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- Publisher
- Springer Journals
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- Copyright © 2018 by The Author(s).
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- Medicine & Public Health; Public Health; Medicine/Public Health, general; Epidemiology; Environmental Health; Biostatistics; Vaccine
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- 1471-2458
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- 1471-2458
- DOI
- 10.1186/s12889-018-5583-6
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Abstract
Background: Socioeconomic status (SES) is a well-established determinant of health status and home ownership is a commonly used composite indicator of SES. Patients in low-income households often stay in public rental housing. The association between public rental housing and mortality has not been examined in Singapore. Methods: A retrospective, cohort study was conducted involving all patients who utilized the healthcare facilities under SingHealth Regional Health (SHRS) Services in Year 2012. Each patient was followed up for 5 years. Patients who were non-citizens or residing in a non-SHRS area were excluded from the study. Results: A total of 147,004 patients were included in the study, of which 7252 (4.9%) patients died during the study period. The mean age of patients was 50.2 ± 17.2 years old and 7.1% (n = 10,400) of patients stayed in public rental housing. Patients who passed away had higher utilization of healthcare resources in the past 1 year and a higher proportion stayed in public rental housing (p < 0.001). They also had higher rates of co-morbidities such as hypertension, hyperlipidaemia and diabetes. (p < 0.001) After adjustment for demographic and clinical covariates, residence in public rental housing was associated with increased risk of all-cause mortality (Adjusted hazard ratio: 1.568, 95% CI: 1.469–1.673). Conclusion: Public rental housing was an independent risk factor for all-cause mortality. More studies should be conducted to understand health-seeking behavior and needs of public rental housing patients, to aid policymakers in formulating better plans for improving their health outcomes. Keywords: Public rental housing, Social determinant of health, Low socioeconomic status, Mortality Background during healthcare encounters or comprehensively at the Socioeconomic status (SES) is a well-recognized deter- population level. minant of health status. Low SES influences one’s health, Public housing is a widely used composite SES measure rate of morbidity and mortality [1]. SES influence health and various studies have shown a positive correlation be- via the interaction between the individual’s socioeco- tween public housing and poor overall health status [5–7]. nomic characteristics as well as their area’s socioeco- Underprivileged housing condition had been associated nomic condition [2, 3]. A multitude of measures are with poorer health such as a higher prevalence of injuries, available for assessment of SES such as home ownership, infectious diseases and chronic medical conditions [6]. For income level, educational status and occupation [4]. example, in the HOPE VI panel study, residents staying in Some of these information are not routinely collected public housing were found to have a two-fold risk of de- veloping chronic medical conditions such as hypertension and hyperlipidemia [8]. Likewise, the Fragile Families and * Correspondence: low.lian.leng@singhealth.com.sg Child Wellbeing Study found that public housing resi- Jun Jie Benjamin Seng and Yu Heng Kwan contributed equally to this work. SingHealth Regional Health System, Singapore Health Services, Singapore, dency is linked with obesity and poorer health statuses of Singapore mothers [9]. Poorer health outcomes are also contributed Department of Family Medicine and Continuing Care, Singapore General by overcrowding, inadequate sanitation and ventilation Hospital, Outram Road, Singapore 169608, Singapore Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Seng et al. BMC Public Health (2018) 18:665 Page 2 of 8 that result in communicable diseases. Importantly, resi- of any SRHS related healthcare facilities. Each subject dence in underprivileged housing is also a marker of lower was followed up for 5 years. SES that underpins potential social instability, and lack of Patients who were non-citizens were excluded as they access to basic healthcare [8]. were unlikely to be under long-term medical care from Globally, home ownership has been shown to be in- SRHS. In