Bridging Data Gaps: Predicting Sub-national Maternal Mortality Rates in Kenya Using Machine Learning Models

Background: The global maternal mortality rate (MMR) in 2022 is 91.46 per 100,000 live births with the cause of death being bleeding while the indirect causes of maternal mortality are during pregnancy wheere many pregnant women experience nutritional problems such as Chornic Energi Deficiency ( CHD) and Nutritional Anemia at 8.43% (WHO, 2022). The maternal mortality rate in ASEAN is 235 per 100,000 live births with the majority of causes of death being bleeding (ASEAN Secretariat, 2022). Until now, the maternal mortality rate in Indonesia alone in 2020 was 91.45/100,000 live births. In 2021, maternal mortality will increase to 305/100,000 live births. Meanwhile, in 2022, the maternal mortality rate will decrease by 230/100,000 live births. Within a period of 3 years, the causes of maternal death included Covid 19, bleeding, hypertension in pregnancy, and heart disease (Ministry of Health of the Republic of Indonesia, 2022). The maternal mortality rate in Central Java in 2020 was 84.6/100,000 live births, in 2021 it will be 199/100,000 live births. In 2022 there will be a decrease to 98.6/100,000 live births, hypertension in pregnancy and bleeding are still the cases of death in that year (Central Health Office, 2022). Brebes Regency shows that the maternal mortality rate in 2020 was 62 cases. In 2021 there were 105 cases and in 2022 there was a decrease to 50 cases with the causes of death being bleeding, hypertension, infection and heart disorders (Brebes District Health Office, 2021) Objective: Reduce maternal and infant morbidity and mortality rates in Brebes Regency, especially the Bumiayu Community Health Center area Conclusion: After providing comprehensive midwifery care to Mrs. S aged 32 years with Chronic Energy Deficiency (KEK) at BPM Mrs. The R working area of the Bumiayu Community Health Center from the third trimester of pregnancy, childbirth, newborns, postpartum and family planning is in good condition.

