To estimate state-level neonatal and under-five mortality rates, we analyzed data from the MICS conducted in Nigeria from 2016 to 2017. MICS have been conducted in Nigeria since 1995 by the Nigeria National Bureau of Statistics in concert with the United Nations International Children’s Emergency Fund (UNICEF). These surveys track important indicators of health and social welfare for children, mothers, and their families. Prior to the 2016–17 MICS, previous MICS had estimated child mortality through indirect methods and by using partial birth histories obtained from mothers. The 2016–17 MICS was the first to obtain full birth histories from mothers that would enable more reliable estimates of child mortality using direct estimation methods. Households included in the survey were selected through a multi-stage stratified random sampling. Full birth history information was obtained for every woman present in the sampled household. Details of the survey design and data collection methods have been described by the UNICEF MICS team.

We estimated both neonatal and under-five mortality rates using the full birth histories contained in the 2016–17 MICS datasets. We defined NMR as the probability of a newborn dying in the first 28 days of life, expressed per 1,000 live births, and U5MR as the probability of a child dying before reaching age 5 years, also expressed per 1,000 live births. See Table 1 for details of our NMR and U5MR calculations.

For each state, we estimated the required average annual rate of decline (AARD) needed for the state to achieve the SDG neonatal mortality target of 12 deaths per 1000 live births and the SDG under-five mortality target of 25 deaths per 1000 live births.

To assess if states were on track to meet their SDG targets for NMR and U5MR, we calculated historical trends in AARD for each state using mortality rate estimates reported in the Nigeria Demographic and Health Surveys (NDHS) of 2003, 2008, and 2013. Like the 2016–17 MICS (and unlike previous MICS), the previous NDHS used direct estimation of child mortality through full birth histories so it was possible to compute trends over the timeframe of interest. We calculated separate estimates for NMR and U5MR for five-year periods (2003 to 2008, and 2008 to 2013), with the assumption that mortality rates changed using exponential decay over the period. Then, we applied the annual rate of decline for each state to the its 2017 mortality rate estimates to project how long it would take for that state to achieve its SDG targets for NMR and U5MR. Since state-level estimates were not available prior to 2011, we calculated AARDs for each of the six regions in the country using equation 1 in Table 1, and applied the regional AARD to all the states in the region.

To quantify the number of avertable deaths between 2018 and 2030 from potential improvements in neonatal and under-five mortality, we applied our projections of state-level mortality rate estimates to state-level population projections from the Nigeria National Population Commission for four different scenarios and computed the difference.

First, we modelled a baseline “do nothing” (or “business as usual”) scenario, where we assumed that NMR and U5MR do not change from 2017 levels, i.e., they remain unchanged between 2017 and 2030. The second, “historical trends” scenario assumed that states will experience AARD trends similar to the trends they experienced in the period leading up to the MDGs. In this scenario, each state starts out with its NMR and U5MR for 2017, and then experiences an annual reduction in both mortality rate estimates between 2018 and 2030 based on its historical AARD estimates. In our third, “best in country” scenario, we assumed that between 2018 and 2030, each state in the country would attain the AARD observed in the best performing zone during the years leading up to the MDGs. Finally, we modeled an “SDG target” scenario where we assumed that between 2018 and 2030, each state will experience the AARD that is required for it to achieve the NMR and U5MR SDG targets by 2030.

In all four scenarios, we estimated the number of neonatal and under-five deaths that would occur for each year between 2018 and 2030 using mortality fractions (see Table 2) and estimates of average annual rates of change in mortality rates (see Table 3). Deaths averted were then calculated by subtracting the number of deaths from each scenario from the number of deaths calculated for the “do nothing” scenario.

Finally, we concluded our analysis by ranking states based on their NMR (SDG 3.2.1) and U5MR (SDG 3.2.2) estimates in 2017. We also rank states based on the estimated annual number of neonatal and under-five deaths they have, in order to identify the states that account for most of the burden of neonatal and under-five mortality in Nigeria.

Mortality rates estimates were computed using Stata software version 15.1 and we used Microsoft Excel for Mac version 16.22 to make projections. An expanded description of methods applied is available as Extended data ¹⁰ .

