Nurse Staffing and Healthcare-Associated Infections: What the Evidence Says

Healthcare-associated infections (HAIs) remain a persistent challenge in hospitals worldwide, and the relationship between nurse staffing levels and infection rates has become a central question for infection prevention programs. The evidence reviewed here shows that understaffing, measured as the number of patients assigned to each nurse, is consistently associated with higher HAI risk across multiple care settings. This article examines the peer-reviewed literature on nurse staffing and HAIs, explains the mechanisms that link staffing adequacy to infection outcomes, and provides a practical framework that infection prevention teams and nursing leaders can use to assess whether their staffing levels are adequate for infection control.

The intended readers are students, researchers, life-science professionals, and informed general readers who want to understand the current state of evidence on this topic. The practical outcome is an evidence-based review that summarizes key studies and offers a structured approach for evaluating staffing adequacy as part of an infection prevention program.

At a Glance: Staffing and Infection Risk

The table below summarizes the main findings from the most relevant studies on nurse staffing and HAIs. These studies vary in design, setting, and outcome measures, so the results should be interpreted with attention to each study's methodology.

The Scope of the Problem: Why Staffing Matters for Infection Control

Nursing care sits at the center of most infection prevention activities in hospitals. Nurses perform hand hygiene, manage indwelling devices, administer antibiotics, monitor surgical sites, and coordinate the daily tasks that either prevent or permit pathogen transmission. When staffing levels fall below what patient acuity requires, these tasks compete for limited time and attention.

The prevalence of indwelling devices in hospitalized patients illustrates the scale of the infection risk that nurses manage daily. A point prevalence study conducted at a tertiary care hospital surveyed 857 non-critically ill adult patients on general care, telemetry, and surgical floors. The study found 1,229 devices among these patients, with 91.0% of surveyed patients having at least one indwelling device. Intravenous catheters were the most common device type at 90.1%, followed by gastrointestinal devices at 12.8% and urinary catheters at 10.2%. The median nurse-to-patient ratio in this study was 3 patients to 1 nurse, and no difference in nurse-to-patient ratio was observed based on the number of devices present. This finding suggests that device burden does not automatically adjust staffing assignments, even though each device represents a potential entry point for infection.

The practical implication is that infection prevention cannot be separated from workload planning. A unit can have excellent hand hygiene protocols and evidence-based device care bundles, but if the nurse-to-patient ratio does not allow enough time to execute those protocols consistently, the risk of infection rises. Infection prevention programs should therefore include staffing assessment as a core component instead of treating staffing as an administrative concern outside their scope.

Core Evidence: What Studies Show About Staffing and HAIs

Unit-Level Analysis of Understaffing and Infection Risk

A study published in The Journal of Nursing Administration examined the association between HAIs and nurse staffing using unit-level staffing data from a large urban hospital system. The researchers analyzed cross-sectional data collected between 2007 and 2012, with HAIs diagnosed using the Centers for Disease Control and Prevention's National Healthcare Safety Network definitions. They used Cox proportional-hazards regression to examine the association of nurse staffing two days before HAI onset with subsequent infection, after adjusting for individual risks.

The results showed that 15% of patient-days had one shift understaffed, defined as staffing below 80% of the unit median for a shift, and 6.2% had both day and night shifts understaffed. Patients on units where both shifts were understaffed were significantly more likely to develop HAIs two days later. The study concluded that understaffing is associated with increased risk of HAIs.

This study is notable for its use of unit-level staffing data instead of hospital-level averages. Hospital-level analyses can mask important variation between units, because a hospital with adequate overall staffing may still have specific units or shifts that are chronically understaffed. The two-day lag between understaffing and infection onset also aligns with the biological plausibility that lapses in infection prevention practices during understaffed periods create conditions for subsequent infection.

