Objectives: To identify and map all trials in maternal health conducted in low- and middle-income countries (LMIC) over the 10-year period 2010-2019, to identify geographical and thematic trends, as well as compare to global causes of maternal death and pre-identified priority areas. Design: Systematic scoping review. Primary and secondary outcome measures: Extracted data included location, study characteristics and whether trials corresponded to causes of mortality and identified research priority topics. Results: Our search identified 7,269 articles, 874 of which were included for analysis. Between 2010 and 2019, maternal health trials conducted in LMICs more than doubled (50 to 114). Trials were conducted in 61 countries – 231 trials (26.4%) were conducted in Iran. Only 225 trials (25.7%) were aligned with a cause of maternal mortality. Within these trials, pre-existing medical conditions, embolism, obstructed labour, and sepsis were all under-represented when compared with number of...

Sources:a National Institute for Population Research and Training, MEASURE Evaluation, International Centre for Diarrhoeal Disease Research (2012) Bangladesh Maternal Mortality and Health Care Survey 2010. Available: http://www.cpc.unc.edu/measure/publications/tr-12-87. Accessed October 15, 2012.b World Health Organization (ND) WHO Maternal Mortality Country Profiles. Available: www.who.int/gho/maternal_health/en/#M. Accessed 1 March 2015.c Lozano R, Wang H, Foreman KJ, Rajaratnam JK, Naghavi M, Marcus JR, et al. (2011) Progress towards Millennium Development Goals 4 and 5 on maternal and child mortality: an updated systematic analysis. Lancet 378(9797): 1139–65. 10.1016/S0140-6736(11)61337-8d UNFPA, UNICEF, WHO, World Bank (2012) Trends in maternal mortality: 1990–2010. Available: http://www.unfpa.org/public/home/publications/pid/10728. Accessed 7 October 2012.e Bangladesh Bureau of Statistics, Statistics Informatics Division, Ministry of Planning (December 2012) Population and Housing Census 2011, Socio-economic and Demographic Report, National Series–Volume 4. Available at: http://203.112.218.66/WebTestApplication/userfiles/Image/BBS/Socio_Economic.pdf. Accessed 15 February, 2015.f Mozambique National Institute of Statistics, U.S. Census Bureau, MEASURE Evaluation, U.S. Centers for Disease Control and Prevention (2012) Mortality in Mozambique: Results from a 2007–2008 Post-Census Mortality Survey. Available: http://www.cpc.unc.edu/measure/publications/tr-11-83. Accessed 6 October 2012.g Ministerio da Saude (MISAU), Instituto Nacional de Estatística (INE) e ICF International (ICFI). Moçambique Inquérito Demográfico e de Saúde 2011. Calverton, Maryland, USA: MISAU, INE e ICFI.h Mudenda SS, Kamocha S, Mswia R, Conkling M, Sikanyiti P, et al. (2011) Feasibility of using a World Health Organization-standard methodology for Sample Vital Registration with Verbal Autopsy (SAVVY) to report leading causes of death in Zambia: results of a pilot in four provinces, 2010. Popul Health Metr 9:40. 10.1186/1478-7954-9-40i Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University Teaching Hospital Virology Laboratory, University of Zambia, and ICF International Inc. 2014. Zambia Demographic and Health Survey 2013–14: Preliminary Report. Rockville, Maryland, USA. Available: http://dhsprogram.com/pubs/pdf/PR53/PR53.pdf. Accessed February 26, 2015.j Centers for Disease Control and Prevention (2014) Saving Mothers, Giving Life: Maternal Mortality.Phase 1 Monitoring and Evaluation Report. Atlanta, GA: Centers for Disease Control and Prevention, US Dept of Health and Human Services. Available at: http://www.savingmothersgivinglife.org/doc/Maternal%20Mortality%20(advance%20copy).pdf. Accessed 26 February 2015.k Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University of Zambia, and Macro International Inc. 2009. Zambia Demographic and Health Survey 2007. Calverton, Maryland, USA: CSO and Macro International Inc.Comparison of Maternal Mortality Estimates: Zambia, Bangladesh, Mozambique.

Abstract

The 2019 Pakistan Maternal Mortality Survey (2019 PMMS) was the first stand-alone maternal mortality survey conducted in Pakistan. A nationally representative sample of 1,396 primary sampling units were randomly selected. The survey was expected to result in about 14,000 interviews with ever-married women age 15-49.

The primary objective of the 2019 PMMS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the survey was designed and carried out with the purpose of assessing where Pakistan stands on maternal health indicators and how well the country is moving toward these targets. Overall aims of the 2019 PMMS were as follows: - To estimate national and regional levels of maternal mortality for the 3 years preceding the survey and determine whether the MMR has declined substantially since 2006-07 - To identify medical causes of maternal deaths and the biological and sociodemographic risk factors associated with maternal mortality - To assess the impact of maternal and newborn health services, including antenatal and postnatal care and skilled birth attendance, on prevention of maternal mortality and morbidity - To estimate the prevalence and determinants of common obstetric complications and morbidities among women of reproductive age during the 3 years preceding the survey

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2019 PMMS used a multistage and multiphase cluster sampling methodology based on updated sampling frames derived from the 6th Population and Housing Census, which was conducted in 2017 by the Pakistan Bureau of Statistics (PBS). The sampling universe consisted of urban and rural areas of the four provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan), Azad Jammu and Kashmir (AJK), Gilgit Baltistan (GB), Federally Administered Tribal Areas (FATA), and the Islamabad Capital Territory (ICT). A total of 153,560 households (81,400 rural and 72,160 urban) were selected using a two-stage and two-phase stratified systematic sampling approach. The survey was designed to provide representative results for most of the survey indicators in 11 domains: four provinces (by urban and rural areas with Islamabad combined with Punjab and FATA combined with Khyber Pakhtunkhwa), Azad Jammu and Kashmir (urban and rural), and Gilgit Baltistan. Restricted military and protected areas were excluded from the sample.

The sampled households were randomly selected from 1,396 primary sampling units (PSUs) (740 rural and 656 urban) after a complete household listing. In each PSU, 110 randomly selected households were administered the various questionnaires included in the survey. All 110 households in each PSU were asked about births and deaths during the previous 3 years, including deaths among women of reproductive age (15-49 years). Households that reported at least one death of a woman of reproductive age were then visited, and detailed verbal autopsies were conducted to determine the causes and circumstances of these deaths to help identify maternal deaths. In the second phase, 10 households in each PSU were randomly selected from the 110 households selected in the first phase to gather detailed information on women of reproductive age. All eligible ever-married women age 15-49 residing in these 10 households were interviewed to gather detailed information, including a complete pregnancy history.

Note: A detailed description of the sample design is provided in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the 2019 PMMS: the Short Household Questionnaire, the Long Household Questionnaire, the Woman’s Questionnaire, the Verbal Autopsy Questionnaire, the Community Questionnaire, and the Fieldworker Questionnaire. A Technical Advisory Committee was established to solicit comments on the questionnaires from various stakeholders, including representatives of government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the National Bioethics Committee, the Pakistan Health Research Council, and the ICF Institutional Review Board. After being finalised in English, the questionnaires were translated into Urdu and Sindhi. The 2019 PMMS used paper-based questionnaires for data collection, while computer-assisted field editing (CAFE) was used to edit questionnaires in the field.

