Research Consultant and
ERA Gender Mainstreaming in the Military
Tabasum Akseer is a post-doctoral research fellow at the Center for International and Defence Policy, in the School of Policy Studies at Queen’s University. She is working with a team led by Dr. Stéfanie von Hlatky, on ‘Gender Mainstreaming in the Military: Economic and Social Implications for Ontario.’ She obtained her doctorate from Queen’s (Cultural Studies) in 2016, where her dissertation explored the impact of surveillance, immigration and security policies on racialized men.
Over the past six years, Tabasum has gained teaching experience as a teaching assistant in the School of Religion, Department of Languages, Literatures and Cultures, and the Gender Studies department, where she also acted as a teaching fellow.
In addition to her teaching, Tabasum has been engaged as a consultant on projects including the use of technology and surveillance in military and peacekeeping operations, smart weapons technology, monitoring and surveillance; development and peace operations in Afghanistan; women’s rights and access to health in Afghanistan; the socio-emotional and cognitive development of children; children’s religiosity and spirituality; and teachers’ perceptions of gender differences in the classroom. Her work has been presented at various national and international conferences and published in journals including; Forced Migration Review, Canadian Journal of School Psychology, Alberta Journal of Educational Research, and the International Electronic Journal of Elementary Education.
Dramatic increases in health funding occurred among all countries in this study since 2000, with increased focus on the MDGs. Low-income countries relied substantially on external support in order to rapidly increase health spending, something that will need to continue in the near future to achieve the SDGs, but brings concerns of fiscal sustainability. Conclusions on whether this increase in external support has led to a displacement of government health spending as found in previous literature [42, 43, 44] could not be concluded on such few observations. The dependency on external resources is much less among the studied middle-income countries, with a majority of the health funding coming from government as well as OOP health spending. Some studies highlight that high OOP health spending is regressive by exposing them to potential catastrophic spending [45, 46, 47]. An 89 cross-country study found that catastrophic health spending and impoverishment remained high where OOP spending on health that was more than 15–20 % of total health spending . Pakistan, Afghanistan, and Peru all have substantially higher OOP spending than this threshold in 2012 of 55, 73 and 37 %, respectively. Exploration into the causes and economic impact of the high OOP spending among the case study countries, especially for Afghanistan, a LIC, is needed.
RMNCH funding also substantially increased across all studied countries, especially after the first review of the MDG progress in 2005. Contributing factors that led to an increase in RMNCH funding and improvements in RMNCH outcomes are political stability; consistent political commitment to health; rapid economic growth; engagement with community; decentralisation; anti-poverty programmes with explicit focus on RMNCH; and, for some LICs, increased external support [1, 13, 14, 15, 16, 17, 18]. Malawi RMNCH financing is heavily reliant on external support, consisting of 70 % or more of total RMNCH funding in 2012, along with Ethiopia’s RMNH funding comprising of 47 % from external support in 2011. High OOP spending for RMNCH activities in Tanzania and CH activities in Ethiopia are of particular concern given that maternal and child health services are to be free for everyone. Possible explanations might be heavy reliance on private pharmacies for drugs and supplies when public health facilities are out of stock, imposing costs on the individual or individuals using private providers for CH services that may be closer to home or perceived better quality of services [49, 50, 51].
Substantial progress was made toward MDG 4 and 5 among some countries, even with per capita RMNCH spending below $50. One possible explanation for this finding is that health spending was targeted toward effective RMNCH interventions. The Lives Saved Tool (LiST) analysis conducted under the Countdown case studies demonstrates that certain interventions were particularly effective in reducing child mortality rates. For example, almost half a million of children’s’ lives were saved in Ethiopia due to key interventions implemented from 2000–2011; 44 % of which was due to activities that focused on reducing stunting . On other hand, resource inefficiencies may be contributing factors that detracted Pakistan’s ability to achieve MDG 4 and 5. Such inefficiencies were found with duplication of program implementation and routine PHC services, along with an overlap of human resource roles between health workers such as the lady health workers, lady health visitors, and community midwives . The possibility that countries with low RMNCH per capita spending were able to achieve MDG 4 or make substantial progress towards MDG 5 by targeting health spending towards more effective RMNCH interventions needs to be tested. Understanding the determinants behind such successful cases could provide a way forward for achievements during the post-MDG era.
