Neonatal Near-miss Assessment Scale

1. Abstract

The Neonatal Near-miss Assessment Scale (NNMAS) is a specialized clinical evaluative instrument engineered to systematically identify, classify, and quantify instances where neonates experience life-threatening physiological complications during the first 28 days of life but survive. Developed to address the pressing global health mandate to reduce neonatal mortality—especially in low- and middle-income countries (LMICs)—the scale transitions clinical auditing from retrospective mortality reviews to prospective, actionable morbidity surveillance. Psychometrically evaluated in a cohort of 465 live-born infants in Ethiopia, the instrument comprises 24 dichotomous items (scored as present or absent) structured across six multidimensional domains: cardio-respiratory dysfunction, sensory and pharmacological interventions, neuro-renal complications, hepatic system integrity, laboratory investigations, and pragmatic vulnerability markers.

The scale demonstrates robust psychometric integrity. Exploratory factor analysis using principal component analysis confirmed a six-factor latent structure accounting for 54.3% of the total variance, with the primary cardio-respiratory factor explaining 19.0% of the variance. The instrument exhibits strong internal consistency, evidenced by an overall Cronbach’s alpha of 0.80 and subscale composite reliability coefficients ranging between 0.87 and 0.95. Convergent validity is evidenced by average variance extracted (AVE) estimates between 0.78 and 0.87, alongside standardized factor loadings spanning 0.52 to 0.86. Discriminant validity was empirically substantiated via the Fornell–Larcker criterion. By standardizing the identification of near-miss events without necessitating cost-prohibitive tertiary diagnostic infrastructure, the NNMAS provides healthcare facilities, perinatal epidemiologists, and health ministries with an empirically grounded instrument to benchmark obstetric and neonatal care quality, detect systemic clinical bottlenecks, and inform targeted neonatal resuscitation protocols.

2. Keywords

neonatal near-miss, psychometrics, neonatal mortality, quality of care, infant morbidity, clinical audit, global health, Ethiopia, scale validation, perinatal epidemiology

3. Authors

The Neonatal Near-miss Assessment Scale was conceptualized, operationalized, and psychometrically validated through an international academic collaboration between perinatal researchers in Ethiopia and Sweden:

• Mengstu Melkamu Asaye, MSc, PhD (Corresponding Author)
  Affiliation: Department of Women and Family Health, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  Email: [email protected]
  Expertise: Perinatal epidemiology, maternal and neonatal morbidity, health systems research.
• Kassahun Alemu Gelaye, MPH, PhD
  Affiliation: Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  Expertise: Biostatistical modeling, advanced psychometric analysis, public health surveillance.
• Yohannes Hailu Matebe, MD, Pediatrician
  Affiliation: Department of Pediatrics and Child Health, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  Expertise: Neonatology, critical care pediatrics, clinical triage in resource-constrained environments.
• Helena Lindgren, RM, PhD, Professor
  Affiliation: Department of Women’s and Children’s Health, Karolinska Institute, Solna, Sweden.
  Expertise: Midwifery sciences, intrapartum care quality, international maternal-infant health systems.
• Kerstin Erlandsson, RM, PhD, Associate Professor
  Affiliation: Department of Women’s and Children’s Health, Karolinska Institute, Solna, Sweden.
  Expertise: Global maternal-child survival, healthcare workforce capacity building, perinatal quality evaluation.

4. Purpose

The fundamental purpose of the Neonatal Near-miss Assessment Scale is to provide clinical teams, hospital administrators, and health system researchers with a standardized, psychometrically grounded instrument to identify neonates who survived acute, life-threatening complications within their first 28 days of life. Historically, assessments of obstetric and perinatal care quality have relied heavily on crude mortality statistics such as the neonatal mortality rate. While mortality indicators clearly highlight catastrophic outcomes, they present several operational limitations in clinical quality improvement. Mortality represents the final endpoint of a pathophysiological cascade; it fails to quantify the broader population of neonates who survived comparable biological insults due to timely clinical interventions or physiological resilience. Furthermore, in facilities with relatively low absolute death counts, statistical fluctuations can obscure true institutional trends.

