Improving the Information Availability and Accessibility of Antenatal Measurements to Ensure Safe Delivery: A Research-Based Policy Recommendation to Reduce Neonatal Mortality in Indonesia.

PURPOSE: Assessing the risks and preventable causes of maternal and neonatal mortality requires the availability of good-quality antenatal information. In Indonesia, however, access to reliable information on pregnancy-related results remains challenging. This research has proposed a research-based policy recommendation to improve availability and accessibility to vital information on antenatal examinations. PATIENTS AND METHODS: Descriptive statistics were used to characterize midwives' capabilities in routinely gathering and recording antenatal information during pregnancy. The investigation was carried out among 19 midwives in South Kalimantan, Indonesia, from April 2016 to October 2017. Antenatal data on 4946 women (retrospective study) and 381 women (prospective study) have been accessed through a scientific and technical training program.
RESULTS: To date, lack of timely access to antenatal information has hampered the process of reducing neonatal mortality in Indonesia. The post-training statistical analysis showed that the training has significantly improved midwives' scientific knowledge and technical abilities in providing more reliable data on antenatal measurements.
CONCLUSION: Consistent scientific and technical training among midwives is required to update their knowledge and skills, particularly those relating to documenting the results of antenatal examinations at different stages of pregnancy and using that information to assess potential risks and identify necessary interventions. This should also be followed by routine monitoring on the quality of collected antenatal data. This can be one of the enabling actions to achieve the 2030 Sustainable Development Goals target in reducing neonatal mortality in Indonesia.

Introduction

Antenatal care (ANC) is a preventive intervention to promote optimal pregnancy results. Access to this has been recognized as one of the best mediations to avoid poor well-being. ANC services are required to conform to the quality standard of ANC to improve maternal and child health, including recording and reporting the after-effects of ANC assessments.1,2 This investment enables the provision of adequate data to be analyzed and utilized for informed planning, decision making and monitoring policy progress to lower the risks of having maternal and neonatal mortality.3–5

Suitable utilization of antenatal data and its methodical examination during different periods of pregnancy are vital when observing, identifying, and measuring the risks and preventable factors associated with maternal and neonatal mortality. In Indonesia, the access to routine information on pregnancy-related results, causes and the effects of intercessions is deficient. This prevents actions to diminish maternal, fetal, and neonatal mortality, such as planning programs, resource allocation, and decision making.2,6-8

World-side improvement of antenatal data accessibility, consistency, and quality has turned out to be badly needed in improving well-being for babies.4,9-11 Although good quality of data will not solely improve the life survival of both mother and infant,5 it can enable medical practitioners to identify the signs of abnormalities; thus, appropriate interventions can be initiated in an expedient way.4,5,9-11

In Indonesia, midwives are the primary practitioners across territories.12,13 They are required to give comprehensive antenatal support to pregnant women and document the eventual outcomes of evaluations on local health recording and reporting systems.2 They are moreover expected to detect alarm signals of potential risks and provide proper interventions or referrals during pregnancy and delivery. However, their abilities in reporting the results of antenatal evaluations are low both in hospitals (20%) and primary healthcare (PHC) centres (42.5%).8 Unrecorded data or an inability to access local data on maternal, fetal, and neonatal characteristics have been perceived as a significant reason in hindering evidence-based interventions to evaluate the causes and predictable components related to maternal and newborn mortality.10,14

In developing countries, access to individual data of mother, fetus, and neonate during ANC remained inadequate. Although many factors influence the well-being of the fetus, routine and reliable measurement of fundal height (FH) at a given gestation age (GA) throughout pregnancy remains significant to estimate fetal weight15 and monitor fetal growth during pregnancy.16–22 In Indonesia, the lack of these data hampered the development of standard growth charts for fetus and neonate.23

The measurement of FH is straightforward and less expensive. It remains a significant screening tool during routine ANC, particularly in rural areas where ultrasound facilities are not always accessible.20,24 When the information of the last menstrual period (LMP) is unreliable and ultrasonic data are not available, FH can potentially be used as a proxy to estimate GA, which is one of the basic measures to recognize preterm birth and low birth weight (LBW).4 Therefore, the examination of FH records can help midwives to improve the quality of maternal and neonatal care.2,25

The aim of this investigation was to propose a research-based policy recommendation to improve the awareness and ability among midwives on the importance of routinely recording essential data on antenatal measurements, essentially for FH measurements.

