“Inequities in Healthcare-Associated Infections Across North America- A Systematic Review”
by
Chandni Shahdev, ScD, MPH, BDS1
1 Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, Massachusetts, USA
Corresponding author (s). E-mail (s): chandnishahdev@gmail.com
The Department of Public Health, Zuckerberg College of Health Sciences
University of Massachusetts Lowell
61 Wilder St., O’Leary 540, Lowell, MA 01854
Abstract
Background
Healthcare-associated infections (HAIs) remain a major concern in North America, with an estimated 687,700 HAIs and nearly 72,000 associated deaths. Studies show that social determinants of health (SDOH), including socioeconomic status, insurance, poverty, and race/ethnicity substantially influence HAI risk, severity and outcomes. However, these disparities have not been systematically synthesized. Therefore, this review aims to examine how SDOH shape HAIs incidence, severity, and outcomes.
Methods
Following PRISMA guidelines, PubMed, MEDLINE, and CINAHL were searched for studies published between 2014 and 2024 using HAIs terms (MRSA, C. difficile, CAUTI, CLABSI, SSI) and SDOH (race, income, insurance, poverty, area deprivation). Studies conducted in the U.S. or Canada and included at least one HAI and one SDOH. Of 3,068 records, 21 studies met inclusion criteria.
Results
Across 21 studies, SDOH consistently predicted higher HAI incidence, readmission, or mortality. Medicaid insurance was strongly associated with increased CDI and SSI burden; low-income neighborhoods predicted greater pediatric CLABSI; and higher MRSA odds were observed in areas of greater deprivation. Multiple studies documented racial inequities, with Black patients experiencing higher MRSA risk and postoperative morbidity.
Conclusions
Findings highlight the need to integrate SDOH into HAI surveillance and prevention strategies. Longitudinal studies are needed to explore HAI outcomes among socially disadvantaged populations.
Keywords:
healthcare-associated infections
Social determinants of health
health disparities
infection prevention
health equity
A
1. Introduction
Healthcare-associated infections (HAIs) remain a major threat to patient safety in North America. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that about 687,000 HAIs occur each year, leading to nearly 72,000 hospital-associated deaths[1]. Despite decades of prevention efforts, conditions such as surgical site infections (SSIs), central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), methicillin-resistant Staphylococcus aureus (MRSA), and Clostridioides difficile infection (CDI) remain persistent[2, 3], These conditions cause morbidity, mortality, longer hospital stays, and increased healthcare costs [4].
While clinical risk factors and hospital practices are central to prevention, there is growing evidence that HAIs do not occur uniformly across populations [5, 6]. Several studies have documented the role of social determinants of health, including socioeconomic status, insurance type, race and ethnicity, poverty level, household condition, neighborhood deprivation, which significantly influence HAI risk, severity and outcomes. These disparities contribute to patient access to care, exposure to the healthcare environment, timely diagnosis and quality of treatment [7–9].
Moreover, public health reports have documented that marginalized communities in the U.S. and Canada face a disproportionate burden of HAIs. They intersect with healthcare environment exposure, contributing to systematic differences in HAI incidence and severity [10, 11]. Despite efforts to achieve equity in infection prevention and healthcare quality [5], evidence linking SDOH and HAI remains scattered across study designs, populations, and infection types.
Although one recent study has begun to examine how social and structural factors contribute to differences in HAI type and severity, however, the evidence has not been comprehensively synthesized [12]. A systematic understanding of these relationships is essential for developing infection prevention strategies that address not only clinical and procedural risks but also the upstream social factors that shape patients’ vulnerability to acquiring HAIs. Such insights are critical for strengthening health system equity efforts, informing targeted prevention initiatives, and guiding policies aimed at improving outcomes among socially disadvantaged populations disproportionately affected by HAIs.
To our knowledge, no systematic review has evaluated the influence of social determinants on HAIs across North America. Hence, the objective of this systematic review is to synthesize current evidence on the association between social determinants of health and healthcare-associated infections in North America. The review aims to recognize consistent patterns of disparities, methodological gaps, and policy recommendations to strengthen infection prevention strategies and equity-focused efforts to reduce HAIs.