addition, patients who resided in non-SRHS versely associated with mortality [10]. In Europe, this residential areas were excluded as they would likely be protective effect has also been shown to persist into old under the care of a different regional health system. In age [11]. The inverse relationship between home owner- Singapore, residents are eligible to rent 1 to 2 room pub- ship and mortality has also been observed in populations lic housing apartments at government subsidized rates if such as children and African-Americans [12, 13] as well their gross household income do not exceed SGD$1500 as among patients with atrial fibrillation, diabetes and per month. Approval from SingHealth Centralised Insti- stroke [14–16]. A study conducted in Finland showed tutional review board (CIRB) (Reference number: 2016/ that residence in rented housing has been associated 2294) was obtained prior to initiation of the study. with higher mortality, despite adjustment for household Information pertaining to patient’s socio-demographic income, occupation and education level [17]. Home and clinical characteristics was drawn from electronic ownership may hence represent material living standards medical records. Socio-demographic information ex- and economical wealth that is inadequately captured by tracted included patient’s age, gender, ethnicity, as well conventional socio-economic indicators. as the number of patients staying in public rental hous- In Singapore, the majority (82%) of its population at- ing. Codes from International Classification of Diseases tain home ownership by purchasing public housing sold (ICD) [22] were used to extract information pertaining on a 99-years lease agreement [18]. Public housing lo- to major co-morbidities in the Charlson and Elixhauser cally can be broadly stratified into one to five bedrooms comorbidity index [23] such as diabetes mellitus, hyper- flats, studio apartments and executive condominiums. In tension and renal disease etc. A total of 26 major comor- 2010, the average monthly household income for citizens bidities were extracted for this study. The healthcare was SGD$7214 [19]. For households with the lowest in- utilization of each patient in the past one year was also come bracket of ≤SGD$1500 per month, public rental captured as this information enabled identification of pa- housing is made available by the government for rental tients who were frequent users of the healthcare system at highly subsidized rates and this accounts for 6% of the [24, 25]. This data included each patient’s number of public housing stock [20]. public primary care clinic visits, emergency department There has not been any study which has examined the visits, specialist clinic visits and hospital admission. The association between public rental housing and mortality primary endpoint in this study was all-cause mortality. in Singapore. Locally, public rental housing residents present as a unique population with high healthcare Statistical analyses utilization [21]. Thus, by utilizing public rental housing All statistical analyses were performed with SPSS version as an indicator of low SES, we aimed to assess the asso- 23 (SPSS Inc., Chicago, IL, USA). Student’s t-test and ciation between public rental housing and mortality risk. Chi-square test were utilized to examine differences be- tween the socio-demographic and clinical characteristics Methods of patients who were alive or died at the end of the study A total of six regional health systems were created by period, where appropriate. To assess the association be- the Ministry of Health Singapore for integration of care tween mortality with public rental housing, multivariate geographically across Singapore in 2011. Among the 6 Cox regression analyses was performed, adjusting for clusters, Singhealth Regional Health System (SRHS) is age, gender, ethnicity, past one-year utilization and 26 the largest cluster, responsible for the provision of major comorbidities. Survival probabilities from healthcare in South-Central Singapore and also provid- all-cause mortality were stratified by residence in public ing care for patients from other areas of Singapore. It is rental housing and analysed using the Kaplan-Meier supported by primary care facilities such as polyclinics curve. The results were then compared using as well as the largest tertiary hospital in Singapore, two-sample log-rank test. A two-tailed p-value of < 0.05 Singapore General Hospital, which oversees over 88,000 was considered statistically significant. inpatient admissions each year. We