Cleaning operations

The processing of the 2019 PMMS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via the Internet File Streaming System (IFSS) to the NIPS central office in Islamabad. These data files were registered and checked for inconsistencies, incompleteness, and outliers. A double entry procedure was adopted by NIPS to ensure data accuracy. The field teams were alerted about any inconsistencies and errors. Secondary editing of completed questionnaires, which involved resolving inconsistencies and coding open-ended questions, was carried out in the central office. The survey core team members assisted with secondary editing, and the NIPS data processing manager coordinated the work at the central office. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate.

Response rate

In the four provinces, the sample contained a total of 116,169 households. All households were visited by the field teams, and 110,483 households were found to be occupied. Of these households, 108,766 were successfully interviewed, yielding a household response rate of 98%. The subsample selected for the Long Household Questionnaire comprised 11,080 households, and interviews were carried out in 10,479 of these households. A total of 12,217 ever-married women age 15-49 were eligible to be interviewed based on the Long Household Questionnaire, and 11,859 of these women were successfully interviewed (a response rate of 97%).

In Azad Jammu and Kashmir, 16,755 households were occupied, and interviews were successfully carried out in 16,588 of these households (99%). A total of 1,707 ever-married women were eligible for individual interviews, of whom 1,666 were successfully interviewed (98%). In Gilgit Baltistan, 11,005 households were occupied, and interviews were conducted in 10,872 households (99%). A total of 1,219 ever-married women were eligible for interviews, of whom 1,178 were successfully interviewed (97%).

A total of 944 verbal autopsy interviews were conducted in Pakistan overall, 150 in Azad Jammu and Kashmir, and 88 in Gilgit Baltistan. The Verbal Autopsy Questionnaire was used in almost all of the interviews, and response rates were nearly 100%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019 Pakistan Maternal Mortality Survey (2019 PMMS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 PMMS is only one of many samples that could have been selected from the same population, using the same design and sample size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 PMMS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios and use the Jackknife repeated replication method for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey report.

Data appraisal

Data Quality Tables

- Household age distribution

BackgroundFor countries to contribute to Sustainable Development Goal 3.1 of reducing the global maternal mortality ratio (MMR) to less than 70 per 100,000 live births by 2030, identifying the drivers of maternal mortality is critically important. The ability of countries to identify the key drivers is however hampered by the lack of data sources with sufficient observations of maternal death to allow a rigorous analysis of its determinants. This paper overcomes this problem by utilising census data. In the context of Indonesia, we merge individual-level data on pregnancy-related deaths and households’ socio-economic status from the 2010 Indonesian population census with detailed data on the availability and quality of local health services from the Village Census. We use these data to test the hypothesis that health service access and quality are important determinants of maternal death and explain the differences between high maternal mortality and low maternal mortality provinces.MethodsThe 2010 Indonesian Population Census identifies 8075 pregnancy-related deaths and 5,866,791 live births. Multilevel logistic regression is used to analyse the impacts of demographic characteristics and the existence of, distance to and quality of health services on the likelihood of maternal death. Decomposition analysis quantifies the extent to which the difference in maternal mortality ratios between high and low performing provinces can be explained by demographic and health service characteristics.FindingsHealth service access and characteristics account for 23% (CI: 17.2% to 28.5%) of the difference in maternal mortality ratios between high and low-performing provinces. The most important contributors are the number of doctors working at the community health centre (8.6%), the number of doctors in the village (6.9%) and distance to the nearest hospital (5.9%). Distance to health clinics and the number of midwives at community health centres and village health posts are not significant contributors, nor is socio-economic status. If the same level of access to doctors and hospitals in lower maternal mortality Java-Bali was provided to the higher maternal mortality Outer Islands of Indonesia, our model predicts 44 deaths would be averted per 100,000 pregnancies.ConclusionIndonesia has employed a strategy over the past several decades of increasing the supply of midwives as a way of decreasing maternal mortality. While there is evidence of reductions in maternal mortality continuing to accrue from the provision of midwife services at village health posts, our findings suggest that further reductions in maternal mortality in Indonesia may require a change of focus to increasing the supply of doctors and access to hospitals. If data on maternal death is collected in a subsequent census, future research using two waves of census data would prove a useful validation of the results found here. Similar research using census data from other countries is also likely to be fruitful.

Abstract

The Government of The Gambia has always been committed to the “Health for All” year 2000 and beyond Alma Ata Declaration (1978) as well as other conventions such as the 1987 Global Conference on Safe Motherhood, the 1990 Convention on the Rights of the Child and the 1994 ICPD-Cairo Plan of Action, amongst others. A unique recommendation from all these conventions was the reduction of maternal mortality by half by the year 2000 and the provision of a comprehensive reproductive health programme using the life cycle approach from birth to death.

The 1987 conference on safe motherhood brought about increased awareness in the health sector on the issue of maternal mortality following which the “sisterhood” method of estimating levels of maternal mortality was first tested in The Gambia in 1987. This field test was done by the MRC field station located in one of the rural divisions of The Gambia and a total of 90 maternal deaths were identified. The lifetime risk of maternal death was estimated to be higher than one woman in twenty (Greenwood et al.). Subsequently, this revelation by the MRC study sparked a new impetus into the “silent epidemic” of maternal mortality following which the Department of State for Health through its MCH/FP programme commissioned a national survey in 1990. The results, which were quite startling, revealed a maternal mortality level of 1,050 per 100,000 live births nationally. There were variations between urban (600 per 100,000) and rural communities with trained birth attendants (894 per 100,000), and communities without trained birth attendants (1,600 per 100,000).

Recent isolated studies on maternal mortality have suggested a general decline in those areas. However, in the absence of a viable vital registration system in The Gambia, there has been a felt need to conduct another national survey, since the 1990 survey. Furthermore, the Department of State for Health’s proposed shift from MCH/FP service provision into a broad-focussed reproductive health programme also requires the availability of current baseline information and the identification of relevant process indicators, all of which justify the need to establish current levels of mortality and use of contraceptives.

It should be noted that current national policies and programmes continue to refer to data obtained from the 1990 maternal mortality study, the 1990 Gambia contraceptive prevalence and fertility determinants survey as well as the 1993 population and housing census as baseline benchmarks both for programme intervention and implementation. This long period to some extent renders the data quite obsolete and unsuitable for many national and development purposes. A simple compromise has been that of making comprehensive demographic, health and socio-economic projections. However, one important limitation of statistical projections is the period between the time the base data were collected and the time span of the projections. The probable margin of error in making projections with reference periods of eight or more years ago could be so large to warrant the acceptance of such projections within any reasonable statistical intervals.

Since there has been no comprehensive national survey on maternal, infant and child mortality during the past 10 years, and given that it would take a number of years before the final analyses of data obtained from the forthcoming census, it was found prudent to carry out a comprehensive study that would collect information on key reproductive health indicators. Furthermore, the complexity involved in studying maternal mortality compounded by its rarity of occurrence in the general population has necessitated conduction of a specialised study. Such a study would be useful in filling in the data deficiencies and providing baseline data for programme intervention and evaluation, especially in an era of a general shift of emphasis of population programmes from vertical family planning activities in favour of a more generally accepted concept - reproductive health.

