harding O, de Haro Moro MT, McCartney G, et al. Widening of inequalities in COVID-19 years of life lost from 2020 to 2021: a Scottish Burden of Disease Study. J Epidemiol Community Health. 2022;76:746–9.

    Article  Google Scholar 

  13. Public Health England. Disparities in the risk and outcomes of COVID-19. London: Public Health England; 2020.

  14. Cramer H. Mathematical methods of statistics. Princeton: Princeton University Press; 1946. p. 282.

    Google Scholar 

  15. Teräsvirta T, Mellin I. Model selection criteria and model selection tests in regression models. Scand J Stat. 1986;13:159–71.

    Google Scholar 

  16. Perez-Guzman PN, Daunt A, Mukherjee S, Crook P, Forlano R, Kont MD, et al. Clinical characteristics and predictors of outcomes of hospitalized patients with coronavirus disease 2019 in a multiethnic London national health service trust: a retrospective cohort study. Clin Infect Dis Off Publ Infect Dis Soc Am. 2021;73:e4047–57.

    Article  CAS  Google Scholar 

  17. Dziak JJ, Coffman DL, Lanza ST, Li R, Jermiin LS. Sensitivity and specificity of information criteria. Brief Bioinform. 2020;21:553–65.

    Article  PubMed  Google Scholar 

  18. Hosmer D. The multiple logistic regression model. In: Applied logistic regression. Hoboken, NJ: Wiley; 2013. pp. 35–47.

  19. Bray I, Gibson A, White J. Coronavirus disease 2019 mortality: a multivariate ecological analysis in relation to ethnicity, population density, obesity, deprivation and pollution. Public Health. 2020;185:261–3.

    Article  CAS  PubMed  Google Scholar 

  20. de Souza RC, Almeida ERM, Fortaleza CMCB, Miot HA. Factors associated with COVID-19 mortality in municipalities in the state of São Paulo (Brazil): an ecological study. Rev Soc Bras Med Trop. 2022;55.

  21. Dukhovnov D, Barbieri M. County-level socio-economic disparities in COVID-19 mortality in the USA. Int J Epidemiol. 2022;51:418–28.

    Article  PubMed  Google Scholar 

  22. Murayama H, Sugiyama M, Inagaki H, Ura C, Miyamae F, Edahiro A, et al. The differential effects of age on the association between childhood socioeconomic disadvantage and subjective symptoms of dementia among older Japanese people. J Epidemiol. 2019;29:241–6.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Cadar D, Lassale C, Davies H, Llewellyn DJ, Batty GD, Steptoe A. Individual and area-based socioeconomic factors associated with dementia incidence in England: evidence from a 12-year follow-up in the English longitudinal study of ageing. JAMA Psychiat. 2018;75:723.

    Article  Google Scholar 

  24. Nash SD, Cruickshanks KJ, Klein R, Klein BEK, Nieto FJ, Ryff CD, et al. Socioeconomic status and subclinical atherosclerosis in older adults. Prev Med. 2011;52:208–12.

    PubMed  Google Scholar 

  25. Braveman P, Gottlieb L. The social determinants of health: it’s time to consider the causes of the causes. Public Health Rep. 2014;129:19–31.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mackes NK, Golm D, Sarkar S, Kumsta R, Rutter M, Fairchild G, et al. Early childhood deprivation is associated with alterations in adult brain structure despite subsequent environmental enrichment. Proc Natl Acad Sci U S A. 2020;117:641.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Velupillai YN, Packard CJ, Batty GD, Bezlyak V, Burns H, Cavanagh J, et al. Psychological, social and biological determinants of ill health (pSoBid): study protocol of a population-based study. BMC Public Health. 2008;8:126.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Marmot M, Bell R. Social inequalities in health: a proper concern of epidemiology. Ann Epidemiol. 2016;26:238–40.

    Article  PubMed  Google Scholar 

  29. Sumpter C, Russell CD, Mackintosh C. Inequitable access to an outpatient parenteral antimicrobial therapy service: linked cross-sectional study. Int J Equity Health. 2020;19:150.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Gordon D. Census based deprivation indices: their weighting and validation. J Epidemiol Community Health. 1995;49(Suppl 2):S39-44.

    Article  PubMed  PubMed Central  Google Scholar 

  31. McLoone P, Boddy FA. Deprivation and mortality in Scotland, 1981 and 1991. BMJ. 1994;309:1465–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Clelland D, Hill C. Deprivation, policy and rurality: the limitations and applications of area-based deprivation indices in Scotland. Local Econ. 2019;34:33–50.

    Article  Google Scholar 

  33. Watt G, Brown G, Budd J, Cawston P, Craig M, Jamieson R, et al. General practitioners at the deep end: the experience and views of general practitioners working in the most severely deprived areas of Scotland. Occas Pap R Coll Gen Pract. 2012:i–40.

  34. Fischbacher C. Identifying ‘deprived individuals’: are there better alternatives to the Scottish Index of Multiple Deprivation (SIMD) for socioeconomic targeting in individually based programmes addressing health inequalities in Scotland? Glasgow: ScotPHO; 2014. Accessed 22 Feb 2023.

  35. Jannot A-S, Countouris H, Van Straaten A, Burgun A, Katsahian S, Rance B, et al. Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris. J Epidemiol Community Health. 2021;75:1143–6.

    Article  PubMed  Google Scholar 

  36. Das A, Ghosh S, Das K, Basu T, Das M, Dutta I. Modeling the effect of area deprivation on COVID-19 incidences: a study of Chennai megacity, India. Public Health. 2020;185:266–9.

