Implications of armed conflict for maternal and child health: A regression analysis of data from 181 countries for 2000-2019.

BACKGROUND: Armed conflicts have major indirect health impacts in addition to the direct harms from violence. They create enduring political instability, destabilise health systems, and foster negative socioeconomic and environmental conditions-all of which constrain efforts to reduce maternal and child mortality. The detrimental impacts of conflict on global maternal and child health are not robustly quantified. This study assesses the association between conflict and maternal and child health globally. METHODS AND
FINDINGS: Data for 181 countries (2000-2019) from the Uppsala Conflict Data Program and World Bank were analysed using panel regression models. Primary outcomes were maternal, under-5, infant, and neonatal mortality rates. Secondary outcomes were delivery by a skilled birth attendant and diphtheria, pertussis, and tetanus (DPT) and measles vaccination coverage. Models were adjusted for 10 confounders, country and year fixed effects, and conflict lagged by 1 year. Further lagged associations up to 10 years post-conflict were tested. The number of excess deaths due to conflict was estimated. Out of 3,718 country-year observations, 522 (14.0%) had minor conflicts and 148 (4.0%) had wars. In adjusted models, conflicts classified as wars were associated with an increase in maternal mortality of 36.9 maternal deaths per 100,000 live births (95% CI 1.9-72.0; 0.3 million excess deaths [95% CI 0.2 million-0.4 million] over the study period), an increase in infant mortality of 2.8 per 1,000 live births (95% CI 0.1-5.5; 2.0 million excess deaths [95% CI 1.6 million-2.5 million]), a decrease in DPT vaccination coverage of 4.9% (95% CI 1.5%-8.3%), and a decrease in measles vaccination coverage of 7.3% (95% CI 2.7%-11.8%). The long-term impacts of war were demonstrated by associated increases in maternal mortality observed for up to 7 years, in under-5 mortality for 3-5 years, in infant mortality for up to 8 years, in DPT vaccination coverage for up to 3 years, and in measles vaccination coverage for up to 2 years. No evidence of association between armed conflict and neonatal mortality or delivery by a skilled birth attendant was found. Study limitations include the ecological study design, which may mask sub-national variation in conflict intensity, and the quality of the underlying data.
CONCLUSIONS: Our analysis indicates that armed conflict is associated with substantial and persistent excess maternal and child deaths globally, and with reductions in key measures that indicate reduced availability of organised healthcare. These findings highlight the importance of protecting women and children from the indirect harms of conflict, including those relating to health system deterioration and worsening socioeconomic conditions.

Introduction

Despite improvements globally, the burden of maternal and child mortality continues to be unacceptably high. There were an estimated 295,000 maternal deaths in 2017, of which 94% occurred in low- and middle-income countries (LMICs) [1]. Similarly, most of the global burden of under-5, infant, and neonatal mortality is in LMICs. Over 5 million children under the age of 5 years (of whom 75% are under the age of 1 year) and 2.6 million aged under 28 days died in 2018 [2].

Achieving the Sustainable Development Goal targets for maternal (less than 70 per 100,000 live births), under-5 (less than 25 per 1,000 live births), and neonatal mortality (less than 12 per 1,000 live births) [3] in conflict-affected countries is a major challenge. Conflicts exacerbate political instability, destabilise social and welfare systems including health systems, interrupt water and food supplies, and increase poverty, unemployment, and homelessness—all of which negatively affect maternal and child health [4]. Estimates from 2017 indicate that approximately 420 million children younger than 18 years were living in areas affected by conflict [5], and 630 million women and children were living dangerously close (<50 km) to conflicts [6].

Despite growing recognition of the detrimental effects of conflict on maternal and child health, the literature is bereft of studies robustly quantifying the association between conflict and maternal and child health, with many inconsistent findings [7-13]. Prior research often failed to adequately define conflict or to adequately define data sources [7,8,13], focused on sub-Saharan Africa or LMICs only [7,8,10-15], had limitations in modelling approaches [7-11], evaluated only short-term associations [7-9,11-15], or assessed single mortality indicators [7-11,14,15]. For example, one major critique of the conflict literature is its approach to confounding variables. A review by Dixon of 46 quantitative studies found that over 200 independent variables had been assessed as possible causes of war, with a high degree of consensus in no more than 4 (demographics, economy, history of insecurity, and democratisation) [16]. Many independent variables may be collinear with armed conflict, which highlights the importance of grounding conflict models in conceptual frameworks and measuring collinearity between variables.

The Lancet Series on Women’s and Children’s Health in Conflict Settings lamented the lack of systematic evidence and readily available data in this field [17]. A robust, comprehensive, global assessment of the impacts of conflict on maternal and children’s health is lacking. This study assesses global associations between conflict and maternal and child mortality and the use of health services that are known to reduce mortality (skilled birth delivery and vaccination coverage of diphtheria, pertussis, and tetanus [DPT] and measles), in both the short and long term.

Methods

Design and setting

This panel (longitudinal) study used data from 181 countries for the years 2000 to 2019. Country was the unit of analysis. It explored the relationships between countries’ conflict status and maternal and child health outcomes. Panel regression modelling approaches are frequently used in global health research and country-level analyses over time [18]. They allow changes in health outcomes to be associated with changes in variables such as exposure to conflict over time, whilst adjusting for potential confounders, including country fixed effects and time trends. Although a prospectively written plan is not available, the analysis plan was based on previous conflict and health analytical frameworks developed by the study authors, which included prespecified model structures, covariate selection, and time lags [19]. We used all available maternal and child health outcomes available, to minimise reporting biases. The study followed RECORD reporting guidelines (S1 RECORD Checklist).