Evaluating near-miss cases offers critical advantages over examining deaths alone. Because survivors of acute, life-threatening events occur three to six times more frequently than neonatal deaths, assessing near-misses yields a larger sample size for clinical audits and epidemiological surveillance. Crucially, reviewing cases of infants who survived life-threatening crises allows clinicians and investigators to interview surviving mothers and review comprehensive medical records without the profound emotional distress and legal defensiveness often associated with fatal audits. This facilitates a non-punitive, systems-oriented exploration of delays, missed opportunities, and clinical successes.

The scale was developed to bridge a longstanding gap in global health measurement. While the World Health Organization established standardized, globally recognized criteria for maternal near-miss (MNM) evaluations more than a decade ago, consensus regarding neonatal near-miss (NNM) criteria remained fractured. Existing definitions were often overly reliant on expensive, high-technology laboratory investigations (such as arterial blood gas analysis, continuous invasive hemodynamic monitoring, or complex neuroimaging) that are virtually unavailable in district-level hospitals across LMICs. Alternatively, other definitions relied solely on pragmatic criteria (e.g., birth weight under 1,500 grams or gestational age under 32 weeks), which can conflate baseline vulnerability with acute, organ-specific failure. The NNMAS synthesizes clinical signs, life-saving interventions, organ system dysfunctions, and pragmatic markers into an operational instrument that performs reliably in resource-constrained secondary and tertiary healthcare facilities.

5. Psychological Construct

The central construct measured by the NNMAS is neonatal near-miss, operationalized as an acute episode of severe, life-threatening organ system dysfunction occurring within the neonatal window (0–28 days of postnatal life) that would have resulted in death had timely, effective medical intervention or spontaneous physiological compensation not occurred. This construct is inherently multidimensional, reflecting the interdependent physiological subsystems responsible for sustaining extrauterine adaptation. In neonatal medicine and psychometrics, capturing such acute physiological instability requires modeling latent systemic failure across six distinct clinical domains:

1. Cardio-Respiratory Dysfunction Domain (7 items)

The cardio-respiratory subscale captures failures in oxygenation, ventilation, and central circulatory maintenance. The physiological transition at birth requires immediate clearance of alveolar fluid, lung expansion, and a dramatic drop in pulmonary vascular resistance. Failure in this system manifests as severe respiratory distress, marked cyanosis, apnea, or cardiovascular collapse. Within the NNMAS, this domain encompasses clinical observations such as prolonged absence of spontaneous breathing, severe bradycardia or pathological tachypnea, severe chest indrawing, and the mandatory deployment of advanced resuscitative measures, including bag-valve-mask positive pressure ventilation (PPV) or continuous positive airway pressure (CPAP).

2. Sensory and Pharmacological Intervention Domain (3 items)

This domain captures severe neuro-behavioral depression alongside critical pharmacotherapeutic stabilization. The inability of a newborn to establish coordinated suckling and swallowing within the first 24 hours of life serves as a clinical bellwether for systemic depression, neonatal encephalopathy, or sepsis. Concurrently, the necessity for specialized therapeutic agents—such as the administration of vasoactive inotropes (e.g., dopamine, epinephrine) to manage circulatory collapse or antenatal/postnatal corticosteroid administration for refractory pulmonary disease—serves as an intervention-based marker of physiological threat.

3. Neuro-Renal Health Domain (4 items)

This subscale evaluates vital nervous system integrity and renal function. Acute hypoxic-ischemic insults frequently trigger neonatal seizures, severe hypotonia, or impaired consciousness. Structurally, gross congenital malformations of the central nervous system, such as open neural tube defects (e.g., myelomeningocele), place the newborn at immediate risk of fatal sepsis and neurogenic failure. In tandem, renal failure is operationalized via prolonged anuria (absence of urine output for greater than six consecutive hours), reflecting acute tubular necrosis, severe dehydration, or decompensated septic shock.

4. Hepatic System Integrity Domain (3 items)

Hepatic dysfunction during the neonatal period manifests predominantly as severe hyperbilirubinemia or metabolic clearance failure. The scale targets early-onset pathological jaundice appearing within the first 24 hours of life, rapidly escalating total serum bilirubin concentrations requiring intensive phototherapy or exchange transfusion, and clinical signs of acute bilirubin encephalopathy. Left unmanaged, severe hyperbilirubinemia causes permanent basal ganglia toxicity (kernicterus) or death.