Fertility decline in most countries of sub-Saharan Africa has thus far started later and proceeded more slowly than in countries in Asia and Latin America and the Caribbean undergoing the transition in fertility since the 1950s from high levels to near-replacement or even below-replacement levels (Bongaarts and Casterline 2013). Yet there is considerable variation among countries in sub-Saharan Africa: in the duration and magnitude of fertility decline, whether stalls in fertility decline have occurred, shifts in the timing of births, and even the economic and population subgroups that have led declines in family size (Caldwell, Orubuloye, and Caldwell 1992; Bongaarts and Casterline 2013; Cleland, Onuoha, and Timæus 1994; Cohen 1998; Ezeh, Mberu, and Emina 2009; Garenne 2008; Kirk and Pillet 1998; Rossier, Corker, and Schoumaker 2015; Timæus and Moultrie 2008). With current fertility estimated at 5.1 births per woman in the region and 19 countries in sub-Saharan Africa at or above that level and another 21 countries with at least four births per woman on average (United Nations 2015a), the pathways that future fertility takes will significantly determine population growth and age structure shifts not only in the region, but increasingly for the world. Sub-Saharan Africa is projected to grow from 840 million people in 2010 to nearly 1.4 billion in 2030 (United Nations 2015a). Above-replacement fertility is projected to account for 61 percent of this population increase from 2010 to 2030 compared to 4 percent from mortality reduction, 37 percent from a young age structure in 2010 (population momentum), and a small negative contribution from migration (United Nations 2015b). These projections draw on the United Nations medium variant and do not take into account the uncertainty around current and future fertility levels, uncertainty that only increases the farther the projection period extends. For high-fertility countries in sub-Saharan Africa, the wide uncertainty around where fertility is headed can result in substantial differences in population projections (Ezeh, Mberu, and Emina 2009; Fuchs and Goujon 2014; Gerland et al. 2014). Beyond population numbers alone, the uncertainty about fertility decline also bears on policy-relevant questions such as the degree to which the region may realize a demographic dividend (e.g., how fast the shift will occur toward a higher ratio of working-age population to non-working-age population and the consequent effects on economic growth) (Bloom et al. 2013) or the extent to which greenhouse gas emissions might be reduced by slowing population growth (O'Neill et al. 2010). Earlier reviews of the fertility transitions in sub-Saharan Africa through the 1980s and 1990s showed fertility declines underway in most countries and particularly rapid declines in several countries in Eastern Africa (Kenya, Rwanda, and Zimbabwe) and Southern Africa (Botswana and South Africa) (Cleland, Onuoha, and Timæus 1994; Cohen 1998; Kirk and Pillet 1998). Subsequent survey data suggested an apparent slowdown in the pace of fertility decline in more than ten sub-Saharan African countries (Bongaarts 2008). Further analyses of the data indicated far fewer stalls in fertility decline had in fact occurred in the region, with evidence strongly supportive of stalls in Kenya and Rwanda, and the stalls that do occur have been of relatively short duration (Garenne 2011; Machiyama 2010; Schoumaker 2009, 2014). Most countries in sub-Saharan Africa still lack complete and accurate vital registration data on births, so these and other analyses of fertility trends will continue to rely heavily on survey data and require reconciling estimates from different sources (Alkema et al. 2012; United Nations 2015c). Our aim in this chapter is to provide an updated and concise description of the diversity of fertility decline patterns among countries1 in sub-Saharan Africa, drawing on the latest series of fertility estimates that take into account many different data sources and that are harmonized with other demographic components (United Nations 2015d). We focus on the level of fertility prior to the start of fertility decline, the time period of the fertility transition, and the estimated pace of decline. We also explore the implications of different fertility decline patterns for future fertility and population projections in the region. We draw on the distinct patterns of fertility decline among countries worldwide that are advanced in (or have completed) their first fertility transition to construct probabilistic fertility and population projections for sub-Saharan African countries. The illustrative comparisons of projections highlight the demographic impact if future fertility decline in sub-Saharan countries were to accelerate and follow the rapid pace of decline already experienced by a diverse group of countries. The UN Population Division publishes estimates and projections of period total fertility rates in World Population Prospects (WPP) every two years. The estimates of total fertility presented in this chapter are from the 2015 Revision and are for five-year time periods for countries or areas with 90,000 persons or more in 2015 (United Nations 2015a). The most recent data underlying the total fertility estimates from the 2015 Revision for 50 sub-Saharan African countries2 (United Nations 2015c) are from the period 2013–2014 for 14 countries, 2010–2012 for 30 countries, and 2005–2009 for six countries. A common challenge in estimating total fertility over time, especially for countries without accurate or complete vital registration data,3 is that estimates vary across data sources and by the methodology used to derive those estimates. Schoumaker (2014) showed that the underlying data from standardized, high-quality surveys such as the Demographic and Health Surveys vary considerably within and across countries, yielding total fertility estimates from recent fertility data of good quality (e.g., Gabon, Lesotho, Namibia, and Zimbabwe) and of poor quality (e.g., Benin, Burkina Faso, Cameroon, Chad, Ethiopia, Guinea, Madagascar, Mali, Mozambique, Niger, Nigeria, and Uganda). Total fertility estimates based on births in the last three years tend to be under-estimated by 10 percent or more in most of the surveys with poor quality fertility data from retrospective birth histories. Figure 1 illustrates the variation in total fertility estimates based on survey data and estimation methods (direct methods (D) and cohort-completed (C) fertility) for Nigeria for the period 1985 to 2015. The thick lines show the total fertility estimates from the 2010, 2012, and 2015 Revisions of WPP. Given new data from the 2008 DHS and other surveys, total fertility in the 2012 Revision was re-estimated at a higher level than the 2010 Revision beginning in the mid-1980s, resulting in almost half a birth per woman difference in the 2005–2010 period. The 2015 Revision used new data from the 2013 Demographic and Health Survey. The 2013 survey estimates highlight a recurring pattern in which fertility estimates based on a recent reference period are consistently lower than fertility estimates from reconstructed birth histories for the same time point. Looking only at fertility estimates from a three-year reference period, the 2013 DHS shows a decline in total fertility to 5.5 births per woman from a stalling pattern of 5.7 births per woman in the 2003 and 2008 DHS. Yet the absolute differences are large between these three-year reference period estimates and those for the same time point from the reconstructed birth histories: about half a birth difference in the mid-2000s (comparing the 2008 and 2013 survey estimates) and about one birth difference in the early 2000s (comparing the 2003 survey estimate to those from the 2008 and 2013 surveys). Estimates of the total fertility rate for Nigeria 1985–2015 based on various data sources and estimation methods, and WPP estimates from the 2010, 2012, and 2015 Revisions NOTES: DHS = Demographic and Health Survey; MICS = Multiple Indicator Cluster Survey; MIS = Malaria Indicator Survey. (C) refers to cohort completed fertility (i.e., average number of children ever born) for women aged 40–44 and 45–49 at the date of the survey and backdated using their mean age of childbearing. (D) refers to direct fertility estimates based on maternity histories or recent births in the 12 or 24 months preceding the survey. SOURCES: Federal Office of Statistics of Nigeria (1992); National Bureau of Statistics (2008, 2012); National Population Commission (2000, 2002, 2004, 2009, 2012, 2014). WPP fertility estimates consider as many types and sources of empirical estimates as possible, including retrospective birth histories and direct and indirect fertility estimates (Gerland 2014). The 2015 Revision updated all total fertility estimates taking into account new data and the inconsistencies among estimates. Moreover, total fertility estimates are derived to ensure as much internal consistency as possible with all other demographic components and intercensal cohorts enumerated in successive censuses (United Nations 2015d). The advantages of this approach are that the estimates are internally consistent within a country over time and with respect to other related demographic information. A disadvantage is that the estimates can depart from a country's official estimates of fertility. Figure 2 shows the estimated trends in period total fertility for sub-regions of sub-Saharan Africa from 1950 to 2015. It was high (above six births per woman) in all sub-regions in 1950–1955. Fertility remained high in Eastern and Western Africa until the 1980s, after which it began a slow decline to 4.9 births per woman in Eastern Africa and 5.5 in Western Africa in 2010–2015. Fertility in Middle Africa began to decline a decade later and more slowly, reaching 5.8 births per woman in 2010–2015. Southern Africa departed from the overall trends with a decline beginning in the 1950s and dropping below three births per woman in the 2000s. The 2010–2015 estimate of 2.5 births per woman in Southern Africa is about half the total fertility level in Eastern, Middle, and Western Africa. Sub-regional trends in total fertility, sub-Saharan Africa, 1950–2015 SOURCE: United Nations 2015a The sub-regional fertility levels mask diverse levels among countries. Figure 3 shows country-specific total fertility levels in 2010–2015. Among the 16 countries in Western Africa, total fertility ranged from 2.4 in Cabo Verde to 7.6 in Niger. One additional country had a fertility level of more than six births per woman (Mali), six countries had fertility between five and six births per woman (Burkina Faso, Côte d'Ivoire, Gambia, Guinea, Nigeria, and Senegal), and seven had fertility between four and five births per woman. Total fertility levels in countries in Africa, 2010–2015 SOURCE: United Nations 2015a. Total fertility levels ranged even more widely among the 20 countries in Eastern Africa, from 1.5 in Mauritius to 6.6 in Somalia. One additional country in Eastern Africa still had a fertility level above six in 2010–2015 (Burundi) and six countries had fertility between five and six births per woman (Malawi, Mozambique, South Sudan, Uganda, Tanzania, and Zambia). Eight countries had fertility levels between four and five births per woman. The lowest levels of fertility were in Djibouti (3.3 births per woman) and in the small island countries of Mauritius, Réunion, and Seychelles (less than three births per woman). The nine countries of Middle Africa all had fertility levels at or above four children per woman. In Angola, Chad, and DR Congo, fertility levels were six or more births per woman, and in the remaining six countries fertility levels were between four and five births per woman. While fertility in Southern Africa is largely dominated by South Africa's pattern, the range in fertility among the five countries in the sub-region is narrow, from 2.4 births per woman in South Africa to 3.6 in Namibia. Both Botswana and South Africa now have fertility levels below three births per woman. The start of the fertility transition also varies widely across sub-Saharan Africa. We analyze when and at what level of fertility a country experienced a maximum total fertility level before the onset of fertility decline. This maximum is defined as the most recent five-year time period where total fertility is within half a child of the overall maximum fertility in the country over the 1950–2015 estimation period, thus excluding random fluctuations in pre-transition fertility (Alkema et al. 2011). Figure 4 shows the diversity across countries and within sub-regions in the level and time period of the maximum fertility level before the onset of fertility decline, as assessed in the 2015 Revision. By the late 1970s, 29 sub-Saharan countries were on the verge of a fertility decline, increasing to 40 countries by the early 1980s. While the maximum fertility before the onset of fertility decline was reached in all countries in Southern Africa by the late 1970s, the range of experiences was much wider among countries in Eastern Africa (from the early 1950s in Réunion to the late 1990s in Somalia), Middle Africa (from the late 1960s in Angola to the late 1990s in Chad and DR Congo), and Western Africa (from the early 1960s in Cabo Verde to the late 1990s in Niger). Maximum total fertility in the time period before the onset of fertility transition, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The maximum fertility before the onset of