Objectives of the survey: a) To establish current levels of maternal, peri-natal, neonatal and infant mortality rates. b) To establish the current levels of contraceptive prevalence rates and barriers to use. c) To elicit how the situation has improved or otherwise during the last ten years. d) Make practical recommendations to Department of State for Health for subsequent and long-term actions required.

Geographic coverage

National.

Analysis unit

• Households
• Women and men (both in child-bearing age)

Universe

The survey covered women age 15 to 49 years old and men age 18 years and over.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage stratified cluster sampling procedure was used for this study. The country is divided into 41 Districts and each of these districts was identified as a stratum. Stratification by districts increases the efficiency of the sample given the homogeneity of the districts. The sample size for the study was 4000 households and was based on the level of maternal mortality which was estimated at 1050 per 100,000 at the time of the study. According to WHO/UNICEF, 1997 publication on Sisterhood Method in Estimating Maternal Mortality, 4,000 households or less would be adequate for study of maternal mortality if the level of maternal mortality is at least 500 per 100,000.

Based on the Rule of Thumb, a 15 per cent sample of EAs (240) was selected for this study, which is also more than adequate for the study of other variables like contraceptive prevalence, infant mortality, fertility and its determinants. The selection of population elements were done at two stages; a representative sample of 240 Enumeration Areas (EAs) were randomly selected and allocated based on the Probability Proportional to the Size (PPS) of the district using random numbers. The EA is a cluster of settlements with an estimated population of 500 peoples.

A total of 4,000 households were then allocated to the districts with probability proportional to the size of each district. For the 240 selected EAs, a specified number of households were randomly selected for interview using a systematic sampling procedure. A complete listing of selected household members was done and all eligible male and female respondents were interviewed.

Sampling deviation

There were no discrepancies between the sample units obtained and the iniitial planned samples.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey tools included a compound and household schedules, female and male questionnaires. The compound and household schedules were used to collect information on local government area, health division and household number, together with residence, sex, age, education and eligibility status of the household members. The female and male questionnaires were administered to women aged 15-49 years and men aged 18 years and above respectively. The survey instruments were similar to the core modules of the Demographic and Health Survey questionnaires (Macro International), with adaptation to suit The Gambian needs. In addition a review of medical records in the three main hospitals in The Gambia (Royal Victoria Hospital, Farafenni Hospital and Bansang Hospital) was carried out in November 2001 to undertake first-hand assessment of the maternal mortality situation at the major referral facilities.

The Survey team with support and guidance of the Technical Team prepared the survey instruments by adapting the Demographic and Health survey modules. The main instruments for this study are: - Male questionnaire which was used to obtain information from males 18 years and above; - Female questionnaire, which obtained information from females, 15-49 years ; - Household questionnaire contains information on Local Government Area (LGA), Districts and Household numbers.

For each person listed on the household questionnaire, relationship to head of household, age, and sex are recorded.

The female questionnaire contains the following key information: - Respondent's background - Reproduction - Contraception - Marriage - Fertility preferences - Maternal mortality

The male questionnaire on the other hand, contained the following information: - Respondent's background - Contraception - Marriage - Maternal mortality

Response rate

All respondents with missing age were excluded from the model. There were about 15 percent of the responses with missing information on the deaths. Imputations were made to establish whether or not they qualified to be classified as maternal deaths. For instance, those missing sex of the sibling but had correctly answered maternal death-related questions, the sex was taken to be female and therefore included in the maternal mortality model. Responses with no information on the type of maternal death, but had indicated the death as having been as a result of complications of pregnancy or child birth, were imputed to be pregnancy-related. On the other hand, responses on symptoms before death were used to impute the type of death in case it was missing.

Data appraisal

All respondents with missing age were excluded from the model. There were about 15 percent of the responses with missing information on the deaths.

Characteristics of the study countries: trends in the maternal mortality ratio (MMR) proportion of maternal deaths due to PPH, and scale-up of the NASG.

Global health indicators such as infant and maternal mortality are important for informing priorities for health research, policy development, and resource allocation. However, due to inconsistent reporting within and across nations, construction of comparable indicators often requires extensive data imputation and complex modeling from limited observed data. We draw on Ahmed et al.’s 2012 paper – an analysis of maternal deaths averted by contraceptive use for 172 countries in 2008 – as an exemplary case of the challenge of building reliable models with scarce observations. The authors’ employ a counterfactual modeling approach using regression imputation on the independent variable which assumes no estimation uncertainty in the final model and does not address the potential for scattered missingness in the predictor variables. We replicate their results and test the sensitivity of their published estimates to the use of an alternative method for imputing missing data, multiple imputation. We also calculate alternative estimates of standard errors for the model estimates that more appropriately account for the uncertainty introduced through data imputation of multiple predictor variables. Based on our results, we discuss the risks associated with the missing data practices employed and evaluate the appropriateness of multiple imputation as an alternative for data imputation and uncertainty estimation for models of global health indicators.

BackgroundThe WHO estimates that 13% of maternal mortality is due to unsafe abortion, but challenges with measurement and data quality persist. To our knowledge, no systematic assessment of the validity of studies reporting estimates of abortion-related mortality exists. Study DesignTo be included in this study, articles had to meet the following criteria: (1) published between September 1st, 2000-December 1st, 2011; (2) utilized data from a country where abortion is “considered unsafe”; (3) specified and enumerated causes of maternal death including “abortion”; (4) enumerated ≥100 maternal deaths; (5) a quantitative research study; (6) published in a peer-reviewed journal. Results7,438 articles were initially identified. Thirty-six studies were ultimately included. Overall, studies rated “Very Good” found the highest estimates of abortion related mortality (median 16%, range 1–27.4%). Studies rated “Very Poor” found the lowest overall proportion of abortion related deaths (median: 2%, range 1.3–9.4%). ConclusionsImprovements in the quality of data collection would facilitate better understanding global abortion-related mortality. Until improved data exist, better reporting of study procedures and standardization of the definition of abortion and abortion-related mortality should be encouraged.

This dataset provides an extensive view of global population statistics and health metrics across various countries from 2014 to 2024. It combines population data with vital health-related indicators, making it a valuable resource for understanding trends in population growth and health outcomes worldwide. Researchers, data scientists, and policymakers can utilize this dataset to analyze correlations between population dynamics and health performance at a global scale.

Key Features: - Country: Name of the country. - Year: Year of the data (2014–2024). - Population: Total population for the respective year and country. - Country Code: ISO 3-letter country codes for easy identification. - Health Expenditure (health_exp): Percentage of GDP spent on healthcare. - Life Expectancy (life_expect): Average life expectancy at birth in years. - Maternal Mortality (maternal_mortality): Maternal deaths per 100,000 live births. - Infant Mortality (infant_mortality): Deaths of infants under 1 year per 1,000 live births. - Neonatal Mortality (neonatal_mortality): Deaths of newborns (0–28 days) per 1,000 live births. - Under-5 Mortality (under_5_mortality): Deaths of children under 5 years per 1,000 live births. - HIV Prevalence (prev_hiv): Percentage of the population living with HIV. - Tuberculosis Incidence (inci_tuberc): Estimated new and relapse TB cases per 100,000 people. - Undernourishment Prevalence (prev_undernourishment): Percentage of the population that is undernourished.