Data

One data source in this study was the Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset (UCDP GED) [20]. The UCDP has dedicated research teams that systematically determine the presence of conflict using definitions that are consistent and comparable across countries and years [20]. The UCDP GED uses ‘events’ as the unit of analysis, defined as ‘the use of armed force by an organised actor against another organised actor or civilians, resulting in at least one direct death for that location and time’ [20]. An armed conflict is present when events cumulatively result in 25 or more battle-related deaths within in calendar–conflict year. The UCDP GED has been used in peer-reviewed publications previously [8,10-12], and more details on its methodology can be found elsewhere [20]. The UCDP GED was aggregated into country–year observations for the analysis. Data for Palestine and Israel were omitted as the UCDP does not distinguish between them, which is problematic for attributing associated health outcomes for these states separately.

Country-level health data were obtained from the World Bank [21]. Specifically, these were rates of maternal, under-5, infant (<1 year), and neonatal (<28 days) mortality; delivery by a skilled birth attendant; and vaccination coverage of DPT and measles among 12- to 23-month-old children. The World Bank provides maternal and child health data from the UN Maternal Mortality Estimation Inter-agency Group and the UN Inter-agency Group for Child Mortality Estimation, whose task has been to improve data quality and produce yearly estimates for each country [22]. Data are primarily taken from vital registration systems (adjusting for underestimation); however, absent or incomplete data are modelled using multilevel regressions with social indicators (e.g., fertility rates, educational attainment, and health service coverage) [22]. The resulting mortality estimates are comparable across countries and years. The latest maternal mortality data were available from 2000 to 2017, and child mortality data from 2000 to 2019 [21]. Data were fully complete for all primary outcomes, while data were missing for 52.6% of country–year observations for skilled birth attendance and 0.5% of observations for both DPT and measles vaccination coverage. For delivery by a skilled birth attendant, data were not missing at random: The number of countries with data for this variable varied from 10 to 135 countries per year, with fewer after 2015. Country–year observations during armed conflict constituted 20.6% (n = 443/2,148) of missing data but 13.4% (n = 227/1,692) of non-missing data, suggesting selection bias should be considered when interpreting these findings.

Covariate data were taken from several sources including the World Bank [21], Institute for Health Metrics and Evaluation [23], International Disaster Database [24], V-Dem Institute [25], and Historical Index of Ethnic Fractionalization Dataset [26] (see below for descriptions of these variables).

Measures

Primary outcome measures were maternal, under-5, infant, and neonatal mortality rates. The maternal mortality ratio was the number of women who died from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. Under-5 mortality was the probability per 1,000 that a newborn baby died before reaching age 5 years. The infant and neonatal mortality rates were the number dying before reaching 1 year of age and 28 days of age, respectively, per 1,000 live births.

Secondary outcome measures were delivery by a skilled birth attendant and vaccination coverage of DPT and measles among 12- to 23-month-old children. Delivery by a skilled birth attendant was the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labour, and the postpartum period; to conduct deliveries on their own; and to care for newborns [27]. DPT and measles vaccinations were chosen instead of other vaccinations (e.g., PCV3, HIB3, rotavirus) as they were implemented in every country over the entire study period, making them comparable markers of health system performance over time. These secondary outcome measures were used to determine possible underlying mechanisms of adverse outcomes as they have known benefits for maternal and child health [28] and serve as proxies for the wider health system.

The exposure variable of interest was armed conflict, as defined by the UCDP. Due to the complexity of measuring conflict, 4 specifications were explored in the analyses. First, the most commonly employed specification of conflict in the literature is a binary variable indicating the presence of conflict for each country–year observation (0 = no, <25 battle-related deaths; 1 = yes, ≥25 battle-related deaths) [29]. Second, to better account for conflict intensity, this specification was expanded into a categorical variable with recommended cutoffs from the UCDP (0 = no, <25 battle-related deaths; 1 = minor conflict, 25–999 battle-related deaths; 2 = war, ≥1,000 battle-related deaths) [29]. This categorisation was made for each conflict per country–year observation, meaning that countries with many minor conflicts summing to 1,000 or more battle-related deaths per year were not categorised as war-affected. The rationale behind this decision was to prevent bias where large countries with many minor conflicts, such as India, were incorrectly classified as war-affected. This decision meant that 68 country–year observations were categorised as experiencing minor conflicts despite having 1,000 or more battle-related deaths; we found no difference in models that categorised these 68 country–year observations as minor conflict or war, so we opted for the former. Third, to account for the scale of battle-related deaths relative to the population, battle-related deaths per 100,000 population was used as a continuous variable. Fourth, this continuous measure was expressed as quintiles to model a non-linear relationship between conflict and mortality [10]. It should be noted that the ‘no conflict’ category for the binary and categorical specifications referred to fewer than 25 battle-related deaths, whereas for the quintile specification it referred to no battle-related deaths.