performed a retrospective, cohort study involving Results patients who were under the care of SRHS and residents Figure 1 shows the flowchart for inclusion of a patient in in the SRHS coverage area of South Central Singapore this study. Of the initial 870,665 patients, 112,640 and in Year 2012. Patients aged 21 years old and above were 611,022 patients were excluded as they were included if they were hospitalized or utilized the services non-citizens and did not reside in SHRS areas Seng et al. BMC Public Health (2018) 18:665 Page 3 of 8 Eligible patients (n=870665) Excluded (n=723661) - Non-citizens (n=112640) - Resided in non SingHealth Residential Health System area (n=611022) Included patients (n=147003) Patients who were alive Patients who were dead (n= 139752) (n= 7251) Fig. 1 Flowchart for inclusion of patients during study period from January 2012 to December 2016 respectively. A total of 147,003 patients were included in 20 co-morbidities examined in this study were associated the study, of which 7251 patients died during the study with increased all-cause mortality (p < 0.05). period. Each patient was followed up for a mean dur- Figure 2 shows the Kaplan Meier curve for all-cause ation of 2.78 ± 1.55 years. mortality stratified by residence in public rental housing. Table 1 shows the baseline socio-demographic and clin- The 5-year mortality of patients living in public rental ical characteristics of patients included in the study. The housing was significantly higher (p < 0.001). majority of patients were female (57.8%) and of Chinese ethnicity (78.5%), with a mean age of 50.2 ± 17.2 years. Discussion Overall, 7.1% (n = 10,400) of patients stayed in public We found that residing in public rental housing was as- rental housing. Compared to patients who were alive, pa- sociated with increased all-cause mortality among pa- tients who passed away during the study period were tients, after adjustment for demographic and clinical older, had higher utilization of healthcare resources in the characteristics. This concurred with findings from other past 1 year and a higher proportion stayed in public rental studies which showed a positive correlation between low housing (p < 0.001). The rates of all 26 co-morbidities SES and adverse health outcomes [26, 27]. examined in the study were higher in patients who died Underprivileged housing condition is tied closely to compared to patients who were alive (p <0.001). poorer health as inadequate household conditions due to Table 2 shows the results of multivariate cox regres- overcrowding, sanitation, and poor indoor air quality sion analyses. After adjustment for covariates which in- often contribute to communicable diseases and exacer- cluded patients’ demographics, co-morbidities and past bations of chronic illnesses [6]. It is also a marker of low healthcare utilization, residence in public rental housing SES and social instability which compromises residents’ remained significantly associated with all-cause mortality access to health care [6]. In Singapore, potential causes [Hazard ratio (HR): 1.568, 95% confidence interval (CI): for our finding may be related to public rental housing 1.469–1.673, p < 0.001]. Other demographic characteris- residents’ lower health literacy, difficult financial condi- tics associated with increased mortality included age, tions and health beliefs. Chan et al. summarized the male gender and Chinese ethnicity (p < 0.001). With the health status, health seeking behaviour and healthcare exception of comorbidities which included chronic ob- utilisation of low socioeconomic status populations res- structive pulmonary disease with cor pulmonale, depres- iding in public rental housing in Singapore [28]. A study sion, collagen vascular disease, atrial fibrillation, by Wee et al. showed that public rental housing residents peripheral vascular disease and spine fracture, all other were more likely to seek medical attention when there is Seng et al. BMC Public Health (2018) 18:665 Page 4 of 8 Table 1 Baseline characteristics of patients (n = 147,003) Characteristics All Patients Patients who Patients who were P-value (n = 147,004) died (n = 7251) alive (n = 139,752) Patient demographics Age, Mean (SD) 50.2 (17.2) 63.9 (16.2) 50.1 (17.2) < 0.001 Gender, Male (%) 62,171 (42.3%) 4030 (54.8%) 58,141 (41.6%) < 0.001 Ethnicity < 0.001 Chinese (%) 115,456 (78.5%) 6212 (85.7%) 109,244 (78.2%) Indian (%) 11,263 (7.7%) 439 (6.1%) 10,824 (7.8%) Malay (%) 14,582 (9.9%) 535 (7.4%) 14,047 (10.1%) Others (%) 5804 (5.9%) 167 (2.3%) 5637 (4.0%) Social Determinants