Materials and Methods

Research Aim and Design

This research is the extension of the two earlier published studies, the details have been provided elsewhere.26,27 The primary aim was to propose a research-based policy recommendation, based on the results of the two previously published research, to improve the awareness and ability among midwives on the importance of routinely recording essential data on antenatal measurements. This is particularly for FH measurements that are used to estimate fetal weight at different stages of pregnancy in the absence of ultrasound facilities.

This research has been conducted using a descriptive and exploratory design using both quantitative and qualitative methods. During the quantitative phase, local pregnancy registers were reviewed to evaluate midwives’ ability to collect and correctly record the ANC examination results before and after scientific and technical training. After the training program, electronic questionnaires were distributed to the midwives to qualitatively gather information regarding their opinions on the challenges of documenting routine ANC examination results.

Research Setting

This research was conducted between April 2016 and October 2017 in one province of Indonesia, South Kalimantan with the estimated population of almost 4 million in 2015.28 This province is one of the five provinces with the highest neonatal mortality rates (NMR) in Indonesia.29–31 Administratively South Kalimantan is structured as two municipalities (urban areas) – Banjarmasin and Banjarbaru – and eleven districts (rural areas) – Banjar, Barito Kuala, Tapin, Hulu Sungai Selatan, Hulu Sungai Tengah, Hulu Sungai Utara, Balangan, Tabalong, Tanah Laut, Tanah Bumbu and Kotabaru. All these urban and rural areas were proportionally represented in the study.

In the capital of the province, public and private hospitals are available as tertiary health facilities. Each administrative area is served by hospitals as secondary health facilities that provide referral services in that area and health centres as primary health facilities. In this study, primary health care centres are the main concern because they are located near where people live and are the most commonly recommended and most cost-effective first level of health care in Indonesia, particularly in rural settings.6,32,33

There are two common types of primary health care centres in South Kalimantan province: public health centres (Puskesmas or PHCs) and independent midwifery practices (Praktik Bidan Mandiri or IMPs). These facilities are situated in both urban and rural areas to ensure services are equally accessible and distributed across the communities. In total, there are 230 PHCs28 and 187 IMPs (unpublished data from the provincial midwifery association in 2015) across the province.

Research Participants

For this study, 14 PHCs and 5 IMPs, proportionally distributed across the administrative areas of the province, were selected using multistage purposive sampling with the type of healthcare system and administrative location as the stratification factors. The selection criteria for the individual strata were based on the population density of the PHC’s catchment area, taken in conjunction with recommendations from the provincial midwifery association. Being administered by the Ministry of Health of the Republic of Indonesia, the participating health centres all followed the standard operational procedures for ANC.34,35 Therefore, it is assumed that the systematic variation between midwives’ measurements is not significant and has been ignored. The study also involved 19 midwives who had experienced in providing ANC services for a minimum of 5 years at the selected PHCs.

Research Instrument and Data Collection

The detailed information on research instrument and data collection procedures can be found in the formerly published studies.26,27 Table 1 presents the summary.

Table 1

Summary of Research Instrument and Data Collection

Phase 1: Quantitative Design

Phase 2: Qualitative Design

Activity:Scientific and technical training was conducted, between 21 and 22 May 2016, among 19 Indonesian midwives. The training aimed to educate and update midwives with scientific knowledge and the technical abilities to allow for routine collection of significant maternal and fetal characteristics during ANC. Scientific training discussed the maternal and fetal characteristics that play a vital role in evidence-based intervention decisions to reduce neonatal mortality, while technical training introduced and explained the use of an electronic pregnancy register as well as demonstrated how to record and manage the data appropriately.Research instrument and data collection:During training:Manual pregnancy registers, from 1 January 2012–31 May 2016, were retrospectively reviewed. Access was granted to manually recorded ANC information on 4946 women who enrolled, received care and gave birth in the centres (Figure 1).After training:Electronic pregnancy registers, from 1 June 2016 to 30 June 2017, were prospectively applied to record the recommended ANC results on 381 women who enrolled, received care and gave birth in the centres (Figure 2).