fertility transition ranged from less than six births per woman in five countries (Central African Republic, Equatorial Guinea, Gabon, Lesotho, and Seychelles) to more than eight births per woman in two countries (Kenya and Rwanda). There was a positive but weak relationship between the maximum fertility level and the timing of when fertility transition commenced (R2 = .04). The transition from the maximum fertility to the current estimated fertility level in 2010–2015 has been slow for most countries in sub-Saharan Africa. We examine both the maximum decline in fertility in a five-year period and the duration of time between the maximum fertility level and when a country achieved a 10 percent decline in total fertility. A 10 percent decline in total fertility is one of several empirical rules that researchers have used to identify when fertility has begun to decline in a sustained manner from a pre-transitional maximum (Bongaarts and Casterline 2013; Coale and Treadway 1986; United Nations 2014), including differences in the time period selected to identify the onset of fertility decline (Casterline 2001) and the magnitude of decline (e.g., 5 percent decline with further conditions for subsequent changes applied, see Bryant 2007). While the different rules all strive to distinguish sustained decline from random fluctuations in fertility levels, each rule will have different repercussions for interpretations about the timing of onset of fertility decline, duration and pace of decline, and correlations with levels of development and other indicators. In seven countries the first steps of fertility decline took place over a long time period. In Angola, Gambia, and Uganda, a 10 percent decline from the maximum fertility level took 40 years; and in Lesotho, Mozambique, Niger, and Tanzania, it took 30 to 35 years (Appendix Table 1).4 In other countries the fertility decline never gained speed. Overall declines from the country-specific maximum fertility level to the level in 2010–2015 were very slow in nine countries (Angola, Congo, Gambia, Mali, Mozambique, Niger, Nigeria, Uganda, and Tanzania), where average fertility declines were 0.2 children per woman or less, a pace at which it would take at least 25 years to realize a decline of one birth per woman (Appendix Table 1). During the fertility transition, some countries experience an acceleration of fertility decline that, based on historical experience of past transitions in Latin America and the Caribbean and Asia, might reach a decline of more than one birth per woman in a five-year period (United Nations 2015a). In most countries in sub-Saharan Africa, however, the maximum fertility decline is much smaller. The largest declines of more than one birth per five-year period were registered in the early-transition countries in Eastern Africa before 2000 (Djibouti, Mauritius, Mayotte, Réunion, Rwanda, Seychelles, and Zimbabwe). For some countries the maximum fertility decline is projected to be reached in the future: one country in Eastern Africa (Mozambique), four in Middle Africa (Angola, DR Congo, Equatorial Guinea, and Sao Tome and Principe), and five in Western Africa (Gambia, Guinea, Mali, Niger, and Nigeria). Figure 5 shows that countries reaching the maximum fertility decline later tend to reach it at lower levels (the correlation across the 50 countries is R2 = .45). Maximum five-year decline in total fertility since the onset of fertility transition, actual and projected, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The pace of fertility decline is illustrated in Figure 6 for Ethiopia and Nigeria, the two most populous countries in sub-Saharan Africa. Estimated levels of total fertility from 1950–1955 to 2010–2015 are shown with a fitted model of the fertility transition. Both countries reached maximum fertility levels before the onset of fertility decline between the late 1970s and early 1980s, and both reached a 10 percent decline from this maximum in the early 2000s. While Ethiopia reached the peak pace of fertility decline in 2005–2010 (a decline of nearly one birth in the five-year period), Nigeria's fertility decline has been consistently slow, with a low peak pace of decline. The maximum fertility decline per five-year period in Nigeria is projected to be reached in the future (a decline of 0.3 births in 2020–2025). The difference in the pace results in current total fertility of 4.6 births per woman in Ethiopia and 5.7 in Nigeria. The 80 percent prediction intervals (dashed lines in Figure 6) around the probabilistic projections of total fertility indicate the magnitude of the different fertility changes that could reasonably happen. For example, by 2045–2050 there is a one in ten chance that total fertility in Nigeria could be as low as 2.6 or as high as 4.4 (Figure 6 and Appendix Table 2). Observed decline in total fertility from 1950–1955 to 2010–2015, predicted decline, and time period of maximum TFR and maximum decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. Given the slow pace of fertility decline in most sub-Saharan African countries thus far and the uncertainty around the pace of future declines, what might be the impact on future fertility levels and the growth in total population if fertility declines in the region followed an accelerated pace that has already been experienced by countries that have completed or are nearing completion of the fertility transition? To answer this question, we generated probabilistic fertility scenarios for 2015–2100 using a modeling approach described in Alkema et al. (2011) and implemented in a publicly accessible software package BayesTFR (Ševčíková, Alkema, and Raftery 2011). The standard United Nations 2015 probabilistic results pool the experience of all countries having similar fertility levels and trends (the baseline scenario). An alternative probabilistic scenario was created using only the fertility transition experiences of 21 countries5 that shared a similar pattern of accelerated decline in fertility (the accelerated scenario): specifically, a slow pace at the start of the transition that sharply rises to a peak pace of decline before steadily tapering off after total fertility has reached about four births per woman. These 21 countries, which include—among countries with more than 10 million inhabitants in 2015—Bangladesh, China, El Salvador, Morocco, Peru, South Africa, Sri Lanka, Thailand, Turkey and Uzbekistan, represent disparate institutional, social, economic, and cultural contexts and yet experienced a similar pattern of a relatively abrupt acceleration of fertility decline. The objective of this exercise is to examine the impact on population size of fertility decline that is more rapid than currently projected, and to illustrate the implications of such a hypothetical scenario. While we do not theorize or analyze the factors that produced these rapid fertility declines, the fact that a similar pattern of fertility decline took place within such a diverse group of countries raises the possibility that a similar decline could occur within a region that also reflects quite distinctive and diverse contexts. Each fertility scenario is used to simulate 10,000 probabilistic population projections for 2015–2100 under identical conditions (i.e., using the same mortality and migration assumptions) that show the population growth trajectories if sub-Saharan African countries were to follow a specific fertility decline pattern.6 The implications of this specific fertility decline pattern for future fertility trends are shown in Figure 7 for Ethiopia and Nigeria. The accelerated fertility decline scenario leads to much more rapid declines for these countries. By 2045–2050, median projected total fertility in Ethiopia would decline from 2.3 (baseline) to 2.0 (accelerated decline) and in Nigeria from 3.6 (baseline) to 2.6 (accelerated decline) (see Appendix Table 2). Probabilistic fertility projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. The accelerated fertility scenario has greater implications for projected fertility in Nigeria, where the recent pace of fertility decline has been much slower than in Ethiopia. The projected medians of the accelerated scenario are similar to the lower bound of the baseline scenario for parts of the projections, suggesting that Ethiopia and Nigeria would need to experience a rapid fertility decline, similar to the rapid declines experienced by the group of 21 countries, in order to realize the lower-bound fertility level of the baseline scenario. The assumption that Ethiopia and Nigeria would follow the fast fertility decline experiences of these 21 countries leads to substantially lower population projections compared with the baseline scenario (Figure 8 and Appendix Table 3). If Ethiopia experienced an accelerated fertility decline, the projected population grows from 99 million in 2015 to 182 million by 2100 (80 percent prediction interval of 93 million to 324 million) compared with the higher median projection of 234 million under the baseline scenario. If Nigeria adopted the accelerated fertility decline pattern, the projected population grows from 182 million in 2015 to a median of 466 million by 2100 (80 percent prediction interval of 282 million to 777 million) rather than the faster projected growth to 737 million under the baseline scenario. Probabilistic population projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia, Nigeria, and sub-Saharan Africa SOURCES: United Nations 2015a and computations by authors. The counterfactual of sub-Saharan African countries following an accelerated fertility decline, as experienced by the group of 21 countries, results in a projected increase in total population in the region from 962 million in 2015 to 3.2 billion by the end of the century (with 80 percent probability of being between 2.8 and 3.7 billion). The total population projection for the baseline UN 2015 scenario would lead to a median projection of about 4 billion, or a difference of 770 million people with an 80 percent prediction interval mostly above the upper bound of the accelerated fertility decline scenario. We presented here an updated description of fertility decline in sub-Saharan Africa and have explored the implications for fertility and population projections if sub-Saharan African countries follow a pattern of accelerated fertility decline. Because estimates of fertility in the region rely almost entirely on survey and census data, there is still uncertainty about fertility change over time. Our analyses of trends over time were based on a new historical time series of total fertility estimates for the period 1950 to 2015 from the 2015 Revision of World Population Prospects. Fertility has remained persistently high in Eastern, Middle, and Western Africa (4.9 births per woman or higher as of 2010–2015) and declined rapidly in Southern Africa, which currently has about half the total fertility level of other sub-regions births per woman). Southern Africa is dominated by South Africa, TFR of 2.4 is the lowest in the Yet there is wide variation in total fertility across countries, even within from below fertility births per woman) in Mauritius to 7.6 births per woman in Niger. The fertility level before the onset of decline was high in all countries in sub-Saharan Africa, from less than six births per woman in five countries to more than eight births per woman in two countries. By the late 1970s, 29 countries were on the verge of fertility decline, increasing to 40 countries by the early 1980s. The transition from these maximum levels of fertility prior to the onset of fertility decline to current levels has been slow for most countries, including seven in which it took 30 years or more to a 10 percent decline in total fertility. While countries in Asia and in Latin America and the Caribbean experienced an acceleration in fertility decline of more than one birth per five-year time period, this level of acceleration has been experienced thus far by only seven countries in Eastern Africa before the The pace of fertility decline in sub-Saharan Africa will a large in the magnitude of future growth in We showed through an illustrative scenario that an acceleration in the pace of fertility decline to that already experienced by 21 countries South Africa) would the growth in future population in the region from a projected billion by to billion and total population size from a projected 4 billion people by the end of the century to 3.2 While these 21 countries represent a wide range of institutional, social, economic, and cultural a similar fast decline from fertility levels of six or more children per woman in the past to less than two or three in recent years. this accelerated pace of fertility decline to countries at of transition illustrates the substantial impact on future population growth of other possible patterns of fertility decline in sub-Saharan Africa. of this are assumption that the fertility estimates from the World Population Prospects are and the fact that uncertainty around the estimates of period total fertility in recent is not into By scenarios on the distinct fertility decline patterns that have been experienced thus far by countries that have completed or are advanced in their fertility we new patterns of fertility decline in the Because most countries in Middle Africa and Western Africa are still in the early of fertility transition, an sub-Saharan African pattern of fertility decline may still and Timæus The is not for the or of by the authors. than be to the for the