Use Cases: - Health Policy Analysis: Understand trends in healthcare expenditure and its relationship to health outcomes. - Global Health Research: Investigate global or regional disparities in health and nutrition. - Population Studies: Analyze population growth trends alongside health indicators. - Data Visualization: Build visual dashboards for storytelling and impactful data representation.

In 2015, the World Health Organization (WHO) released global targets and strategies for reducing maternal mortality in the Sustainable Development Goal (SDG) period developed through broad stakeholder consultations. The targets and strategies identified in the “Strategies toward Ending Preventable Maternal Mortality (EPMM)” report are grounded in a systemic and human rights approach to maternal health and aim to address the broad spectrum of key social, political, economic, and health system determinants of maternal health and survival, as exemplified by 11 Key Themes. These upstream determinants of maternal survival are not well represented in maternal health measurement efforts, which tend to focus on service delivery. Thus, work was undertaken to develop a core set of maternal health indicators that could drive progress toward achieving the recommendations laid out in the EPMM Strategies that identified a menu of 25 indicators and 7 standard stratifiers related to the legal and policy environment, accountability mechanisms, inequities in access and quality, and empowerment of women, girls, families, and communities. Measurement experts have called for more research to ensure that indicators for monitoring maternal health, including its upstream determinants, are valid, which is critical if such measures are to be effective for driving and tracking progress toward ending preventable maternal deaths. This paper describes nine research reports emanating from seven discrete research studies to validate ten indicators in India, Ghana and Argentina that are compiled in a PLOS Collection with the aim of illustrating the breadth of the validation work, extracting some unifying themes and common findings, and discussing the implications for policy and practice they suggest.

Introduction

As facility-based deliveries increase globally, maternity registers offer a promising way of documenting pregnancy outcomes and understanding opportunities for perinatal mortality prevention. This study aims to contribute to global quality improvement efforts by characterizing facility-based pregnancy outcomes in Kenya and Uganda including maternal, neonatal, and fetal outcomes at the time of delivery and neonatal discharge outcomes using strengthened maternity registers.

Methods

Cross sectional data were collected from previously strengthened maternity registers at 23 facilities over 18 months. Pregnancy outcomes were classified as live births, early stillbirths, late stillbirths, or spontaneous abortions according to birth weight or gestational age. Discharge outcomes were assessed for all live births. Outcomes were assessed by country and by infant, maternal, and facility characteristics. Maternal mortality was also examined.

Results

Among 50,981 deliveries, 91.3% were live born and, of those, 1.6% died before discharge. An additional 0.5% of deliveries were early stillbirths, 3.6% late stillbirths, and 4.7% spontaneous abortions. There were 64 documented maternal deaths (0.1%). Preterm and low birthweight infants represented a disproportionate number of stillbirths and pre-discharge deaths, yet very few were born at ≤1500g or <28w. More pre-discharge deaths and stillbirths occurred after maternal referral and with cesarean section. Half of maternal deaths occurred in women who had undergone cesarean section.

Conclusion

Maternity registers are a valuable data source for understanding pregnancy outcomes including those mothers and infants at highest risk of perinatal mortality. Strengthened register data in Kenya and Uganda highlight the need for renewed focus on improving care of preterm and low birthweight infants and expanding access to emergency obstetric care. Registers also permit enumeration of pregnancy loss <28 weeks. Documenting these earlier losses is an important step towards further mortality reduction for the most vulnerable infants.

Abstract

The Afghanistan Mortality Survey (AMS) 2010 was designed to measure mortality levels and causes of death, with a special focus on maternal mortality. In addition, the data obtained in the survey can be used to derive mortality trends by age and sex as well as sub-national estimates. The study also provides current data on fertility and family planning behavior and on the utilization of maternal and child health services.

OBJECTIVES

The specific objectives of the survey include the following: - National estimates of maternal mortality; causes and determinants of mortality for adults, children, and infants by age, sex, and wealth status; and other key socioeconomic background variables; - Estimates of indicators for the country as a whole, for the urban and the rural areas separately, and for each of the three survey domains of North, Central, and South, which were created by regrouping the eight geographic regions; - Information on determinants of maternal health; - Other demographic indicators, including life expectancy, crude birth and death rates, and fertility rates.

ORGANIZATION OF THE SURVEY

The AMS 2010 was carried out by the Afghan Public Health Institute (APHI) of the Ministry of Public Health (MoPH) and the Central Statistics Organization (CSO) Afghanistan. Technical assistance for the survey was provided by ICF Macro, the Indian Institute of Health Management Research (IIHMR) and the World Health Organization Regional Office for the Eastern Mediterranean (WHO/EMRO). The AMS 2010 is part of the worldwide MEASURE DHS project that assists countries in the collection of data to monitor and evaluate population, health, and nutrition programs. Financial support for the survey was received from USAID, and the United Nations Children’s Fund (UNICEF). WHO/EMRO’s contribution to the survey was supported with funds from USAID and the UK Department for International Development and the Health Metrics Network (DFID/HMN). Ethical approval for the survey was obtained from the institutional review boards at the MoPH, ICF Macro, IIHMR, and the WHO.

A steering committee was formed to coordinate, oversee, advise, and make decisions on all major aspects of the survey. The steering committee comprised representatives from various ministries and key stakeholders, including MoPH, CSO, USAID, ICF Macro, IIHMR, UNICEF, UNFPA, WHO, and local and international NGOs. A technical advisory group (TAG) made up of experts in the field of mortality and health was also formed to provide technical guidance throughout the survey, including reviewing the questionnaires, the tabulation plan for this final report, the final report, and the results of the survey.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

The AMS 2010 is the first nationwide survey of its kind. A nationally representative sample of 24,032 households was selected. All women age 12-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. The survey was designed to produce representative estimates of indicators for the country as a whole, for the urban and the rural areas separately, and for each of the three survey domains, which are regroupings of the eight geographical regions. The compositions of the domains are given below: - The North, which combines the Northern region and the North Eastern region, consists of nine provinces: Badakhshan, Baghlan, Balkh, Faryab, Jawzjan, Kunduz, Samangan, Sari Pul, and Takhar. - The Central, which combines the Western region, the Central Highland region, and the Capital region, consists of 12 provinces: Badghis, Bamyan, Daykundi, Farah, Ghor, Hirat, Kabul, Kapisa, Logar, Panjsher, Parwan, and Maydan Wardak. - The South, which combines the Southern region, the South Eastern region, and the Eastern region, consists of 13 provinces: Ghazni, Hilmand, Kandahar, Khost, Kunar, Laghman, Nangarhar, Nimroz, Nuristan, Paktika, Paktya, Uruzgan, and Zabul.

The sample for the AMS 2010 is a stratified sample selected in two stages from the 2011 Population and Housing Census (PHC) preparatory frame obtained from the Central Statistics Organization (CSO). Stratification was achieved by separating each domain into urban and rural areas. Because of the low urban proportion for most of the provinces, the combined urban areas of each domain form a single sampling stratum, which is the urban stratum of the domain. On the other hand, the rural areas of each domain are further split into strata according to province; that is, the rural areas of each province form a sampling stratum. In total, 34 sampling strata have been created after excluding the rural areas of Hilmand, Kandahar, and Zabul from the domain of the south. Among the 34 sampling strata, 3 are urban strata, and the remaining 31 are rural strata, which correspond with the total number of provinces and their rural areas. Samples were selected independently in each sampling stratum by a twostage selection process. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels within a sampling stratum, by sorting the sampling frame according to administrative units at different levels within each stratum, and by using a probability proportional to size selection at the first stage of sampling.

