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Background: Most maternal health programs in low- and middle- income countries estimate gestational age to provide appropriate antenatal care at the correct times throughout the pregnancy. Although various gestational dating methods have been validated in research studies, the performance of these methods has not been evaluated on a larger scale, such as within health systems. The objective of this research was to investigate the magnitude and impact of errors in estimated delivery dates on health facility delivery among women enrolled in a maternal health program in Zanzibar. Methods: This study included 4225 women who were enrolled in the Safer Deliveries program and delivered before May 31, 2017. The exposure of interest was error in estimated delivery date categorized as: severe overestimate, when estimated delivery date (EDD) was 36 days or more after the actual delivery date (ADD); moderate overestimate, when EDD was 15 to 35 days after ADD; accurate, when EDD was 6 days before to 14 days after ADD; and underestimate, when EDD was 7 days or more before ADD. We used Chi-squared tests to identify factors associated with errors in estimated delivery dates. We performed logistic regression to assess the impact of errors in estimated delivery dates on health facility delivery adjusting for age, district of residence, HIV status, and occurrence of past home delivery. Results: In our data, 28% of the estimated delivery dates were a severe overestimate, 23% moderate overestimate, 41% accurate, and 8% underestimate. Compared to women with an accurate delivery date, women with a moderate or severe overestimate were significantly less likely to deliver in a health facility (OR = 0.71, 95% CI: [0.59, 0.86]; OR = 0.74, 95% CI: [0.61, 0.91]). When adjusting for multiple confounders, women with moderate overestimates were significantly less likely to deliver in a health facility (AOR = 0.76, 95% CI: [0.61, 0.93]); the result moved slightly towards null for women with severe overestimates (AOR = 0.84, 95% CI: [0.69, 1.03]). Conclusions: The overestimation of women’s EDDs reduces the likelihood of health facility delivery. To address this, maternal health programs should improve estimation of EDD or attempt to curb the effect of these errors within their programs.
In 2015, the maternal and neonatal mortality ratios in Zanzibar, Tanzania were 307 maternal deaths per 100,000 live births and 29 neonatal deaths per 1000 live births, respectively [21]. The Safer Deliveries program aimed to reduce the high rates of maternal and neonatal mortality by increasing the number of pregnant women who deliver in a health care facility and attend prenatal and postnatal check-ups. This program began in 2011 and has been implemented in phases, each phase increasing in scope and scale with the third phase starting in January 2016. As of May 2017, the Safer Deliveries program was active in six (out of 11) districts in Zanzibar on the islands of Unguja and Pemba. The program trains CHWs selected by the Ministry of Health to participate in the program based on their literacy, expressed commitment to the improvement of health, and respectability in their communities. The CHWs work with community leaders and staff at nearby health facilities to identify and register pregnant women. Typically, a pregnancy is confirmed at the health facility during the first antenatal care visit using a pregnancy test. In the absence of reagent for the pregnant test, missing two consecutive periods and clinical evidence of an enlarged uterus is used to determine pregnancy status. During registration, the CHW meets with the woman, her husband and/or other influential members of the family to discuss and enroll in the program. The CHW visits the woman in her home three times during pregnancy; before 7 months; between 7 and 8 months and again between 8 and 9 months gestational age, to screen for danger signs and provide education, counseling and support to help the woman prepare for a facility delivery. The timing of the visits is based on the estimated delivery date. The program is supported by a digital platform developed by D-tree International, built using Mangologic software. All CHWs have a mobile app running on a low-end Android smartphone which supports case management and decision support to guide the health worker through each visit. The mobile app guides development of a tailored birth plan based on the woman’s obstetric history and risk factors to identify the most appropriate health facility based on her risk profile. The app then uses the woman’s estimated gestational age to provide tailored messages at the appropriate phase of her pregnancy, screen for danger signs and coordinate referrals to a health facility, calculate and track savings needed for transportation and delivery expenses, and links the woman with a community driver for transportation during delivery. All data collected by the CHWs on the mobile app are synchronized in quasi-real time to the Safer Deliveries server, which is available as raw data and visualized on a program dashboard to support monitoring and programmatic decision-making. This study included women enrolled in the Safer Deliveries program by May 31, 2017 (n = 9740) who had a live birth by May 31, 2017 (n = 4511). We excluded: 253 women from the newly-added Mkoani district of Pemba Island, 2 women with missing LMP date and EDDs, and 31 women with invalid enrollment times. Our final study population included 4225 women. We used data collected in the mobile app as part of routine care. At enrollment, the CHW collected demographic and health information to support clinical care for the mother. The CHW used the woman’s Reproductive and Child Health (RCH) card, if available, to record information about the EDD and antenatal care visits. The EDD on the RCH card was ascertained at a facility via last menstrual period or ultrasound, if available at the facility. If the woman does not have an RCH card or there is no EDD, the date of LMP and timing of ANC visits were calculated using self-reported date of LMP and this date was used to calculate an EDD. Although we did not collect information on whether EDD was ascertained by date of LMP or ultrasound, only two Primary Health Care Centers and two hospitals in Unguja have an ultrasound machine available; however, these machines may not be commonly used. Further, only 419 (10%) women reported an ANC visit at one of these health centers, and the distribution of preterm, term, and post-term classifications did not significantly differ from women without ultrasound access at their ANC visit(s) (P = 0.834). Due to this, we believe that the vast majority of the delivery dates were calculated based on LMP at an antenatal care or community health worker visit. The CHW also recorded information about obstetric history at the enrollment and categorized the pregnancy as low, medium, or high risk. Based on the risk category, the woman was advised to deliver at a specific facility and given a target amount of money that she should save for transportation to the facility based on pre-negotiated rates with local drivers participating in the program. At enrollment and each subsequent visit, the CHW collected details about how the woman has prepared for delivery, such as amount of money saved, transportation plan, and number of antenatal visits at a health facility. After the woman delivered, the CHW recorded the date and location of delivery. Two continuous measures were calculated based on the EDD: (1) difference between actual delivery date (ADD) and EDD and (2) estimated gestational age at delivery (weeks). The difference in delivery dates was coded as a categorical variable with four levels: severe overestimate (EDD was 36 days or more after ADD), moderate overestimate (EDD was 15 to 35 days after ADD), accurate (EDD was 6 days before to 14 days after ADD), and underestimate (EDD was 7 days or more before ADD). The accurate category was defined based on multiple studies that reported actual delivery dates to be accurate within 7 and 14-days of the estimated delivery date due to LMP dating error and/or natural variability in length of gestation [1, 2, 13, 14]. We chose to categorize overestimate as moderate and severe to allow the odds of health facility delivery to vary by severity of LMP misestimation. The cutoff of 36 days for severe overestimation was based on the low proportion ( 99% of sample). All statistical analyses were performed in Stata V15 (StataCorp, College Station, Texas, USA).
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