Obstetric anesthesia in China: associated challenges and long-term goals.

BackgroundDespite considerable efforts to reduce the maternal mortality ratio, numerous pregnant women continue to die in many developing countries, including Nigeria. We conducted a study to determine the incidence and causes of maternal mortality over an 8-month period in a rural-based secondary health facility located in Jahun, northern Nigeria.MethodsA retrospective observational study was performed in a 41-bed obstetric ward. From October 2010 to May 2011, demographic data, obstetric characteristics, and outcome were collected from all pregnant women admitted. The total number of live births during the study period was recorded in order to calculate the maternal mortality ratio.ResultsThere were 2,177 deliveries and 39 maternal deaths during the study period, with a maternal mortality ratio of 1,791/100,000 live births. The most common causes of maternal mortality were hemorrhage (26%), puerperal sepsis (19%), and obstructed labor (5%). No significant difference (P = 0.07) in mean time to reach the hospital was noted between fatal cases (1.9 hours, 95% confidence interval [CI] 1.1–2.6) and nonfatal cases (1.4 hours, 95% CI 1.4–1.5). Two hundred and sixty-six women were admitted presenting with stillbirth. Maternal mortality was higher for unbooked patients than for booked patients (odds ratio 5.1, 95% CI 3.5–6.2, P < 0.0001). The neonatal mortality rate was calculated at 46/1,000 live births. The main primary causes of neonatal deaths were prematurity (44%) and birth asphyxia (22%).ConclusionMaternal and neonatal mortality remains unacceptably high in this setting. Reducing unbooked emergencies should be a priority with continuous programs including orthodox practices in order to meet the fifth Millennium Development Goal.