The primary sampling unit was the enumeration area (EA). After selection of the EA and before the main fieldwork, a household listing operation was carried out in the selected EAs to provide the most updated sampling frame for the selection of households in the second stage. The household listing operation consisted of (1) visiting each of the 751 selected EAs, (2) drawing a location map and a detailed sketch, and (3) recording on the household listing forms all structures found in the EA and all households residing in the structure with the address and the name of the household head. The resulting lists of households serve as the sampling frame for the selection of households at the second stage of sampling. In the second stage of sampling, a fixed number of 32 households was selected randomly in each selected cluster by an equal probability systematic sampling technique. The household selection procedure was carried out at the IIHMR office in Kabul prior to the start of fieldwork. An Excel spreadsheet prepared by ICF Macro to facilitate the household selection was used. A level of non response, or refusals on the part of households and individuals, had already been taken into consideration in the sample design and sample calculation.

The survey interviewers interviewed only pre-selected households, and no replacements of pre-selected households were made during the fieldwork, thus maintaining the representativeness of the final results from the survey for the country. Interviewers were also trained to optimize their effort to identify selected households and to ensure that individuals cooperated to minimize non-response. It is important to note here that interviewers in the AMS were not remunerated according to the number of questionnaires completed but given a daily per diem for the number of days they spent in the field; in addition, it is also important to note that respondents were neither compensated in any way for agreeing to be interviewed nor coerced into completing an interview.

For security reasons, the rural areas of Kandahar, Hilmand, and Zabul, which constitute less than 9 percent of the population, were excluded during sample design from the sample selection; however, the urban areas of these provinces were included. Of the 751 EAs that were included in the sample, 34 EAs (5 urban and 29 rural) were not surveyed. Six of the selected EAs in Ghazni, 16 in Paktika, 1 in Uruzgan, 3 in Kandahar, 3 in Daykundi, and 2 in Faryab were not surveyed because of the security situation. In addition, two EAs from Badakshan and one from Takhar were not surveyed because base maps from the CSO were unavailable. The non-surveyed EAs-which were primarily in rural areas-represent 4 percent of the total population of the country,

Table 1.1 - Sample coverage (Percentage of the population represented by the sample surveyed in the Afghanistan Mortality Survey, Afghanistan 2010) Region / Urban / Rural / Total North / 97 / 98 / 98 Central / 100 / 98 / 99 South / 94 / 63 / 66 Total / 98 / 84 / 87

Overall, approximately 13 percent of the country was not surveyed; most of these areas were in the South zone. As shown in Table 1.1, the survey covered only 66 percent of the population in the South zone. Sample weights were adjusted accordingly to take into account those EAs that were selected but not completed for security or other reasons.

Overall, the AMS 2010 covered 87 percent of the population of the country, 98 percent of the urban population and 84 percent of the rural population. Nevertheless, the lack of total coverage and the disproportionate exclusion of areas in the South, and particularly the rural South, should be taken into consideration when interpreting national level estimates of key demographic indicators and estimates for the South zone and regions within. For this reason key indicators will be presented for all Afghanistan and Afghanistan excluding the South zone. Despite these exclusions, the AMS is the most comprehensive mortality survey conducted in Afghanistan in the last few decades in terms of geographic coverage of the country.

Throughout this report, numbers in the tables reflect weighted numbers unless indicated otherwise. In most cases, percentages based on 25-49 cases are shown in parentheses and percentages based on fewer than 25 unweighted cases are suppressed and replaced with an asterisk, to caution readers when interpreting data that a percentage may not

Hypertensive disorders in pregnancy, of which the multisystem pathology pre-eclampsia is most severe, often lead to preterm delivery, maternal mortality and life-long complications¹. Pre-eclampsia lacks early screening tools^2–4 and causal therapies^5,6, illustrating the urgent need for a better understanding of early disease dynamics. Here we present the first study comparing single-nuclei transcriptomes of human diseased preterm preeclamptic placentae and healthy controls, embedding the characterisation of the maternal-foetal barrier dysfunction in the context of a comprehensive spatio-temporal study including early and late gestational placentae.

Our results highlight and contextualise a disturbed communication from foetal to maternal side during the development of pre-eclampsia starting with a disturbed trophoblast stem-cell maturation. We provide new targets for potential early disease prevention in order to protect mother and child from increased gestational mortality and morbidity but also from life-long increased cardiovascular disease risk.

This repository contains microscopy and ISS data used in the study. It contains the 37 TIFF files produced on a digital slide scanner (Olympus SLIDEVIEW VS200) connected to external LED source (Excelitas Technologies, X-Cite Xylis) and the MATLAB output files.

1. Say, L. et al. Global causes of maternal death: A WHO systematic analysis. The Lancet Global Health 2, (2014).

2. Brown, M. A. et al. Hypertensive disorders of pregnancy: ISSHP classification, diagnosis, and management recommendations for international practice. Hypertension vol. 72 24–43 (2018).

3. Zeisler, H. et al. Predictive Value of the sFlt-1:PlGF Ratio in Women with Suspected Preeclampsia. New England Journal of Medicine 374, 13–22 (2016).

4. National Institute for Health and Care Excellence. 1 Recommendations | PlGF-based testing to help diagnose suspected pre-eclampsia (Triage PlGF test, Elecsys immunoassay sFlt-1/PlGF ratio, DELFIA Xpress PlGF 1-2-3 test, and BRAHMS sFlt-1 Kryptor/BRAHMS PlGF plus Kryptor PE ratio) | Guidance | NICE. https://www.nice.org.uk/guidance/dg23/chapter/1-Recommendations (2019).

5. Gestational Hypertension and Preeclampsia: ACOG Practice Bulletin, Number 222. Obstetrics and gynecology 135, e237–e260 (2020).

6. Hauth, J. C. et al. Pregnancy outcomes in healthy nulliparas who developed hypertension. Calcium for Preeclampsia Prevention Study Group. Obstetrics and gynecology (2000).