Table 4 shows our estimates of the national and state NMRs from MICS 2016–2017. The estimated national NMR in 2017 was 37 per 1,000 live births (95% CI: 36-39). But at the state level, NMR ranged from 12 per 1,000 live births (95% CI: 7-22) in Kwara state to 65 per 1,000 live births (95% CI: 59-72) in Kano state.

For Nigeria to achieve its SDG target for NMR of 12 deaths per 1,00 live births (SDG 3.2.1), its national NMR will have to decrease by 9% every year, on average, between 2018 and 2030. However, the AARD required to reach SDG 3.2.1 at the state level varies widely. While Kwara state has already achieved the SDG 3.2.1 target, some other states such as Zamfara and Kano will need to achieve an average annual decrease in NMR of 11%, and 13% respectively, in order to meet the NMR SDG targets.

Our estimates of historical AARD in NMR ranged between 1.4% per year and 2.6% per year at the national level. At the subnational level, the AARD for NMR varied across zones. The South-South zone of the country recorded the largest AARD of 8% per year between 2008 and 2013, while the South-West zone of the country showed worsening NMR in the same period with an increase in NMR 1.3% per year (see Table 3).

At historical AARDs, Nigeria, as a country, will achieve its SDG target for NMR in 2060, 30 years behind the SDG target year of 2030. However, progress across the different states would vary. At historical rates of decline, 12 out of 37 states would have met or surpassed the SDG NMR target by 2030, while 19 states will achieve the SDG NMR target between 2031 (1 year later) and 2088 (58 years later). We did not estimate target years for the five states in the South-West zone of the country because, unlike other zones, the South-West zone showed increases in NMR between 2003 and 2013 that ranged from 0.3% per year to 1.3% per year. As such, they would be moving away, not towards, the SDG NMR target (See Table 4, and Figure 2A).

However, if between 2018 and 2030, each state maintains an AARD in NMR equal to the best historical rate of decline in the country in the MDG era (i.e. 8% decline per year), Nigeria will achieve its SDG target for NMR in 2031 (1 year behind schedule). In this scenario, 26 out of 37 states will achieve the NMR target for the SDGs while 11 will not. The years in which states will reach NMR targets for SDG range from 2022 (8 years early) to 2038 (8 years behind schedule).

At the national level, U5MR in 2017 was 118 per 1,000 live births (95% CI: 115-121), but at the state level, U5MR ranged from 32 per 1,000 live births (95% CI: 23-46) in Kwara state, to 209 per 1,000 live births (95% CI: 193-226) in Zamfara state. Consequently, Nigeria will need to maintain a national AARD in U5MR of 12% per year between 2018 and 2030 in order to meet the SDG target for U5MR of 25 deaths per 1,000 live births (SDG 3.2.2). To achieve SDG 3.2.2 targets at state level, states will have to maintain an AARD that ranges from 2% per year in Kwara state to 16% per year in Kano, Jigawa, and Zamfara states ( Table 5).

Historical estimates of AARDs for U5MR ranged from 4.0% to 4.5% per year at the national level. At the subnational level, AARD for U5MR was highest in the South-South zone (at 8.4% per year), but showed reversals in the South-East zone with rates of increase as high as 2.5% per year (see Table 3).

Our model suggests that Nigeria is not on track to meet the SDG target for U5MR (SDG 3.2.2) by 2030. At current national AARDs, it will achieve SDG 3.2.2 in 2052 (22 years behind schedule). At the subnational level, 5 out of 37 states will meet or surpass the SDG 3.2.2 target on or before 2030, while the other 32 states are not on track to achieve SDG 3.2.2 target by 2030. The years in which states will reach U5MR targets for SDG range from 2021 (9 years ahead of schedule) in Kwara state to 2074 (44 years behind schedule) in Zamfara and Osun states (see Table 5 and Figure 2B).

If all states maintained an AARD in U5MR equal to the best performing in country (i.e. 8.4% decline per year), Nigeria will still not meet its U5MR target by 2030, but would do so by 2036 (six years behind schedule). In this scenario, 13 out of 37 states will meet or surpass their targets for U5MR by 2030, while the other 24 will not. The years in which states will reach U5MR targets for SDG range from 2020 (10 years early) in Kwara state to 2042 (12 years behind schedule) in Kano and Zamfara states (see Table 5).

