You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background
==========

Nosocomial infection (NI) is a significant cause of morbidity and mortality in neonatal Intensive Care Units (NICU) globally \[[@b1-medscimonit-25-8213]\]. It is defined as an infection acquired during the period of hospitalization that was absent before or at the time of admission and that must occur up to 48 hours after hospital admission. NI has strong influence on many aspects of the healthcare system as it increases the duration of hospitalization and use of medical resources, in addition to an increased cost of care, particularly in developing countries where this is a major public health issue. The incidence of NI in NICU is approximately 30% and is responsible for up to 40% of reported neonatal deaths in developing countries \[[@b2-medscimonit-25-8213],[@b3-medscimonit-25-8213]\].

Newborns, particularly those hospitalized in an NICU, are one of the most vulnerable groups among different age groups to acquire NI. Neonates in the NICU face significant exposure to medical devices, antimicrobial drugs, and various other causative organisms. A slower maturation of the infant's immune system increases the likelihood of NI incidence. NI is a blanket term for a range of infections including pneumonia, urinary tract infections, bloodstream infections, meningitis, secondary skin infections, and infections of the throat, eye, ear, or nose. These may be caused by bacteria, viruses, fungi, or several of these combined \[[@b4-medscimonit-25-8213],[@b5-medscimonit-25-8213]\].

NI continue to be a substantial source of avoidable morbidity and mortality in developing countries where rates of infection are excessive because of inadequate infection control practices, overcrowding in hospitals, inappropriate use of limited resources, and lack of supervision \[[@b6-medscimonit-25-8213],[@b7-medscimonit-25-8213]\]. The major pathogens causing infections in neonates are different not only from country to country and in different units, but also change over time within the same unit \[[@b8-medscimonit-25-8213]\].

Antibiotic resistance is a significant risk factor for adverse outcomes in infants with NI \[[@b9-medscimonit-25-8213]\], which can increase mortality risk by 27% for every additional resistance to an antibiotic class \[[@b10-medscimonit-25-8213]\]. Drug-resistant infections such as *Serratia marcescens* infect blood, urinary tract, and surgical wounds and may cause keratitis, conjunctivitis, pneumonia, sepsis, and meningitis \[[@b11-medscimonit-25-8213]\]. Gram-negative infections are more resistant than gram-positive organisms \[[@b12-medscimonit-25-8213]\].

The present investigation aimed at identifying the risk factors of NI in NICUs by using the incidence rates of NI reported by the relevant studies to perform a meta-analysis of relative risks of important risk factors.

Material and Methods
====================

This study was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol.

Search strategy
---------------

The electronic databases PubMed, Cochrane, Embase, VANFUN, CNKI, and VTTMS were searched for studies reporting the incidence of NI in the NICU published before November 2018. Search terms used were newborn, neonatal, infant, NICU, Neonatal Intensive Care Unit, nosocomial infection, hospital infection, hospital-acquired infection, pathogenic bacteria, and pathogen. These terms were used with "AND" or "OR" in logical combinations. References lists of related studies were also screened. Two reviewers performed literatures search independently and while unifying the results, the help of a third reviewer was sought if any disagreement arose.

Inclusion and exclusion criteria
--------------------------------

Studies were included if they reported the incidence of NI in infant cohorts studied at an NICU and published in English or Chinese language. Studies were excluded if they lacked a control group, or if they were case reports, theoretical research, systematic reviews and/or meta-analysis, conference reports, expert comments, or economic analysis.

Data extraction and quality assessment
--------------------------------------

Data were extracted from eligible studies and organized into 2 categories: (I) basic information and (II) key outcomes. Basic information included author name, sample size, publication year, and identified risk factors. Key outcomes included clinical outcome data pertaining to infant body weight, gestational age, mechanical ventilation use, venipuncture, incidence of asphyxia, and feeding intolerance. Data were extracted by 2 independent reviewers and the help of a third author was sought when any disagreement arose while unifying the results.

Statistical analysis
--------------------

Categorical data reported by the individual studies were used to calculate relative risk (RR). For the identification of risk factors, meta-analyses of RR in the incidence of NI were performed between: (i) under *vs.* over 2500 g body weight, (ii) less *vs.* more than 37 weeks gestation, (iii) use *vs.* no use of mechanical ventilation, (iv) venipuncture *vs.* no venipuncture, (v) incidence of asphyxia *vs.* no incidence), and (vi) prevalence of feeding intolerance *vs.* no prevalence.