Systematic Review Evidence Across Multiple Studies

A systematic review published in the Joint Commission Journal on Quality and Patient Safety synthesized research on hospital staffing and HAI risk. The review searched MEDLINE, PubMed, and the Cumulative Index to Nursing and Allied Health Literature for studies published between January 1, 2000, and November 30, 2015, and included 54 articles. The majority of studies examined the relationship between nurse staffing and HAIs, with 50 of 54 studies addressing this question. Among those, 40 studies, or 74.1%, found nurse staffing variables to be associated with an increase in HAI rates.

Only 5 studies addressed non-nurse staffing, and those had mixed results. Physician staffing was associated with an increased HAI risk in 1 of 3 studies. The review authors noted that studies varied in design and methodology, as well as in their use of operational definitions and measures of staffing and HAIs. Despite this lack of consistency, the overall results demonstrated that increased staffing is related to decreased risk of acquiring HAIs. The authors called for more rigorous and consistent research designs, definitions, and risk-adjusted HAI data in future studies.

The practical takeaway from this review is that the association between staffing and HAIs is robust across many studies, even though individual studies may differ in their specific findings. Infection prevention teams should not dismiss conflicting results from single studies as evidence that staffing does not matter. Instead, the weight of evidence supports including staffing adequacy in infection risk assessments.

Patient-Nurse Ratio and Infection Risk in Pediatric Care

A surveillance study conducted at a tertiary-level pediatric hospital in Mexico analyzed the association between patient-nurse ratio and HAIs. The researchers conducted a descriptive and prospective study from July 2017 to December 2018, documenting nursing attendance and HAI records. They calculated patient-nurse ratio using nurse staffing records and patient census, obtaining 63,114 staff attendance data points from five hospital departments across morning, evening, and night shifts.

The results showed that a patient-nurse ratio greater than 2:1 was associated with a 54% increased risk for HAIs, with a 95% confidence interval of 42 to 167%, adjusted by shift staff, special conditions, and surveillance periods. The HAIs most associated with patient-nurse ratio were urinary tract infections with an odds ratio of 1.83, procedure-related pneumonia with an odds ratio of 2.08, and varicella with an odds ratio of 2.33. The study concluded that a high number of patients per nurse increased the probability of various types of HAI and recommended that patient-nurse ratio be established in HAI guidelines and policies.

This study is particularly useful because it provides a specific threshold, a patient-nurse ratio greater than 2:1, that was associated with increased infection risk. While this threshold comes from a pediatric hospital and may not transfer directly to adult settings, it offers a concrete reference point for staffing assessment. The finding that different infection types had different odds ratios also suggests that staffing pressure may affect some infection prevention practices more than others.

Nurse-Patient Ratios in Intensive Care Units

A systematic review published in Nursing in Critical Care evaluated the impact of nurse-to-patient ratios on patient outcomes and nurse well-being in intensive care units. The review included 20 studies conducted across various locations, incorporating retrospective cohort studies, cross-sectional designs, and other methodologies. The studies were analyzed to determine the influence of staffing levels on patient and nurse outcomes.

The review found that safe nurse staffing levels were associated with a 14% reduction in hospital mortality, shorter ICU stays, a 20% improvement in infection prevention, and an average ICU stay reduction of 1.5 days. Enhanced patient satisfaction by 18% was observed in units with adequate staffing. Conversely, lower staffing ratios were linked to a 25% increase in adverse events, nurse fatigue, and diminished patient safety outcomes.

The review highlighted the critical role of nurse-to-patient ratios in improving patient outcomes and nurse well-being in ICUs. The authors recommended that future research focus on standardizing methodologies to evaluate staffing strategies and exploring their long-term impacts on both patient and nurse outcomes. They also emphasized the importance of implementing evidence-based staffing policies and integrating supportive technologies.

The 20% improvement in infection prevention associated with safe staffing levels is a substantial effect size. For an ICU with a baseline infection rate of 5 per 1,000 device-days, a 20% reduction would bring the rate to 4 per 1,000 device-days. Over the course of a year, this difference could translate into several prevented infections in a single unit.