We estimated the number of neonatal and under-five deaths that would occur between 2018 and 2030 under four different scenarios described in the Methods section above (sub-section on quantifying avertable deaths between 2018 and 2030). Figure 3 shows trends in mortality rates and number of annual deaths for each of the four scenarios over the period 2018 to 2030. In the “do nothing” scenario, in which each state maintains its 2017 NMR and U5MR throughout the period 2018 to 2030, a total of 4.3 million neonatal deaths and 12.4 million under-five deaths would occur between 2018 and 2030 (see Table 6). This number of deaths translates to an average of 330,000 neonatal deaths per year and 950,000 under-five deaths per year between 2018 and 2030. In the second scenario, the “historical trends” scenario, in which we applied the historical rates of decline for each zone to all states within the zone, there would be an estimated 3.5 million neonatal deaths and 9.3 million under-five deaths between 2018 and 2030. In the third scenario, the “best in country” scenario, in which every state would attain the AARD observed by the best performing in country, there would be an estimated 2.5 million neonatal deaths and 7.5 million under-five deaths between 2018 and 2030. In our last scenario, the “SDG target” scenario, in which each state will maintain from 2018–2030 the AARD required to reach the SDG targets for NMR and U5MR by 2030, there would be an estimated 2.4 million neonatal deaths and 6.4 million under-five deaths over this time period.

Comparing each scenario with the baseline “do nothing” scenario, we estimate that if states maintain historical AARDs, at least 850,000 neonatal deaths will be averted between 2018 and 2030 while 3.1 million or more under-five deaths will be averted in the same period compared to the baseline. However, if every state achieves and maintains the best AARD achieved during the MDG era, at least 1.83 million neonatal deaths and 4.9 million under-five deaths would be averted between 2018 and 2030. In the event that each state in Nigeria achieves and maintains the AARD required for it to meet its SDG targets for NMR and U5MR, we estimate that compared to the baseline scenario, at least 1.89 million neonatal deaths and 5.96 million under-five deaths will be averted between 2018 and 2030 ( Table 7).

Our models suggest that even if Nigeria achieves its SDG targets for NMR of 12 per 1,000 live births by 2030, there would still be at least 139,000 neonatal deaths per year if the NMR remains at the SDG target rate with no further improvement. In the same vein, if Nigeria achieves its SDG targets for U5MR of 25 per 1,000 live births, we project that on average, there would still be at least 308,000 under-five deaths per year if the U5MR remains at the SDG target rate with no further improvement.

We ranked states based on their most recent levels of NMR and U5MR, as well as their contributions to the annual number of neonatal and under-5 deaths. Table 8 lists the states in the top and bottom performance quintiles measured by mortality rates, while Table 9 lists the states that account for the largest portion of neonatal and under-five mortality based on 2018 estimates.

States performing in the top quintile for NMR include Kwara, Taraba, Cross River, Adamawa, Anambra, Ebonyi, and the Federal Capital Territory, with NMRs between 12 and 23 per 1,000 live births in 2017. States performing in the bottom quintile for NMR include Bauchi, Kebbi, Nasarawa, Osun, Niger, Zamfara, and Kano, with NMRs between 41 and 65 per 1,000 live births in 2017.

For U5MR, the states performing in the top quintile include Kwara, Enugu, Ebonyi, Cross River, Anambra, Delta, and Lagos, with U5MRs between 32 and 64 per 1,000 live births in 2017. By contrast, states performing in the bottom quintile for U5MR include Sokoto, Gombe, Bauchi, Kebbi, Jigawa, Kano, and Zamfara, with U5MR between 134 and 209 per 1,000 live births in 2017.

Our model shows that Kano state accounts for 13% of projected national neonatal mortality in 2018 with an estimated 34,000 neonatal deaths, and 14% of projected national under-five mortality in 2018 with an estimated 108,000 under-five deaths. In sum, only a small number of states account for about 40% of total neonatal and under-five deaths in 2018. They include Kano, Lagos, Oyo, Katsina, Niger, Bauchi, and Kaduna states for neonatal mortality, and Kano, Jigawa, Bauchi, Katsina, Zamfara, Lagos, and Niger for under-five mortality (see Table 9 for details)

Based on recent survey data and analysis of historical trends, we modeled future trends in state-level neonatal and under-five mortality in Nigeria between 2018 and 2030. Our findings show that if historical trends continue, Nigeria as a country, and multiple states within the country, are unlikely to meet the SDG targets for neonatal and under-five mortality by 2030. While some states have already attained or will attain the SDG targets several years before 2030, others are projected to attain the SDG targets as late as 2088 (58 years behind schedule).

