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Lives Saved Analyses for Child Survival Projects: Using LiST to Estimate the Impact of Maternal, Newborn and Child Health Interventions
- 1. Lives Saved Analyses for Child Survival Projects: Using LiST to Estimate the Impact of Maternal, Newborn and Child Health Interventions Debra Prosnitz, MPH; Rebecca Levine, MPH; James Ricca MD, MPH; and Ingrid Friberg, PhD
- 21. Stunting Zinc Diarrhea incidence IUGR Appropriate Complementary Feeding Complementary feeding education and/or supplementation Previous Stunting
- 22. Malaria Mortality Disease Specific Treatments Disease Specific Preventions Risk factors ITN/IRS Antimalarials Stunting
- 23. Stunting Post-Neonatal Diarrheal Mortality Rotavirus vaccine Vitamin A Zinc Water/Sanitation (5) Zinc Antibiotics for dysentery ORS Disease Specific Treatments Disease Specific Preventions Risk factors IUGR Syphilis detection and treatment IPTp Maternal energy and/or micronutrient supplementation Zinc Complementary Feeding Complementary feeding education and/or supplementation Diarrhea incidence Improved H 2 O source within 30 minutes Hand washing with soap Water connection in the home Improved excreta disposal (latrine, toilet) Hygienic disposal of children’s stools Distant Factors Breast Feeding Breast Feeding Promotion
- 32. Modeling Mortality Rates and Equity
- 38. LiST Contributors Institute for International Programs
Editor's Notes
- “ At the turn of the millenium, there was a wide-spread feeing in the child health community that the over 10 million annual deaths of under five children were not receiving the attention they deserved. A group of concerned scientists and policymakers gathered for a week in Bellagio, Italy, to prepare a series of articles arguing for increased funding for child survival actions, which became known as the Lancet Child Survival Series. The second article in the series included a formal attempt to estimate how many deaths could be saved by each intervention then available.” “ The Bellagio spreadsheets were sufficiently accurate for the purposes of this initial exercise and made an important contribution to placing child survival back on the international agenda. However, from a methodological standpoint there was substantial room for improvement.” “ This software was inspired by the Bellagio exercise, but went much further in terns of the background mortality data used, the quality of the literature reviews on intervention effectiveness and of the modelling process itself.
- This tool is a multi-cause model, in which we assume an interaction between causes. This means that we assume that people who are ‘saved’ from one cause of death are still at risk of dying from a separate cause at the standard population risk.
- LiST was built into Spectrum, a software from the Future’s Institute which is already used by many organizations for family planning and HIV projections, among other things. The first benefit of this is that we can start with DemProj, which is the demographic platform underlying Spectrum. The data in this module is based on the 2008 UN population projections which allow estimates of population through 2050. By using this platform, links have been built into other modules within Spectrum. AIM, which models UNAIDS data for each country and FamPlan which estimates of the impact of family planning on the number of births. The Child survival module itself is a cohort based model in which each child goes through time periods at risk of dying, ensuring that each child cannot be saved more than once, and cannot die more than once. This also allows the impacts of the interventions to be lagged to the future. For example, IUGR and past stunting both affect the probability of future stunting.
- The general framework of the model starts with the UN estimates and projections of population, which allows us to estimate the number of child deaths in each year. We overlay this number with information on the cause of death in individual countries as well as information on both the current and future status of a wide variety of child survival interventions. These allow us to calculate the impact of each intervention and present the number of deaths averted by both cause and intervention. These results then get fed back into the model to adjust population projections for the future year. We can also enter country specific data on the health status of the population, including nutritional status and this also feeds into the impacts of the interventions, the resultant stunting and the numbers of deaths averted.