    Article  CAS  PubMed  Google Scholar 

  37. Bilal U, Tabb LP, Barber S, Diez Roux AV. Spatial inequities in COVID-19 testing, positivity, confirmed cases, and mortality in 3 U.S. cities: an ecological study. Ann Intern Med. 2021;174:936–44.

    Article  PubMed  Google Scholar 

  38. Baena-Díez JM, Barroso M, Cordeiro-Coelho SI, Díaz JL, Grau M. Impact of COVID-19 outbreak by income: hitting hardest the most deprived. J Public Health Oxf Engl. 2020;42:698–703.

    Article  Google Scholar 

  39. Hamer M, Kivimäki M, Gale CR, Batty GD. Lifestyle risk factors, inflammatory mechanisms, and COVID-19 hospitalization: a community-based cohort study of 387,109 adults in UK. Brain Behav Immun. 2020;87:184–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pro G, Gilbert PA, Baldwin JA, Brown CC, Young S, Zaller N. Multilevel modeling of county-level excessive alcohol use, rurality, and COVID-19 case fatality rates in the US. PLoS ONE. 2021;16:e0253466.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Katikireddi SV, Whitley E, Lewsey J, Gray L, Leyland AH. Socioeconomic status as an effect modifier of alcohol consumption and harm: analysis of linked cohort data. Lancet Public Health. 2017;2:e267–76.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Scotland’s Census. Scotland’s census. Scotl. Census. Accessed 22 Feb 2023.

  43. Bambra C. Pandemic inequalities: emerging infectious diseases and health equity. Int J Equity Health. 2022;21:6.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We would like to acknowledge The DataLoch Covid-19 Collaborative contributors for their assistance in data collection and curation.

The DataLoch Covid-19 Collaborative Contributors

Clinical Lead: Atul Anand5,6

Programme Lead: Kathy Harrison5,6

DataLoch technical and data team: Ally Hume5,6, Catriona Waugh5,6, Catherine Stables5,6, Chloe Brook5,6, Chris Duncan5,6, David Homan5,6, Erin Cadger5,6, Ioanna Lampaki5,6, Jennifer Daub5,6, Jilly McKay5,6, Neil Murray5,6, Ronnie Harkess5,6, Shedrack Ezu5,6, Sophie McCall5,6, Stela McLachlan5,6

Infectious Diseases clinical team: Claire L Mackintosh5,6, Daniella A Ross5,6, Meghan R Perry5,6, Oliver Koch5,6

NHS Lothian including Lothian Analytical Services: Alastair Thomson5,6, Alistair Stewart5,6, Daniella Ene5,6, Hazel Neilson5,6, Juergen Caris5,6, Maria McMenemy5,6, Nazir Lone5,6, Nicola Rigglesford5,6, Paul Schofield5,6, Sophie David5,6, Stephen Young5,6, Tracey McKinley5,6, Tracey Rapson5,6

COVID-19 manual data extraction and review: Anda Bularga5,6, Anna K Jamieson5,6, Arjuna A Sivakumaran5,6, Arun Parajuli5,6, Daniella A Ross5,6, Ed Whittaker5,6, Emma K Watson5,6, Ha Bao Trung Le5,6, Hannah MM Preston5,6, Jason Yang5,6, John P Kelly5,6, Jonathan Wubetu5,6, Julia Guerrero Enriquez5,6, Kathryn AW Knight5,6, Louisa R Cary5,6, Meghan R Perry5,6, Oscar C N Maltby5,6, Rosie Callender5,6, Sarah H Goodwin5,6, Thomas H Clouston5,6, Thomas J McCormick5,6, XinYi Ng5,6, Zaina Sharif5,6

DataLoch clinical and management team: Anoop Shah5,6, Atul Anand5,6, Colan Mehaffey5,6, Kathy Harrison5,6, Ken Lee5,6, Laura Woods-Dunlop5,6, Michael Gray5,6, Nicholas Mills5,6, Pamela Linksted5,6, Peter Cairns5,6, Rob Baxter5,6, Stephen Lavenberg5,6, Susan Buckingham5,6


Open Access and publication charges supported by the Wellcome Trust grant, 218492/Z/19/Z, to the University of Edinburgh Library.

Author information

Authors and Affiliations




MRP, CLM, OK and MSS conceived the original design. DR, CPM, MRP and DataLoch collected the data, which was analysed by RNRC and MSS with assistance from MRP and MCT. MSS, RNRC, MRP, and CLM drafted the manuscript which was critically revised by all authors who agree now to be accountable for all aspects of the work in ensuring that questions related to the accuracy and integrity of any part of the work are appropriately investigated and resolved. MSS and RNRC are joint first authors. MRP and CLM are joint last authors.

Corresponding author

Correspondence to Marcello S. Scopazzini.

Ethics declarations

Ethics approval and consent to participate

The study received full ethical approval granted by the Lothian NHS Board (reference number CG/DF/2087) and the study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Individual patient consent was not sought as the study team analysed summary data alone compiled from deidentified patient data linked and stored in a secure data repository within a secure safe haven at DataLoch, a collaboration between NHS Lothian, the Scottish Government and the University of Edinburgh.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Multivariable logistic regression analysis between mortality and three SIMD indicators carried forward from univariable analysis.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Scopazzini, M.S., Cave, R.N.R., Mutch, C.P. et al. Scottish Index of Multiple Deprivation (SIMD) indicators as predictors of mortality among patients hospitalised with COVID-19 disease in the Lothian Region, Scotland during the first wave: a cohort study. Int J Equity Health 22, 205 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: