Document Type : Original Research


1 Social Determinants of Health Research Center, Semnan University of Medical Sciences, Semnan, Iran

2 Department of Health Information Technology, Sorkheh School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran

3 Department of Pediatric, Semnan University of Medical Sciences, Semnan, Iran

4 Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran



Background: Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies.
Objective: This study aimed to probe the studies, including premature infants’ registries, adapt the needed minimum data set, and provide an offered framework for premature infants’ registries.
Material and Methods: For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed.
Results: Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure. 
Conclusion: To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.


Shahrbanoo Pahlevanynejad (Google Scholar)



Low birth weight is considered one of the most important indicators of community health and the main cause of infant mortality [ 1 ]. About 120 million babies are born worldwide each year that about 25 million of whom are underweight at birth (the proportion is about 50% in some parts of Asia) [ 2 ]. Approximately, 32 million low birth weight infants are born in low-income countries, of whom two-thirds belong to Asia [ 3 , 4 ]; the World Health Organization (WHO) has also presented a 10% prevalence of low birth weight in Iran [ 5 ]. Due to the importance of the issue, November 17 is named World Prematurity Day to raise awareness in the world [ 6 - 8 ].

Premature birth is caused by several factors, such as premature rupture of the amniotic sac or membrane, maternal infection during pregnancy, and special economic and social conditions, which is the second underlying cause of death in children under 5 years (44%) [ 8 ].

In addition to various illnesses and diseases, premature birth leads to the hospitalization of newborns in intensive care units. According to Kramer’s studies, 43 potential factors were considered for the birth of premature babies, changing based on the social, economic, and cultural situations in any region [ 9 , 10 ]. The determination of the factors, the disease, and treatment methods can reduce the morbidity and mortality rate [ 11 ].

Information technology is perceived as an important potential panacea for healthcare organizations and institutes to manage and improve healthcare services in the face of increased demand [ 12 , 13 ]. The registry and information systems aimed to improve the diagnosis process and provide assistance to healthcare professionals through the examination of patients’ data [ 14 ].

The combination of this information and registry in the medical field is of great importance and it is contemplated as one of the most important infrastructures for promoting health and social development [ 15 , 16 ]. Thus, the use of registration systems (registries) facilitates activities such as tracking patients’ follow-up trends, evaluating the follow-up process, assessing patient care quality, and collecting the necessary data for planning and creating valuable health care systems [ 12 , 17 ].

The aims of this study were to investigate the existing studies, including premature infants’ registries, identify, extract the required minimum data set, and propose a framework for premature infants’ registry.

Material and Methods

Search strategy

This is a descriptive study that has systematically reviewed prematurity-related registries. A protocol adhered to review articles, based on preferred items to report in systematic reviews and meta-analyses (PRISMA) [ 18 ]. Five major search engines, including Web of Science, ProQuest, PubMed, Scopus, and Embase were queried, and then the sources of contained publications were explored to distinguish additional applicable gray letters. The search strategy included four categories of keywords: maternal, newborn and neonatal, prematurity, and registry. These keywords should appear in conjunction with the title or abstract of the article. In Table 1, the key points and tactics of the search were represented.

Item Category name Search terms
1 Prematurity Prematurity, premature, light for gestational age, low birth weight, small for gestational age, SGA, LBW, extremely low birth weight, preterm, preterm birth, preemies, premmies, early birth, preterm delivery, preterm labor.
2 Newborn Neonatal, neonate, infant or baby, neonate, neonatal.
3 1 and 2
4 Maternal Maternal, mother, maternity, pregnancy, pregnant, motherly
5 3 and 4
6 Registry Electronic health record, Information system, Dataset, Database, Computerized or Registry.
7 5 and 6
SGA: Small for Gestational Age, LBW: Low Birth Weight
Table 1.Literature search terms

Study selection

Firstly, repetitive articles were recognized and removed using Mendeley desktop software (version 1.19.4 by Mendeley Ltd.), and a handmade modification was used for validation. If a study was reported in more than one publication and presented the same data, it only included the most recent publication. In the next step, the abstract and title of articles were studied according to the inclusion/exclusion criteria. According to the inclusion/exclusion criteria, the summary and caption of the papers were read. Captions and summaries were tested separately with health information management expertise. The Cochrane risk of bias assessment tool (version 15 March 2019) was used to assess the quality of the study. Risk of bias was performed using the risk of bias template on Microsoft excel which is available in an online supplement [ 19 , 20 ] (Figure 1).

Figure 1. Preferred Reporting Items for Systematic Reviews Flow Diagram (PRISMA) flow diagram.