Pakistan is the sixth most populous country in the world & is ranked 53^rd in the list of countries contributing towards high maternal mortality ratios. According to a recent maternal mortality survey, the current maternal mortality ratio of Pakistan is 186 per 100,000 live births with high disparities among provinces; Balochistan being the highest contributor with MMR of 298 per 100,000 live births. This study specifically focuses on the causes & contributory factors of high maternal deaths in Balochistan based on the evidence generated by the Maternal and Perinatal Death Surveillance & Response system. MPDSR provides the decision maker with reliable and timely data to take required action and to reduce the preventable maternal deaths. Maternal mortality data was collected from the three pilot tertiary health care facilities and data was analyzed using Statistical Package for Social Sciences version (20.0). Out of 40 notified maternal deaths in the period January 2020 till July 2020 around 39 deaths were reviewed & analyzed. This study found out that of these 39 maternal deaths around 32% were attributed to hemorrhage, and around 15% & 12% resulted from eclampsia and sepsis respectively. Other major causes of maternal deaths included obstructed labour (10%), embolism (10%) and anesthesia complications (2%) respectively. Non-medical causes of these maternal deaths included shortage of human resource (7.7%), lack of medical equipment (7.7%) and failure to recognize the danger signs earlier (5.1%). The major underlying factors of these maternal deaths included low antenatal visits, underutilization of family planning services, poor referral system and delays at all levels in the three delay model. The study concluded that almost all of these maternal deaths can be avoided by undertaking appropriate measures & timely actions.