of Health Resided in Public rental housing (%) 10,400 (7.1%) 1162 (16.0%) 9238 (6.6%) < 0.001 Past 1-year Healthcare Utilization during first year of inclusion Public Primary Care Clinic visits, Mean (SD) 2.45 (4.16) 4.08 (7.14) 2.45 (4.15) < 0.001 ED visits, Mean (SD) 0.15 (0.72) 2.87 (4.81) 0.14 (0.67) < 0.001 Specialist Clinic visits, Mean (SD) 2.50 (5.65) 12.90 (16.50) 2.48 (5.59) < 0.001 Hospital admissions, Mean (SD) 0.12 (0.54) 2.79 (3.75) 0.11 (0.50) < 0.001 Medical Comorbidities Diabetes without complications (%) 20,808 (14.1%) 2605 (35.4%) 18,203 (13.0%) < 0.001 Hypertension (%) 43,057 (29.3%) 4802 (65.3%) 38,255 (27.4%) < 0.001 Hyperlipidaemia (%) 42,437 (28.8%) 4090 (55.6%) 38,347 (17.9%) < 0.001 Chronic Kidney Disease Stage 3–4 (%) 4614 (3.1%) 1255 (17.1%) 3359 (2.4%) < 0.001 Asthma (%) 4958 (3.4%) 359 (4.9%) 4599 (3.3%) < 0.001 Chronic Obstructive Pulmonary Disease (%) 3085 (2.1%) 681 (9.3%) 2404 (1.7%) < 0.001 Chronic Obstructive Pulmonary Disease with cor pulmonale (%) 2574 (1.8%) 548 (7.5%) 2026 (1.5%) < 0.001 Osteoarthritis (%) 16,787 (11.4%) 1186 (16.1%) 15,601 (11.2%) < 0.001 Diabetes with complications (%) 2169 (1.5%) 434 (5.9%) 1735 (1.2%) < 0.001 Cerebrovascular accident (%) 5173 (3.5%) 1355 (18.4%) 3818 (2.7%) < 0.001 Chronic kidney disease stage V or End-stage renal failure (%) 1807 (1.2%) 827 (11.2%) 980 (0.7%) < 0.001 Depression (%) 2810 (1.9%) 303 (4.1%) 2507 (1.8%) < 0.001 Schizophrenia (%) 561 (0.4%) 108 (1.5%) 453 (0.3%) < 0.001 Dementia (%) 513 (0.4%) 268 (3.6%) 245 (0.2%) < 0.001 Collagen vascular disease (%) 517 (0.4%) 102 (1.4%) 415 (0.3%) < 0.001 Parkinson disease (%) 481 (0.3%) 185 (2.5%) 296 (0.2%) < 0.001 Epilepsy (%) 715 (0.5%) 138 (1.9%) 577 (0.4%) < 0.001 Coronary heart disease (%) 9509 (6.5%) 2035 (27.7%) 7474 (5.3%) < 0.001 Atrial fibrillation (%) 1286 (0.9%) 479 (6.5%) 807 (0.6%) < 0.001 Heart failure (%) 2196 (1.5%) 910 (12.4%) 1286 (0.9%) < 0.001 Peripheral vascular disease (%) 1124 (0.8%) 355 (4.8%) 769 (0.6%) < 0.001 Hip fracture (%) 279 (0.2%) 113 (1.5%) 166 (0.1%) < 0.001 Spine fracture (%) 452 (0.3%) 125 (1.7%) 327 (0.2%) < 0.001 Chronic liver disease (%) 1074 (0.7%) 225 (3.1%) 849 (0.6%) < 0.001 Pressure ulcer (%) 243 (0.2%) 145 (2.0%) 98 (0.07%) < 0.001 Malignancy (%) 4893 (3.3%) 1297 (17.6%) 3596 (2.6%) < 0.001 SD standard deviation, ED emergency department, ICD international classification of diseases Continuous variables were analyzed using Student’s -test and categorical variables were analysed using chi-square test or Fisher’s exact test when appropriate Based on ICD codes in the preceding five years Seng et al. BMC Public Health (2018) 18:665 Page 5 of 8 Table 2 Multivariable Cox regression analysis Variable Adjusted HR (95% CI) P-value Patient demographics Age 1.084 (1.082, 1.086) < 0.001 Gender (Male) 1.581 (1.508, 1.658) < 0.001 Ethnicity Others Reference Chinese 1.114 (1.009, 1.230) 0.033 Indian 1.481 (1.353, 1.621) < 0.001 Malay 1.041 (0.891, 1.217) 0.613 Social Determinants of Health Residing in Public Rental Housing 1.568 (1.469, 1.673) < 0.001 Past One Year of Healthcare Utilization ED visits 0.983 (0.964, 1.001) 0.071 Specialist Clinic visits 1.015 (1.013, 1.017) < 0.001 Hospital admissions 1.085 (1.060, 1.110) < 0.001 Medical Comorbidities Diabetes without complications 1.249 (1.177, 1.326) < 0.001 Hypertension 1.083 (1.011, 1.160) 0.001 Hyperlipidaemia 0.653 (0.612, 0.698) 0.034 Chronic Kidney Disease Stage 3–4 1.308 (1.181, 1.449) < 0.001 Asthma 0.869 (0.769, 0.983) 0.025 Chronic Obstructive Pulmonary Disease 1.391 (1.164, 1.663) < 0.001 Chronic Obstructive Pulmonary Disease with cor pulmonale 1.032 (0.850, 1.254) 0.749 Osteoarthritis 0.675 (0.632, 0.720) < 0.001 Diabetes with complications 1.238 (1.113, 1.377) < 0.001 Cerebrovascular accident 1.605 (1.502, 1.716) < 0.001 Chronic kidney disease stage V or End-stage renal failure 1.546 (1.365, 1.752) < 0.001 Depression 1.037 (0.914, 1.175) 0.575 Schizophrenia 1.835 (1.501, 2.244) < 0.001 Dementia 1.365 (1.191, 1.565) < 0.001 Collagen vascular disease 1.191 (0.968, 1.464) 0.098 Parkinson disease 1.589 (1.365, 1.850) < 0.001 Epilepsy 1.792 (1.500, 2.140) < 0.001 Coronary heart disease 1.244 (1.168, 1.326) < 0.001 Atrial fibrillation 1.103 (0.993, 1.226) 0.066 Heart failure 1.721 (1.580, 1.874) < 0.001 Peripheral vascular disease 1.299 (1.155, 1.462) 0.809 Hip fracture 1.434 (1.185, 1.736) < 0.001 Spine fracture 1.031 (0.854, 1.245) 0.753 Chronic liver disease 1.796 (1.564, 2.063) < 0.001 Pressure ulcer 1.390 (1.155, 1.673) 0.001 Malignancy 2.967 (2.786, 3.160) < 0.001 HR Hazards ratio, ED emergency