Inclusion criteria

In this review, papers were published as follows: 1) in English, 2) in full-text (exclude summaries and conference summaries), 3) for neonates, 4) presenting neonates on prematurity or low birth weight, and 5) reported the outcome and registries and databases related to it.

Exclusion criteria

This study aimed to investigate neonates on prematurity and low birth weight, and publishes about not neonates or related to other prenatal diseases, systematic review, review papers, no registry, and none implemented were excluded.

Data Synthesis

In the current review article, different prematurity registries and databases were reviewed based on a list with the required data elements presented in Table 2. They included the basic registry information (country, registry or database name, acronym, year of establishment, resource maintenance), scope, registry type, and important variables. The validity of the data elements in this list was evaluated by two researchers for extracting data from registries. Based on the articles’ aims, only the qualitative data elements from the included registries and databases were extracted.

Date elements Description
Important registry information Continent, region, Registry or database name, Acronym, Year of establishment, Resource maintain.
Scope Nation-wide, Government-wide
Type of Registry Population or Hospital-based
Important variables Maternal characteristics: demographic (age, parity, weight, gestational age, ethnicity), society, and economic elements.
Pregnancy data and information
Neonate information
Clinical information
Administrative information
Death information
Table 2.Data elements definitions in neonatal prematurity registries and databases.


Overview of Studies

Table 3 includes a summary of the subjects collected during this review in which 8726 papers were recaptured. Overall, there were 6732 duplicates among the databases, which were excluded. After removing duplicates, the abstract and title of 1994 articles were studied concerning the inclusion criteria. At this stage, 1875 articles were excluded, considering the irrelevance of the article title or abstract. The full texts of 119 articles seemed relevant to the objectives. In the final analysis, 56 articles, considered eligible, were included. Eight articles were found and added through searching the Google Scholar engine manually (Figure 1).