2. Methods
A
A review protocol was conducted in line with recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13].2.1 Literature search strategy
Three databases were searched to explore the relationship between different types of HAI infection and SDOH indicators in North America, in collaboration with a university librarian. The database included were PubMed, MEDLINE and CINHAL PLUS with full text. The search keywords included MeSH terms and text were used related to HAI infection: “hospital acquired infection" OR "hospital-acquired infection" OR "healthcare associated infection" OR "healthcare-associated infection” OR "nosocomial infection" OR "surgical site infection” OR "central line-associated bloodstream infection” OR "catheter-associated urinary tract infection" OR "ventilator-associated pneumonia” OR "methicillin-resistant Staphylococcus aureus" OR "clostridioides difficile" OR "c difficile"; and related to SDOH indicators: "social determinants of health” OR “socioeconomic” OR "socio-economic" OR “SES” OR “income” OR “poverty” OR “deprivation” OR "area deprivation index" OR "social vulnerability index" OR "health disparities" OR "healthcare disparities" OR "racial disparities" OR “race” OR “racial” OR “ethnicity” OR “minority” OR “insurance” OR “Medicaid” OR “uninsured.”
2.2. Eligibility Criteria
Inclusion
We included peer-reviewed articles that examined the relationship between at least one HAI and one SDOH, conducted in the United States or Canada, and were published in English in a last ten years (January 2014- December 2024). Eligible study designs included quantitative or mixed-methods analysis, such as retrospective cohorts, cross-sectional studies, case-control as well as qualitative studies that explored association between HAI and SDOH.
Exclusion
Systematic reviews, narrative reviews, scoping reviews, and meta-analyses were excluded; however, their reference lists were screened to identify additional eligible primary studies. Studies conducted outside the United States were excluded, as were those published in languages other than English. Non-peer reviewed publications including unpublished manuscripts, conference abstracts or presentations, dissertations, news articles, and organizational reports were also excluded from the review.
2.3 Study Selection
As this is a single-author review study, all titles, abstracts and full text were independently screened and selected by reviewer based on predefined eligibility criteria. EndNote reference manager was used to export all references from the databases and duplicates were removed. First, titles and abstracts were screened to identify potentially eligible studies based on inclusion and exclusion criteria. Then full texts of these studies were reviewed.
2.4 Data Extraction and Synthesis
A
Data was extracted on study design, setting, population, HAI type, SDOH variables and key findings. Due to heterogeneity in outcome and measures, a narrative synthesis was conducted.3. Results
The search yielded 3,068 records, of which 1,355 duplicates were removed, leaving 1,713 records for screening. After title and abstract review, 1,633 records were excluded. 80 full-text articles were assessed, and 59 were excluded for reasons including non–North American setting, review design, lack of HAI outcomes, or evaluation of only clinical (non-SDOH) risk factors. A total of 21 studies met the inclusion criteria and were included in this review.
The included studies evaluated a range of HAIs (MRSA, CDI, CLABSI, CAUTI, VAP, and SSI); and examined SDOH indicators (race/ethnicity, insurance type, neighborhood deprivation, and poverty). Data sources included hospital surveillance systems, statewide datasets, pediatric registries, and large national databases from the United States and Canada (Fig. 1).
Fig. 1
PRISMA Flow Diagram of Study Selection
4. Discussion
A
This systematic review synthesizes 21 studies examining how social determinants of health (SDOH) influence healthcare-associated infections (HAIs) across North America. Across HAI types including CDI, MRSA, CLABSI, CAUTI, VAP, and SSI, the findings consistently show that structural and social inequities significantly impact HAI risk, severity and outcomes (Table 1).
Table:1 Characteristics of studies on social determinants of health and healthcare-associated infections: HAI Type, Study Design, Setting,
SDOH Variable and Findings
A
|
Study (Author, Year)
|
HAI Type
|
Study Design &
Data Source
|
Population/ Setting
|
SDOH
Assessed
|
Findings
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
1. Shaka et al.,
2022 [14]
|
VAP
|
Retrospective (2016–2017); NIS
|
33,140 VAP; nationwide
|
Insurance
|
|
|||||||||
|
2. Argamany et al.,
2016 [8]
|
CDI
|
Retrospective (2001–2010); NHDS
|
1,676,903 CDI;
nationwide
|
Race
|
• Black race ↑ mortality (aOR 1.12; 95% CI 1.09–1.15).
• Severe CDI (aOR 1.09; 95% CI 1.07–1.11).
• Shorter LOS (aOR 0.94; 95% CI 0.93–0.94).
• CDI incidence ↑ White patients (7.7 vs 4.9 per 1,000 discharges, p < 0.0001).
|
|||||||||
|
3. Olsen et al.,
2023 [9]
|
CDI
|
Retrospective (2011–2017); MarketScan Commercial and Medicaid
|
71,668 CDI;
nationwide
|
Insurance
|
• ↑ CDI incidence: Dual-eligible had 3.1 ↑CDI than Medicare-only; younger Medicaid had 2.7 ↑CDI than commercially insured.
• Trend: HCA-CDI rates ↓ across Medicaid and Medicare groups but remained stable in commercially insured younger adults.