Study question Can a machine learning (ML) model, developed using modern neural network architecture produce comparable annotation data; utilisable for algorithmic outcome prediction, to manual time-lapse annotations? Summary answer The model automatically annotated unseen embryos with comparable results to manual methods, generating morphokinetic data to enable comparably predictive outputs from an embryo selection algorithm. What is known already The application of artificial intelligence across healthcare industries, including fertility, is increasing. Several ML models are available that seek to generate or analyse embryo images and morphokinetic data, and to determine embryo viability potential. Along with photographic images, the use of time-lapse in IVF laboratories has amassed numeric data, resulting predominantly from annotated manual assessment of images over time. Embryo annotation practice is variable in quality, can be subjective and is time-consuming; commonly taking several minutes per embryo. The development of rapid, accurate automatic annotation would represent a significant time-saving as well as an increase in reproducibility and accuracy. Study design, size, duration Multicentre quality assured annotation data from 63,383 time-lapse monitored embryos (EmbryoScope®), comprising over 400 million individual images, were used to train a ML model to automatically generate morphokinetic annotations. Data was derived from 8 UK clinics within a cohesive group between 2012-2021. Accuracy was assessed using 900 unseen embryos (with live birth outcome) by comparing the output of an established in-house, prospectively validated embryo selection model when the input was either ML-automated, or manual annotations. Participants/materials, setting, methods Multi-focal plane images were processed on the Azure cloud (Microsoft) and resampled to 300x300 pixels. A Laplacian-based focal stacking algorithm merged frames into a single image. The model consisted of an EfficientNetB4 Convolutional Neural Network classifier to extract features and classify the stage of embryo images. A Temporal Convolutional Network interpreted a time-series of image features; producing annotations from pronuclear fading through to blastocyst. Soft localisation loss function used QA data to integrate annotation subjectivities. Main results and the role of chance The ML model rapidly and automatically generated annotations. Efficacy and comparability of the ML model to automate reliable, utilisable annotations was demonstrated by comparison with manual annotation data and the ML model’s ability to auto-generate annotations which could be used to predict live birth by providing annotation data to an established, validated in house embryo selection model. Live birth-predictive capability was measured, and benchmarked against manual annotation, using the area under the receiver operating characteristic curve (AUC). When tested on time-lapse images, collected from pronuclear fading to full blastulation, representing 900 previously unseen, transferred blastocysts where live birth outcomes were blinded, the in-house developed auto-annotation ML model resulted in an AUC of 0.686 compared with 0.661 for manual annotations, for live birth prediction. Auto annotation using the developed model took only milliseconds to complete per embryo. The developed auto-annotation model, built and tested on large data, is considered suitable for productionisation with the aim of being validated and integrated into an application to support IVF laboratory practice. Limitations, reasons for caution Whilst this model was trained to recognise key morphokinetic events, there are other morphokinetic variables that may be useful in the prediction of live birth and further improve embryo selection, or deselection, ability. Akin to manual interpretation, some embryos may fail to be annotated or need second opinion. Wider implications of the findings There is increasing evidence supporting the application of ML to utilise big data from time-lapse imaging and fertility care generally. Whilst promising benefits to IVF clinics and patients, responsible use of data is required alongside large high-quality datasets, and rigorous validation, to ensure safe and robust applications. Trial registration number N/A

During the 12-month period from September 1982 to August 1983, 9,317 live births and 58 maternal deaths were recorded in Melanda and Islampur upazilas in the Jamalpur district of rural Bangladesh, giving a maternal mortality rate of 62.3 per 10,000 live births. Maternal mortality was positively related to maternal age and parity, with the mortality risk rising very sharply beyond age 35 years, and beyond parity four among women aged 25-34 years in particular. The most common causes of maternal death were eclampsia (20.7 percent), septic abortion (20.7 percent), postpartum sepsis (10.3 percent), obstructed labor (10.3 percent), and antepartum and postpartum hemorrhage (10.3 percent). These findings indicate that family planning, by decreasing the likelihood of pregnancy after age 35 and parity four, can help reduce the proportion of women at risk of maternal mortality.

Maternal near miss are the cases that those womens who nearly died but survived as a complications during pregnancy and childbirth or within 42 days of termination of pregnancy. Maternal mortality has been the leading cause of illness and death among women of reproductive age in India. In the recent years, the concept of the WHO maternal near miss (MNM) has been adopted by the tertiary level hospitals as it has an added advantage of offering a large number of cases for intervention and for the evaluation of the maternal healthcare being provided by the health-care system to reduce maternal mortality. Goal and objectives: The main objectives of the study are to identify the incidence of the maternal near-miss events, observe the trend of near-miss events, and compare the nature of near-miss events with maternal mortality. Materials and Methods: It was an prospective study conducted in the department of obstetrics and gynecology at Chengalpattu medical college and hospital, Tamilnadu, India. From January 2023 – December 2023, over a period of one year. Potentially life-threatening maternal complications and maternal mortalities were identified and recorded in the case sheets of the patients and near miss registers were maintained in the department, and monthly near miss audit was conducted in the department. Near-miss cases were analysed based on the Health and Family Welfare Government of India guidelines 2014 and WHO criteria. Datas were collected on demographic characteristic including age, parity, nature of complications, intensive care unit admissions, and timing of near miss events in relation with admissions. Similarly statistical analysis was also done. Results: The total no of near miss admission of the obstetrics and gynecology department during the study period was 62 and the maternal mortality was 11 cases. During the study periods the total no of deliveries were 10856 and total live births were 10,847. The incidence of Near miss was high in the age group of 26-30 years (22%) and the lowest incidence was seen in the extremes of age less than 20 years and more than 35 years each carries the risk of (9%). Among the etiology assessed Hypertensive disorders of pregnancy contributes majority of the near miss cases (33%) of this Hypertensive disorders of pregnancy Antepartum eclampsia contributes most of the cases, followed by Heart disease (20%) and post-partum hemorrhage (11%). With reference to the obstetric code near miss events is most commonly seen in primigravidas which contributes about (45%) of the cases, (98%) of the cases are being referred from secondary care referral centers and (2%) came as self. Comparing with educational qualifications (98%) of the near miss cases were educated. From this study the maternal near miss incidence of the institution is 5.7/100000 live births which indicates optimum quality of the treatment given at this institution and the focus has been aimed against 0 mortality. The maternal mortality ratio was 563:1which indicates for every 563 deliveries there is 1 maternal mortality. Conclusions: Enlighting the causes of near miss events of pregnancy of our institution, Hypertensive disorders in pregnancy, heart disease complicating pregnancy and postop partum hemorrhage were the three leading major causes of maternal near-miss events and mortality followed by sepsis. As the near-miss analysis indicates the quality of health care and causes are almost similar to maternal mortality, so its registry should be followed along with maternal mortality.