Funding Details:

F. H. and D.N.M. were supported by Deutsche Forschungsgemeinschaft (HE6249/5-1; HE6249/7-1; HE6249/7-2; D.N.M.: Projektnummer 394046635 - SFB 1365). D.N.M. was supported by grants from the German Centre for Cardiovascular Research (DZHK; BER 1.1 VD). O. N. was supported through the PhD program Inflammatory Disorders in Pregnancy (DP-IDP) by the Austrian Science Fund (FWF): Doc 31-B26, PhD program Molecular Medicine at the Medical University of Graz, and through the Marietta Blau Grant by the Austrian Federal Ministry for Education, Science and Research (OeAD; BMBWF), and with grants from the MeFo Graz (PS-Stipendium 2019/2020), German Association of Prenatal Diagnostics and Obstetrics (DGPGM, 2020). K.S., T.K. and A.E-H. were supported by the K1 COMET Competence Center CBmed (Center for Biomarker Research in Medicine), which is funded by the Federal Ministry of Transport, Innovation and Technology (BMVIT), Land Steiermark (Department 12, Business and Innovation), BMWFW, the Styrian Business Promotion Agency (SFG), and the Vienna Business Agency. The COMET programme is executed by the Austrian Research Promotion Agency (FFG).
K.S. was supported by the Doctoral School in Translational Molecular and Cellular Biosciences at the Medical University of Graz.
M.G. was supported by the FWF: (P 29639, P33554, I 3304, and Doc 31-B26) and the Medical University Graz through the PhD programs Inflammatory Disorders in Pregnancy (DP-IDP) and MolMed. B.H. was supported by the FWF: (Doc 31-B26) and the Medical University Graz through the PhD program Inflammatory Disorders in Pregnancy (DP-IDP). Sebastian Tiesmeyer was supported by Federal Ministry of Education and Research of Germany in the framework of SAGE (031L0265). This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI)

Carbetocin Duratocin Market Outlook

The Carbetocin Duratocin market size was valued at approximately USD 320 million in 2023 and is projected to reach around USD 540 million by 2032, growing at a healthy CAGR of 5.8% during the forecast period. The market's expansion is primarily driven by the increasing incidence of postpartum hemorrhage (PPH) globally, which is a leading cause of maternal mortality. The rising awareness about maternal health, coupled with advancements in healthcare infrastructure, further propels the market's growth.

One of the primary growth factors for the Carbetocin Duratocin market is the rising prevalence of postpartum hemorrhage (PPH) worldwide. PPH remains one of the leading causes of maternal mortality, accounting for approximately 27% of maternal deaths globally. The increasing awareness and initiatives by various organizations and governments to reduce maternal mortality rates have significantly propelled the demand for effective treatments like Carbetocin Duratocin. Additionally, the development of healthcare infrastructure in emerging economies plays a crucial role in increasing the accessibility and availability of Carbetocin Duratocin, thereby driving market growth.

Another significant driver is the advancements in medical technology and pharmaceutical research. Innovations in drug formulations and delivery methods enhance the efficacy and safety profiles of Carbetocin Duratocin, making it a preferred choice among healthcare professionals. The injectable form of Carbetocin Duratocin, which offers rapid onset of action, is especially instrumental in emergency situations such as severe PPH. Furthermore, ongoing clinical trials and research studies aim to expand the therapeutic indications of Carbetocin Duratocin, which could open new avenues for market growth.

Additionally, the growing emphasis on women's health and the increasing healthcare expenditure globally are expected to fuel the demand for Carbetocin Duratocin. Governments and health organizations are investing heavily in maternal health programs to improve outcomes and reduce complications during childbirth. This focus on maternal health ensures that highly effective drugs like Carbetocin Duratocin are made available in hospitals and healthcare centers, thereby driving market growth. Moreover, the increasing number of childbirths in developing countries is another crucial factor contributing to the market's expansion.

From a regional perspective, North America dominates the Carbetocin Duratocin market due to the high awareness about maternal health and the well-established healthcare infrastructure. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, owing to the increasing population, rising healthcare expenditure, and improving access to maternal healthcare services. The governments in this region are actively working to reduce maternal mortality rates, which is likely to boost the demand for Carbetocin Duratocin. Europe also holds a significant market share, driven by the high adoption of advanced medical treatments and increasing awareness about postpartum care.

In the context of postpartum hemorrhage management, Carboprost Tromethamine emerges as a vital alternative to Carbetocin Duratocin. This synthetic prostaglandin analog is primarily used to control severe bleeding after childbirth, particularly when other uterotonic agents are ineffective. Its mechanism of action involves stimulating uterine contractions, thereby reducing blood loss and preventing further complications. While Carboprost Tromethamine is often reserved for cases where first-line treatments fail, its role in emergency obstetric care cannot be understated. The availability of multiple uterotonic options, including Carboprost Tromethamine, ensures that healthcare providers can tailor interventions based on individual patient needs and clinical scenarios.

Product Type Analysis

The Carbetocin Duratocin market by product type is segmented into injectable and oral forms. The injectable form dominates the market, owing to its rapid onset of action and high efficacy in emergency situations such as severe postpartum hemorrhage (PPH). The injectable Carbetocin is often the first line of treatment in many healthcare settings, making it a critical component in managing PPH effectively. The rapid ab

Abstract

The 2017 Ghana Maternal Health Survey (GMHS) was implemented by the Ghana Statistical Service (GSS). Data collection took place from 15 June to 12 October 2017. ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Financial support for the 2017 GMHS was provided by the Government of Ghana through the Ministry of Health (MOH) and by USAID, the European Union (EU) delegation to Ghana, and the United Nations Population Fund (UNFPA).

SURVEY OBJECTIVES The primary objectives of the 2017 GMHS were as follows: - To collect data at the national level that will allow an assessment of the level of maternal mortality in Ghana for the country as a whole and for three zones: Coastal (Western, Central, Greater Accra, and Volta regions), Middle (Eastern, Ashanti, and Brong Ahafo regions), and Northern (Northern, Upper East, and Upper West regions) - To identify specific causes of maternal and non-maternal deaths, in particular deaths due to abortionrelated causes, among adult women - To collect data on women’s perceptions of and experiences with antenatal, maternity, and emergency obstetrical care, especially with regard to care received before, during, and following the termination or abortion of a pregnancy - To measure indicators of the utilisation of maternal health services, especially post-abortion care services - To allow follow-on studies and surveys that will be used to observe possible reductions in maternal mortality as well as abortion-related mortality

The information collected through the 2017 GMHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

Household Woman

Universe

the survey covered all household members, all women aged 15-49 and for autopsy questionnaire women aged 12-49.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017 GMHS was designed to provide estimates of key reproductive health indicators for the country as a whole, for urban and rural areas separately, for three zonal levels (Coastal, Middle, and Northern), and for each of the 10 administrative regions in Ghana (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West).

The sampling frame used for the 2017 GMHS is the frame of the 2010 Population and Housing Census (PHC) conducted in Ghana. The 2010 PHC frame is maintained by GSS and updated periodically as new information is received from various surveys. The frame is a complete list of all census enumeration areas (EAs) created for the PHC. An EA is a geographic area that covers an average of 161 households (per updates to the PHC frame from the 2014 Ghana Demographic and Health Survey [GDHS]). Individual EA size is the number of residential households in the EA according to the 2010 PHC. The average size of urban EAs (185 households) is slightly larger than the average size of rural EAs (114 households). The sampling frame contains information about the EA’s location, type of residence (urban or rural), and estimated number of residential households.

The 2017 GMHS sample was stratified and selected from the sampling frame in two stages. Each region was separated into urban and rural areas; this yielded 20 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before the sample selection, according to administrative units at different levels, and by using a probability proportional to size selection at the first stage of sampling.

In the first stage, 900 EAs (466 EAs in urban areas and 434 EAs in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was implemented from 25 January to 9 April 2017 in all of the selected EAs. The resulting lists of households then served as a sampling frame for the selection of households in the second stage. The household listing operation included inquiring of each household if there had been any deaths in that household since January 2012 and, if so, the name, sex, and age at time of death of the deceased person(s).