Activity:The performance of the current ANC documentation systems, the potential challenges to completing the tasks and the impact of the training program were assessed.Research instrument and data collection:After training:An electronic feedback questionnaire was distributed among 19 participating midwives through a social media platform. In this study, the responses were electronically collected between August and October 2016 and considered as preliminary data in the analysis.

Retrospective data for assessing training outcomes.

Statistical Analysis

The detailed information of statistical analysis used to assess the quality of routine ANC data collection can be found in the earlier published studies.26,27 The summary is given in Table 2.

Table 2

Summary of Statistical Analysis

Quantitative Method

Qualitative Method

Statistical analysis:Descriptive statistics were performed to calculate the quantity of records of ANC characteristics in both manual and electronic pregnancy registers at each PHC center. A two-sample t-test was used to compare the performance of ANC data documentation process, before and after the training program, across the health care facilities.Data management and analysis:Microsoft Excel 2010 and Minitab 17

Statistical analysis:Non-parametric statistics, frequencies, cross tabulations, percentages, and Cramer’s V test were used to analyze the midwives’ responses regarding factors affecting their ability to successfully complete the ANC data collection tasks.Data management and analysis:Microsoft Excel 2010 and SPSS 23

Results

Baseline Characteristics of Midwives

Overall, the average age of the participating midwives was 41 years (29–56 years) with the average working experience in antenatal and midwifery services was 19 years. The descriptive summary of midwives’ characteristics is given in Table 3.

Table 3

Description of Midwives’ Characteristics (Number of Participating Midwives = 19)

Characteristics	Mean (Range)	No. of Women (Percent)
Age	41 (29–56) years old
25–35 years old		2 (10.5)
36–45 years old		13 (68.4)
>46 years old		4 (21.1)
Working experience	19 (6–36) years
6–10 years		4 (21.1)
11–20 years		9 (47.4)
21–30 years		5 (26.3)
>31 years		1 (5.3)

General Information on the Study Population (Retrospective Study, n = 4946 Manually Recorded Data)

In total, 14 public health centres (PHCs) and 5 independent midwifery practices (IMPs) were involved in the study. Between 1 January 2012 and 31 May 2016, 4946 pregnant women received ANC services in these centres and their information was manually recorded (Figure 2). Descriptive statistics on the baseline characteristics of mothers and newborns in the study population are presented in Table 4.

Figure 2

Prospective data for assessing training outcomes.

Notes: Adapted from Anggraini D, Abdollahian M, Marion K, et al. The impact of scientific and technical training on improving routine collection of antenatal care data for maternal and foetal risk assessment: a case study in the Province of South Kalimantan, Indonesia. J Pregnancy. 2018;2018. Copyright © 2018 Dewi Anggraini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.26

Table 4

Baseline Characteristics and Antenatal Events (Retrospective Study, Number of Pregnant Women = 4946)