Statistical indices, I^2^, and chi-squared (χ^2^) test were used to evaluate data heterogeneity and to select the meta-analysis model. When results had a χ^2^ P≤0.05 and/or I^2^\>50%, data were considered heterogeneous and the random-effects model was used; otherwise, the fixed-effects model was used. Publication bias assessment was performed with Egger's test or Begg's test after visualizing the funnel plot of an outcome measure. Stata software (version 10; Stata Corp., TX, USA) was used for meta-analyses.

Results
=======

Characteristics of included studies
-----------------------------------

The literature search identified 982 abstracts, of which 923 were excluded after inspection of the title and/or abstract, which left a total of 59 articles for further consideration, which were then downloaded from databases. A review of these research articles led to the exclusion of 37 articles for failing to meet the inclusion criteria; among these, 9 were descriptive studies, 10 had no clinically relevant outcomes, and 4 were duplicate studies. In total, the meta-analysis included 22 cohort studies with an overall population of 2270 infants with and 21 605 infants without NI \[[@b13-medscimonit-25-8213]--[@b34-medscimonit-25-8213]\]. [Figure 1](#f1-medscimonit-25-8213){ref-type="fig"} presents the study selection method, and key characteristics of the studies are displayed in [Table 1](#t1-medscimonit-25-8213){ref-type="table"}. The funnel plot of log RR of infant body weight showed the absence of significant publication bias, which was confirmed by the outcomes of Begg's test (Z=1.49, p=0.137) and Egger's test (p=0.104).

Infant body weight as a risk factor
-----------------------------------

Sixteen studies reported body weight of 1666 infants with and 18 532 infants without NI. Random-effects meta-analysis showed that the incidence of NI was significantly higher in infants with a body weight of \<2500 grams (g) in comparison with those with ≥2500 g weight (RR: 3.44, 95% CI: 2.31--5.11; [Figure 2](#f2-medscimonit-25-8213){ref-type="fig"}).

Gestational age as a risk factor
--------------------------------

Ten studies reported the gestational age of 791 infants with and 9774 infants without NI. Random-effects meta-analysis showed that a gestational age of \<37 weeks posed a significantly higher NI risk (RR: 3.85, 95%CI: 1.87--7.92; [Figure 3](#f3-medscimonit-25-8213){ref-type="fig"}).

Mechanical ventilation as a risk factor
---------------------------------------

Ten studies (981 infants with and 5953 infants without NI) reported the use of mechanical ventilation for infants. Random-effects meta-analysis showed that infants who underwent mechanical ventilation had higher NI incidence compared with the infants who did not undergo mechanical ventilation (RR: 3.16, 95% CI: 2.21--4.50; [Figure 4](#f4-medscimonit-25-8213){ref-type="fig"}).

Venipuncture as a risk factor
-----------------------------

Nine studies (724 infants with and 4890 infants without NI) reported that infants underwent venipuncture. Random-effects meta-analysis showed that infants who underwent venipuncture had a higher incidence of NI than infants who did not undergo venipuncture (RR: 3.01, 95% CI: 1.20--7.57).

Asphyxia incidence as a risk factor
-----------------------------------

Six studies (654 infants with and 3913 infants without NI) reported the incidence of asphyxia. Random-effects meta-analysis indicated that the incidence of NI was greater in infants with asphyxia than normal infants (RR: 1.68, 95% CI: 1.04--2.71).

Feeding Intolerance as a risk factor
------------------------------------

Five studies (506 infants with and 2654 infants without NI) reported the prevalence of feeding intolerance. Random-effects meta-analysis showed that the incidence of NI was greater in infants with feeding intolerance in comparison with those without feeding intolerance (RR: 2.12, 95% CI: 1.60--2.81).

Discussion
==========

In this meta-analysis of 22 studies that reported outcomes of 2270 infants with and 21605 infants without NI, we found that infant body weight of under 2500 grams, a gestational age of less than 37 weeks, use of mechanical ventilation, venipuncture, incidence of asphyxia, and feeding intolerance during infancy are the risk factors for NI.