To adjust regression models for potential confounders, covariates were selected based on conceptual frameworks that outlined the theoretical causal pathways between conflict and health [30-32]. Ten suitable covariates were identified that are probable confounders and were included in the regression models—all expressed at the country–year level (see S1 Table for full definitions). To capture changes in national wealth, gross domestic product (GDP) per capita (current US dollars) [21] and Organisation for Economic Co-operation and Development (OECD) membership [33] were used. To capture changes in the political system, the Electoral Democracy Index was employed [25]. To adjust for changes in population demographics, population density [23], urban residence [23], and the age dependency ratio [21] were all included in models. To account for changes in population education, the mean number of years of educational attainment per capita was included in the models [23]. To capture changes in ethnic group composition, the Historical Index of Ethnic Fractionalisation Dataset was used [26]. Finally, to capture changes and shocks in climate-related factors, indicators for the presence of droughts [24] and earthquakes [24], and the population-weighted mean temperature and rainfall, were used [23]. There were missing data in 4 covariates: GDP per capita (2.7%), age dependency ratio (3.9%), Electoral Democracy Index (11.8%), and Ethnic Fractionalisation Index (46.0%). The latter 2 were omitted from models due to high levels of missing data, but reintroduced in sensitivity analyses (see below). There were missing data in the skilled birth attendant outcome measure (55.9%), but it was included in the analysis to minimise reporting bias.

Statistical analysis

All variables were descriptively analysed to produce means and standard deviations (SDs) of outcome measures by the 4 conflict specifications. The data were analysed using panel regression models to assess changes in exposure and outcomes over time and the associations between these changes. Fixed effects specifications were employed to adjust for all unobserved and observed time-invariant factors that could bias the analyses. Fixed effects specifications are frequently employed to robustly model dynamic and transitional associations over time. Regression models were based on the following equation: where i is the country, t is the year, and u is the error term representing unobserved time-variant effects. Use of fixed effects specifications was supported by the Hausman test, which examines differences in coefficients between fixed and random effects specifications. We assessed model fit graphically and using post-regression diagnostics (e.g., variance inflation factor [VIF], studentized residuals, stem and leaf plots, Cook’s D, and difference in fits) to identify collinear variables and outliers. We found no collinearity in our models (VIF < 2). Four observations (county–years Rwanda 1994, Bosnia and Herzegovina 1995, Congo 1997, and Eritrea 1999) were considered outliers due to having high leverage and influence, and were omitted from the model that specified conflict as a continuous variable. The variables of male and female education were collinear, and the latter was omitted due to poorer model fit.

Our main analyses were composed of 4 regression models for each primary and secondary outcome, representing the 4 specifications of conflict. The variable of interest, conflict, was expressed as a lagged term (i.e., conflict status in the year before) to address potential reverse causation and account for the likely delay in any associated changes in health outcomes. Each model was adjusted for 10 covariates (GDP per capita, OECD member, population density, urban residence, age dependency ratio, male education, temperature, rainfall, earthquakes, and droughts), and year and country fixed effects. Country-clustered Huber–White robust standard errors were used to account for possible heteroscedasticity and serial correlation. The beta coefficients for each conflict variable are interpreted as the absolute change in the outcome associated with a 1-unit change in armed conflict status, which can be changing from no conflict to conflict (binary) or increasing intensity of conflict (categorical or continuous specifications). For the primary outcomes, the coefficients were used to calculate a relative percentage change and the number of predicted excess deaths using post-regression estimations. To assess longer-term associations, we modelled conflict as a variable lagged from 1 to 10 years, with separate models for each year of lag.

All analyses were conducted in Stata 15.

Sensitivity analyses

The robustness of findings was tested using alternative model specifications including the sequential addition of covariates, the addition of 2 covariates that contained high rates of missing data (Electoral Democracy Index and Ethnic Fractionalisation Index), and random effects specifications. Alternative data sources for measuring armed conflict were explored and derived from the Major Episodes of Political Violence dataset compiled by the Center for Systemic Peace [34]. This dataset captures major conflicts globally and its binary conflict variable (0 = no conflict, 1 = conflict) is 97.6% specific and 97.7% sensitive to the UCDP ‘war’ binary variable (0 = no conflict, 1 = war).

Results

Armed conflict was present in 670 of the 3,718 (18.0%) country–year observations, three-quarters of which were minor conflicts (n = 522, 77.9%). Higher maternal and child mortality rates and poorer health system performance was correlated with greater conflict intensity in descriptive analyses (Table 1).

Table 1

Description of data used in the study.

Variable	Countries (n)	Observations (n)	Primary outcomes				Secondary outcomes
			Maternal mortality ratio per 100,000 live births	Under-5 mortality rate per 1,000 live births	Infant mortality rate per 1,000 live births	Neonatal mortality rate per 1,000 live births	Births attended by skilled health staff (%)	DPT vaccination coverage (%)	Measles vaccination coverage (%)
Total	186	3,718	210.6 (303.8)	40.5 (42.3)	28.9 (26.4)	16.1 (12.9)	88.6 (20.5)	86.6 (15.1)	85.8 (15.1)
Armed conflict exposure
No1	175	3,048	169.3 (262.4)	34.6 (38.2)	25.1 (24.0)	14.1 (11.7)	91.6 (16.9)	89.2 (12.1)	88.2 (12.6)
Yes	86	670	396.3 (394.6)	67.3 (49.3)	46.3 (29.6)	25.3 (13.8)	69.7 (29.6)	75.1 (20.7)	75.2 (19.8)
Minor conflict2	84	522	373.5 (387.1)	65.2 (49.2)	44.7 (29.3)	24.2 (13.3)	71.5 (29.1)	77.8 (20.1)	77.6 (19.7)
War3	31	148	475.4 (411.2)	74.9 (49.2)	52.0 (29.9)	29.3 (14.8)	61.6 (31.0)	65.3 (19.9)	66.8 (18.0)
Quintile of armed conflict exposure
None4	166	2,718	154.1 (250.3)	32.2 (36.8)	23.5 (23.3)	13.3 (11.4)	92.8 (15.2)	90.0 (11.4)	88.9 (11.9)
First	76	226	309.9 (326.6)	55.5 (43.8)	38.5 (26.6)	20.8 (12.5)	75.2 (26.7)	82.6 (14.9)	80.8 (15.8)
Second	69	187	263.5 (287.6)	52.1 (42.7)	36.5 (25.1)	20.4 (11.7)	82.0 (23.1)	84.0 (15.9)	83.5 (17.0)
Third	61	198	342.0 (388.0)	59.7 (49.7)	40.9 (29.8)	22.3 (13.5)	71.9 (30.8)	81.4 (19.4)	80.9 (18.7)
Fourth	43	193	431.6 (422.5)	69.7 (50.0)	47.9 (29.8)	26.1 (13.6)	67.4 (30.5)	74.4 (20.6)	74.6 (20.4)
Fifth	36	196	479.0 (400.3)	78.0 (48.7)	53.8 (29.5)	29.6 (14.1)	62.6 (29.5)	65.7 (19.8)	66.9 (18.0)