Characteristics	Mean (Standard deviation)	No of women (Percent)
Number of antenatal care (ANC) visits	3.4 (2.2)
Sex of neonate
Male		1010 (20.4)
Female		967 (19.6)
Not stated/recorded		2969 (60.0)
Gestational age (GA) at delivery (weeks)	37.7 (3.6)
Premature birth (less than 37 weeks)		693 (14.0)
Term birth (37–41 weeks)		1446 (29.2)
Not stated/recorded		2807 (56.8)
Maternal age (years)	26.9 (6.1)
13–22		1165 (23.6)
23–32		2138 (43.2)
33–42		793 (16.0)
Not stated/recorded		850 (17.2)
Maternal body mass index (BMI) (kg/m2)36	22.1 (3.8)
Underweight (less than 18.5)		79 (1.6)
Normal (18.5–24.9)		358 (7.2)
Overweight (25–29.9)		86 (1.7)
Obese (more than or equal 30)		24 (0.5)
Not stated/recorded		4399 (88.9)
Maternal nutritional status(Maternal middle upper arm circumference or MUAC)	25.5 (2.9)
Chronic energy shortage(if middle upper arm circumference (MUAC) is less than 23.5 cm)		648 (13.1)
Normal (if MUAC is more than or equal 23.5 cm)		2974 (60.1)
Not stated/recorded		1324 (26.8)
Birth order (Parity)
1st birth (Nulliparous)		1162 (23.5)
2nd or greater (Multiparous)		2086 (42.2)
Not stated/recorded		1698 (34.3)
Mode of delivery
Spontaneous		560 (11.3)
Sectio caesarean		52 (1.1)
Vacuum extraction		5 (0.1)
Not stated/recorded		4329 (87.5)
Birth weight	3076.3 (439.5)
Low birth weight (LBW) (less than 2500 g)		145 (2.9)
Average birth weight (2500–3999 g)		2388 (48.3)
Macrosomia (more than 4000 g)		66 (1.3)
Not stated/recorded		2347 (47.5)
Birth attendance
Health practitioner		681 (13.8)
Midwife		987 (20.0)
Specialist/obstetrician		52 (1.1)
Not stated/recorded		3226 (65.2)

Not all pregnant women included in the retrospective study attained the minimum recommendation of 4 ANC visits (range between 1 and 12 visits). Overall, the pregnant women were between 23 and 32 years old (43.2%), well nourished (60.1%) with middle-upper arm circumference (MUAC) ≥23.5 cm, nulliparous (24.4%) and had normal BMI at the first visit of ANC (between 18.5 and 24.9 kg/m2) (7.2%). Of these 4946 women, 1446 (29.2%) delivered the baby at term pregnancy, 560 (11.3%) delivered the baby through the spontaneous normal mode of delivery, 693 (14%) delivered preterm (<37 completed weeks of gestation); 1010 (20.4%) delivered male newborns, 967 (19.6%) delivered female newborns; and 987 (20.0%) were assisted by midwives. Before scientific and technical training among midwives, the information gap of each study population characteristic was substantial (from 17.2% to 88.9%) (Table 4).

Description of the Study Population (Prospective Study, n = 381)

Between 1 June 2016 and 30 June 2017, there were 381 pregnant women who enrolled, received ANC and were followed up until they gave birth at the study centers. Descriptive statistics on the baseline characteristics of mothers and newborns in the study population are presented in Table 5.

Table 5

Baseline Characteristics and Antenatal Events (Prospective Study, Number of Pregnant Women = 381)

Characteristics	Mean (Standard Deviation)	No of Women (Percent)
Number of antenatal care (ANC) visits	6 (2)
Sex of neonate
Male		213 (55.9)
Female		164 (43.0)
Not stated		4 (1.0)
Gestational age (GA) at delivery (weeks)	38.9 (1.6)
Premature birth (less than 37 weeks)		33 (8.7)
Term birth (37–41 weeks)		348 (91.3)
Maternal age (years)	27.7 (5.7)
13–22		73 (19.2)
23–32		233 (61.2)
33–42		74 (19.4)
More than 42		1 (0.3)
Maternal body mass index (BMI) (kg/m2)36	22.2 (3.6)
Underweight (less than 18.5)		49 (12.9)
Normal (18.5–24.9)		258 (67.7)
Overweight (25–29.9)		58 (15.2)
Obese (more than or equal 30)		15 (3.9)
Not stated		1 (0.3)
Maternal nutritional status(Maternal middle upper arm circumference or MUAC)	26.6 (3.4)
Chronic energy shortage(if middle upper arm circumference (MUAC) less than 23.5 cm)		11 (2.9)
Normal (if MUAC more than or equal 23.5 cm)		109 (28.6)
Not stated		261 (68.5)
Birth order (Parity)
1st birth (Nulliparous)		109 (28.6)
2nd or greater (Multiparous)		272 (71.4)
Mode of delivery
Spontaneous		346 (90.8)
Sectio caesarean		27 (7.1)
Vacuum extraction		3 (0.8)
Not stated		5 (1.3)
Birth weight (g)	3076.9 (373.6)
Low birth weight (LBW) (less than 2500g)		15 (3.9)
Average birth weight (2500–3999 g)		366 (96.1)
Birth attendance
Health practitioner		21 (5.5)
Midwife		302 (79.3)
Specialist/obstetrician		44 (11.5)
Others		4 (1.0)
Not stated		10 (2.6)