A similar meta-analysis by Zhang et al. \[[@b35-medscimonit-25-8213]\] that included 8 studies found body mass of ≤2500 g (OR: 2.71, 95% CI: 1.59--4.59), mechanical ventilation (OR: 2.66, 95% CI: 1.39--5.11), use of central venous indwelling (OR: 6.91, 95% CI: 3.87--12.35), total parenteral nutrition (OR: 2.45, 95% CI: 1.06--5.68), nasogastric tube indwelling (OR: 3.22, 95% CI: 1.08--9.61), and the use of prophylactic antibacterial drugs (OR: 2.57, 95% CI: 0.84--7.80) are significant risk factors for the incidence of NI.

Li et al. \[[@b36-medscimonit-25-8213]\] identified 13 risk factors related to colonization or infection with extended-spectrum β-lactamase generating bacteria in the NICU. These included parenteral nutrition (OR: 7.51), mechanical ventilation (OR: 4.8), birthweight (standardized mean difference; SMD: 1.17), caesarean delivery (OR: 1.76), gestational age (SMD: 1.36), length of NICU stay (SMD: 0.72), endotracheal intubation (OR: 2.82), continuous positive airway pressure (OR: 5.0), central venous catheter use (OR: 2.85), malformations (OR: 2.89), previous antibiotic use (OR: 6.72); cephalosporin use (OR: 6.0), and ampicillin/gentamicin use (OR: 2.31).

In our study, the results showed weight \<2500 g, gestational age of \<37 weeks, mechanical ventilation, venipuncture, asphyxia, and feeding intolerance were the risk factors for NI in infants who were kept in the NICU. This suggests that the NICU environment during early life may be of significance in NI incidence. As compared to vaginal delivery, neonates delivered by caesarean section have a higher prevalence of abnormal respiratory function and have weaker immune function.

Body mass is the most important independent risk factor for critically ill premature infants. Newborns with low weight have poor physical development, underdeveloped body organ function, and poor immune system function, and therefore have weaker ability to remove invading bacteria \[[@b37-medscimonit-25-8213]\]. Mechanical ventilation is also a key independent risk factor for infant NI. Preventing the occurrence of respiratory distress syndrome, routine use of non-invasive ventilators as much as possible, evaluating the need for mechanical ventilation, and reducing the mechanical ventilation time wherever possible are key recommendations to reduce the incidence of NI \[[@b38-medscimonit-25-8213]\]. Overall, these outcomes suggest that invasive procedures should be avoided, and alternative approaches should be taken to reduce the incidence of infection in NICUs.

The prevalence of NI is high. Sohn et al. (2001) reported an 11.4% prevalence of NI after surveying 827 patients in 29 Pediatric Prevention Network NICUs in the USA \[[@b39-medscimonit-25-8213]\]. NI not only poses risk of morbidity and mortality, but may also alter neurobehavioral development. Early development has long-lasting consequences in later life, which may be affected by NI-altered physiology and increased NICU stay, during which neonates may be exposed to invasive procedures and antibiotics \[[@b40-medscimonit-25-8213]\]. Nurses can play a vital role in reducing the incidence of NI by implementing a systematic protocol for improvement. In one example, a nurse-driven quality improvement program at an NICU for the reduction of central line-associated bloodstream infection incidence has successfully led to reduced NICU stay and healthcare costs \[[@b41-medscimonit-25-8213]\].

NI rates are found to be well-managed with good teamwork. A survey found that the odds of an infant having NI decreased by 18% with each 10% increase in NICU staff responding that there was good teamwork in the NICU \[[@b42-medscimonit-25-8213]\]. Overall burnout prevalence in NICU staff has not been found to be associated with the prevalence of NI, but perceptions of hardships in work were reported to be associated with increased rates of NI in very low birthweight infants \[[@b43-medscimonit-25-8213]\].

There are several strengths of this meta-analysis. Included cohort studies offered several advantages including: (i) NI risk factors could be quantitatively estimated using large pooled sample sizes; (ii) stringent inclusion and exclusion criteria were used for the selection of infants; (iii) to ensure accuracy, standard statistical methods were used for data analysis, and (iv) the number of included studies was sufficient for meta-analytical power. Many of these studies had long follow-up times, making the conclusions clinically relevant.

There are, however, some limitations of this study which should also be noted: (I) only cohort studies were included, (II) only English and Chinese articles were included, (III) the infants' conditions were not consistent across the included studies, (IV) numerous treatment regimens were used during NICU stay, (V) the severity of risk factors was variable, and (VI) pooled data were used for the meta-analysis, as individual patient data were unavailable.