Some of the selected EAs were very large. To minimise the task of household listing, each large EA selected for the 2017 GMHS was segmented. Only one segment was selected for the survey with probability proportional to segment size. Household listing was conducted only in the selected segment. Thus, in the GMHS, a cluster is either an EA or a segment of an EA. As part of the listing, the field teams updated the necessary maps and recorded the geographic coordinates of each cluster. The listing was conducted by 20 teams that included a supervisor, three listers/mappers, and a driver.

The second stage of selection provided two outputs: the list of households selected for the main survey (Household Questionnaire and Woman’s Questionnaire) and the list of households selected for the verbal autopsy survey (Verbal Autopsy Questionnaire).

Selection for Main Survey In the second stage of selection for the main survey, a fixed number of 30 households were selected from each cluster, resulting in a total sample size of 27,000 households. Replacement of nonresponding households was not allowed. Due to the non-proportional allocation of the sample to the different regions and the possible differences in response rates, sampling weights are required for any analysis that uses the 2017 GMHS data. This ensures the representativeness of the survey results at the national and regional levels. Results shown in this report have been weighted to account for the complex sample design.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed.

Selection for Verbal Autopsy Survey In the second stage of selection for the verbal autopsy survey, all households in which a female resident age 10-54 died in 2012 or later were selected to be visited by an interviewer. However, only the deaths of female residents who were age 12-49 at the time of death were eligible to be included in the survey. A wider age range was used for the initial selection in case of minor inaccuracies on the part of the person who provided information during the household listing operation; the first questions in the Verbal Autopsy Questionnaire established true eligibility, and interviews ended if the deceased woman was discovered to have died before age 12, after age 49, or before 2012.

There is a chance that some households were both purposively selected for the verbal autopsy survey and randomly selected for the main survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2017 GMHS: the Household Questionnaire, the Woman’s Questionnaire, and the Verbal Autopsy Questionnaire. The survey protocol was reviewed and approved by the ICF Institutional Review Board.

The Household Questionnaire and the Woman’s Questionnaire were adapted from The DHS Program’s standard Demographic and Health Survey questionnaires and the questionnaires used in the 2007 GMHS to reflect the specific interests and data needs of this survey. The Verbal Autopsy Questionnaire was adapted from the recent 2016 World Health Organization (WHO) verbal autopsy instrument.

For all questionnaires, input was solicited from stakeholders representing government ministries and development partners. After the finalization of the questionnaires in English, they were translated into three major languages: Akan, Ga, and Ewe. The Household and Woman’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the four languages for either of the questionnaires.

The Verbal Autopsy Questionnaire was filled out on paper during data collection and entered into the CAPI system afterwards. The tablet computers were equipped with Bluetooth® technology to enable remote electronic transfer of files, such as assignments from the team supervisor to the interviewers, individual questionnaires among survey team members, and completed questionnaires from interviewers to team supervisors. The CAPI data collection system employed in the 2017 GMHS was developed by The DHS Program using the mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, The DHS Program, and Serpro S.A.

Household Questionnaire The Household Questionnaire was used to list all members of and visitors to selected households. Basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, marital status, education, and relationship to the head of the household. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women who were eligible for individual interviews. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, materials used for the floor of the dwelling unit, and ownership of various

BackgroundPeripartum mistreatment of women contributes to maternal mortality across the globe and disproportionately affects vulnerable populations. While traditionally recognized in low/low-middle-income countries, the extent of research on respectful maternity care and the types of mistreatment occurring in high-income countries is not well understood. We conducted a scoping review to 1) map existing respectful maternity care research by location, country income level, and approach, 2) determine if high-income countries have been studied equally when compared to low/low-middle-income countries, and 3) analyze the types of disrespectful care found in high-income countries.MethodsA systematic search for published literature up to April 2021 using PubMed/MEDLINE, EMBASE, CINAHL Complete, and the Maternity & Infant Care Database was performed. Studies were included if they were full-length journal articles, published in any language, reporting original data on disrespectful maternal care received from healthcare providers during childbirth. Study location, country income level, types of mistreatment reported, and treatment interventions were extracted. This study was registered on PROSPERO, number CRD42021255337.ResultsA total of 346 included studies were categorized by research approach, including direct labor observation, surveys, interviews, and focus groups. Interviews and surveys were the most common research approaches utilized (47% and 29% of all articles, respectively). Only 61 (17.6%) of these studies were conducted in high-income countries. The most common forms of mistreatment reported in high-income countries were lack of informed consent, emotional mistreatment, and stigma/discrimination.ConclusionsMapping existing research on respectful maternity care by location and country income level reveals limited research in high-income countries and identifies a need for a more global approach. Furthermore, studies of respectful maternity care in high-income countries identify the occurrence of all forms of mistreatment, clashing with biases that suggest respectful maternity care is only an issue in low-income countries and calling for additional research to identify interventions that embrace an equitable, patient-centric empowerment model of maternity care.

Childbirth Experience Instrument Market Outlook

The global market size for Childbirth Experience Instruments was valued at approximately USD 1.2 billion in 2023 and is expected to reach around USD 2.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. The increasing awareness and emphasis on improving maternal and neonatal outcomes are significant growth factors driving the market. This growth can be attributed to the rising demand for comprehensive tools that can assess and enhance childbirth experiences, ensuring better healthcare services for mothers and their newborns.

Several growth factors underpin the expansion of the Childbirth Experience Instrument market. Firstly, the increasing focus on patient-centered care in maternity services has necessitated the need for detailed feedback mechanisms. Healthcare providers are increasingly relying on surveys, questionnaires, and digital tools to gain insights into the birthing experience, which in turn helps in improving the quality of care. Secondly, advancements in healthcare IT solutions have paved the way for the development of sophisticated digital tools that offer real-time data collection and analysis, making it easier for healthcare providers to monitor and enhance maternal care services.

Furthermore, the growing emphasis on mental health during and post-pregnancy is another critical factor driving the market. The emotional and psychological well-being of mothers has become an essential aspect of maternity care, and childbirth experience instruments play a crucial role in assessing and addressing these needs. These tools help in identifying issues such as postpartum depression and anxiety, enabling timely intervention and support. Additionally, the increasing number of research studies focusing on maternal health and childbirth experiences is fueling the demand for these instruments, as researchers seek comprehensive data to support their findings.

Technological advancements and increasing digitization in healthcare are also contributing to market growth. The integration of artificial intelligence and machine learning in digital tools has enhanced the accuracy and efficiency of data collection and analysis. These technologies allow for personalized care plans based on individual feedback and experiences, further improving maternal and neonatal outcomes. Moreover, the availability of mobile applications and online platforms has made it easier for mothers to share their childbirth experiences, providing valuable data for healthcare providers and researchers.

Regionally, North America holds a significant share of the market, driven by advanced healthcare infrastructure and high awareness levels among healthcare providers and patients. Europe follows closely, with substantial investments in maternal health and a strong focus on patient-centered care. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare expenditure, rising awareness about maternal health, and the growing adoption of digital healthcare solutions. Latin America and the Middle East & Africa also show promising growth potential, albeit at a slower pace, due to improving healthcare infrastructure and rising awareness levels.