Trends in maternal mortality in Suriname: 3 confidential enquiries in 3 decades

To investigate the burden of maternal near-miss and death due to rupture of the gravid uterus, the indicators of quality of care, and avoidable factors associated with care deficiencies for ruptured uterus in Nigerian tertiary hospitals. Secondary analysis of a nationwide cross-sectional study. Forty-two tertiary hospitals. Women admitted for pregnancy, childbirth or puerperal complications. Cases of severe maternal outcome [SMO: maternal near-miss (MNM) or maternal death (MD)] following uterine rupture were prospectively identified over 1year. Incidence of SMO, indicators of quality of care, and avoidable factors associated with deficiencies in care. There were 91724 live births and 3285 women with SMO during the study period. SMO due to uterine rupture occurred in 392 women: 305MNM and 87 MD. Uterine rupture accounted for 11.9, 13.3, and 8.7% of all SMO, MNM, and MD, respectively. SMO, MNM, and intra-hospital maternal mortality ratios due to uterine rupture were 4.3/1000 live births, 3.3/1000 live births, and 94.8/100000 live births, respectively. Mortality index (% of MD/SMO) was 22.2%, and MNM:MD ratio was 3.5. Avoidable factors contributing to deaths were related to patient-orientated problems, especially late hospital presentation and lack of insurance to cover life-saving interventions. Medical personnel problems contributed to care deficiencies in one-third of women who died. Uterine rupture significantly contributes to SMO in Nigerian tertiary hospitals. Strategies to improve maternal survival should address avoidable institutional factors and include community-based interventions to encourage skilled attendance at birth and early referral of complications. Uterine rupture remains an important cause of maternal death in Nigerian tertiary hospitals.

Introduction: In many developing countries, maternal mortality is still high. Although an effort has been made to achieve Millennium Development Goals, the positive results and significant decrease in maternal mortality is yet to come, especially in the sub-Saharan African countries. Ethiopia is a member of sub-Saharan countries with high maternal mortality. Since health education and promotion activities are insufficient, most births take place at home by non-health professionals and the public awareness in health facility delivery remains low. Because of these underlying factors, many Ethiopian women are dying from pregnancy and other pregnancy related causes. It is important to evaluate the women that deliver at health facilities versus the home. Objective: To identify factors associated with health facility births in Ethiopia using demographic characteristics, pregnancy-related factors, and media-related factors and to know users of health facility births. Method: This study used the data from the Ethiopian Demographic and Health Survey (EDHS) 2011, which is a national representative sample data. This study analyzed specific health facility births. The demographic characteristics, pregnancy- related factors, and media-related factors were used as independent variables. Delivery places, the outcome variables were treated as categorical variables both bivariately and multivariately. A logistic regression analysis was conducted to identify factors associated with delivery place. Result: Maternal mortality in Ethiopian still high 676/100,000 per life births. Health facility births were still low. A total of 9,429 home deliveries and 1,467 health facility deliveries were identified from the EDHS 2011. Only 13.5% of pregnant women gave birth at a health facility. Women’s residence regions, residential place, educational level, wealth index, husband/partner education level, antenatal care visit and media exposure were significantly associated with their delivery place choice. Whereas older age, high number of parity and married women were negatively associated with health facility deliveries. The outcome of birth (child alive or not) had no association with delivery place selection. Conclusion: This study verifies the factors associated with health facility births in Ethiopia. Region, place of residence, education level of pregnant women and partner and media exposure had significant association. To promote health facility utilization in birth and decrease maternal mortality special efforts will be needed taking account the significant factors. As a positive tool for breaking regional inequality and health education, media and social media may serve an important role in facilitating health facility births.

Context: Maternal death signifies the quality of health care provided in the population. Pregnancy although a physiological state, carries risk of serious maternal morbidity and mortality sometimes. Majority of the maternal deaths in developing countries are preventable. Objective: To find the causes of maternal mortality over a period of 5.3years at a tertiary care hospital. Material And Methods: Retrospective study of 45 deaths from JAN 2009 TO MARCH 2014. Maternal mortality ratio and causes were analysed and compared. Results: Over the study period MMR was 277/1, 00,000 LIVE BIRTHS .The leading direct cause was SEPSIS (51.11%) & Indirect cause was ANAEMIA (62.2 %). Conclusions: Maternal mortality can be prevented with proper identification and care .There is change in the trend of causes of maternal mortality. Strengthening of the first referral units with equipments, adequately competent staff and blood bank can decrease maternal mortality and morbidity. Contributing factors should be tackled to decrease death of young female. Auditing the reasons for maternal mortality in a resource poor country is extremely helpful in not only identifying the reasons but also in identifying the preventable causes of maternal mortality.