Mathematical Modeling of Staffing and MRSA Transmission

A modeling study published in Antimicrobial Stewardship and Healthcare Epidemiology used a stochastic mathematical model of methicillin-resistant Staphylococcus aureus to study the impact of changes in staffing and a finite pool of tasks on hospital-associated acquisitions. The model simulated a 15-bed intensive care unit with one intensivist and nurse-patient ratios set at 1:1, 1:1.5, 1:2.5, 1:3, 1:5, and 1:7.5. Each model was run 1,000 times, and the outcome was the median number of hospital-associated MRSA acquisitions in one year.

Treating the 1:3 nurse-patient ratio as the baseline, with 45 MRSA acquisitions per year, the model found that increasing the number of nurses from 5 to 6, moving to a 1:2.5 nurse-patient ratio, had a relative risk of 0.77. This finding suggested that a small change in nurse staffing levels might have an outsized impact on rates. More dramatic changes had correspondingly larger swings in MRSA acquisition rates, with 1:1 nurse-patient ratio scenarios having a relative risk of 0.17, and at the other extreme, a 1:7.5 nurse-patient ratio having a relative risk of 4.66.

This modeling study is valuable because it demonstrates a dose-response relationship between staffing and infection risk. The relationship is not linear, meaning that the benefit of adding one nurse is greater when staffing is already low. The model also incorporated a finite pool of tasks, which is more realistic than models that assume an endless series of tasks. When nurses have a finite set of tasks, adding staff allows those tasks to be completed more consistently and reduces the likelihood that infection prevention tasks are skipped.

Mechanisms Linking Staffing to Infection Risk

Missed Nursing Care as a Pathway

The concept of missed nursing care provides a mechanistic explanation for how understaffing leads to infections. A systematic review published in the Journal of Clinical Nursing examined the impact of nursing care left undone on patient outcomes. The review searched Medline, CINAHL, and Scopus for studies examining the association of missed nursing care and at least one patient outcome, retaining studies regarding registered nurses, healthcare assistants, support workers, and nurses' aides. Only adult settings were included.

Fourteen studies reported associations between missed care and patient outcomes. Four studies found significantly decreased patient satisfaction associated with missed care. Seven studies reported associations with one or more patient outcomes including medication errors, urinary tract infections, patient falls, pressure ulcers, critical incidents, quality of care, and patient readmissions. Three studies investigated whether there was a link between missed care and mortality, and from these results no clear associations emerged.

The review noted that a considerable body of evidence supports the hypothesis that lower levels of registered nurses on duty increase the likelihood of patients dying on hospital wards and the risk of many aspects of care being either delayed or left undone. However, the direct consequence of missed care remains unclear. The review showed a modest evidence base of studies exploring missed care and patient outcomes, generated mostly from nurse and patient self-reported data.

For infection prevention, the relevance of missed care is direct. When nurses are understaffed, they must prioritize tasks. Hand hygiene before and after patient contact, daily assessment of catheter necessity, and timely removal of indwelling devices are tasks that can be delayed or omitted when workload exceeds capacity. Each omitted task creates a small increase in infection risk, and across many patients and many shifts, these omissions accumulate into measurable differences in HAI rates.

The Role of Nurse Burnout and Workload

The relationship between staffing, burnout, and patient safety outcomes is complex and requires careful interpretation. A critical interpretive synthesis published in Frontiers in Psychology examined the empirical evidence linking burnout to patient safety failures. The authors reviewed quantitative systematic reviews and interrogated the primary studies they included, allowing direct comparison between review-level conclusions and the underlying empirical evidence.

Across eight reviews, only a minority of primary studies examined objective safety outcomes, and findings were inconsistent. Despite this, review conclusions often implied stronger and more generalisable effects than the evidence warranted. The authors identified four recurring problems in the literature: a narrow occupational focus, limited theoretical positioning of burnout within patient safety systems, extrapolation beyond objective evidence, and conflation of reported with observed safety events.

The authors argued that burnout cannot currently be justified as a direct predictor of patient safety outcomes. Instead, burnout is better understood as a system-level condition that shapes care processes, reporting practices, and organisational adaptation. They proposed an open-systems framework for theorising burnout and patient safety that aligns psychological constructs with the realities of complex socio-technical healthcare systems.