Product Type Analysis

The market for Childbirth Experience Instruments is segmented by product type into surveys, questionnaires, and digital tools. Surveys are traditional yet essential tools in capturing comprehensive feedback from mothers about their childbirth experiences. These tools are widely used due to their simplicity and effectiveness in gathering qualitative and quantitative data. Surveys are often administered in hospitals and maternity clinics, ensuring that healthcare providers receive detailed insights into various aspects of the birthing process, including pain management, emotional support, and overall satisfaction.

Questionnaires, similar to surveys, are structured tools used to collect data on specific aspects of the childbirth experience. They are often more focused and detailed, allowing for in-depth analysis of particular areas such as labor pain, delivery methods, and postpartum care. Questionnaires can be customized to meet the specific needs of healthcare providers or researchers, making them versatile and valuable tools in improving maternal care. The increasing demand for personalized care and the need for detailed feedback are driving the adoption of questionnaires in maternity services.

Digital tools represent a significant advance

We obtained the analyzed data from the public repository of the Global Burden of Disease (GBD) study (https://vizhub.healthdata.org/sdg/#0 and http://ghdx.healthdata.org/record/ihme-data/gbd-2017-health-related-sdgs-1990-2030). However, under the request of the International Journal for Equity in Health in order to contribute to transparency and replicability of research, the authors of the study entitled “Human resources for health and maternal mortality in Latin America and the Caribbean over the last three decades: a systemic-perspective reflections”, made the data available. Any other use than exploring or replicating the results of the above-mentioned paper is not authorized and shall not be used without the previous authorization of the investigators. If you are interested in analyzing this database for original research purposes please contact Edson Serván Mori (eservan@insp.mx).

This dataset was used in a facility-based cross-sectional analysis in which we (1) measured facility readiness to manage common obstetric emergencies using the clinical cascades and signal function tracers; (2) compared these readiness estimates by facility characteristics; and (3) measured cascading drop-offs in resources.Data were collected in 2016 from 23 hospitals (10 designated comprehensive emergency obstetric care (CEmOC) facilities) in Migori County, western Kenya, and Busoga Region, eastern Uganda, in the Preterm Birth Initiative (PTBi) study in East Africa. Research assistants used standardised forms to visually identify emergency resources during the on-sitephysical inventory of resources. They captured data about facility characteristics, obstetric drugs, consumable supplies,durable goods and the presence of emergency guidelines and protocols. Researchers recorded both the presence/absence of the item and its location (ie, unit). In this analysis, we used a resource’s presence or absence at the facility level to estimate facility-level readiness regardless of the unit in which the items were located.Baseline data were used to estimate a facility’s readiness to manage common obstetric emergencies using WHO’s Service Readiness Index (SRI)/signal function tracers and the clinical cascade model. We compared emergency readiness using the proportion of facilities with tracers (signal functions) and the proportion with resources for identifying and treating the emergency (cascade stages 1 and 2). Date Submitted: 2022-03-18

Abstract

The 1997 Yemen Demographic Maternal and Child Health Survey (YDMCHS) is part of the worldwide Demographic and Health Surveys (DHS) program. The DHS program is designed to collect data on fertility, family planning and maternal and child health.

The YDMCHS-97 has the following objectives: 1. Provide policymakers and decisionmakers with a reliable database and analyses useful for policy choices and population programs, and provide researchers, other interested persons, and scholars with such data. 2. Update and expand the national population and health data base through collection of data which will allow the calculation of demographic rates, especially fertility rates, and infant and child mortality rates; 3. Analyse the direct and indirect factors which determine levels and trends of fertility. Indicators related to fertility will serve to elaborate plans for social and economic development; 4. Measure the level of contraceptive knowledge and practice by method, by rural and urban residence including some homogeneous governorates (Sana’a, Aden, Hadhramaut, Hodeidah, Hajjah and Lahj). 5. Collect quality data on family health: immunizations, prevalence and treatment of diarrhea and other diseases among children under five, prenatal visits, assistance at delivery and breastfeeding; 6. Measure the nutritional status of mothers and their children under five years (anthropometric measurements: weight and height); 7. Measure the level of maternal mortality at the national level. 8. Develop skills and resources necessary to conduct high-quality demographic and health surveys.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

The 1997 YDMCHS was based on a national sample in order to provide estimates for general indicators for the following domains: Yemen as a whole, urban and rural areas (each as a separate domain), three ecological zones identified as Coastal, Mountainous, and Plateau and Desert, as well as governorates with a sample size of at least 500 completed cases. The survey sample was designed as a two-stage cluster sample of 475 enumeration areas (EA), 135 in urban areas and 340 in rural areas. The master sample, based on the 1994 census frame, was used as the frame for the 1997 YDMCHS. The population covered by the Yemen survey was the universe of all ever-married women age 15-49. The initial target sample was 10,000 completed interviews among eligible women, and the final sample was 10,414. In order to get this number of completed interviews, and using the response rate found in the 1991-92 YDMCHS survey, a total of 10,701 of the 11,435 potential households selected for the household sample were completed.

In each selected EA, a complete household listing operation took place between July and September 1997, and was undertaken by nineteen (19) field teams, taking into consideration the geographical closeness of the areas assigned to each team.

Note: See detailed description of sample design in APPENDIX B of the final survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two Questionnaires were used to collect survey data:

Household Questionnaire: The household questionnaire consists of two parts: a household schedule and a series of questions relating to the health and socioeconomic status of the household. The household schedule was used to list all usual household members. For each of the individuals included in the schedule, information was collected on the relationship to the household head, age, sex, marital status (for those 10 years and older), educational level (for those 6 years and older) and work status (for those 10 years and older). It also collects information on fertility, general mortality and child survival. The second part of the household questionnaire included questions on housing characteristics including the type of dwelling, location, materials used in construction, number of rooms, kitchen in use, main source of drinking water and health related aspects, lighting and toilet facilities, disposal of garbage, durable commodities, and assets, type of salt the household uses for cooking, and other related residential information.

Individual Questionnaire: The individual questionnaire was administered to all ever-married women age 15-49 years who were usual residents. It contained 10 sections on the followings topics: - Respondent's background - Reproduction - Family planning - Pregnancy and breastfeeding - Immunization and health - Birth preferences - Marriage and husband's background - Maternal mortality - Female circumcision - Height and weight

Response rate

10,701 households, distributed between urban (3,008 households) and rural areas (7,693), households which were successfully interviewed in the 1997 YDMCHS. This represents a country-wide response rate of 98.2 percent (98.7 and 98.0 percent, respectively, for urban and rural areas).

A total of 11,158 women were identified as eligible to be interviewed. Questionnaires were completed for 10,414 women, which represents a response rate of 93.3 percent. The response rate in urban areas was 93 percent; and in rural areas it was 93.5 percent.

Note: See summarized response rates by place of residence in Table 1.1 of the final survey report.

Sampling error estimates

The estimates from a sample surveys are affected by two types of errors: (1) non-sampling error, and (2) sampling error. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the YDMCHS-97 to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the YDMCHS-97 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would have yielded results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of standard error of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistics in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the YDMCHS-97 sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the YDMCHS-97 is the ISSA Sampling Error Module (SAMPERR). This module used the Taylor linearization method of variance estimate for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimate of more complex statistics such as fertility and mortality rates.

Note: See detailed estimate of sampling error calculation in APPENDIX C of the final survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women and men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX D of the final survey report.