We also show that if current mortality rates remain unchanged, up to 4.3 million neonatal deaths and 12.5 million under-five deaths are likely to occur in Nigeria between 2018 and 2030. However, if each state attains the required annual rate of decline needed to achieve the SDG targets for neonatal and under-five mortality, 1.9 million neonatal deaths and 5 million under-five deaths can be averted between 2018 and 2030.

Another important finding from this study is that if Nigeria only meets the SDG targets and does not surpass them—in other words, if it only achieves a U5MR of 25 deaths per 1,000 live births and an NMR of 12 deaths per 1,000 live births by 2030—the country would still have a high burden of avertable mortality. In a scenario in which it reaches these two SDG targets, at least 139,000 neonatal deaths and 308,000 under-five deaths would still occur every year in Nigeria. This annual number of deaths translates to more than 800 under-five deaths per day.

Our estimates are consistent with the 2016 estimates of the NMR and U5MR in Nigeria from the Global Burden of Disease (GBD) study. The 2016 GBD estimated an NMR for Nigeria of 35.7 per 1,000 live births (95% CI: 28.8-43.5) for NMR and a U5MR of 108.7 per 1,000 live births (95% CI: 93.0- 126.2) ⁶ .

In addition, our projections are consistent with projections from three other groups: (a) the UN Inter-agency Group for Child Mortality Estimation (UN-IGCME) ¹¹ , (b) the GBD 2016 SDG collaborators ¹² , and (c) McArthur and colleagues ¹³ . All three studies found that if current trends persist, Nigeria will not reach its SDG targets for child mortality by 2030.

However, our study goes a step further to provide state-level estimates and assessment of states’ performance. Our estimates are also consistent with those of Liu and colleagues, who showed that by 2030, Nigeria would account for up to 7% the estimated 4.4 million annual child deaths in 2030 ¹⁴ .

Our study contributes to the current literature in important ways. To the best of our knowledge, this is the first study to make subnational projections of neonatal and under-five mortality in Nigeria. Sub-national modeling is important because primary health care is mostly managed at the state and local government levels in Nigeria, not at the federal government. Our finding of a large disparity in mortality rates between states shows that they have performed very differently in adopting and scaling up child survival policies, thus we hope that our state level analysis will be helpful to policymakers. The lack of state-level estimates and projections make it difficult for policy makers at the state level to track their performance or make informed decisions about the future effects of certain health policies.

Our study uses the recently released MICS 2016–2017 household survey data, which has a number of advantages. This is the most recent household survey conducted in Nigeria, it has both national and state-level estimates for neonatal and under-five mortality, and it is the only one conducted in the post-2015 SDG era. Therefore, our analysis provides a good baseline for policy making.

Another strength of our study is that we adopted reasonable assumptions of rates of change. While we could have chosen to model scenarios with the rates of change of the best performing countries outside Nigeria, we decided to keep our analysis grounded in historical evidence. We therefore restricted our analysis of scenarios to rates of decline that have been achieved within Nigeria, and compared them to what will need to be achieved for the SDG targets to be met.

One important limitation of our study is that although we show differences in state-level performance for both neonatal and under-five mortality, we do not analyze the reasons for these differences. So, our study has not shown why some states perform better than others or which policies might have improved their performance. This is an important subject for future research.

The wide variation in performance in neonatal and under-five mortality seen across states in this study creates an opportunity for states to learn from each other. While a state like Kwara has already met the SDG target for NMR, other states like Zamfara and Kano will only meet their SDG targets for NMR around 2079 (a 49-year delay) and 2088 (a 58-year delay) respectively, if their current rates of decline are maintained. For under-five mortality, while Kwara state is projected to reach its U5MR targets for SDG3 around 2021 (9 years ahead of schedule), others like Zamfara and Osun states are projected to reach their U5MR targets for SDG3 by 2074 (44 years behind schedule).