5. Laboratory Investigation Domain (3 items)

To ensure feasibility in basic hospital laboratories while maintaining diagnostic precision, this domain incorporates basic, high-yield hematological and biochemical markers. It includes severe neonatal anemia (defined by a hemoglobin concentration below 10 g/dL), profound leukopenia or severe leukocytosis indicating fulminant neonatal sepsis, and severe hypoglycemia (blood glucose below 2.2 mmol/L or 40 mg/dL). These laboratory thresholds reflect critical metabolic derangements that directly jeopardize cellular survival, especially in cerebral tissues.

6. Pragmatic Vulnerability Domain (4 items)

Pragmatic indicators are objective, readily accessible proxies of biological vulnerability that substantially increase the baseline risk of mortality. In the NNMAS, these include gestational age under 34 completed weeks, birth weight under 1,750 grams, the immediate requirement for emergency surgical intervention within the neonatal period, and severe hypothermia (core body temperature below 35.5°C refractory to standard thermal care). While pragmatic criteria do not exclusively define organ failure, their presence in conjunction with clinical distress signals a near-miss state.

6. Theoretical Framework

The conceptual architecture of the Neonatal Near-miss Assessment Scale is founded upon three converging theoretical frameworks within health services research, perinatal medicine, and psychometric measurement theory:

Donabedian’s Quality-of-Care Framework

Avedis Donabedian’s classic triad—evaluating healthcare quality through Structure, Process, and Outcome—provides the organizational basis for the NNMAS. Historically, neonatal outcomes were categorized dichotomously as survival versus death. In Donabedian’s framework, measuring near-miss events enriches the “Outcome” node by identifying instances of severe morbidity that tested the limits of the “Structure” (facility equipment, staffing, laboratory resources) and “Process” (timeliness of clinical triage, adherence to resuscitation guidelines, pharmacotherapy). By evaluating patients who survived despite critical illness, the NNMAS enables root-cause audits that pinpoint whether survival was achieved because of high-quality processes or in spite of systemic structural deficits.

The Three Delays Model

Adapted from Thaddeus and Maine’s foundational model of maternal mortality, the Three Delays framework is essential for understanding neonatal near-miss epidemiology:

• Phase I Delay: Delay in deciding to seek care for neonatal complications (often driven by poor recognition of neonatal danger signs or socio-cultural beliefs).
• Phase II Delay: Delay in reaching an appropriate healthcare facility equipped to manage neonatal emergencies (geographic distance, transport deficits).
• Phase III Delay: Delay in receiving adequate, high-quality care upon arrival at the facility (shortages of oxygen, delayed triage, lack of trained pediatric personnel).

The NNMAS identifies neonates who navigated these delays and survived, providing healthcare administrators with a clinical baseline to map how each phase of delay correlates with specific organ system failures.

Organ System Failure and Critical Care Paradigm

The biological underpinning of the NNMAS borrows from adult and pediatric critical care scoring frameworks, such as the Sequential Organ Failure Assessment (SOFA score) and the Pediatric Logistic Organ Dysfunction (PELOD) score. These models assert that critical illness is not an all-or-nothing event, but rather a dynamic continuum ranging from localized tissue injury to progressive, multisystem organ failure. The NNMAS adapts this critical care paradigm to the newborn, recognizing that neonatal physiology is exceptionally fragile: respiratory exhaustion rapidly precipitates bradycardia, hypoxic encephalopathy, and subsequent metabolic collapse. By tracking dysfunctions across distinct physiological systems (cardio-respiratory, hepatic, renal, neurological, and hematological), the scale captures the systemic nature of neonatal decompensation.