This distinction matters for infection prevention programs. A nurse who is burned out may not directly cause an infection through a single error. However, burnout can reduce the consistency of infection prevention practices, decrease vigilance in device care, and increase the likelihood that tasks are missed. The pathway from burnout to infection is indirect and mediated through care processes. Infection prevention teams should therefore address burnout as a system-level condition instead of blaming individual nurses for lapses.

Staffing Composition and Skill Mix

The composition of the nursing team, beyond the total number of staff, affects infection risk. A study presented at a conference and published in Infection Control and Hospital Epidemiology examined changes in nursing team composition and the risk of device-associated infections in intensive care units. The study used daily staffing records from December 2018 to August 2019 for a medical-respiratory intensive care unit and a cardiac surgery unit. Both units staffed a fixed 2:1 patient-nurse ratio, with 1:1 for specific cardiac surgeries.

Staff deficiency was defined as assignments filled by nurses pulled from other units, supplemental staff, or clinical coordinator roles. Staff support comprised nursing assistants and unit secretaries. The study used National Healthcare Safety Network definitions for central-line-associated bloodstream infections and catheter-associated urinary tract infections. The Spearman correlation coefficient was used to determine the relationship between staffing, acuity, and the risk window for HAI, defined as days 1 to 10 preinfection.

The study found that when clinical coordinator roles changed to include 50% of time in direct patient care instead of supportive roles, this affected staffing patterns. The findings suggest that the type of nurse filling a shift matters, beyond the count. A nurse pulled from another unit may be less familiar with the equipment, protocols, and patient population, which could affect the quality of infection prevention practices.

The Financial Dimension of Staffing and Infections

The relationship between HAIs, nurse staffing, and hospital financial performance was examined in a study published in Inquiry, a journal of medical care organization, provision, and financing. The study used contingency theory as a framework and analyzed publicly available data on 2,059 hospitals from 2014 to 2016. The key independent variables were available infection rates and nurse staffing, and the dependent variables were indicators of financial performance: operating margin, total margin, and days cash on hand.

The study found nearly identical negative direct associations between infections and operating margins and total margins at negative 0.07%, and positive associations between the interaction of infections and nurse staffing at 0.05%. A 10% higher infection rate would be predicted to be associated with only a 0.2% lower profit margin. The associations between HAIs, nurse staffing, and days cash on hand were insignificantly different from zero.

These findings suggest that the direct financial penalty for HAIs may be smaller than often assumed, at least in the period studied. However, the interaction between infections and nurse staffing was positive, meaning that the relationship between infections and financial performance may differ depending on staffing levels. For infection prevention programs, this study provides context for making the business case for staffing investments. The financial argument for adequate staffing may rest more on avoided costs of care, reputation, and regulatory consequences than on direct margins.

Staffing Assessment Framework for Infection Control

Step 1: Define Staffing Adequacy for Your Unit

The first step in assessing staffing adequacy for infection control is to define what adequate staffing means for your specific unit. The evidence reviewed above suggests several approaches to this definition. The unit-level analysis used a threshold of staffing below 80% of the unit median for a shift as the definition of understaffing. The pediatric surveillance study used a patient-nurse ratio greater than 2:1 as the threshold associated with increased infection risk. The ICU systematic review found that safe staffing levels were associated with improved outcomes, though it did not specify a single ratio.

For most units, a combination of approaches will be most useful. Track the patient-nurse ratio for each shift and compare it to a target based on unit acuity and professional standards. Also track the percentage of shifts that fall below a minimum staffing threshold. Both measures provide different information about staffing adequacy.

Step 2: Collect Unit-Level Staffing Data

Hospital-level staffing data can mask important variation between units. The unit-level analysis study found that 15% of patient-days had one shift understaffed and 6.2% had both day and night shifts understaffed. These patterns would be invisible in hospital-level data. Infection prevention teams should therefore collect staffing data at the unit level, ideally for each shift.