- CHERG (Child Health Epidemiology Reference Group) was responsible for reviews of effectiveness of interventions. To do this, they first they defined an intervention: a biological agent or action that is intended to reduce morbidity or mortality. Then, they only looked at interventions which would typically be implemented by the health department. This meant that they ignored distal causes of mortality such as poverty, lack of education and household crowding or birth spacing. They also looked only at interventions which would be feasible to implement in a low-income country. That meant that technologically intensive or expensive interventions, such as secondary care of newborns was excluded. So yes, some effective interventions were excluded simply because it is unlikely that they could be easily implemented in one of the priority countries. Finally, all the interventions had to have cause-specific evidence of an effect on child mortality. The evidence was only included in the model if there was sufficient or limited data that was suggestive of efficacy. Interventions with inadequate data to assume efficacy were not included even if the general impression of the intervention was of a positive impact. One example is immediate initiation of breastfeeding after birth. To reiterate, since the initial publication of the efficacy data, a series of reference groups have been convened, focusing on the efficacy of interventions in their field, such as newborn health or nutrition and they are continually reviewing and refining this data. Thus if new data is available which changes the results of a meta-analysis or review, then these interventions can be added to or removed from the model easily.
- We can look at the interventions that are included in the model in a variety of ways. One is as part of a continuum of care. Thus there are interventions which can impact the health of the child peri-conceptually, ante-natally, during the birthing process and post-natally. We could also look at the interventions as being either preventive or curative.
- Over 40 interventions are included in the model
- ential Care: % of women with essential care during delivery and immediate newborn care. Includes: monitoring labor progress with partograph, detection of complications and infection control via a clean delivery. Episiotomy is available if needed. For the neonate, this includes immediate drying and wrapping, skin-to-skin contact and thermal care as well as immediate breastfeeding initiation BEmOC: Refers to management of delivery at health center and covers case mgmt of direct obstetric complications. Includes: Case mgmt of abortion, ectopic pregnancy, hypertensive diseases of pregnancy, ante-partum hemorrhage, prolonged/obstructed labor, PPH, and severe infection. Methods include: shock mgmt, MgSO4, pain relief, ABC, parenteral antibiotiocs, parenteral oxytocis, IV fluids, instrumental delivery and manual removal of the placenta and retained products, CEmOC: This refers to the mgmt of delivery at a hospital and covers case management of direct obstetric complications. This is in addition to all interventions included in Basic Emergency Obstetric Care. Additional methods include: ultrasound, culdocentesis, induction, laparotomy, salpingectomy, blood transfusion, caesarian section, hysterectomy, symphisiotomy, ballon tamponade, uterine ligature, MROP, surgical infection control and episiotomy. Clean practices & Immediate ENC: ie clean delivery kit
- There are two kinds of data that are needed to get LiST to work: Country-specific data and global data. For the country-specific data, first we have the national population data and trends which are built into Spectrum. We also have a cause of death structure for each country, estimated for the year 2003. We also need intervention coverage data both past and present. This default data in LiST is the data that was available from the date closest to the year 2003. However, it varies from country to country based on the quality of the data available. In addition, we use global estimates of intervention efficacies which was developed by the CHERG. It is really important to realize that this data can and should be changed to use the program to fit your exact needs. We only have baseline data to make things easy, but anytime you use it, you should make sure that the data fits what you are planning on doing with the software. For example, some people use DHS data, others prefer UNICEF values. Which one you use depends on your situation.
- The pieces of the model include preventions, treatments and risk factors. Each prevention prevents a death. Each treatment stops a death from occurring. And each risk factor modifies the probability of a child dying from that specific cause. Each of these individual interventions has an impact on a specific cause of death, such as Hib vaccination on pneumonia, or on a specific risk factor, such as breast feeding on stunting, which then affects several causes of death. So if you are working with one intervention, it is very straightforward, but how do we deal with multiple interventions. First, all the preventions are entered into the model and the proportionate deaths averted are calculated to prevent double counting of deaths averted. Only then is the same done for the all of the treatments. Thus, treatments only impact lives which were not already saved by a prevention (treatment only saves residual lives).
- The impact of the interventions is also age specific. LiST uses 5 different age categories. We have a cause of death profile which is divided into neonatal and post-neonatal. We would like to have the cause of death profile match the effectiveness data, but right now, the data just isn’t available.