Highlighting these differences is only a first step. Opportunities are needed for greater dialogue among policymakers within the different states. These dialogues could take the form of study visits to states performing in the top quintile for neonatal mortality (Kwara, Taraba, Cross River, Adamawa, Anambra, Ebonyi, and the Federal Capital Territory) and under-five mortality (Kwara, Enugu, Ebonyi, Cross River, Anambra, Delta, and Lagos states). They could also take the form of collaborative learning networks, or joint programming. Fostering these dialogues will encourage partnerships, stimulate homegrown innovation, and create an environment where evidence-based learning and decision making becomes the norm. One model for this kind of shared learning is the Joint Learning Network for Universal Health Coverage.

Another important policy implication of this study is that it highlights the importance of not just tracking mortality rates, but also tracking rates of change. While current mortality rates create a snapshot of performance at the time of the survey, it is impossible for policy makers to project how much progress is possible or feasible without paying attention to rates of change. For example, while neonatal mortality rates in the south-west region of the country were better than other regions between the period 2003 and 2013, they ended the period with NMR worse than at the beginning because of the rates of change they experienced. By contrast, the north-west and north-east zones have the highest neonatal mortality rates by recent estimates, but over time they have shown some progress in the form of a downward trend.

The importance of rates of decline are further seen in the modeling of different scenarios in our study. For example, if every state within the country achieves an AARD equal to the best performers (8% per year for NMR, and 8.4% per year for U5MR), a total of 1.8 million neonatal or 4.9 million under-five deaths would be averted between 2018 and 2030. If, on the other hand, each state achieves the AARD required to meet its SDG3 target (between 0% and 13% for NMR, and between 2% and 16% per year for U5MR), a total of 1.9 million neonatal deaths or 5.9 million under-five deaths could be averted between 2018 and 2030. However, achieving such high rates of decline will require sustained efforts on the part of policy makers. For comparison, in the period between 2006 and 2016, Rwanda, with all its investments in health reform, recorded an average rate of decline in under-five mortality of 9.2% per year ¹³ . Our study found that the best rates of decline achieved in Nigeria were 8% per year for NMR and 8.4% per year for U5MR over a 10-year period from 2003 to 2013. Furthermore, long-run estimates of average rates of decline in U5MR for Nigeria between 1990 and 2016 (a 26-year period) were between 2.7% and 3.3% per year ^{6, 7} . Therefore, a policy maker seeking to achieve an AARD of up to 16% in Nigeria should be prepared to make the required investments and sustain these investments over the long-run. This would require innovation, resources, persistence, and political will.

Finally, although our study did not analyze the reasons for variation across states, there is sufficient evidence in the literature on interventions that can reduce neonatal and under-five mortality in low- and middle-income countries. From recent estimates, the main causes of under-five mortality in Nigeria include malaria, diarrheal diseases, neonatal encephalopathy, lower respiratory tract infections, and neonatal preterm births ^{5, 15} . These are all conditions for which there are simple, low-cost, lifesaving interventions ^{16– 18} . Reliable platforms also exist to deliver these interventions ^{18– 20} and are captured in the Global Strategy for Women’s, Children’s and Adolescent Health (2016–2030) ²¹ .

Even though there are wide variations in mortality rates exist across the country, there is evidence that the main causes of child mortality do not vary significantly across the country. Uthman and colleagues conducted an exploratory spatial analysis of under-five mortality in Nigeria using data from the 2008 Nigeria Demographic and Health Survey, and concluded that the “vast majority of states (73%)” do not show evidence of special-cause variation in under-five mortality ²² . Therefore, there is potential for remarkable progress by addressing the common causes.

All datasets used in this study (Nigeria 2016-07 Survey) are publicly available at https://dhsprogram.com/ and http://mics.unicef.org/surveys. Interested readers can access the data by filling out an online form describing the intended use of the data. Once approved, data can be downloaded from the respective sites. The online form is accessible following clicking to download the file via the MICS site.

Open Science Framework: Supplementary data: How many child deaths can be averted in Nigeria? Assessing state-level prospects of achieving 2030 sustainable development goals for neonatal and under-five mortality. https://doi.org/10.17605/OSF.IO/8QMKE ¹⁰

This project contains the following extended data:

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).