The data elements to collect include the number of registered nurses on duty, the number of patients on the unit, the patient-nurse ratio for each shift, the number of shifts below the staffing threshold, and the number of shifts filled by nurses pulled from other units or supplemental staff. This data should be collected prospectively and linked to HAI surveillance data.

Step 3: Link Staffing Data to HAI Surveillance Data

The unit-level analysis study found that patients on units with both shifts understaffed were significantly more likely to develop HAIs two days later. This finding suggests that the risk window for staffing-related infections is relatively short. Infection prevention teams should therefore link staffing data to HAI surveillance data with attention to the timing of understaffing relative to infection onset.

A practical approach is to review staffing data for the 2 to 10 days before each HAI is identified. The ICU study used a risk window of days 1 to 10 preinfection. This review can identify patterns, such as infections that cluster after periods of understaffing or after shifts filled by nurses from other units.

Step 4: Assess the Impact of Staffing Changes

When staffing changes occur, whether planned or unplanned, infection prevention teams should monitor HAI rates before and after the change. The study of nursing team composition changes in ICUs provides an example of this approach. When clinical coordinator roles changed to include 50% of time in direct patient care, the study examined whether this change affected HAI risk.

The modeling study on nurse scheduling reorganization provides another example. The study used contact data from wearable sensors at a short-stay geriatric ward and proposed a proof-of-concept modeling study that reorganized nurse schedules for efficient infection control. The strategy switched and reassigned nurses' tasks through the optimization of shift timelines, while respecting feasibility constraints and satisfying patient-care requirements. Through a Susceptible-Colonized-Susceptible transmission model, the study found that schedule reorganization reduced HAI risk by 27% with a 95% confidence interval of 24 to 29%, while preserving timeliness, number, and duration of contacts. The study noted that more than 30% of nurse-nurse contacts should be avoided to achieve an equivalent reduction through simple contact removal.

This finding suggests that how nurses are scheduled matters, beyond how many nurses are on duty. Reorganizing schedules to break potential chains of transmission can substantially limit HAI risk while ensuring the timeliness and quality of healthcare services. Infection prevention programs should consider including optimization of nurse scheduling practices in their programs.

Step 5: Use the Framework for Continuous Quality Improvement

Staffing assessment should be an ongoing process, not a one-time evaluation. Infection prevention teams should review staffing and HAI data on a regular schedule, such as quarterly, and use the findings to inform staffing recommendations. The framework should be integrated into existing quality improvement processes instead of operating as a separate silo.

Records and Measurements for Staffing Assessment

The following records and measurements are essential for assessing the relationship between staffing and HAIs in your facility:

Staffing records. Maintain daily records of nurse assignments for each unit and shift. Include the number of registered nurses, licensed practical nurses, nursing assistants, and unit secretaries. Record the number of patients on the unit and the patient-nurse ratio for each shift. Flag shifts that fall below your defined staffing threshold.

Staffing deficiency records. Track the number of shifts filled by nurses pulled from other units, supplemental staff, or clinical coordinator roles. The ICU study defined staff deficiency in this way, and the findings suggested that the type of nurse filling a shift matters for infection risk.

HAI surveillance data. Use standardized definitions for HAI surveillance, such as the Centers for Disease Control and Prevention's National Healthcare Safety Network definitions used in the unit-level analysis study. Record the date of infection onset, the infection type, and the unit where the infection occurred.

Device utilization data. Track the number and type of indwelling devices in use on each unit. The point prevalence study found that 91.0% of surveyed patients had at least one indwelling device, with intravenous catheters the most common. Device burden affects the workload associated with infection prevention tasks.

Missed care reports. If your facility collects data on missed nursing care, review this data in relation to staffing levels and HAI rates. The systematic review of missed care found associations with urinary tract infections and other outcomes.

Staffing change logs. Document any changes to staffing models, skill mix, or scheduling practices. The study of nursing team composition changes in ICUs provides a model for evaluating the impact of such changes.

Common Failure Patterns in Staffing and Infection Control

Several recurring patterns emerge from the literature on staffing and HAIs. Recognizing these patterns can help infection prevention teams identify problems before they result in infections.