• Comorbidity effect: Medicaid-insured with chronic or immunocompromising conditions had 2.7–2.8 ↑ CDI; even without comorbidities CDI remained ↑ (67.5 vs 45.6/100k PY).
|
|||||||||
|
4. Ramai et al.,
2020 [15]
|
CDI
|
Retrospective (2006–2015); HCUP- NIS
|
76,124 CDI; nationwide
|
Insurance; hospital type; region
|
|
|||||||||
|
5. Scaria et al.,
2021 [16]
|
CDI
|
Retrospective (11 months of 2014); Medicare
|
19,490 CDI
|
Neighborhood ADI; dual eligibility; race; rurality; neighborhood poverty
|
• Most disadvantaged neighborhood 16% ↑ odds of readmission than least groups (OR 1.16; 95% CI 1.04–1.28)
|
|||||||||
|
6. Vader et al
2021 [17]
|
CDI
|
Case–control
(2014–2018);
EMR
|
170 CDI cases, 324 controls; safety-net hospital
|
SDI; insurance; race
|
• No association with SDI or race.
• Medicare pts had 2× odds (aOR 2.04; 95% CI 1.31–3.20) vs private insurance
|
|||||||||
|
7. Warren et al.,
2024 [18]
|
CDI
|
Retrospective (2015–2021); EHR
|
35,160 tests;
3 hospitals
|
Race
|
• White patients received more CDI tests than Black or NWNB patients (14.46 vs 12.96 vs 10.27/1,000 patient-days)
• Test positivity is similar for White and Black patients (15% each), but lower in Non-white and non-black patients (12%; P = .006).
|
|||||||||
|
8. Gutierrez et al.,
2023 [19]
|
CLABSI
(Pediatric)
|
Retrospective (2018–2023); PHIS
|
1,210 CLABSI;
24 hospitals
|
Low-income neighborhood
|
• Low hospital-level neighborhood income was linked to ↑CLABSI risk (RR 1.43; 95% CI, 1.10–1.84; p < 0.01).
|
|||||||||
|
9. Lyren et al.,
2024 [7]
|
CLABSI
(Pediatric)
|
Cross-sectional,
SPS-
Multicenter study data
|
148 SPS; hospitals
|
Race/Ethnicity
|
• CLABSI rates were 2.6–3.6 SD ↑ in Multiracial Hispanic and Combined Hispanic–Pacific Islander patients, White patients consistently had significantly lower harm rates than reference values.
|
|||||||||
|
10. Gettler et al.,
2023 [20]
|
CLABSI, CAUTI
|
Retrospective (2018–2021)
Academic Medical Center Surveillance data, Durham NC
|
450 CLABSI,
233 CAUTI
|
Race/Ethnicity
|
• CLABSI rates were ↑ among non-Hispanic Black (IRR 1.27; 95% CI 1.02–1.58) and other race patients (IRR 2.25; 95% CI 1.31–3.88)
• CAUTI rates were ↑ among non-Hispanic Black (IRR 1.42; 95% CI 1.05–1.92) and Asian patients (IRR 2.49; 95% CI 1.16–5.36) compared with non-Hispanic White patients
|
|||||||||
|
11. Andreatos et al.,
2018 [21]
|
MRSA
|
Retrospective (2013–2015);
Medicare
|
26,928 MRSA
|
Poverty level, Education, Housing quality
|
• Poverty ↑ MRSA
(Coeff 0.094, 95% CI 0.034–0.155, P = 0.002).
• Higher education levels ↓ MRSA (Coeff − 0.037, 95% CI − 0.068 to − 0.005, P = 0.024).
• Better housing quality (more rooms per home) ↓ MRSA (Coeff − 0.107, 95% CI − 0.134 to − 0.081, P < 0.001).
• Income slightly above poverty line ↓ MRSA (Coeff − 0.257, 95% CI − 0.314 to − 0.199, P < 0.001).• |
|||||||||
|
12. McGarry et al.,
2023 [22]
|
MRSA, MSSA
(Pediatric)
|
Longitudinal cohort; Cystic Fibrosis Foundation Patient Registry
|
10,640 CF
|
Race/Ethnicity (Hispanic vs non-Hispanic White).
|
• Hispanic pwCF had a 19%↑risk of MSSA acquisition (HR 1.19, 95% CI 1.10–1.28, p < 0.001) than non-Hispanic pwCF.
• Hispanic pwCF had a 13% ↑ risk of MRSA acquisition (HR 1.13, 95% CI 1.02–1.26, p = 0.02) than non-Hispanic pwCF.
