Testing the assumptions of an indicator of unmet need for obstetric surgery in Ghana: A cross-sectional study of linked hospital and population-based delivery data.

BACKGROUND: The Unmet Obstetric Need (UON) indicator has been widely used to estimate unmet need for life-saving surgery at birth; however, its assumptions have not been verified. The objective of this study was to test two UON assumptions: (a) Absolute maternal indications (AMIs) require surgery for survival and (b) 1%-2% of deliveries develop AMIs, implying that rates of surgeries for AMIs below this threshold indicate excess mortality from these complications. METHODS: We used linked hospital and population-based data in central Ghana. Among hospital deliveries, we calculated the percentage of deliveries with AMIs who received surgery, and mortality among AMIs who did not. At the population level, we assessed whether the percentage of deliveries with surgeries for AMIs was inversely associated with mortality from these complications, stratified by education. RESULTS: A total of 380 of 387 (98%) hospital deliveries with recorded AMIs received surgery; an additional eight women with no AMI diagnosis died of AMI-related causes. Among the 50 148 deliveries in the population, surgeries for AMIs increased from 0.6% among women with no education to 1.9% among women with post-secondary education (P < .001). However, there was no association between AMI-related mortality and education (P = .546). Estimated AMI prevalence was 0.84% (95% CI: 0.76%-0.92%), below the assumed 1% minimum threshold. DISCUSSION: Obstetric providers consider AMIs absolute indications for surgery. However, low rates of surgeries for AMIs among less educated women were not associated with higher mortality. The UON indicator should be used with caution in estimating the unmet need for life-saving obstetric surgery; innovative approaches are needed to identify unmet need in the context of rising cesarean rates.

Unmet Obstetric Need Indicator for Estimating Maternal Mortality by Severe Obstetric Complications in Tambacounda Region, Senegal / 国際保健医療

<b>Introduction</b><br> Maternal mortality ratio is widely used to provide a general sense of size of the problem of maternal deaths. However, it cannot be used to measure progress of maternal health programme on an annual basis and to compare geographic areas, because of its wide range of errors. This research estimates maternal mortality in six districts by using “unmet obstetric need” indicator in Tambacounda region, Senegal and describes possible application of the indicator to monitor, evaluate and facilitate maternal mortality reduction.<br><b>Methods</b><br> We used data on caesarean sections performed in seven health facilities in Tambacounda and Kaolack regions in 2005, and calculated rates of the intervention for the residents of Tambacounda. We estimated maternal mortality ratios for selected severe obstetric complications in six districts by calculating the number of deficits for the caesarean sections for absolute maternal indications, which were the number of women who developed life-threatening events but could not receive the appropriate interventions for the diseases.<br><b>Results</b><br> The rates of caesarean sections for all indications and for absolute maternal indications in the six districts ranged from 0.3 to 2.0% and from 0.1 to 0.9%, respectively. The estimated maternal mortality ratio for the absolute maternal indications in Tambacounda region was 651 (95%CI 554-761). Statistically significant differences in the ratios were observed between Koumpentoum district (maternal mortality ratio 966, 95%CI 741-1239) and Goudiry (877, 588-1260), and Kédougou (249, 119-457) and Bakel (296, 128-584).<br><b>Conclusions</b><br> This study method enabled us to distinguish the difference in maternal mortality ratios for the selected severe obstetric complications between the small districts. It implies that the “unmet obstetric need” indicator can be used to compare geographic areas, to monitor trends, and to evaluate programme impact as well as baseline data to establish necessary measures to decrease maternal deaths.