The invisible understaffing pattern. Hospital-level staffing data shows adequate staffing, but unit-level data reveals chronic understaffing on specific shifts. The unit-level analysis study found that 15% of patient-days had one shift understaffed and 6.2% had both shifts understaffed. This pattern is invisible in aggregate data and requires unit-level monitoring to detect.

The task prioritization pattern. When workload exceeds capacity, nurses prioritize immediate clinical tasks over infection prevention tasks. Hand hygiene, device care, and daily assessment of device necessity are the tasks most likely to be missed. The missed care review found associations between missed care and urinary tract infections, suggesting that this pattern has measurable consequences.

The float nurse pattern. Units fill staffing gaps with nurses pulled from other units or supplemental staff. The ICU study found that staff deficiency defined in this way was associated with HAI risk. Float nurses may be less familiar with unit-specific protocols, equipment, and patient populations.

The staffing change without evaluation pattern. Facilities change staffing models or ratios without planning an evaluation of the impact on HAI rates. The study of clinical coordinator role changes in ICUs provides a model for evaluating such changes, but many facilities do not conduct this evaluation.

The single-threshold pattern. Facilities rely on a single staffing threshold, such as a minimum patient-nurse ratio, without considering other factors that affect workload. The point prevalence study found no difference in nurse-to-patient ratio based on the number of devices present, suggesting that device burden does not automatically adjust staffing assignments.

Limitations of the Evidence

The evidence on nurse staffing and HAIs has several important limitations that should be considered when applying these findings to practice.

Methodological heterogeneity. The systematic review published in the Joint Commission Journal on Quality and Patient Safety noted that studies varied in design and methodology, as well as in their use of operational definitions and measures of staffing and HAIs. This heterogeneity makes it difficult to compare results across studies and to establish a single staffing threshold that applies to all settings.

Observational study designs. Most studies on staffing and HAIs are observational, meaning they cannot establish causation. The unit-level analysis used cross-sectional data, and the pediatric surveillance study was descriptive and prospective. While the consistency of findings across studies strengthens the case for a causal relationship, confounding remains a possibility.

Variable definitions of understaffing. Studies use different definitions of understaffing, from staffing below 80% of the unit median to specific patient-nurse ratios. The unit-level analysis used the 80% threshold, while the pediatric study used a ratio greater than 2:1. These different definitions make it difficult to compare findings across studies.

Limited generalizability. Studies conducted in specific settings, such as pediatric hospitals or ICUs, may not generalize to other settings. The pediatric surveillance study was conducted in a tertiary-level pediatric hospital in Mexico, and the findings may not transfer directly to adult settings or to other countries with different staffing norms.

Inconsistent findings on burnout. The critical interpretive synthesis on burnout and patient safety found that only a minority of primary studies examined objective safety outcomes and that findings were inconsistent. The authors argued that burnout cannot currently be justified as a direct predictor of patient safety outcomes. This limitation should be considered when interpreting studies that link burnout to HAIs.

Publication bias. Studies that find a significant association between staffing and HAIs may be more likely to be published than studies that find no association. This potential publication bias could inflate the apparent strength of the staffing-HAI relationship.

Safety and Regulatory Context

The relationship between nurse staffing and patient outcomes has attracted regulatory attention in several jurisdictions. The study of a national healthcare system that implemented a nurse staffing directive, published in the International Journal of Nursing Studies, examined the impact of such a directive using multi-level interrupted time series analyses. While the specific findings are not summarized here, the existence of this study reflects the policy interest in mandating minimum staffing levels.

The pediatric surveillance study explicitly recommended that patient-nurse ratio be established in HAI guidelines and policies, arguing that regulating the number of patients per nurse can prevent HAIs and their complications. This recommendation reflects a growing view that staffing should be considered an infection prevention intervention, beyond an administrative concern.

Infection prevention teams should be aware of the regulatory context in their jurisdiction. Some jurisdictions have implemented nurse staffing directives or minimum ratio requirements, while others rely on professional standards and voluntary guidelines. The evidence reviewed here can inform advocacy for staffing policies that support infection prevention, but specific regulatory requirements vary by location.