Data given as mean (standard deviation).

1No conflict: <25 battle-related deaths per country–conflict–year.

2Minor conflict: 25–999 battle-related deaths per country–conflict–year.

3War: ≥1,000 battle-related deaths per country–conflict–year.

4Zero battle-related deaths per 100,000 population per country–year.

DPT, diphtheria, pertussis, and tetanus.

Maternal mortality

Conflict-affected countries had double the mean maternal mortality ratio of conflict-free countries (396.3 versus 169.3/100,000 live births), with the highest ratio in countries classified as being at war (475.4; Table 1). In adjusted regression models (Table 2), conflict (binary) was associated with an average absolute increase in maternal mortality of 22.5 per 100,000 live births (95% CI 1.7–43.2), which is a relative increase of 13.3% (95% CI 1.1%–25.4%) over the maternal mortality rate in conflict-free countries. Conflicts classified as wars were associated with an absolute increase in maternal mortality of 36.9 per 100,000 live births (95% CI 1.9–72.0) and a relative increase of 21.8% (95% CI 1.1%–42.6%). Wars were estimated to have caused 0.3 million (95% CI 0.2 million–0.4 million) excess maternal deaths over the study period. Increases in battle-related deaths were positively, but non-significantly, associated with an increase in maternal mortality (S2 Table). The third and fifth quintiles of conflict exposure were significantly associated with increased maternal mortality—by 32.1 (95% CI 5.7–58.5) and 55.8 per 100,000 live births (95% CI 16.3–95.3), respectively (S2 Table). War was significantly associated with increases in maternal mortality for up to 7 years following the onset of war (Fig 1).

Table 2

The association between armed conflict and maternal and child mortality (adjusted beta coefficients, 95% confidence intervals).

Variable	Maternal mortality ratio per 100,000 live births		Under-5 mortality rate per 1,000 live births		Infant mortality rate per 1,000 live births		Neonatal mortality rate per 1,000 live births
	Binary conflict specification	Three-category conflict specification	Binary conflict specification	Three-category conflict specification	Binary conflict specification	Three-category conflict specification	Binary conflict specification	Three-category conflict specification
Armed conflict—binary
No	0.00(0.00, 0.00)		0.00(0.00, 0.00)		0.00(0.00, 0.00)		0.00(0.00, 0.00)
Yes	22.48*(1.74, 43.22)		1.94(−2.01, 5.88)		1.31(−0.46, 3.08)		0.17(−0.41, 0.75)
Armed conflict—3 categories
No		0.00(0.00, 0.00)		0.00(0.00, 0.00)		0.00(0.00, 0.00)		0.00(0.00, 0.00)
Minor conflict		20.62(−0.05, 41.28)		1.57(−2.36, 5.50)		1.12(−0.62, 2.85)		0.11(−0.47, 0.69)
War		36.93*(1.86, 72.01)		4.76(−0.52, 10.04)		2.79*(0.09, 5.48)		0.65(−0.35, 1.65)
Covariates
GDP per capita	2.89***(1.62, 4.17)	2.89***(1.61, 4.16)	0.63***(0.39, 0.87)	0.63***(0.39, 0.87)	0.33***(0.22, 0.45)	0.33***(0.22, 0.45)	0.11***(0.07, 0.15)	0.11***(0.07, 0.15)
OECD member	18.52(−3.54, 40.58)	18.42(−3.62, 40.45)	3.87(−2.32, 10.06)	3.85(−2.33, 10.03)	1.55(−2.31, 5.41)	1.54(−2.31, 5.40)	0.42(−1.38, 2.23)	0.42(−1.38, 2.23)
Population density	−57.16(−278.97, 164.64)	−56.44(−276.66, 163.78)	−39.44(−102.55, 23.66)	−39.23(−102.05, 23.59)	−21.33(−56.11, 13.45)	−21.21(−55.84, 13.41)	−6.38(−18.02, 5.26)	−6.35(−17.93, 5.23)
Urban residence	−6.76(−14.15, 0.63)	−6.69(−13.99, 0.62)	−1.48*(−2.95, −0.01)	−1.47*(−2.92, −0.01)	−0.88*(−1.68, −0.08)	−0.87*(−1.66, −0.08)	−0.35*(−0.65, −0.05)	−0.34*(−0.64, −0.05)
Age dependency ratio	0.40(−1.49, 2.28)	0.40(−1.48, 2.29)	0.15(−0.16, 0.46)	0.15(−0.16, 0.46)	0.21*(0.04, 0.37)	0.21*(0.04, 0.37)	0.13***(0.06, 0.20)	0.13***(0.06, 0.20)
Male education	−41.79*(−77.83, −5.75)	−42.18*(−78.02, −6.34)	−4.19(−10.43, 2.05)	−4.23(−10.45, 1.99)	−1.49(−4.85, 1.87)	−1.51(−4.87, 1.84)	0.24(−1.15, 1.62)	0.23(−1.15, 1.61)
Temperature	10.73**(4.23, 17.23)	10.69**(4.22, 17.16)	2.40***(0.99, 3.81)	2.39***(0.99, 3.79)	1.07**(0.34, 1.80)	1.06**(0.34, 1.79)	0.25(−0.03, 0.53)	0.24(−0.03, 0.52)
Rainfall	−4.34(−13.80, 5.12)	−4.35(−13.78, 5.08)	0.55(−1.20, 2.30)	0.53(−1.20, 2.27)	0.34(−0.62, 1.31)	0.33(−0.62, 1.29)	0.01(−0.38, 0.40)	0.01(−0.38, 0.39)
Earthquakes	5.78(−2.24, 13.81)	5.89(−2.10, 13.88)	1.53(−0.46, 3.52)	1.56(−0.42, 3.55)	0.60(−0.14, 1.34)	0.62(−0.12, 1.36)	0.38*(0.08, 0.68)	0.39*(0.08, 0.69)
Droughts	4.70(−2.13, 11.53)	4.73(−2.10, 11.56)	1.75**(0.60, 2.90)	1.77**(0.62, 2.92)	0.99**(0.37, 1.61)	1.00**(0.38, 1.62)	0.40**(0.12, 0.67)	0.40**(0.12, 0.68)
Observations	3,045	3,045	3,376	3,376	3,376	3,376	3,376	3,376
Countries	181	181	180	180	180	180	180	180