Ref. No Continent Registry, database name, or abbreviation Year of establishment Scope Registry type Resource maintains
[ 21 ] Asia / Afghanistan Facility-based maternal and perinatal health care surveillance system (BABIES) 2002 Popular wide Hospital-based Women’s hospital quality assurance team
[ 22 ] America / Ohio Pediatric Health Information System. (PHIS) 2006 Popular wide Population-based Department of Health
[ 23 ] America / United States National Assisted Reproductive Technology Surveillance System. (NASS) 1995 Popular wide Population-based U.S. Department of Health and Human Services Centers for Disease Control and Prevention
[ 24 ] America / Arkansas Pediatric Health Information System (PHIS) 1999 Popular wide Population-based AHRQ (Agency for Healthcare Research and Qualities)
[ 25 , 26 ] America / Washington Washington State Immunization Information System. (WAIIS) 1991 Government-wide Hospital-based Washington State Department of Health
[ 27 ] America / Texas Pediatric Health Information System. (PHIS) 2001 Government-wide Hospital-based AHRQ
[ 28 ] America / North Carolina Case Management Information System. (CCNC’s) 2011 Popular wide Population-based AHRQ
[ 29 ] America / Montevideo / Uruguay Perinatal Information System Database of the Latin American Centre for Perinatology and Women’s Reproductive Health. (CLAP/WR) 1970 Popular wide Population-based Pan American Health Organization (PAHO)
[ 30 ] Africa / Ethiopia Dabat Health and Demographic Surveillance System. (HDSS) 1996 Popular wide Population-based Ethiopian Public Health Association.
[ 31 , 32 ] Europe / Italy/ Umbria regional Standard Certificate of Live Births in Umbria (SCLB) 1990 Government-wide Population-based Italian Ministry of Health
[ 33 ] Asia / Bangladesh Chakaria Chakaria Health and Demographic Surveillance System. (CHDSS) 1994 Popular wide Population-based International Centre for Diarrheal Disease Research, Bangladesh (ICDDRB)
[ 4 ] Asia / China Beijing Chinese Maternal and Newborn’s Health Monitoring System. (MNHMS) 1988 Government-wide Population-based National Health and Family planning Commission (NHFPC) & Alliance for Health Policy and Systems Research (AHPSR)
[ 34 ] Asia / China Beijing China’s Information System of Prevention of Mother-to-Child Transmission of Syphilis manage-ment. (iPMTCT) 1980 Popular wide Population-based National Center for Women and Children’s Health, China (CDC)
[ 35 ] Asia / China/ Wuhan The Electronic Perinatal Health Care Information System. (EPHCIS) 1994 Popular wide Population-based Wuhan Medical and Health Center for Women and Children (WMHCWC)
[ 36 ] Asia / China/ Guangzhou Guangzhou Perinatal Health Care and Delivery Surveillance System (GPHCDSS) 2000 Government-wide Population-based Guangzhou Women and Children’s Health Information Center
[ 37 ] Europe / Sweden Swedish Medical Birth Register (SMBR) 1973 Popular wide Population-based Swedish National Council-based Epidemiology Center for Health and Welfare Population
[ 37 ] Europe / Sweden Prescribed Drug Register (PDR) 2005 Popular wide Population-based Center for Health and Welfare Population
[ 37 ] Europe / Sweden Cause of Death Register (CDR) 1997 Popular wide Population-based WHO
[ 37 ] Europe / Sweden National Patient Register 1987 Popular wide Population-based Center for Health and Welfare Population
[ 38 - 40 ] Europe / France Programmed de medicalization des systems information (PMSI) 1997 Popular wide Hospital-based Ministry of Health
[ 41 ] America / Canada Canadian Preterm Birth Network (CPTBN) 2017 Popular wide Population-based CIHI (Canadian Institute for Health Information)
[ 41 ] America / Canada discharge abstract database (DAD) 1963 Popular wide Hospital-based CIHI
[ 42 ] America / Canada / Nova Scotia Nova Scotia Atlee Perinatal Data-base (NSAPD) 1986 Government-wide Hospital-based Fertility Care Program
[ 14 ] Asia / Pakistan Pakistan Demographic Survey (PDS) 1997 Popular wide Population-based Federal Bureau of Statistics (FBS)
[ 43 ] America / Peru Perinatal Information System 16] 2000 Popular wide Hospital-based University of Pennsylvania and the Universidad Peruana Cayetano Heredia
[ 44 ] Europe / Spain European Information System (EuroNeoStat) 2002 Popular wide Population-based Health and Consumer Protection Directorate General (DG SANCO)
[ 45 ] Europe / Spain del sistema de vigilancia delas sepsis nosocomiales en (RNMBP (NeoKissEs)) 2005 Popular wide Population-based Spanish Society for Neonatology
[ 46 ] Europe / Spain Spanish National Health Service (Sistema Nacional de Salud) (SNS) 1986 Popular wide Population-based Ministry of Health and Social Policy
[ 47 ] Europe / Scotland discharge summaries (SMR2) 1975 Popular wide Hospital-based Scottish Health Service Common Services Agency.
[ 48 ] Europe / Scotland Scottish Birth Record (SBR) 2003 Popular wide Population-based NHS Net (National Health Service)
[ 49 ] Europe / Scotland Universal Newborn Hearing (UNHS) 2005 Popular wide Population-based NHS Net
[ 50 ] America / Brazil / São Paulo Live Births Information System (Sistema de Informações de Nasci-dos Vivos (SINASC) 1990 Popular wide Population-based Federal Bureau of Statistics (FBS)
[ 51 ] America / Brazil / Rio de Janeiro Live Births Information System (Sistema de Informações de Nasci-dos Vivos (SINASC) 1990 Popular wide Population-based Federal Bureau of Statistics (FBS)
[ 52 ] America / Brazil / Rio de Janeiro Unified Health System 1990 Popular wide Population-based The Ministry of Health (MH)
[ 53 ] America / Brazil / Porto Alegre Information System on Live Births of the Municipal Health Department (ISLBMHD) 1990 Popular wide Hospital-based Municipal Health Department
[ 54 ] Africa / Uganda Demographic and Health Surveys (DHS) 1990 Popular wide Hospital-based government health facilities and maternal health services
[ 55 , 56 ] Europe / Slovenia National Perinatal Information System of Slovenia (NPIS) 1968 Popular wide Hospital- based National Institute of Public Health (Slovenia)
[ 57 ] America / Pennsylvania Certificate of Live Births (CLBs) 1963 Popular wide Population-based Department of Health
[ 58 ] Europe / Italy / Lombardy Hospital Discharge database (SDO) 1991 Popular wide Population-based Regional Health System
[ 59 ] America / Brazil / Para-ná Mortality and Live Birth Information System (SLBIM) 1990 Popular wide Population-based Federal Bureau of Statistics (FBS)
[ 60 ] Africa / Kenya / Nairobi Nairobi Urban Health and Demographic Surveillance system (NUHDSS) 2002 Popular wide Population-based African Population and Health Research Center (APHRC)
[ 61 ] Asia / India / Karnataka maternal and newborn health registry (MNHR) 1992 Popular wide Population-based Ministry of Health
[ 62 ] Europe / Norway Medical Birth Registry of Norway. (MBRN) 1967 Popular wide Hospital-based Norwegian Institute of Public Health
[ 63 ] Asia / Sri Lanka Yellow form 2009 Popular wide Hospital-based WHO Collaborating Centre
[ 63 , 64 ] Asia / Taiwan Taiwan National Medical Birth Register database (TNMBRD) 1995 Popular wide Population-based Maternal and child health research in Taiwan.
[ 65 ] Europe / Island Rhode Island’s integrated child health information system (KIDSNET) 2005 Popular wide Population-based Department of Health
[ 66 , 67 ] Europe / Finland/ Helsinki Medical Birth Register (MBR) 1983 Popular wide Population-based National Institute for Health and Welfare (THL)
[ 68 ] America / Florida Pediatrix Clinical Data Warehouse (PCDW) 2009 Popular wide Population-based Baylor Research Institute
[ 69 ] Africa / Ghana Dodowa Health and Demographic Surveillance System (DHDSS) 1990 Popular wide Population-based Government of Ghana and the British Oversees Development Agency (ODA now DFID)
[ 70 ] Asia / Japan Japan Society of Obstetrics and Gynecology Perinatal Database. (JSOG) 1979 Popular wide Population-based Ministry of Health, Labour and Welfare
[ 71 ] Australia / New Zealand Australian and New Zealand Neo-natal Network. (ANZNN) 1994 Popular wide Population-based National Health and Research Council's (NHMRC)
Table 3.Main Evidence.