Most pregnant women included in this study followed the minimum recommendation of ANC visits (average of 6 visits) with a range between 1 and 14 visits. Overall, the pregnant women were between 23 and 32 years old (61.2%), well-nourished (28.65%) with middle-upper arm circumference (MUAC) ≥23.5 cm, multiparous (71.4%) and had normal BMI at the first visit of ANC (between 18.5 and 24.9 kg/m2) (67.7%). Of these 381 women, 348 (91.3%) delivered the baby at term pregnancy, 346 (90.8%) delivered the baby through the spontaneous normal mode of delivery, 33 (8.7%) delivered preterm (<37 completed weeks of gestation); 213 (55.9%) delivered male newborns, 164 (43.0%) delivered female newborns; and 302 (79.3%) were assisted by midwives. After training, the information gap of each study population characteristic (prospective study) is smaller (from 0.32% to 68.5%) (Table 5).

The Impact of Scientific and Technical Training on Improving Routine Collection of ANC Data

Based on our reviews in the previous study,26 scientific and technical training has generally and significantly improved the average amount (%) of recorded ANC data across all primary health care providers. A two-sample t-test shows an overall increase in data recorded from 17.5% to 62.1% (p-value <0.0005) (Table 6).

Table 6

Data Recorded Before and After Training

Treatment	Mean (Percent)	Standard Deviation (Percent)	Standard Error of Mean (Percent)	Estimate for Difference (Percent)	95 Percent Confidence Interval for Difference (Percent)		T-value	Pooled Standard Deviation (Percent)
					Lower Bound	Upper Bound
After training	62.1	40.4	1.7	44.6***	40.5	48.8	21.1***	35.0
Before training	17.5	28.5	1.2

Notes: ***Significant at p-value < 0.0005. Table adapted from Anggraini D, Abdollahian M, Marion K, et al. The impact of scientific and technical training on improving routine collection of antenatal care data for maternal and foetal risk assessment: a case study in the Province of South Kalimantan, Indonesia. J Pregnancy. 2018;2018. Copyright © 2018 Dewi Anggraini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.26

The outcomes also demonstrate a general improvement, especially in recording individual data, obstetric history, delivery plans, ANC utilization criteria, maternal measurements, provision of supplements, clinical fetal estimates and delivery time (from 0.8–47.9% to 57.4–99.2%). These results have been shown and discussed in the previous study.26 Nonetheless, midwives’ responsiveness to the significance of gathering and recording the results of laboratory tests, maternal risk detection, ultrasonic fetal estimates and fetal risk detection provide an opportunity for improvement (<12%). A comprehensive investigation that compares the performance of midwives in documenting the results of the antenatal examinations before and after the training program has been done and published in the previous studies.26,27

The Impact of Scientific and Technical Training on Improving Routine Collection of FH Measurements

Scientific and technical training has also significantly improved the awareness and ability among midwives in recording FH measurements. Table 7 shows that the training has increased the average amount of recorded FH information across PHC providers, from 37.1% to 58.5% (p-value < 0.0005).

Table 7

Fundal Height Assessment Recorded Before and After Training

Intervention	Number of Samples	Mean (Percent of FH Records)	Standard Deviation (Percent of FH Records)	SE Mean (Percent of FH Records)	Mean Difference (After Training – Before Training)	T value for Two-Sample t-Test of the Difference		95 Percent Confidence Interval of the Difference
						Assume Not Equal Variance	Assume Equal Variance	Assume Not Equal Variance	Assume Equal Variance
Before training	195	37.10	34.10	2.40	21.40	8.56***	11.46***	(16.47, 26.33)	(17.73, 25.07)
After training	402	58.50	10.80	0.54

Note: ***The t-test is significant at p-value < 0.0005.