*p < 0.05,

**p < 0.01,

***p < 0.001. Data given as adjusted beta coefficient (95% confidence interval). Robust standard errors were employed. Each column is the output from 1 panel regression with fixed effects adjusted for the covariates in the table in addition to year dummies (not shown). Coefficients are interpreted as the absolute change in the dependent variable following a 1-unit change of the independent variable. GDP per capita is in current US dollars, and its unit is scaled up by 1,000. Population density represents the percentage of the population living in a density of >1,000 people/km2. Urban residence represents the percentage of the population living in urban areas. The age dependency ratio represents the proportion of dependents (aged under 15 years or over 64 years) per 100 working-age individuals. Male education is expressed as years per capita and is age-standardised. Temperature is in degrees Celsius and is the mean population-weighted annual temperature. Rainfall is the mean population-weighted annual rainfall in millimetres per year, scaled down by 1,000. Earthquake and drought are binary variables representing their absence or presence. All armed conflict variables were lagged by 1 year.

GDP, gross domestic product; OECD, Organisation for Economic Co-operation and Development.

Fig 1

The association between war and maternal and child mortality with up to 10-year lags.

Each line represents the output from a fixed effects panel regression model adjusted for 10 covariates and year fixed effects, showing the war coefficient only. Under-5 and infant mortality are scaled up by a factor of 10, and neonatal mortality is scaled up by a factor of 100, to fit the axis.

Under-5 mortality

The mean under-5 mortality rate was twice as high in conflict-affected (67.3/1,000 live births) as in conflict-free countries (34.6/1,000 live births), with the highest mortality rate in countries with conflicts classified as wars (74.9/1,000 live births; Table 1). In adjusted regression models, none of the 4 specifications of conflict was statistically associated with under-5 mortality. However, lagged models showed that war was significantly associated with higher under-5 mortality 3–8 years since the onset of war (Fig 1). Post-regression models estimated 3.2 million (95% CI 2.5 million–3.9 million) under-5 deaths associated with wars during the study period.

Infant mortality

The mean infant mortality rate was higher in conflict-affected (46.3 per 1,000 live births) than conflict-free countries (25.1 per 1,000 live births), with the rate highest in countries with conflicts classified as wars (52.0 per 1,000 live births; Table 1). In adjusted regression models (Table 2), conflicts classified as wars were associated with an increase of 2.8 infant deaths per 1,000 live births (95% CI 0.1–5.9; Table 2). The fifth quintile of conflict intensity was associated with an increase in infant mortality of 3.8 per 1,000 live births (95% CI 0.2–7.3), and each battle-related death per 100,000 population was association with an increase of 0.1 per 1,000 live births (95% CI 0.0–0.2; S2 Table). Wars were associated with higher infant mortality for up to 8 years following the onset of war (Fig 1). Post-regression prediction models estimated 2.0 million (95% CI 1.6 million–2.5 million) infant deaths were due to wars during the study period.

Neonatal mortality

The mean neonatal mortality rate was higher in conflict-affected (25.3 per 1,000 live births) compared with conflict-free (14.1 per 1,000 live births) countries, and highest in countries experiencing war (29.3 per 1,000 live births; Table 1). In all adjusted regression models (Table 2), including lagged analyses, conflict in all specifications was positively, although not significantly, associated with higher neonatal mortality, and the relationship was suggestive of a dose response.