- There are three sets of interventions which are tied to other interventions. Each one has been dealt with slightly differently because of the types of interventions that we are dealing with. Antenatal care, where the three components that are part of the model are a proportion of the total women receiving at least 4 ANC visits. For antenatal care, the coverage actually refers to the percentage of pregnant women with at least 4 ANC visits. The three components are calculated to be a proportion of those women. So the number that is displayed, reflects the percent of women with each component. For example, in 2003, syphilis detections and treatment is expected to have occurred for 6% of pregnant women. Child birth care is divided into two parts, which are forced to sum to 100% - facility based deliveries and home based deliveries. For each of these, there are several component interventions. The value seen is a proportion of the facility delivery or home delivery with access to the intervention. Births are divided into two categories: facility and home based births. These are forced to add to 100%. There are 4 different interventions associated with facility birth. Instead of indicating the percent of women who had access to corticosteroids, we indicate the percent of women delivering in a facility who have access to corticosteroids. We did this so that countries who were improving the quality of their child birth care could reflect that in the numbers. Water: Two different interventions are associated with clean water, use of an improved water source within 30 minutes of the home or use of a water connection in the home (which included water piped into the home or a protected source in the yard). However, these are not independent - Piped water is a subset of improved water. So, the impact of an improved source is the difference between improved and piped.
- This is a diagram which depicts the interventions which can affect stunting, a risk factor for mortality. Although not explicitly shown here, you should realize that stunting has a time lag built in as well. A child who was stunted in a previous time period has a higher risk of currently being stunted than one who was not previously stunted. The pink for appropriate complementary feeding indicates that this is not something that is directly observed, only that this is the actual link between the education and the stunting.
- This diagram is an example of one of the most simple flow-charts depicting the associations in the model. Note that the blue indicates a treatment while green is a prevention. And red represents a risk factor which modifies the probability of a child dying of this particular disease. This means that reducing stunting will reduce the probability of getting the disease. Thus in the model, it acts like a prevention.
- And finally, just to show that this is not just a simplistic model, you can see all of the associations that are built in. This is just the model of post-neonatal diarrhea. You can see the obvious preventions and treatments as well as the risk factors for mortality that modulate the probability of a child dying. And each of these risk factors is associated with a variety of more distal factors which modulate the probability of having that particular risk factor.
- LiSt is linked to FamPLan, a Family planning module that can reduce the number of births with an increase in used of contraception. To demonstrate this are 4 projections of mortality. Each is identical except for the child survival and family planning interventions. The first is a baseline model with neither family planning nor child survival scaled up in the final year. The second has only family planning coverage increased in the final year. In the third projection, only child survival interventions have been scaled up. In the final projection, both family planning and child survival interventions have been increased. As you can see, in the first row, the number of deaths in each year increases due to population increases. If you scale up family planning, you can reduce the number of deaths by about 7000 children, but these are mostly due to fewer births. In reality, we also save fewer lives than with the same level of child survival interventions just because fewer children are being born. Also note that the under 5 mortality rate has not changed. However, in the third projection, we have only scaled up the child survival interventions and have saved approximately 31,000 deaths due to the child survival interventions. This does not reflect any decrease in the number of children born as family planning is not yet scaled up, however there is a dramatic reduction in the under 5 mortality rate. In the final model, both family planning and child survival have been scaled up. So we have a reduced number of births as well as an increase in children surviving. Although once again fewer children are saved with the child survival interventions because of the reduced number of births.
- Emphasize: LiST is a Multi-Cause Model NOT a Natural History Model. As a multi-cause model, it does calculations on residual deaths, and therefore does not save children multiple times. As it is NOT a natural history model, it does not take into account disease transmission rates, etc.
- NOTE: Child Survival Interventions have been validated in LiST. Maternal Health interventions have NOT been validated – modeling and validation of MH interventions is ongoing; please refer to the limitations of MH previously noted.
- Modeled between 20-25 interventions ACSD: UNICEF’s Accelerate Child Survival and Development Initiative
- Trainings and Orientations have also been done for several additional organizations, including UN groups, development organizations and external organizations.
- LiST is not complete. Maternal mortality links are being worked on, and should be available soon. We also know that family planning has benefits beyond just reducing births, it may also have additional benefits to families and these will be included in the near future as well. The programmers are working to tie in the mortality impact to both the MBB and CHOICE costing and budgeting approaches. As new data and reviews are available, the software will be updated with that new information. Finally, right now, the only baseline data in the model is from 2003. They are working on getting models which are current through 2008 entered into the system. We also want to do the same for districts within some larger countries such as India and Pakistan.
- These are the websites where you will be able to get access to the software, although the final versions are not available yet.