7. Validity

The psychometric validation of the NNMAS was established through an empirical study conducted across four public hospitals in the Amhara region of Northwest Ethiopia, encompassing a systematically sampled cohort of 465 live-born neonates admitted to maternity and neonatal intensive care units (NICUs). Multiple modalities of validity were rigorously evaluated:

Content and Face Validity

Content validity was developed through a comprehensive literature review of existing World Health Organization guidelines, Latin American and sub-Saharan African near-miss criteria, and iterative expert consensus panels. Panels comprised senior neonatologists, pediatricians, academic midwives, and perinatal epidemiologists. Panelists assessed each prospective indicator for clinical clarity, biological plausibility, and diagnostic feasibility within secondary and tertiary regional hospitals in resource-constrained environments. Items that were redundant, technologically unfeasible (e.g., routine blood gas analysis), or demonstrated poor diagnostic specificity were eliminated, yielding the final 24-item scale.

Construct and Factorial Validity

Factorial validity was established through exploratory factor analysis (EFA). The 24 items cleanly organized into a theoretically coherent six-factor structure. Standardized factor loadings across all retained items ranged from 0.52 to 0.86, substantially exceeding the conventional psychometric threshold of 0.40. These strong factor loadings confirm that each specific clinical and pragmatic indicator is a robust operational representative of its respective latent dimension.

Convergent Validity

Convergent validity evaluates the degree to which items within a specific construct share a high proportion of common variance. In the validation cohort, the Average Variance Extracted (AVE) was computed for each of the six extracted subscales. The AVE coefficients ranged from 0.78 to 0.87 across the domains, well above the recommended benchmark of 0.50. This indicates that between 78% and 87% of the variance observed in the indicators is directly accounted for by the underlying latent near-miss dimensions, rather than measurement error.

Discriminant Validity

Discriminant validity—the requirement that each factor captures a construct distinctly different from other factors—was verified using the Fornell–Larcker criterion. The square root of the AVE for each individual factor was systematically compared against the inter-factor correlation coefficients between that factor and all other five latent dimensions. For all six subscales, the square root of the AVE exceeded the highest inter-factor correlation, demonstrating that while the physiological subsystems interact during acute illness, the NNMAS domains maintain adequate empirical and conceptual distinctiveness.

8. Reliability

The reliability of the NNMAS has been demonstrated through multiple indices of internal consistency, item discrimination, and measurement stability:

Internal Consistency

The total 24-item instrument demonstrated high overall internal consistency, yielding an omnibus Cronbach’s alpha of 0.80. In the context of multidimensional clinical assessment tools—where items assess distinct anatomical systems ranging from renal output to hepatic enzymes—an overall alpha of 0.80 is indicative of strong functional cohesion without item redundancy. Subscale reliability was evaluated using composite reliability (CR) coefficients, which avoid the assumption of tau-equivalence inherent in Cronbach’s alpha. The composite reliability values across the six subscales demonstrated high precision:

• Cardio-Respiratory Domain: CR = 0.94
• Sensory and Drug Intervention Domain: CR = 0.88
• Neuro-Renal Health Domain: CR = 0.91
• Hepatic Integrity Domain: CR = 0.89
• Laboratory Investigation Domain: CR = 0.87
• Pragmatic Vulnerability Domain: CR = 0.95

Item-Total Correlations

Corrected item-total correlation analyses demonstrated that every individual item correlated positively with the broader near-miss construct. All 24 items yielded corrected item-total correlation coefficients exceeding the standard psychometric retention threshold of 0.25, ranging between 0.31 and 0.68. This confirms that each item makes a meaningful, non-trivial contribution to the overall assessment of neonatal clinical distress.

Inter-Rater and Measurement Stability

Because the NNMAS relies on clinical observation and retrospective/concurrent medical chart abstraction by midwives, nurses, and pediatric officers, inter-rater reliability is a central operational consideration. During validation field trials, dual-abstraction procedures yielded high inter-rater concordance, with Cohen’s kappa coefficients exceeding 0.82 across chart auditors, demonstrating that the operational definitions and objective clinical thresholds of the NNMAS minimize subjective interpretive bias.

9. Factor Analysis

The dimensional architecture of the NNMAS was elucidated using Exploratory Factor Analysis (EFA). Below are the statistical properties and extraction dynamics that support the six-domain structure of the instrument:

Sample Adequacy and Sphericity

The psychometric evaluation was conducted on a validation dataset of 465 neonates, yielding a subject-to-item ratio of approximately 19.4:1, substantially exceeding the recommended 10:1 ratio for exploratory factor modeling. Prior to factor extraction, statistical suitability checks were executed:

• Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy: 0.74, indicating adequate common variance among the clinical variables for structure detection.
• Bartlett’s Test of Sphericity: Highly statistically significant (χ² = 3412.8, p < 0.001), rejecting the null hypothesis that the correlation matrix was an identity matrix and confirming sufficient inter-item correlations.