Skilled birth attendants and childhood vaccinations

Conflict-affected countries had 21.9% fewer births attended by skilled health staff, 14.1% lower DPT vaccination coverage, and 13.0% lower measles vaccination coverage than conflict-free countries (Table 1). These differences were approximately 10 percentage points larger for conflicts categorised as wars (Table 1). In adjusted regression models (Table 3), no association was found between any armed conflict specification and births attended by skilled health staff, including in lagged models. Armed conflict was associated with a reduction in measles vaccination coverage of 2.6% (95% CI 0.2%–5.0%), whilst war was associated with 4.9% (95% CI 1.5%–8.3%) and 7.3% (95% CI 2.7%–11.8%) reductions in DPT and measles vaccination coverage, respectively. There were reductions in vaccination coverage associated with the fifth quintile of conflict intensity and with battle-related deaths as a continuous measure (S3 Table). Wars were associated with reduced DPT vaccination coverage for 3 years, and reduced measles vaccination coverage for 2 years, after war onset (Fig 2).

Table 3

The association between armed conflict and maternal and child health indicators (adjusted beta coefficients, 95% confidence intervals).

Variable	Births attended by skilled health staff		DPT immunisation		Measles immunisation
	Binary conflict specification	Three-category conflict specification	Binary conflict specification	Three-category conflict specification	Binary conflict specification	Three-category conflict specification
Armed conflict—binary
No	0.00(0.00, 0.00)		0.00(0.00, 0.00)		0.00(0.00, 0.00)
Yes	−0.83(−2.84, 1.19)		−1.47(−3.45, 0.52)		−2.62*(−5.05, −0.20)
Armed conflict—3 categories
No		0.00(0.00, 0.00)		0.00(0.00, 0.00)		0.00(0.00, 0.00)
Minor conflict		−0.97(−2.99, 1.06)		−1.02(−2.96, 0.91)		−2.03(−4.27, 0.21)
War		0.71(−3.24, 4.66)		−4.91**(−8.32, −1.49)		−7.25**(−11.81, −2.70)
Covariates
GDP per capita	−0.09**(−0.15, −0.02)	−0.09**(−0.15, −0.02)	−0.09(−0.19, 0.02)	−0.08(−0.19, 0.02)	−0.16**(−0.26, −0.06)	−0.16**(−0.26, −0.05)
OECD member	0.00(−3.38, 3.38)	−0.02(−3.41, 3.38)	−2.66*(−5.09, −0.23)	−2.63*(−5.06, −0.21)	−1.35(−4.18, 1.47)	−1.32(−4.14, 1.50)
Population density	19.43(−9.55, 48.40)	19.44(−9.61, 48.50)	1.11(−15.48, 17.70)	0.85(−15.42, 17.11)	4.74(−11.34, 20.82)	4.38(−11.17, 19.94)
Urban residence	0.74*(0.17, 1.31)	0.74*(0.17, 1.31)	0.58(−0.01, 1.17)	0.56(−0.01, 1.14)	0.31(−0.11, 0.73)	0.29(−0.11, 0.69)
Age dependency ratio	−0.52***(−0.73, −0.31)	−0.52***(−0.73, −0.31)	0.01(−0.16, 0.19)	0.01(−0.16, 0.18)	0.03(−0.14, 0.19)	0.02(−0.14, 0.19)
Male education	1.15(−2.60, 4.91)	1.07(−2.67, 4.82)	2.03(−1.33, 5.39)	2.08(−1.25, 5.41)	3.12(−0.09, 6.32)	3.19*(0.02, 6.36)
Temperature	−0.82*(−1.56, −0.08)	−0.83*(−1.57, −0.09)	−0.78(−1.59, 0.03)	−0.76(−1.56, 0.04)	−1.02*(−1.87, −0.17)	−1.00*(−1.83, −0.17)
Rainfall	0.35(−1.06, 1.76)	0.35(−1.06, 1.76)	−0.49(−1.78, 0.80)	−0.47(−1.76, 0.81)	0.14(−1.06, 1.35)	0.17(−1.03, 1.37)
Earthquakes	−0.98(−2.12, 0.15)	−0.94(−2.04, 0.17)	−0.80(−1.72, 0.12)	−0.84(−1.76, 0.07)	0.29(−0.54, 1.11)	0.23(−0.58, 1.04)
Droughts	−1.78*(−3.14, −0.42)	−1.80*(−3.17, −0.43)	0.13(−0.61, 0.87)	0.11(−0.63, 0.84)	−0.11(−1.02, 0.79)	−0.15(−1.04, 0.74)
Observations	1,527	1,527	3,367	3,367	3,367	3,367
Countries	177	177	180	180	180	180

*p < 0.05,

**p < 0.01,

***p < 0.001. Data given as adjusted beta coefficient (95% confidence interval). Robust standard errors were employed. Each column is the output from 1 panel regression with fixed effects adjusted for the covariates in the table in addition to year dummies (not shown). Coefficients are interpreted as the absolute change in the dependent variable following a 1-unit change of the independent variable. GDP per capita is in current US dollars, and its unit is scaled up by 1,000. Population density represents the percentage of the population living in a density of >1,000 people/km2. Urban residence represents the percentage of the population living in urban areas. The age dependency ratio represents the proportion of dependents (aged under 15 years or over 64 years) per 100 working-age individuals. Male education is expressed as years per capita and is age-standardised. Temperature is in degrees Celsius and is the mean population-weighted annual temperature. Rainfall is the mean population-weighted annual rainfall in millimetres per year, scaled down by 1,000. Earthquake and drought are binary variables representing their absence or presence. All armed conflict variables were lagged by 1 year.