department, ICD international classification of diseases Based on ICD codes in the preceding five years Seng et al. BMC Public Health (2018) 18:665 Page 6 of 8 Kaplan-Meier survival estimates 0 365 730 1095 1460 1825 Days Not Residing in Rental Flat Residing in Rental Flat Interval 0 365 730 1095 1460 1825 (Days) Residing 10385 10171 9964 9777 9553 9337 in Rental Housing Not 136618 135544 134487 133504 132282 131046 Residing in Rental Housing Fig. 2 Kaplan-Meier curve for survival probability stratified by residence in public rental housing manifestation of bothersome symptoms such as chronic over time [32, 33]. Residence in public rental housing pain [29]. Another study found that the costs of screening has been suggested to affect the health of residents both and treatment were the chief barriers deterring public ren- positively and negatively. Postulated reasons for its posi- tal housing residents’ participation in health screening tive effects on health are due to income, quality, gateway programmes [30]. Collectively, these may prevent early and network effects [9]. Income effect refers to the free- detection and treatment of chronic diseases and malignan- ing up of income for procurement of health services, cies which increase their risk of mortality. In addition, while quality effect refers to the tight regulation of pub- studies have demonstrated a higher usage of alternative lic housing quality which minimizes residents’ exposure medicine as well as distrust in doctor-patient relationship to lead and pest infestation [9]. Gateway effect refers to among public rental housing residents, which may prevent locating subsidised housing in close proximity to social them from seeking timely medical attention [16, 31]. Sub- service organisation and network effect refers to sharing optimal housing conditions such as sanitation, poor indoor of information within public rental residents as well as air quality and overcrowding in public rental housing may social support [9]. The positive effects of public rental also contribute to poor health. For example, household air housing could not be evaluated in this study and may be pollution has negative impacts on patients with chronic re- considered in future studies. spiratory diseases such as asthma and chronic obstructive Unsurprisingly, we found that patients who died during pulmonary disease [15]. However, similar comparative stud- study period had higher rates of co-morbidities such as ies are not available in Singapore. Air quality and pollution hypertension and hyperlipidaemia. High disease burden is a is of a lesser concern as public rental housing in Singapore well-recognized predictor of mortality among patients with is formulated by housing policy to be integrated with more different disease states [34]. It is noteworthy that the preva- affluent housing communities and prevent the formation of lence of chronic obstructive disease (COPD) (9.1%) among ghettos. patients who died was significantly higher than the national The relationship of socioeconomic inequality and mor- average of 3.5% [35]. After adjustment for covariates, tality is a complex and involves the interplay of material, COPD was also associated with increased risk of mortality. behavioural and psychosocial factors which may vary A study by Brugge et al. showed a positive correlation Overall Survival Rate 0.88 0.90 0.92 0.94 0.96 0.98 1.00 Seng et al. BMC Public Health (2018) 18:665 Page 7 of 8 between increased respiratory symptoms and residence in public rental housing and mortality due to the retrospect- public housing [36]. Some of the contributing factors sug- ive nature of the study. gested for poorer respiratory health status included envir- onmental and social factors such as mold, poor hygiene Conclusion and smoking in the household. Given the well-established We found that public rental housing was an independent association of smoking with malignancies and other meta- risk factor for all-cause mortality. More studies should be bolic diseases [37], future studies should explore if smoking conducted to understand the health-seeking behaviours, is a prevalent problem among public rental housing resi- healthcare needs and social circumstances of public rental dents to evaluate the need for implementation of targeted housing residents. This will aid policy makers in formulat- smoking cessation programs. In Singapore, cleanliness of ing better policies to improve the health-related outcomes common areas within housing estates is maintained by for this population. government town councils. However, each