• Hispanic children acquired MSSA earlier (median 3.8 vs 4.9 years, p < 0.001) and MRSA earlier (median 20.8 vs 22.4 years, p = 0.02) than non-Hispanic pwCF.
|
|||||||||
|
13. Freeman et al.,
2018 [23]
|
MRSA
|
Ecological cross-sectional study; CDC state-level SIR for HO-MRSA bloodstream infections
|
Population level data
|
Race/Ethnicity poverty rate, income
|
• Poverty, income inequality, % African American, and % diabetes (2013 only) was positively correlated with higher HO-MRSA rates (univariate).
• No association with % Hispanic, median income, or % ≥65.
• In multivariable models, % African American remained the only significant predictor of HO-MRSA across both 2013 and 2014.• |
|||||||||
|
14. Oates et al.,
2019 [9]
|
MRSA
(Pediatric)
|
Cross-sectional observation; Cystic Fibrosis Center Patient Registry
|
231 pediatric CF
|
ADI (high ≥ 5 vs low < 5), rurality, parental education, household income, race
|
• ↑Neighborhood deprivation strongly correlated with rural residence, no parental college education, and lower household income (all P < 0.001).
• High neighborhood deprivation associated with > 2-fold ↑ MRSA infection (OR 2.26; 95% CI 1.14–4.45), P < 0.05.
|
|||||||||
|
15. Sood et al.,
2023 [24]
|
MRSA
|
Retrospective (2016–2018); EMR (Epic) Premier Quality Advisor database; ADI
|
220,849 cases
5 hospitals in one large Maryland-Washington DC health system
|
ADI, Race/ethnicity, Insurance
|
• Black race had 44% ↑ MRSA
(OR 1.44; 95% CI 1.18–1.76).
• After adjusting for ADI, Black race was no longer associated with ho-MRSA, indicating ADI mediated the disparity.
• Asian and “unknown” race remained associated with lower odds of co-MRSA/MSSA in adjusted models.
|
|||||||||
|
16. Arsoniadis et al.,
2016 [25]
|
SSI
|
Retrospective (2005–2013); NSQIP-PUF
|
9,513 CD patients
|
Race (African American AA vs non-AA)
|
• SSI risk ↑ among AA: 17.6% vs 14.8%, p = 0.037
|
|||||||||
|
17. Qi et al.,
2019 [26]
|
SSI
|
Cross-sectional.
State Inpatient Database
|
149,741patients
7 U.S. states
|
Race/ethnicity, income, insurance
|
• 30-day SSI rate: 2.55% after colectomy; 0.61% after hysterectomy.
• Colectomy cohort: Black race associated with lower SSI risk → (aOR 0.71; 95% CI 0.61–0.82).
• Medicare insurance → ↑ SSI risk → (aOR 1.25; 95% CI 1.10–1.41).
• Medicaid insurance → ↑ SSI risk → (aOR 1.23; 95% CI 1.06–1.44).
• Lowest ZIP-code income quartile → ↑ SSI risk → (aOR 1.14; 95% CI 1.01–1.29).
• Hysterectomy cohort: No SDOH (race, insurance, income) showed a significant association with SSI after adjustment.
|
|||||||||
|
18. Stevens et al.,
2023 [27]
|
SSI, CLABSI
|
Retrospective (2010–2020);
Children’s Hospital Colorado trauma registry, EMR, CDC-SVI 2018
|
355 pediatric trauma patients (< 18 y), single center study
|
Overall, SVI (≥ 70th percentile = high vulnerability) SVI subcomponent Socioeconomic status, Household /disability, Minority /language, Housing/transportation
|
• SSI higher in ↑ SVI → 3.9% vs 0.4% (P = 0.03).
• No significant differences in rates of postoperative pneumonia, UTI, DVT, or CLABSI by SVI status.
|
|||||||||
|
19. Welter et al.,
2023 [28]
|
SSI
|
Retrospective (2017);
ACS-NSQIP
|
740,144 patients; 10 surgical specialties: general surgery (GS), vascular surgery (VS), cardiac surgery (CS), thoracic surgery (TS), orthopedics (OS), neurosurgery (NS), urology (US), otolaryngology (ENT), plastic surgery (PS), and gynecology (GYN).
|
Race: Black African American (BAA) to White non-Hispanic (WNH)
|
• WNH had ↑ SSI in GS (4.4% vs 4.1%, p = 0.003) and TS (3.1% vs 1.7%, p = 0.015)
• BAA had ↑ SSI in VS (4.4% vs 3.2%, p < 0.001), OS (1.6% vs 1.2%, p < 0.001), and GYN (3.0% vs 2.4%, p < 0.001), with similar rates in ENT and US.
|
|||||||||
|
20. Yi et al.,
2019 [29]
|
SSI
|
Retrospective (2011–2013); state inpatient discharge database
|
291,757 cesarean deliveries,
California
|
Insurance
|
• Cesarean deliveries covered by Medicaid had ↑ SSI risk
(aOR 1.40; 95% CI 1.20–1.60, p < 0.0001)
• SSI incidence by insurance type: Medicaid: 0.75% (1,055 infections), Private insurance: 0.63% (955 infections)
• Medicaid covered nearly half of cesarean deliveries (48%), indicating large population-level inequality exposure.
|
|||||||||
|
21. Taree et al.,
2021 [30]
|
SSI
|
Retrospective (2012–2014)
HCUP
|
65,121 patients; 191 SSI readmissions (30-day) and 283 SSI readmissions (90-day)
|
Insurance
|
• Medicare and Medicaid patients had significantly higher odds of SSI-related readmission at both 30 and 90 days (P < 0.0001).