As shown in Table 3, the contained 51 information systems were investigated and described prematurity registries based on the continent, region, registry, name of the system, abbreviation, year of development, scope, registry type, and resource focus.

Among the surveyed systems (51 systems), 11 (21.56%), 17 (33.33%), 4 (7.84%), 1 (1.96%), and 18 (35.31%) systems were in Asia, Europe, Africa, the continent of Australia, and related to America, respectively, as seen in Table 3 and Figure 2.

Figure 2. Division of existing registration systems based on 5 continents


In the Asia continent, Afghanistan has a facility-based maternal and perinatal health care monitoring system (BABIES) in 2002, as a popular hospital-based system. The creation of the system aimed to enhance the quality of healthcare services and reduce mortality [ 21 ]. In China, most of the surveyed popular and population-based systems were used in different provinces. Among these, the MNHMS system was under use on a population-based basis since 1988 in six provinces of China. This system was created in Beijing, China, to monitor pregnancy care and review results. China’s information system was also established nationally in Beijing in 1980 to prevent mother-to-child transmission of syphilis. It was then used as a national system to prevent the transmission of syphilis to infants [ 4 , 34 , 36 ].


In continental America, numerous national and state systems were established to record and maintain data on outpatients and inpatients, including the following. The first system was the PHIS information system, containing management and pharmaceutical data from 43 pediatric hospitals that are members of the Children’s Hospital Association with approximately 85% of all independent children’s hospitals in the United States [ 50 , 51 , 59 ]. The PHIS system was used as a national, population-based system in Ohio (since 2006) and Arkansas (since 1999) [ 22 , 24 ]. According to the present study, the purpose of creating this system was child care management and epidemiological studies.


The oldest system in continental Europe was created in 1967 in Norway and then in 1968 in Slovenia. Most of these systems were created to study the health factors and diseases of children as well as infants and contained both maternal and neonatal [ 55 , 56 , 62 ].


In Africa, Ethiopia has been using a national and population-based system since 1996, Uganda since (national and population-based) 1990, Kenya since 2002 (popular and hospital-based), and Ghana since 1990 (national and population-based). Different studies were also conducted on each of the systems, and the results were published [ 30 , 54 , 60 , 69 ].


In Australia, the ANZNN system was established in New Zealand in 1994 as a national, population-based system to maintain and promote neonatal health [ 71 ].

Base on this research’s finding the oldest systems were implemented in 1963 in Norway and Pennsylvania, while the newest system, the Canadian Preterm Birth Network, was established in 2017. Regarding the scope of registry systems, 44 (86.27%) out of the 51 systems were popular-wide, and the rest were provincial-wide. Based on the results, of the 51 systems, the largest number of systems was population-based (n=39, 76.47%), and the systems were hospital-based. Most systems (68%) contained only clinical data, 23% management data, and 12% financial data.

In this study, the Minimum Data Set (MDS) was classified into two main categories, including maternal data category and newborn data category (Figure 3) respectively in four and eight main classes and several subclasses (Figures 4 and 5).

Figure 3. Main Framework of Neonatal Prematurity Information System

Figure 4. Maternal Data Element framework for Iranian Neonatal Prematurity Registry

Figure 5. Neonatal Data Element framework for Iranian Neonatal Prematurity Registry

In the maternal part of the data, subclasses were categorized into demographic, prenatal, pregnancy, delivery, and childbirth as seen in Figure 4.