Professional Escalation Criteria

Infection prevention teams should have clear criteria for escalating staffing concerns to hospital leadership. The following situations warrant escalation based on the evidence reviewed:

Persistent understaffing. When unit-level data shows that a unit has both day and night shifts understaffed on a recurring basis, this pattern should be escalated. The unit-level analysis found that this pattern was significantly associated with increased HAI risk.

Staffing below evidence-based thresholds. When patient-nurse ratios exceed thresholds associated with increased infection risk, such as the ratio greater than 2:1 identified in the pediatric study, this should be escalated. While this specific threshold comes from a pediatric setting, it provides a reference point for discussion.

Infections clustering after understaffed periods. When HAI surveillance data shows a pattern of infections occurring 2 to 10 days after understaffed shifts, this pattern should be escalated. The unit-level analysis found a two-day lag between understaffing and infection onset, and the ICU study used a risk window of days 1 to 10 preinfection.

Staffing changes without infection prevention input. When the facility plans to change staffing models, skill mix, or scheduling practices without consulting infection prevention, this should be escalated. The ICU study of nursing team composition changes provides a model for evaluating such changes, and infection prevention input should be part of the planning process.

High rates of missed infection prevention tasks. When data on missed nursing care shows that infection prevention tasks are being consistently omitted, this should be escalated. The missed care review found associations between missed care and urinary tract infections and other outcomes.

Frequently Asked Questions

What is the role of a nurse in the prevention of cross infection?

Nurses perform the majority of infection prevention tasks in hospitals, including hand hygiene, aseptic technique for device insertion and care, daily assessment of device necessity, and patient and family education. The evidence reviewed here shows that when nurse staffing is inadequate, these tasks are more likely to be missed or delayed, which increases infection risk. The missed care review found associations between missed care and urinary tract infections, and the unit-level analysis found that understaffing was associated with increased HAI risk two days later.

How does nurse staffing affect healthcare-associated infection rates?

Multiple studies have found that lower nurse staffing levels are associated with higher HAI rates. The systematic review of 54 studies found that 74.1% of studies examining nurse staffing found staffing variables associated with increased HAI rates. The ICU systematic review found that safe staffing levels were associated with a 20% improvement in infection prevention. The mechanism is likely through missed nursing care, as understaffed nurses must prioritize tasks and may omit infection prevention activities.

What is the evidence for specific nurse-to-patient ratios and infection risk?

The pediatric surveillance study found that a patient-nurse ratio greater than 2:1 was associated with a 54% increased risk for HAIs. The modeling study of MRSA in ICUs found that moving from a 1:3 to a 1:2.5 nurse-patient ratio reduced MRSA acquisitions with a relative risk of 0.77, while a 1:7.5 ratio had a relative risk of 4.66 compared to baseline. However, the evidence does not support a single universal ratio that applies to all settings, and the systematic review noted methodological heterogeneity across studies.

How does nurse burnout relate to infection risk?

The relationship between burnout and infection risk is complex. A critical interpretive synthesis found that burnout cannot currently be justified as a direct predictor of patient safety outcomes, and that only a minority of primary studies examined objective safety outcomes. Burnout is better understood as a system-level condition that shapes care processes and reporting practices. Infection prevention programs should address burnout as a system-level condition instead of blaming individual nurses.

What is missed nursing care and how does it relate to infections?

Missed nursing care refers to aspects of care that are delayed or left undone when workload exceeds capacity. The systematic review of missed care found associations between missed care and urinary tract infections, medication errors, patient falls, and pressure ulcers. When nurses are understaffed, infection prevention tasks such as hand hygiene and device care are among the tasks most likely to be missed.

How should infection prevention programs assess staffing adequacy?

Infection prevention programs should collect unit-level staffing data for each shift, including patient-nurse ratios and the number of shifts below a defined staffing threshold. This data should be linked to HAI surveillance data with attention to the timing of understaffing relative to infection onset. The unit-level analysis found that patients on units with both shifts understaffed were more likely to develop HAIs two days later, suggesting a relatively short risk window.