• Medicaid insurance was an independent predictor of SSI readmission in adjusted models at 30 and 90 days (multivariable).
|
|||||||||
Note
HAI, healthcare-associated infection; CDI, Clostridioides difficile infection; MRSA, methicillin-resistant Staphylococcus aureus; MSSA, methicillin-susceptible Staphylococcus aureus; CLABSI, central line–associated bloodstream infection; CAUTI, catheter-associated urinary tract infection; VAP, ventilator-associated pneumonia; SSI, surgical site infection.
CF, Cystic Fibrosis; CD, Crohn’s Disease
SDOH, social determinants of health; ADI, Area Deprivation Index; Social Vulnerability Index; SDI, Social Deprivation Index; LOS, length of stay.
NIS, National Inpatient Sample; NHDS, National Hospital Discharge Survey; HCUP, Healthcare Cost and Utilization Project; ACS-NSQIP, American College of Surgeons- National Surgical Quality Improvement Program; PHIS, Pediatric Health Information System; SPS, Solutions for Patient Safety; EMR, electronic medical record; EHR, electronic health record.
aOR, adjusted odds ratio; IRR, incidence rate ratio; HR, hazard ratio; RR, rate ratio; CI, confidence interval; PY, person-years.
Racial and Ethnic Disparities
Several studies in this review show racial disparities across various types of HAIs, with Black people remain the most common affected groups among others [8, 18, 20, 23, 24, 28]. In CDI, Black patients had significantly higher mortality (aOR 1.12; 95% CI 1.09–1.15) and greater odds of severe disease (aOR 1.09; 95% CI 1.07–1.11) compared to Whites, despite White patients having a higher crude incidence rate of CDI-related hospitalizations [8]. CDI testing patterns also reflected inequities: White patients had more CDI tests per 1,000 patient-days than Black or non-White patients, although positivity rates were similar, this suggests potential differences in diagnostic access for clinical decisions [18].
Similarly, device associated infections also show pronounced disparities. Non- Hispanic Black patients had significantly higher CLABSI rates (IRR 1.27; 95% CI 1.02–1.58) and higher CAUTI rates (IRR 1.42; 95% CI 1.05–1.92) compared to non-Hispanic White patients [20]. Pediatric studies also revealed that multiracial Hispanic and Hispanic- pacific Islander children experienced higher CLABSI rates above reference values for race, whereas White children consistently had lower risk [7].
Racial disparities were also dominant in MRSA and MSSA. Hispanic children with cystic fibrosis had a 19% higher risk of MSSA (HR 1.19; 95% CI 1.10–1.28) and a 13% higher risk of MRSA (HR 1.13; 95% CI 1.02–1.26) and acquired these infections earlier than non- Hispanic White children.[22] At a population level analysis by Freeman et al, the author found that proportion of African American residents were the strongest predictor of hospital-onset MRSA bloodstream infections in multivariable models [23]. Disparities in SSI was also found higher in African American demonstrating higher risk of SSI in orthopedic, vascular and gynecological surgical procedures [28].
These findings collectively indicate that racial disparities in HAIs reflect structural inequities in healthcare access, environmental exposure, diagnostic procedure and socioeconomic conditions rather than biological factors. A study by Sood and colleagues also found that when neighborhood deprivation was included in MRSA models, the previously observed racial disparities were no longer significant [24]. This suggests that the higher level of MRSA burden among Black patients may be driven by underlying structural inequalities rather than race itself.
Insurance Status
Insurance type was also one of the most consistent predictors of emerging HAI incidence and severity. In CDI, Medicaid-insured individuals experienced elevated risk: dual-eligible patients gad more than threefold higher incidence (3.1 times) compared to Medicare-only patients, and younger adults on Medicaid had 2.7 times higher CDI incidence than other private insurance types. Even among Medicaid beneficiaries without chronic comorbidities, CDI remained significantly higher (67.5 vs 45.6 per 100,00 person-years) [9].