In Indonesia, the Johnson-Toshach model37 that incorporates maternal FH and fetal station/descent level (FS) is well recognized and performed to predict fetal weight. In this study, however, the attention is not on the accuracy of the equation that has been assessed and talked about in the past investigation,15 but on how accurately the information of FH was recorded on the manual register (before training) and electronic pregnancy register (after training).

Figure 3 presents the ability of the midwives in correctly recording FH measurements which expected to increase during the pregnancy.

Figure 3

FH and GA data available for fetal weight estimation. [Source: Anggraini, et al. 38]. (A) before training (retrospective data, n = 195). (B) after training (prospective data, n = 402).

Figure 3 presents the ability of the midwives in correctly recording FH measurements which expected to increase during the pregnancy. Figure 3(A) clearly shows that before training (retrospective cohort study), some of the recorded FH have not increased by GA. The relationship pattern between FH and GA could not be reliably seen when the data were retrospectively collected. This can be justified by the low correlation value between FH and GA recorded by r = 0.616 (p-value < 0.0005).

However, after training, the awareness and ability among midwives in correctly documenting FH information were significantly improved [Figure 3(B)]. The increase in FH can now obviously be seen by the increase of GA when the data were prospectively collected. This can be justified by the higher correlation value between FH and GA recorded by r = 0.851 (p-value < 0.0005).

Figure 4 presents a similar pattern between FH and GA at different stages of pregnancy for prospective data.

Figure 4

Fundal height growth assessment during critical periods of pregnancy. [Source: Anggraini, et al. 38]. (A) 13–42 weeks (n = 402). (B) 20–40 weeks (n = 402). (C) 20–35 weeks (n = 399). (D) 24–40 weeks (n = 401)

Factors Affecting Midwives in Completing Routine ANC Data Documentation Tasks

Table 8 summarizes the main contributing factors affecting midwives in completing ANC data records and the main reasons for the delay in collecting and reporting routine ANC information.

Table 8

Factors and Reasons Influencing Midwives in Completing Routine Collection of ANC Data

Main Contributing Factors (Percent)	Main Reasons (Percent)
Poor recording and reporting systems (31.3)Unawareness of the pregnancy (25.0)Time limitations (25.0)	Lack of awareness (46.2)High workload (30.8)Insufficient skills and facilities (15.4)

The majority of the participating midwives (n = 17, 89.5%) responded to the effectiveness of the training and 52.6% of them responded to the importance of the developed electronic register, particularly if it could be computationally and automatically linked with the monthly ANC reporting formats. These results have been reported in the previously published work.27

Discussion

Most of the midwives were senior midwives with working experience ranged between 6 and 36 years in ANC services across urban and rural primary health care facilities. They also followed the standard operational procedures for ANC measurements.34,35

Overall, the pregnant women, who were retrospectively and prospectively included in the study, were between 23 and 32 years old. They were well nourished with middle-upper arm circumference (MUAC) ≥ 23.5 cm, had normal BMI at the first visit of ANC (between 18.5 and 24.9 kg/m2), delivered the baby at term pregnancy through the spontaneous normal mode of delivery and were assisted by midwives.

Currently, local health registers, as opposed to periodic household surveys, are utilized to identify risks of maternal and neonatal mortality.6,7,38 With the development of a decentralization approach in Indonesia,6,7 access to timely and reliable local information and the accumulation of maternal and fetal measurements during ANC is urgently required. Given appropriate education and training, monitoring and control as well as innovation; local maternal, fetal, and neonatal well-being information can be utilized as consistent baseline data to improve the quality of care services.38

Access to individual data on maternal and fetal well-being during pregnancy and at delivery is fundamental to fortify routine health data frameworks. The analysis presented in the previous section clearly shows that scientific and technical training has equipped urban and rural midwives with the knowledge of the importance of ANC data measurement and documentation. The improvement of their essential midwifery care in recording the after-effects of ANC assessments was overall greater (62.1%) than the present national report (42.5%).8 This was particularly evident in the gathering and recording of the critical attributes, such as FH, to evaluate the dangers during pregnancy. If the excellent quality of care can be related to its successful information accumulation during labor,11 then it is also crucial to have real-time data collection at various phases of pregnancy to enhance the quality of ANC.