While the Countdown case studies were not designed to test causal relations between financing and RMNCH outcomes, the studies show that reductions in MMR, IMR and U5MR were accompanied by an increase in RMNCH financing. Improvements in other distal factors were noted to have potentially contributed to reducing maternal and child mortality rates in the case studies such as rapid economic growth in Ethiopia, Peru, and Tanzania and improvements in female literacy as documented in Malawi for improved child survival. Other studies have shown that public spending leads to a reduction in infant and child mortality, albeit a small one [5, 6, 7, 8, 9].
Two main limitations of this study are the availability of regularly reported RMNCH expenditure data across the countries and findings presented are not generalisable. Inconsistency in regularly reported RMNCH expenditure data across countries, along with the defined scope of the case study (Table 2), did not allow for a more uniform methodology to conduct the health finance component of the case studies. Not every case study focused on the achievements, progress (or lack thereof) on both MDGs 4 and 5. Moreover, the time periods of focus for the health financing section of the case studies was dependent on health expenditure data availability. For example, Malawi’s period of focus was 2006–2011; Ethiopia’s was 1995–2011, while Pakistan’s was 2001–2010. Data sources used were also mixed, although when available most used NHA data. At times, these data were also supplemented by other country-specific data sources, such as the Household Integrated Economics Surveys for OOP spending estimates for Pakistan and data from the Ministerio de Economía y Finanzas for MNH and CH data for Peru, while others (Pakistan, Peru, and Tanzania) used the OECD-CRS database for external contributions to RMNCH. As a result, the case studies are purely descriptive. Robust econometric analyses to understand the causal relationship between levels and sources of health financing and RMNCH outcomes were not feasible because of inconsistent and limited health and RMNCH expenditure data with a small sample size of only 6 countries. Thus, these findings are not generalisable toward other LICs, LMICs, and UMICs experiences with health financing and RMNCH progress during the MDG era.
While RMNCH expenditure tracking efforts have improved since 2000, such as the inclusion of sub-accounts under the NHA, comprehensive and consistent RMCNH expenditures for many countries is still lacking. Resource tracking tools are used to collect and analyse health expenditure data within countries but many are not institutionalised (conducted on a regular basis), accounting methods for RMNCH expenditures may not be mutually exclusive, or face limitations because of inconsistent or subjective methods used over the years. For example, the government spending for the reproductive and child health subaccounts for Ethiopia are based on assumptions developed from background materials (such as health service reports) and expert opinions [34, 35]. This leaves government estimates of RMNCH expenditure prone to estimation errors due to the use of different accounting assumptions from 1 year to the next. Therefore, identifying time trends in RMNCH expenditures comes with caveats because methods used over time for the NHA subaccounts might be different, as was the case for Ethiopia. Implementation of SHA 2011 may minimise some of these issues by tracking expenditures according to the classification of Global Burden Disease. Furthermore, the OECD-CRS provides specific data around RMNCH expenditures but this only captures donor disbursements for health. This data set does not break out the RMNCH-specific expenditures, which is done by external agencies like Countdown and IHME [52, 53]. The OECD recently added a code specifically for RMNCH funding, but this new indicator still does not disaggregate funding within RMNCH.
Global efforts to collate RMNCH expenditures into one database or report are fragmented. The Commission on Information and Accountability (CoIA) for Women’s and Children’s Health  began an effort to have all 75 Countdown countries to report their total RMNCH expenditure by financing source by 2015. To date, 27 countries have produced this data but only 8 out of the 27 country data were provided for the 2015 Countdown report . Another source for health financing data is the WHO Global Health Expenditure Database, which attempts to collate all NHA data into one “master” database that is accessible to everyone in an open-sourced platform. A draw back in using this database is that key NHA data from the RMNCH subaccounts is not consistently available for most countries. Child health expenditure data is compiled only for Liberia in 2007 and Malawi from 2003–2005, while RMNH expenditure data is compiled for 17 countries but this data is still very limited in terms of years available in the database versus data available from the NHA reports. The WHO does provide the NHA reports for countries but requires one to manually extract the NHA data from the tables. Institutionalising or standardising reporting systems on RMNCH interventions and resource use, along with improving the information collected and provided into an accessible database, is important to monitor programs and understand health progress at the country level, and globally.