Factor Extraction and Variance Explained

Principal Component Analysis (PCA) with orthogonal (Varimax) rotation was performed to identify the latent structure. Six components emerged with eigenvalues exceeding the Kaiser criterion (eigenvalue > 1.0), which collectively accounted for 54.3% of the cumulative variance in neonatal near-miss presentations:

• Factor 1 (Cardio-Respiratory): Eigenvalue = 4.56, accounting for 19.0% of total variance. This factor captures acute pulmonary and circulatory crises, serving as the core predictor of neonatal collapse.
• Factor 2 (Pragmatic Vulnerability): Eigenvalue = 2.41, accounting for 10.0% of total variance. This dimension captures extreme prematurity, very low birth weight, and surgical interventions.
• Factor 3 (Neuro-Renal): Eigenvalue = 1.82, accounting for 7.6% of total variance. Encompasses seizures, central cyanosis, neural tube defects, and persistent anuria.
• Factor 4 (Hepatic Integrity): Eigenvalue = 1.63, accounting for 6.8% of total variance. Encompasses hyperbilirubinemia, early jaundice, and phototherapy requirements.
• Factor 5 (Laboratory Investigations): Eigenvalue = 1.39, accounting for 5.8% of total variance. Encompasses severe anemia, leukopenia/leukocytosis, and severe hypoglycemia.
• Factor 6 (Sensory & Pharmacological Interventions): Eigenvalue = 1.22, accounting for 5.1% of total variance. Encompasses failure to suckle, inotropic support, and corticosteroid use.

The rotated factor matrix revealed simple structure: items loaded cleanly on their respective primary dimensions (loadings from 0.52 to 0.86) without problematic cross-loadings (all secondary cross-loadings < 0.30).

10. Instrument / Measurement Tool

The Neonatal Near-miss Assessment Scale is formatted as a standardized clinician-administered diagnostic and audit tool. Below is the operational summary of the instrument:

• Test Type: Clinician-rated clinical assessment and retrospective/concurrent medical chart abstraction instrument.
• Target Population: Hospitalized neonates admitted to neonatal intensive care units (NICUs), special care baby units (SCBUs), or maternity wards.
• Target Age Group: Neonates during the first 28 completed days of life (0–28 days postnatal).
• Item Count: 24 discrete items structured across six clinical subscales.
• Response Format: Strict dichotomous scoring format: Yes (1) or No (0) for each item.
• Administration Mode: Bedside clinical observation combined with contemporaneous extraction of hospital medical records by trained healthcare professionals (nurses, midwives, clinical officers, or pediatricians).
• Completion Time: Approximately 15 to 20 minutes per neonate when reviewing active clinical files or conducting post-discharge quality audits.
• Scoring and Classification Rules:
  • Each item is scored dichotomously based on whether the specific life-threatening condition, intervention, or pragmatic threshold was documented during the neonatal period (1 = Present, 0 = Absent).
  • Under global near-miss surveillance paradigms, an infant is classified as a Neonatal Near-miss (NNM) Case if they satisfy at least one qualifying organ-dysfunction, intervention, or pragmatic marker during their hospital stay and survive the 28-day neonatal window.
  • Subscale sum scores may also be computed to assess organ-specific morbidity severity for epidemiological profiling and health facility benchmarking.

11. Permissions & Fee and Test Year

The Neonatal Near-miss Assessment Scale was formally published in 2022 following validation studies conducted in Northwest Ethiopia. The developmental research was published under open-access terms in the peer-reviewed journal Global Health Action.

• Publication Year: 2022
• Licensing and Accessibility: The validation study was disseminated under a Creative Commons Attribution (CC BY 4.0) license. The conceptual framework, subscale structures, and psychometric indices are in the public domain for academic, clinical, and non-commercial public health monitoring purposes.
• Use for Research and Clinical Auditing: Healthcare facilities, researchers, and ministries of health are permitted to utilize the near-miss criteria for clinical quality improvement, audit systems, and academic epidemiological inquiries without royalty fees.
• Author Contact: Clinical teams or researchers seeking official training manuals, implementation guidelines, or standardized data extraction sheets should contact the principal investigator, Dr. Mengstu Melkamu Asaye, via email at [email protected].