Moreover, the awareness of midwives in significantly recording FH at a certain GA to estimate the weight of fetus has fundamentally improved after the training (from 0–16.5% to 62.2–65.8%).26 However, the information sufficiency stayed underneath 70%. A comparable pattern of low percentage in recording FH was found in rural Australia (range 36–76%).39 Although many variables impact the well-being of the fetus, FH stays as one of the most prescribed and accessible indicators to estimate fetal weight15 and screen fetal development during pregnancy16–22 to identify preterm birth and LBW.4

The arrangement of the fetal growth chart during pregnancy is critical to evaluate the feasibility of the fetus at various phases of pregnancy and to guarantee neonatal endurance and prosperity. The chart is a compelling screening tool to help midwives in analyzing and identifying the dangers of fetal development variations from the norm, for example, prematurity and LBW. Subsequently, preventive activities and referrals can be adequately and timely initiated.

Indonesia was the fifth-worst for preterm births in the world with expected 154 preterm births for every 1000 live births.40,41 The event of prematurity, which for the most part, results in LBW, has increased by 3% between 1990 and 2013 and is the second most basic reason for death among neonates and children under five.4,8,42 While maternal, neonatal, and child well-being projects have been started and executed to lessen the death ratio in Indonesia, mainly because of prematurity and LBW,2,43,44 less consideration is given on gathering antenatal data and promoting surveillance tools for fetal development during pregnancy.

As far as we know, the scientific and technical training has, for the first time, prepared Indonesian midwives with the updated scientific knowledge and technology in regards to fetal development evaluation during pregnancy. The training has likewise expanded the midwives’ alertness to the significance of routine documentation of the vital information on attributes of mother and fetus in the present manual pregnancy registers.26 The training also explored several factors triggering incomplete and delayed recording and reporting tasks of ANC data that have been discussed in the previous publication.27 These include a high amount of work, lack of time for routine ANC examinations, inadequate skills and training, and lack of awareness about the importance of documenting the examination results. The result is consistent with those of Burke, Suswardany, Michener, Mazurki, Adair, Elmiyati, Rao38 and Sibiya, Cele, Ngxongo45 who found similar factors hampering complete and reliable records of the recommended ANC examination results.

Therefore, the training has the following implications: it can fundamentally improve the quality of information gathered to develop Indonesia’s particular charts and protocols for the surveillance of fetal growth, it gives an understanding into how to execute such agreements to improve patients’ well-being and will prompt better and progressively reliable provision of local antenatal information to carry out evidence-based maternal and fetal hazard appraisal, pregnancy result review, as well as resource planning and allocation. The training focuses on the quality improvement of healthcare services.46 Consistent education and training might then be an important part of investment programs in midwives to lessen maternal and neonatal mortality.29,47

Conclusion

Based on the comprehensive reviews of existing regulations on antenatal examinations, statistical analysis carried out in the previous research, the United Nations International Children’s Emergency Fund (UNICEF) recommendations30 the current project carried out by the National Research Council (USA) and the Indonesian Academy of Sciences (Indonesia),6,7 the following recommendations are proposed as enabling actions to achieve the 2030 SDGs target for the reduction of neonatal mortality:

Promote continuous scientific and technical training among midwives to update their knowledge and skills, particularly those relevant to measuring and documenting the results of antenatal examinations. As well as analyzing the information to assess potential risks and identify necessary interventions/referrals promptly.

Use the outcome of the information analysis to manage and distribute the health resources effectively.

Introduce the protocols of routine collection of antenatal examination results into clinical practice.

Assess/evaluate the adequacy and quality of antenatal information before and after the scientific and technical training to effectively design the structured content of the next training program.