Moreover, in VAP lack of insurance more than doubled the odds of mortality (aOR 2.13; 95% CI 1.49–3.06) than insured groups, suggesting the critical role of financial burden in acute infection outcomes [31]. Several surgical complication studies also indicate similar effects. Medicaid and Medicare were independently associated with higher SSI risk following abdominal surgery [26]. In cesarean deliveries, Medicaid coverage increases SSI risk by 40% (aOR 1.40; 95% CI 1.20–1.60), and Medicaid accounted for nearly half of all cesarean birth, highlighting substantial population-level implications [29]. Medicaid also reported higher odds of SSI-related readmission at both 30 and 90 days [30].
These findings indicate that insurance-based disparities reflect broader financial as well as socioeconomic burden, including delayed treatment, limited perioperative care and challenges with follow-up.
Poverty, Neighborhood Deprivation and Area- Level Social Disparities
Neighborhood-level indicators of deprivation were strongly and consistently associated with increased HAI risk and worse outcomes. Patients living in the most disadvantaged neighborhood had significantly higher CDI readmission rates (26% vs 21%), even after adjusting for comorbidities (aOR 1.16; 95% CI 1.04–1.28) [16]. Similarly, in pediatric care, children from low-income neighborhoods had significantly elevated CLABSI risk (RR 1.43; 95% CI 1.10–1.84) [19].
A study on MRSA risk also found that higher-deprivation neighborhood (ADI > 5) was linked to double the odds of MRSA infections (OR 2.26; 95%CI 1.14–4.45) [32], while state-level ecological analysis found that poverty, income inequality and the proportion of African American residents were all correlated with increased hospital-onset MRSA rates [23]. Conversely, higher community education levels and improved housing quality were associated with lower MRSA rates [21].
In surgical populations, pediatric patients in high SVI neighborhood had nearly 10-fold higher SSI rates (3.9% vs 0.4%), suggesting the profound impact of structural barriers such as overcrowded house, limited transportation and inadequate access to routine care [27].
These findings indicate that housing quality, income level and neighborhood significantly influence HAI risks across various infection types and healthcare settings.
Collectively all domains related to SDOH suggest the need for expanding infection prevention efforts beyond patient-level clinical factors and include social and structural determinants. Hence, this review has several strengths. It is first, to our knowledge, to systematically synthesize evidence across North America on the relationship between social determinants of health and a wide range of healthcare-associated infections, including MRSA, CLABSI, CAUTI, VAP and SSI. The review follows the structured PRISMA protocol to conduct this study and includes multiple large national datasets (e.g. NIS, Medicare, SPS) as well as hospital surveillance data. By integrating findings across diverse populations and infection types, the review highlights consistent and critical inequities among population. Furthermore, the narrative analysis allowed for the meaningful interpretation of heterogenous study designs, SDOH measures, contributing to valuable insights for infection prevention and health equity efforts.
Limitations
However, several limitations also exist in this study. First, the heterogeneity of study designs, data sources, HAI definitions and SDOH measures limited direct comparison and precluded meta-analysis. Many studies relied on administrative datasets, which may contain misclassification of both outcomes and social variables such as race or insurance type. Several studies also lack control of potential confounders, raising the possibility of potential residual confounding. Finally, this review was conducted by single author, there is potential risk of selection or interpretation bias despite adherence to a structured protocol.
5. Conclusions
This systematic review highlights the SDOH play critical role in shaping the risks and outcomes of HAIs across North America. Evidence consistently shows that racial and ethnic inequities, insurance status, neighborhood deprivation, and poverty are associated with higher HAI incidence, severity, readmissions, and mortality. These disparities persist even as overall HAI rates have declined, which suggests that improvements in infection prevention have not been applied equitably.
The findings highlight the need for infection prevention efforts and policies to move beyond a narrow focus on clinical risk factors and incorporate social and structural contexts into surveillance, risk stratification and quality improvement initiatives. Hospitals in low-income areas may require additional resources, enhanced screening and modified quality of care metrics to address structural inequities. Infection prevention team can integrate neighborhood-level indices, insurance status, and racial inequities quality markers into HAI dashboards to facilitate earlier detection of racial disparities and support tailored prevention efforts for patient safety.
Future research is needed to include more longitudinal and prospective studies to better understand casual pathways linking social disparities to HAI risks.