Conclusions
===========

Based on the evidence obtained in this meta-analysis, infant body weight of \<2500 g, gestational age of \<37 weeks, use of mechanical ventilation, venipuncture, incidence of asphyxia, and feeding intolerance during infancy are the significant risk factors for the incidence of NI in infants placed in the NICU. Considering the epidemiological nature of this data, it is recommended that NI risk assessment should be performed in even larger and wider datasets to refine the evidence achieved herein.

**Conflict of interest**

None.

**Source of support:** Departmental sources

![Flow diagram of the literature search and selection process.](medscimonit-25-8213-g001){#f1-medscimonit-25-8213}

![Forest plot showing the relationship between weight and the incidence of NI (RR between infants with under and over 2500 grams body weight in the incidence of NI).](medscimonit-25-8213-g002){#f2-medscimonit-25-8213}

![Forest plot showing the relationship between gestational age and the incidence of NI (RR between infants with under and over 37 weeks of gestation in the incidence of NI).](medscimonit-25-8213-g003){#f3-medscimonit-25-8213}

![Forest plot showing the relationship between mechanical ventilation and the incidence of NI (RR between infants with and without mechanical ventilation use in the incidence of NI).](medscimonit-25-8213-g004){#f4-medscimonit-25-8213}

###### 

Description of included studies and their identified risk factors.

  Study (ref. no.)                               n      Identified risk factors   
  ---------------------------------------------- ------ ------------------------- ------------------------------------------------------------------------------------
  Kawagoe 2001 \[[@b13-medscimonit-25-8213]\]    1544   213                       Weight
  Xu Yan 2007 \[[@b14-medscimonit-25-8213]\]     1026   266                       Weight
  You 2009 \[[@b15-medscimonit-25-8213]\]        618    72                        Weight, gestational age, asphyxia
  Ren X 2010 \[[@b16-medscimonit-25-8213]\]      3075   51                        Weight, gestational age
  Ren J 2011 \[[@b17-medscimonit-25-8213]\]      218    21                        Weight, gestational age, mechanical ventilation, venipuncture, asphyxia
  Zhang S 2011 \[[@b18-medscimonit-25-8213]\]    660    197                       Mechanical ventilation, asphyxia, feeding intolerance
  Chen K 2013 \[[@b19-medscimonit-25-8213]\]     240    20                        Mechanical ventilation, venipuncture
  Chen L 2013 \[[@b20-medscimonit-25-8213]\]     699    198                       Weight
  Zhang X 2013 \[[@b21-medscimonit-25-8213]\]    903    110                       Mechanical ventilation, venipuncture
  Zhang XL 2013 \[[@b22-medscimonit-25-8213]\]   4811   101                       Weight
  Chi 2014 \[[@b23-medscimonit-25-8213]\]        658    30                        Venipuncture, asphyxia, feeding intolerance
  Doaa 2014 \[[@b24-medscimonit-25-8213]\]       418    161                       Weight, gestational age, mechanical ventilation, venipuncture
  Luo 2014 \[[@b25-medscimonit-25-8213]\]        213    37                        Weight
  Behnaz 2015 \[[@b26-medscimonit-25-8213]\]     1000   57                        Weight, gestational age, mechanical ventilation, venipuncture
  Chu 2015 \[[@b27-medscimonit-25-8213]\]        1340   60                        Weight
  Li J 2015 \[[@b28-medscimonit-25-8213]\]       1440   122                       Weight, gestational age
  Luo 2016 \[[@b29-medscimonit-25-8213]\]        644    32                        Weight, gestational age, mechanical ventilation, venipuncture, feeding intolerance
  Yu 2017 \[[@b30-medscimonit-25-8213]\]         760    198                       Mechanical ventilation, venipuncture, asphyxia, feeding intolerance
  Wang Y 2107 \[[@b31-medscimonit-25-8213]\]     726    44                        Weight, gestational age
  Zhao 2017 \[[@b32-medscimonit-25-8213]\]       438    49                        Mechanical ventilation, feeding intolerance
  Peng 2018 \[[@b33-medscimonit-25-8213]\]       773    95                        Weight, gestational age, venipuncture
  Wang H 2018 \[[@b34-medscimonit-25-8213]\]     1653   136                       Weight, gestational age, mechanical ventilation, asphyxia

[^1]: Study Design

[^2]: Data Collection

[^3]: Statistical Analysis

[^4]: Data Interpretation

[^5]: Manuscript Preparation

[^6]: Literature Search

[^7]: Funds Collection

[^8]: Li Wang and Ke-Ning Du contributed equally to this work