Further, in the newborn part of the data, subclasses were categorized to eight classes as seen in Figure 5.


A patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, serving a predetermined scientific, clinical, or policy purpose [ 72 ]. Registries are more comprehensive than similar hospital minimum data sets, such as those of New Zealand, England and France have more data elements, especially in the legal, incident, and geographic data classes [ 40 , 73 , 74 ]. Well-defined data collection was used for patient registration, medical center identification, insurance and reimbursement, medical research, outcome evaluation, and administrative reports [ 75 ].

All systems studied introduced some kinds of minimal data elements. In some systems, only the clinical aspects required for specific or cohort studies were addressed. However, some other systems investigated other aspects of information, such as management, financial, and health information. In this study, the Minimum Data Set (MDS) was classified into two main categories, including maternal data category and newborn data category, respectively in four and eight main classes and several subclasses in Figures 4 and 5 at the top. The MDS was categorized into 12 different data classes based on studied registries, expert focus panels, and patient hospitals. The proposed minimum data set for the prematurity information management system contains clinical data in addition to administrative plus financial data of patients.

In the maternal part of the data, subclasses were categorized into demographic, prenatal, pregnancy, delivery, and childbirth as seen in Figure 4. Further, in the newborn part of the data, subclasses were categorized to eight classes, including 1) demographic data (record no, national code, name, sure name, father name, newborn gender, mother nationality, father nationality, and birth place), 2) admission data (hospital name, refer from, refer to, admit status, and doctor name), 3) birth data (GA, BW, BH, HC, VS, apgarmin1, apgarmin5, newborn count, delivery method, and birth intervention, 4) the NICU admission data (ward entrance weight, ward height, ward HC, ward temperature, ward VS, discharge nurse, and ward admission status), 5) interventions data (respiratory support, medical intervention, noninvasive intervention, operation, and vaccination), 6) discharge data (center name, discharge weight, discharge height, discharge CC, discharge VS, lab result, X-ray result), 7) alerts and reminders data (O2 saturation, respiratory, V/S, parents to hospital, parents to ward, and vaccination), and 8) reporting data (general reports, mother reports, newborn reports, and compound reports) as seen in Figure 5.

All types of identity and demographic, socioeconomic, financial, and administrative data along with clinical data are investigated in designing the minimum data set of this system. While in many previous systems, they did not receive much attention and were more supported clinically [ 40 , 73 , 74 ]. However, current systems can fulfill the needs of owners and investors in conducting clinical trials, these systems cannot directly assist physicians and healthcare teams in deciding and educating parents of infants. Thus, researchers recommend adding a module to the available systems to perform this.

This research contains some limitations as follows: 1) the identification and comparison of data for researchers were hard to work since the sparse dispersion of papers related to the MDS of premature infant’s registry and systems of surveillance, 2) the lack of access to databases in the current context of sanctions, the lack of the authors’ cooperation to grant access to some systems and related sites, and the difficulty of persuading authors to authorize access to the systems.


Registry systems are important to many users, including policymakers, executives, planners, health care providers, and patients. These systems contain valuable information and knowledge that especially help health policymakers in the design and development of future health care information systems for disease management. Also, registration is mentioned as an essential key to improving digital health systems, and communication between doctors, patients or parents. To the best of our knowledge, the present study is a comprehensive study that has systematically examined prematurity-based registrations. With this research, we found that in regard to the preparation of output reports for doctors and care providers, registries play an important role in documenting the data and information of infants to improve their health status during the care period. Investing in the design and development phase of creating data systems can improve the performance of care standards and thus reduce mortality and the cost of premature babies. In particular, the production of information systems in low- and middle-income countries, which are likely to experience a growing trend in the field of neonatal morbidity and mortality and its complications, is of great importance.


The authors appreciate all the neonatologists, pediatricians, and gynecologists in Amir Al-Momenin Hospital affiliated with Semnan University of medical sciences, who have taken part considered within the approval of information components.

Authors’ Contribution

Sh. Pahlevanynejad, R. Safdari and N. Danaee contributed to the conceptualization and study design, data collection and interpretation, drafting of the paper, and critically revising several drafts of the paper for improvement of its intellectual content. All authors have read and approved the submitted and revised final version of the manuscript.

Ethical Approval

The current study was the result of the first phase of the author’s Ph.D. dissertation, which is done in the cooperation of TUMS with Semnan University of medical sciences with research ethics certificate ID: IR.TUMS.SPH.REC.1399.126.

Conflict of Interest



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