List of Abbreviations
ADI
Area Deprivation Index
BAA
Black or African American
CAUTI
Catheter–Associated Urinary Tract Infection
CDI
Clostridioides difficile Infection
CI
Confidence Interval
CLABSI
Central Line–Associated Bloodstream Infection
HCUP
Healthcare Cost and Utilization Project
HAI
Healthcare–Associated Infection
IRR
Incidence Rate Ratio
LOS
Length of Stay
MRSA
Methicillin–Resistant Staphylococcus aureus
MSSA
Methicillin–Susceptible Staphylococcus aureus
NIS
National Inpatient Sample
NSQIP
National Surgical Quality Improvement Program
OR
Odds Ratio
PRISMA
Preferred Reporting Items for Systematic Reviews and Meta–Analyses
SDI
Social Deprivation Index
SDOH
Social Determinants of Health
SSI
Surgical Site Infection
SVI
Social Vulnerability Index
VAP
Ventilator–Associated Pneumonia
WNH
White Non–Hispanic
A
DeclarationsEthical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
A
Availability of data and materials
All data generated or analyzed using this study are included in the manuscript.
Competing interests
The author declares that there is no competing interest.
A
Funding Statement
This research received no specific grant from any funding agency.
A
Author’s contributions
The author solely conducted the study, designed the search strategy, screened articles, extracted and synthesized data and revised the manuscript.
Acknowledgments
The author thanks the university librarian for assistance with refining the search strategy.
Author’s Information
CS is a doctoral student in Public Health Infectious Disease Epidemiology at the University of Massachusetts Lowell with research interest in Infectious diseases, Occupational health and safety and prevention of HIV disease.
References
1.
CDC, Current HAI Progress Report [Internet]. Healthcare-Associated Infections (HAIs). 2025 [cited 2025 Dec 8]. Available from: https://www.cdc.gov/healthcare-associated-infections/php/data/progress-report.html
2.
Weiner-Lastinger LM, Abner S, Edwards JR, Kallen AJ, Karlsson M, Magill SS et al (2020) Antimicrobial-resistant pathogens associated with adult healthcare-associated infections: Summary of data reported to the National Healthcare Safety Network, 2015–2017. Infect Control Hosp Epidemiol 41(1):1–18
3.
Magill SS, O’Leary E, Janelle SJ, Thompson DL, Dumyati G, Nadle J et al (2018) Changes in Prevalence of Health Care-Associated Infections in U.S. Hospitals. N Engl J Med 379(18):1732–1744
4.
AHRQ’s Healthcare-Associated Infections Program [Internet] [cited 2025 Dec 8]. Available from: https://www.ahrq.gov/hai/index.html
5.
McGrath CL, Logan LK, Deloney VM, Rubin LG, Ravin KA, Muller M et al Monitoring health disparities in healthcare-associated infection surveillance: A Society for Healthcare Epidemiology of America (SHEA) Research Network (SRN) Survey. Infect Control Hosp Epidemiol 45(4):526–529
6.
Chen J, Khazanchi R, Bearman G, Marcelin JR (2021) Racial/Ethnic Inequities in Healthcare-associated Infections Under the Shadow of Structural Racism: Narrative Review and Call to Action. Curr Infect Dis Rep 23(10):17
7.
Lyren A, Haines E, Fanta M, Gutzeit M, Staubach K, Chundi P et al (2024) Racial and ethnic disparities in common inpatient safety outcomes in a children’s hospital cohort. BMJ Qual Saf 33(2):86–97
8.
Argamany JR, Delgado A, Reveles KR (2016) Clostridium difficile infection health disparities by race among hospitalized adults in the United States, 2001 to 2010. BMC Infect Dis 16(1):454
9.
Olsen MA, Stwalley D, Tipping AD, Keller MR, Yu H, Dubberke ER (2023 July) Trends in the incidence of Clostridioides difficile infection in adults and the elderly insured by Medicaid compared to commercial insurance or Medicare only. Infect Control Hosp Epidemiol 44(7):1076–1084
10.
Addressing Health Inequities in Canada | Canadian Public Health Association [Internet]. [cited 2025 Dec 8]. Available from: https://www.cpha.ca/health-inequities
11.
kffdrishtip. Disparities in Health and Health Care: 5 Key Questions and Answers [Internet]. KFF (2024) [cited 2025 Dec 8]. Available from: https://www.kff.org/racial-equity-and-health-policy/disparities-in-health-and-health-care-5-key-question-and-answers/
12.
Tarabay J, Nix CD, Doline K, McClusky J, Catalfumo F, Lewin CA et al (2025 July) Exploring the connection of health disparities and inequities with health care-acquired infections in North America: A scoping review of the literature. Am J Infect Control 53(7):778–784
13.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71
14.
Shaka H, El-amir Z, Akhtar T, Wani F, Raghavan S, Khamooshi P et al A nationwide retrospective analysis of ventilator-associated pneumonia in the US. Proc Bayl Univ Med Cent 35(4):410–414
15.
Ramai D, Dang-Ho KP, Lewis C, Fields PJ, Ofosu A, Barakat M et al (2020) Clostridioides difficile infection in US hospitals: a national inpatient sample study. Int J Colorectal Dis 35(10):1929–1935
16.