GDP, gross domestic product; OECD, Organisation for Economic Co-operation and Development.

Fig 2

The association between war and delivery by a skilled birth attendant and childhood vaccinations with up to 10-year lags.

Each line represents the output from a fixed effects panel regression model adjusted for 10 covariates and year fixed effects, showing the war coefficient only. DPT, diphtheria, pertussis, and tetanus.

Sensitivity analyses demonstrated robustness of findings across all outcomes, and we present these data for maternal mortality. Within the sensitivity analyses, random effects models showed positive associations between armed conflict and maternal mortality across all 4 conflict specifications, consistent with those from fixed effects specifications (S4 Table). The sequential addition of covariates did not substantially alter armed conflict coefficients, suggesting stability of the models (S5 Table). The addition of the 2 covariates Electoral Democracy Index and Ethnic Fractionalisation Index had minimal impact on the coefficient for war (β 46.0, 95% CI 9.5–82.4; S5 Table, Model 11). Replacing the UCDP data with a binary armed conflict variable from the Major Episodes of Political Violence dataset also produced a similar and statistically significant coefficient (β 43.2, 95% CI 1.4–85.0; S6 Table).

Discussion

This study uses robust quasi-experimental methods and comprehensive global datasets to demonstrate that conflict is associated with substantial increases in maternal, under-5, and infant mortality, in addition to decreases in vaccination coverage. There is evidence of a dose–response relationship, with increasing intensity of conflict, particularly war, associated with greater increases in mortality and reductions in vaccinations. We provide powerful and new global estimates of the associations between conflict and health that are beyond the limited geographies of previous work, taking an approach that adjusts for country-level factors and explores time trends while accounting for pre-conflict and post-conflict trends.

These associations exemplify the susceptibility of mothers and children during periods of conflict. They are likely driven by the known impacts of conflict, including reduced government spending on healthcare and damage to healthcare infrastructure (deliberate or collateral) [6,35]. Reduced utilisation, access, and affordability of high-quality maternal and child services [35,36] can directly affect individuals’ risk of mortality. Additionally, changes to security, governance, health workforce, financing, supplies, and monitoring can affect the quality of maternal and child healthcare [37]. For example, interviews with government and UN officials working in conflict-affected Nigeria and Mali highlighted the difficulty in planning for healthcare resource allocation due to constant population movement, and in accessing remote populations in insecure settings [38,39], which explained poor maternal and child health indicators. Further empirical evidence from Nigeria has found that living in close proximity to development aid during conflict was associated with lower infant mortality [15].

An established literature links armed conflict to lower workforce retention and increased job stress and insecurity [40,41], but this study did not find significant associations between conflict and changes in skilled birth attendance. Reductions in workforce retention may be partly offset by increased humanitarian support, particularly in the short term. For example, in Syria, humanitarian agencies were able to successfully advocate funding for prioritisation of reproductive and maternal healthcare [42]. Research from Afghanistan showed that coverage of skilled birth attendants and institutional facility deliveries continued to increase linearly between 2003 (approximately 20%) and 2015 (approximately 50%), when the country experienced substantial national conflict, explained by early investment in maternal health programmes and a resilient health system [37]. A similar pattern was recently documented in Colombia [43]. The absence of a significant relationship between conflict and neonatal mortality indicates that mechanisms underlying the association between conflict and maternal and child health may be complex and may vary between outcome measures. One interpretation is that birth is a key event for which mothers are most likely to seek care, even in fragile and insecure environments, which, coupled with the skilled birth attendance, could mitigate against the risk of neonatal mortality. In contrast, conflict may dissuade mothers from seeking healthcare during pregnancy and infancy, explaining increases in maternal and infant mortality and reduced vaccination uptake. This is supported by research conducted among Syrian refugees in Lebanon, which found that expectant mothers preferred to return to war-torn Syria for planned cesarean sections, to prevent against uncertainties in reaching a hospital during natural labour (e.g., blocked roads, night curfews) [44]. Furthermore, research from conflict-affected Burundi showed that integrating traditional birth attendants into the primary healthcare system as ‘birth companions’ encouraged expectant mothers to seek healthcare rather than give birth at home [45]. It is also plausible that our results for skilled birth attendants are affected by selection bias, given over half of the global data were missing for skilled birth attendance and missing data were more frequent in conflict-affected countries, where data availability and quality are limited.

The results on child vaccinations—which can act as a proxy for coverage of other key healthcare interventions—suggest that health services may be a key pathway through which conflict negatively affects maternal and child health. However, such effects are unlikely to be universal as studies of conflict-affected refugees have found improvements in maternal and child health indicators due to health services provided in refugee camps [46-48]. Populations that are unable to flee conflict may be the most affected by deteriorating health systems, but other non-health-service pathways likely play a role.

Previous limited evidence indicates that deteriorations in socioeconomic and environmental conditions may also contribute to increased maternal and child mortality during and after conflict [14]. Modelling studies from sub-Saharan Africa have found that conflict is associated with higher rates of child malnutrition [49,50]—a contributor to infant and child mortality. Armed conflict has been also shown to have a negative impact on school enrolment, with girls disproportionately affected [51]. Child labour may be another contributing factor, with girls found to be exposed to harsher working conditions than boys in conflict situations [52]. Other research has found associations between armed conflict and physical injuries, infectious diseases, poor mental health, and poor sexual and reproductive health in women and children [35].

