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

Figures & data

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.

Figure 1 Retrospective 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

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 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

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)

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/m^2)^Citation36	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)
1^st birth (Nulliparous)		1162 (23.5)
2^nd 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)

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/m^2)^Citation36	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)
1^st birth (Nulliparous)		109 (28.6)
2^nd 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)

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

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.

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 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)

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)

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.

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