Scaria E, Powell WR, Birstler J, Alagoz O, Shirley D, Kind AJH et al (2020) Neighborhood disadvantage and 30-day readmission risk following Clostridioides difficile infection hospitalization. BMC Infect Dis 20(1):762
17.
Vader DT, Weldie C, Welles SL, Kutzler MA, Goldstein ND (2021) Hospital-acquired Clostridioides difficile infection among patients at an urban safety-net hospital in Philadelphia: Demographics, neighborhood deprivation, and the transferability of national statistics. Infect Control Hosp Epidemiol 42(8):948–954
18.
Warren BG, Burch CD, Barrett A, Graves A, Gettler E, Turner NA et al (2024) Racial disparities in Clostridioides difficile testing in three southeastern US hospitals. Infect Control Hosp Epidemiol 45(4):429–433
19.
Gutierrez SA, Chiou SH, Raghu V, Cole CR, Rhee S, Lai JC et al (2024) Associations between hospital-level socioeconomic patient mix and rates of central line-associated bloodstream infections in short bowel syndrome: A retrospective cohort study. JPEN J Parenter Enter Nutr 48(6):678–685
20.
Gettler EB, Kalu IC, Okeke NL, Lewis SS, Anderson DJ, Smith BA et al Disparities in central line-associated bloodstream infection and catheter-associated urinary tract infection rates: An exploratory analysis. Infect Control Hosp Epidemiol. 44(11):1857–1860
21.
Andreatos N, Shehadeh F, Pliakos EE, Mylonakis E (2018) The impact of antibiotic prescription rates on the incidence of MRSA bloodstream infections: A county-level, US-wide analysis. Int J Antimicrob Agents 52(2):195–200
22.
McGarry ME, Huang CY, Ly NP (2023 Sept) Ethnic differences in staphylococcus aureus acquisition in cystic fibrosis. J Cyst Fibros Off J Eur Cyst Fibros Soc 22(5):909–915
23.
Freeman JT, Blakiston MR, Anderson DJ, Hospital-Onset MRSA (2018) Bacteremia Rates Are Significantly Correlated With Sociodemographic Factors: A Step Toward Risk Adjustment. Infect Control Hosp Epidemiol 39(4):479–481
24.
Sood G, Dougherty G, Martin J, Beranek E, Landrum BM, Qasba S et al (2023) Is neighborhood deprivation index a risk factor for Staphylococcus aureus infections? Am J Infect Control 51(12):1314–1320
25.
Arsoniadis EG, Ho YY, Melton GB, Madoff RD, Le C, Kwaan MR (2017) African Americans and Short-Term Outcomes after Surgery for Crohn’s Disease: An ACS-NSQIP Analysis. J Crohns Colitis 11(4):468–473
26.
Qi AC, Peacock K, Luke AA, Barker A, Olsen MA, Joynt Maddox KE (2019) Associations Between Social Risk Factors and Surgical Site Infections After Colectomy and Abdominal Hysterectomy. JAMA Netw Open 2(10):e1912339
27.
Stevens J, Reppucci ML, Pickett K, Acker S, Carmichael H, Velopulos CG et al (2023 July) Using the Social Vulnerability Index to Examine Disparities in Surgical Pediatric Trauma Patients. J Surg Res 287:55–62
28.
Welter M, Grosh K, Jose J, Khalil S, Muharraq A, Elian A et al (2023) Are There Racial Differences in the Rate of Surgical Site Infection Based on Surgical Subspecialty? Surg Infect 24(10):860–868
29.
Yi SH, Perkins KM, Kazakova SV, Hatfield KM, Kleinbaum DG, Baggs J et al (2019 June) Surgical site infection risk following cesarean deliveries covered by Medicaid or private insurance. Infect Control Hosp Epidemiol 40(6):639–648
30.
Taree A, Mikhail CM, Markowitz J, Ranson WA, Choi B, Schwartz JT et al (2021) Risk Factors for 30- and 90-Day Readmissions Due To Surgical Site Infection Following Posterior Lumbar Fusion. Clin Spine Surg 34(4):E216–E222
31.
Cai Y, Booraphun S, Li AY, Kayastha G, Tambyah PA, Cooper BS et al (2024) Cost-effectiveness of a short-course antibiotic treatment strategy for the treatment of ventilator-associated pneumonia: an economic analysis of the REGARD-VAP trial. Lancet Glob Health 12(12):e2059–e2067
32.
Oates GR, Harris WT, Rowe SM, Solomon GM, Dey S, Zhu A et al (2019) Area Deprivation as a Risk Factor for Methicillin-resistant Staphylococcus aureus Infection in Pediatric Cystic Fibrosis. Pediatr Infect Dis J 38(11):e285–e289