What is the financial case for adequate nurse staffing in infection control?

A study of 2,059 hospitals found nearly identical negative direct associations between infections and operating margins and total margins at negative 0.07%, with a 10% higher infection rate predicted to be associated with only a 0.2% lower profit margin. The associations between HAIs, nurse staffing, and days cash on hand were insignificantly different from zero. The financial case for staffing may rest more on avoided costs of care, reputation, and regulatory consequences than on direct margins.

How can nurse scheduling be optimized to reduce infection risk?

A modeling study using contact data from wearable sensors at a short-stay geriatric ward found that reorganizing nurse schedules reduced HAI risk by 27% while preserving timeliness, number, and duration of contacts. The strategy switched and reassigned nurses' tasks through the optimization of shift timelines. The study noted that more than 30% of nurse-nurse contacts should be avoided to achieve an equivalent reduction through simple contact removal, suggesting that scheduling optimization is a more efficient approach than contact reduction alone.

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References and Further Reading

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• Healthcare Occupations. U.S. Bureau of Labor Statistics.
• O*NET OnLine. U.S. Department of Labor.
• Office of Intramural Training and Education. National Institutes of Health.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Healthcare Associated Infections, Nurse Staffing, and Financial Performance.. Inquiry : a journal of medical care organization, provision and financing, 2023.
• The Impact of Nurse-Patient Ratios on Patient Outcomes in Intensive Care Units.. Nursing in critical care, 2025.
• Nurse Staffing and Healthcare-Associated Infection, Unit-Level Analysis.. The Journal of nursing administration, 2019.
• Telenursing: A Concept Analysis.. Computers, informatics, nursing : CIN, 2023.
• Patient-nurse ratio as an index related to healthcare-associated infections: a surveillance study.. Boletin medico del Hospital Infantil de Mexico, 2023.
• What impact does nursing care left undone have on patient outcomes? Review of the literature.. Journal of clinical nursing, 2018.
• Hospital Staffing and Health Care-Associated Infections: A Systematic Review of the Literature.. Joint Commission journal on quality and patient safety, 2018.
• Reorganization of nurse scheduling reduces the risk of healthcare associated infections.. Scientific reports, 2021.
• Management strategies to mitigate burnout and turnover intention while enhancing patient safety in neonatal intensive care: an integrative review.. 2026.
• Prevalence of operating room occupational health cluster: a systematic review and meta-analysis.. 2026.
• Burnout and patient safety: navigating between exaggerated evidence and warranted assertability.. 2026.
• Predictors of work engagement and burnout among rural nurses in Japan during COVID-19: a cross-sectional study.. 2026.
• Perception of the patient safety culture among healthcare workers.. 2026.
• How Common Are Indwelling Devices in Hospitalized Adults? A Contemporary Point Prevalence Study in A Tertiary Care Hospital.. American Journal of Infection Control, 2020.
• Modeling methicillin-resistant Staphylococcus aureus (MRSA) acquisitions in the intensive care unit with different staffing levels and finite direct-care tasks. Antimicrobial Stewardship and Healthcare Epidemiology, 2023.
• Study on the nurse staffing in the public ward of general hospital in Guangzhou. 2010.
• [Nursing sensitive outcomes and staffing: a review on healthcare associated infection].. Assistenza infermieristica e ricerca : AIR, 2017.
• Changes in Nursing Team Composition and Risk of Device Associated Infection in Intensive Care Units. Infection control and hospital epidemiology, 2020.
• Impact of COVID-19 on nurse staffing levels and healthcare-associated infections in medical institutions: a retrospective cohort study. Scientific Reports, 2025.
• Nurse staffing and healthcare-associated infections in a national healthcare system that implemented a nurse staffing directive: Multi-level interrupted time series analyses. International Journal of Nursing Studies, 2020.
• Healthcare-associated infections in adult intensive care units: A multisource study examining nurses’ safety attitudes, quality of care, missed care, and nurse staffing. Intensive and Critical Care Nursing, 2023.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.