resident is Abbreviations responsible for the internal cleanliness of their flats. CIRB: Centralized Institutional review board; ICD: International classification of Studies may wish to consider exploring the hygiene diseases; SES: Socioeconomic status; SRHS: Singhealth regional health system level within the living quarters of public rental housing residentsand itspotential impact on residents’ health Acknowledgements We would like to thank Mr. Tan Wee Boon from Academic Clinical Program outcomes. for Medicine, Singapore General Hospital for the help rendered pertaining to Interestingly, depression was not associated with in- statistical analyses in this study. creased mortality among patients. This contrasted find- ings from by Reynolds et al. who found that depressive Funding This research received grant funding from SingHealth Foundation Health symptoms was associated with shortened life expectancy Services Research (Aging) Startup Grant SHF/HSRAg004/2015 and SingHealth [38]. A potential reason for the differing findings could Nurturing Clinician Scientist Award Academic Clinical Programme Funding be due to the age differences between the study popula- FY 2016 Cycle 2. The funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. tions. Patients included in this study were comparatively younger (50.2 ± 17.2 years old) than patients included in Availability of data and materials the Florida study which involved geriatric patients aged The datasets generated and/or analysed during the current study are not ≥70 years old [38]. publicly available due to institutional restrictions but are available from the corresponding author on reasonable request. Overall, while public rental housing was found to be an independent risk factor for mortality, interpretation Authors’ contributions of results should also take into account Singapore’s LLL was the study’s principal investigator and was responsible for the unique housing and healthcare policies. More than 80% conception and design of the study. YHK, SJJB, JT and HG were the co- investigators. All authors contributed to the interpretation of data and litera- of the home ownership in Singapore is accounted by ture review. YHK, SJJB and HG prepared the initial draft of the manuscript. All public housing sold under long-term lease. Compared to authors revised the draft critically for important intellectual content and Hong Kong, another urbanized Asian city, where 31% of agreed to the final submission. households resides in public rental housing [39], the Ethics approval and consent to participate proportion of households residing in public rental hous- Approval from SingHealth Centralised Institutional review board (CIRB) ing in Singapore is lower (6%). Universal healthcare (Reference number: 2016/2294) was obtained prior to initiation of the study. coverage is also provided to all Singapore citizens Waiver of consent was obtained and approved for this study. through a mixed financing system, which is achieved Competing interests through compulsory medical savings for individuals, The authors declare that they have no competing interests. utilization of market-based mechanisms and technology to improve healthcare outcomes [40]. Publisher’sNote Our study also had several limitations. Firstly, variables Springer Nature remains neutral with regard to jurisdictional claims in that could be analysed in the study included only routinely published maps and institutional affiliations. collected data from electronic databases within SHRS. Author details Consequently, we were unable to evaluate the differential Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore. causes of mortality and other socio-demographic variables 2 Program in Health Services and Systems Research, Duke-NUS Medical such as history of smoking and income level in the study. School, 8 College Road, Singapore 169857, Singapore. Faculty of Science, National University of Singapore, Singapore, Singapore. Health Services Factors that have been linked with poorer health out- Research Centre, Singapore Health Services, Singapore, Singapore. comes among patients with lower SES such as dietary 5 Department of Rheumatology and Immunology, Singapore General quality, level of physical activity, health literacy and educa- Hospital, Singapore, Singapore. SingHealth Regional Health System, Singapore Health Services, Singapore, Singapore. Department of Family tion level could not be assessed [41–44]. 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Journal
BMC Public Health – Springer Journals
Published: May 29, 2018