Aims: To determine prevalence, causes and factors leading to severe acute maternal morbidity and mortality during COVID 19 pandemic.&#x0D; Methods: The was a cross sectional observational study conducted in the department of Obstetrics and Gynaecology of Manipal teaching hospital from March 2020 to February 2022 amid COVID 19 pandemic. Women who sustained severe acute maternal morbidity (according to World health organization organ system criteria) and maternal deaths during pregnancy, labour and six weeks postpartum were included. Maternal characteristics, total live births, pregnancy outcome, causes and factors leading to morbidity and mortality were noted. The acquisitioned data was analyzed using Statistical Package for Social Sciences version 21.&#x0D; Results: There were 37 cases of severe acute maternal morbidity and 2 cases of maternal deaths. The severe acute maternal morbidity ratio was 9.4 per 1000 live births and maternal mortality ratio was 51 per 100,000 live births. Therefore, severe acute maternal morbidity mortality ratio was 18.5:1 and mortality index 5.1%. Haematological and coagulation system (45.9%) and neurological system (29.7%) were the common organ systems involved. Hypertensive disorders (40.5%) and haemorrhage (32.4%) were the main causes of severe acute morbidity. Both maternal deaths occurred due to eclampsia. There was no severe acute maternal morbidity or mortality due to COVID infection in pregnancy. Delay in seeking and reaching to the health centre (65%) was major delay leading to severe morbidity and mortality.&#x0D; Conclusions: Indices and causes of severe acute maternal morbidity and mortality during COVID 19 pandemic was not worse compared to those prior to COVID 19 pandemic. Primary delay in seeking and reaching health care was the main factor leading to severe morbidity and mortality.&#x0D;

Introduction: In Singapore, published maternal mortality rates (MMR) over the last decade (1990 to 1999) have been so low (0.0 to 1.0 per 1000 live births and still births) as to imply that maternal deaths are rare to the point of being non-existent in some years. This inference is counterintuitive, and earlier studies on maternal mortality, amniotic fluid embolism (AFE) and pulmonary thromboembolism (PTE) have also suggested otherwise. Accordingly, local trends in maternal mortality warrant further examination. Materials and Methods: A descriptive and comparative study, comprising a clinico-pathological review of maternal deaths, for which autopsies were conducted by the Centre for Forensic Medicine, during a 10-year period from 1990 to 1999. The annual necropsy-based, MMR (estimated MMR), as well as the prevalence of maternal deaths during this time, were estimated with the aid of the relevant, published demographic data on live births and still births. These were compared with the corresponding rates calculated (calculated MMR) from raw demographic data on maternal deaths classified as such in the published data. Results: Coronial autopsies were conducted on a total of 51 cases of maternal death, of which 33, 17 and 1 were direct, indirect and fortuitous deaths, respectively. The annual, estimated MMR ranged from 0.4 to 1.8 per 10,000 live births and still births. The estimated MMR was twice as high as the calculated MMR and a statistically significant upward linear trend was demonstrated for the former (P = 0.046). AFE (16/51) and PTE (10/51) were the two most common causes of maternal death; their corresponding cause-specific, 10-year prevalence being 0.33 and 0.21 per 10,000 live births and still births, respectively. There was apparent clustering of the cases of PTE during the earlier part of the last decade (1990 to 1995), corresponding to a statistically significant, upward trend in its overall necropsy incidence during that time (P = 0.019). Cardiovascular and pulmonary disorders constituted the bulk of indirect deaths (13/17), while antenatal suicides accounted for 3 of these deaths (0.06 per 10,000 live births and still births). Conclusions: This was an upward trend in MMR, as reflected in coronial casework, over the last decade. It would appear that the local, estimated (direct and indirect) maternal mortality prevalence compares favourably with the MMR reported in developed countries. The apparent rate of AFE was no less than 4 times higher than that reported in the United Kingdom, while the maternal mortality rate from PTE was at least as high. Allowing for the possibility that such deaths were under-reported, the actual annual MMR and 10-year prevalence could be appreciably higher than the estimates presented here. There may well be a case for the establishment of a comprehensive database of maternal deaths, that is updated continually and contemporaneously, in Singapore.

The Maternal Mortality Rate (MMR) is a crucial indicator to assess public health, reflecting female deaths due to complications of pregnancy, childbirth, and postpartum per 100,000 live births. In Indonesia, MMR is still high, reaching 305 per 100,000 live births, mainly due to the lack of early detection of the high risk of pregnancy. Assisted activities for pregnant women in the Working Area of the Tirto I Health Center, Pekalongan Regency, to improve the detection and management of high-risk of pregnancy. This activity was carried out through a community empowerment program involving 32 pregnant women from Samborejo Village and Pandanarum Village, for eight months. The methods used include socialization, mentoring, and continuous education with the Islamic Religion and Health approach. The results of the activity showed an increase in pregnant women's knowledge about pregnancy risks, increased community participation, and early identification of risky medical conditions such as pre-eclampsia, KEK, anemia, and hypertension. Emotional and psychological support is provided through an Islamic values-based approach, which helps reduce stress and improve the mental health of pregnant women. Collaboration between the service team, village midwives, and health cadres strengthens efforts to detect and handle the high risk of pregnancy. This program successfully increased the knowledge of pregnant women by 13% and sufficient knowledge by 3% about the prenatal care. It reduced the risk of pregnancy complications, which ultimately contributed to the reduction of maternal mortality and infant mortality rates in the Tirto I Health Center Working Area.. Evaluation and follow-up show that the main goal is achieved and recommend future improvements. Efforts are needed to increase collaboration between various parties such as health offices, health centers, universities, and community organizations to create a stronger support network for early detection of high-risk pregnancies.