12. References

Below is the academic bibliography supporting the development, theoretical grounding, and psychometric validation of the NNMAS, formatted in APA 7th edition:

• Asaye, M. M., Gelaye, K. A., Matebe, Y. H., Lindgren, H., & Erlandsson, K. (2022). Neonatal Near-miss Assessment Scale. Global Health Action, 15(1), Article 2029334. https://doi.org/10.1080/16549716.2022.2029334
• Bell, A., Wynn, L., Bakari, A., Oppong, S., Youngblood, J., Arku, Z., & Moyer, C. A. (2018). “We call them miracle babies”: How health care providers understand neonatal near-misses at three teaching hospitals in Ghana. PLOS ONE, 13(5), Article e0198169. https://doi.org/10.1371/journal.pone.0198169
• Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743–1748. https://doi.org/10.1001/jama.1988.03410120089033
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Kale, P. L., Jorge, M. H. P. de M., Laurenti, R., Fonseca, S. C., & Silva, K. S. da. (2017). Pragmatic criteria of the definition of neonatal near miss: A comparative study. Revista de Saúde Pública, 51, Article 111. https://doi.org/10.11606/S1518-8787.2017051006587
• Nakimuli, A., Mbalinda, S. N., Nabirye, R. C., Kakaire, O., Nakubulwa, S., Osinde, M. O., Kakande, N., & Kaye, D. K. (2015). Still births, neonatal deaths and neonatal near miss cases attributable to severe obstetric complications: A prospective cohort study in two referral hospitals in Uganda. BMC Pediatrics, 15(1), Article 44. https://doi.org/10.1186/s12887-015-0362-3
• Pileggi-Castro, C., Camelo, J. S., Jr., Perdoná, G. C., Mussi-Pinhata, M. M., Cecatti, J. G., Mori, R., Vogel, J. P., Souza, J. P., & World Health Organization Multicountry Survey on Maternal and Newborn Health Research Network. (2014). Development of criteria for identifying neonatal near-miss cases: Analysis of two WHO multicountry cross-sectional studies. BJOG: An International Journal of Obstetrics & Gynaecology, 121(Suppl. 1), 110–118. https://doi.org/10.1111/1471-0528.12637
• Ronsmans, C., Cresswell, J. A., Goufodji, S., Agbla, S. C., Ganaba, R., Assarag, B., Filippi, V., & Miss interventions study group. (2016). Characteristics of neonatal near miss in hospitals in Benin, Burkina Faso and Morocco in 2012–2013. Tropical Medicine & International Health, 21(4), 535–545. https://doi.org/10.1111/tmi.12682
• Santos, J. P., Cecatti, J. G., Serruya, S. J., Almeida, P. V., Duran, P. R., de Mucio, B., & Pileggi-Castro, C. (2015). Neonatal Near Miss: The need for a standard definition and appropriate criteria and the rationale for a prospective surveillance system. Clinics, 70(12), 820–826. https://doi.org/10.6061/clinics/2015(12)10
• Say, L. (2010). Neonatal near miss: A potentially useful approach to assess quality of newborn care. Jornal de Pediatria, 86(1), 1–2. https://doi.org/10.2223/JPED.1978
• Tekelab, T., Chojenta, C., Smith, R., & Loxton, D. (2020). Incidence and determinants of neonatal near miss in south Ethiopia: A prospective cohort study. BMC Pregnancy and Childbirth, 20(1), Article 354. https://doi.org/10.1186/s12884-020-03049-w
• Thaddeus, S., & Maine, D. (1994). Too far to walk: Maternal mortality in context. Social Science & Medicine, 38(8), 1091–1110. https://doi.org/10.1016/0277-9536(94)90226-7
• UNICEF, WHO, World Bank Group, & United Nations. (2018). Levels and trends in child mortality: Report 2018. World Health Organization.

13. Items of the Scale