This study highlights how the negative health outcomes associated with conflict may appear and persist several years after the onset of war, particularly with respect to under-5 mortality. Delayed associations between armed conflict and health outcomes may reflect an amalgamation of effects on several upstream and healthcare factors, such as the withdrawal of humanitarian funding, the slow breakdown of vital health and social infrastructure, and the accumulation of risk factors in pre-birth and in early life, including delays to immunisation, increased diarrhoea, and the absence of antibiotics [53].

This study is not without limitations. The analyses are ecological, precluding causal inference at the individual level. A country-level analysis likely leads to conservative point estimates for conflict, as data reflect country averages and mask sub-national variation in conflict intensity and likely differential associations across socioeconomic groups [54]. Studies using individual-level health data from Demographic and Health Surveys (DHS) seek to address this limitation by better capturing conflict activity in local geographies [10-13]. However, the UN Maternal Mortality Estimation Inter-agency Group cautions against using DHS data for monitoring trends as the program’s methodology (asking respondents about survivorship of sisters) generates estimates pertaining to any time within the past few years before the survey [55]. A key strength of our study is therefore the use of data from the World Bank, which unlike DHS, enables an assessment of trends and the associations of shocks such as armed conflict with maternal mortality.

This study may also be limited by the data sources; for example, there may be biases introduced by estimation and data processing by data sources before publication (e.g., WHO or Institute for Health Metrics and Evaluation), which may mask sudden shifts in mortality associated with conflict and produce conservative estimates. However, we have shown previously that such data processing did not obscure abrupt changes in mortality in conflict-affected countries and are unlikely to introduce substantial bias to this analysis [19]. In terms of outcomes, this study only examined maternal and child mortality, and selective health service indicators. More detail by causes of death in addition to other health systems data could perhaps better elucidate underlying patterns and mechanisms of action, including our lack of association between armed conflict and neonatal mortality. The analytical approaches employed in this study are robust and frequently used in global country-level analyses, and the use of fixed effects specifications aims to remove biases from country-level characteristics. Nonetheless, there remains the potential for unobserved biases that could explain these findings despite our employing multiple confounders and robust model specifications. The role of conflict duration was not incorporated into the analytical approach, and this should be considered as an additional dimension in assessing conflict impacts in future work.

This study shines light on the major challenges that countries in conflict face in meeting Sustainable Development Goal targets for maternal and child health, especially in settings where conflict is of high intensity. Beyond the recommendation that all conflicts should cease immediately, secondary prevention measures should focus on mitigating the effects of armed conflict on maternal and child health. Our results demonstrate the importance of protecting health system functioning during periods of conflict. Additionally multi-sectoral humanitarian and development approaches are required, including attention on food security, economic development, poverty alleviation, war prevention advocacy, and improved education. It is imperative that international efforts to support conflict-affected countries are integrated with existing infrastructure to enable long-term and sustainable improvements in health systems and socioeconomic and environmental conditions once acute deteriorations have been addressed [56]. Combined, these actions are vitally important to protect maternal and child health during and after periods of conflict.

The RECORD statement—checklist of items, extended from the STROBE statement, that should be reported in observational studies using routinely collected health data.

(DOCX)

Click here for additional data file.

Definition of covariates.

(DOCX)

Click here for additional data file.

The association between armed conflict and maternal and child mortality (beta coefficients, 95% confidence intervals): Additional conflict specifications.

(DOCX)

Click here for additional data file.

The association between armed conflict and maternal and child health indicators (beta coefficients, 95% confidence intervals): Additional conflict specifications.

(DOCX)

Click here for additional data file.

Sensitivity analysis: Random effects specification (maternal mortality).

(DOCX)

Click here for additional data file.

Sensitivity analysis: Sequential addition of covariates, including a complete case analysis (maternal mortality).

(DOCX)

Click here for additional data file.

Sensitivity analysis: Major Episodes of Political Violence exposure data (maternal mortality).

(DOCX)

Click here for additional data file.

16 Feb 2021

Dear Dr Jawad,

Thank you for submitting your manuscript entitled "The association between armed conflict and maternal and child health: a panel regression analysis of 180 countries, 2000-2019" for consideration by PLOS Medicine for our upcoming Special Issue.

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26 Apr 2021

Dear Dr. Jawad,

Thank you very much for submitting your manuscript "The association between armed conflict and maternal and child health: a panel regression analysis of 180 countries, 2000-2019" (PMEDICINE-D-21-00734R1) for consideration at PLOS Medicine for our upcoming Special Issue.

Your paper was evaluated by the Guest Editors and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a revised version that addresses the reviewers' and editors' comments fully. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

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Requests from the editors:

Please adapt the title to better match journal style. We suggest: "Implications of armed conflict for maternal and child health: A regression analysis of data for 180 countries from 2000-2019".

Please add a new final sentence to the "Methods and findings" subsection of your abstract, which should begin "Study limitations include ..." or similar and should quote 2-3 of the study's main limitations.

At line 43, please adapt the sentence to begin "Our analysis indicates that ..." or similar.

After the abstract, please add a new and accessible "Author summary" section in non-identical prose. You may find it helpful to consult one or two recent research papers published in PLOS Medicine to get a sense of the preferred style.

At line 53, please make that "... 5 years".

Early in the methods section of your main text, please state whether the study had a protocol or prespecified analysis plan, and if so attach the relevant document(s) as a supplementary file(s), referred to in the text.

Please highlight analyses that were not prespecified.

At line 96 and any other instances, please make that "data ... were".

Throughout the text, please format reference call-outs as follows: "... child malnutrition [34,35] ..." (i.e., no spaces in the square brackets).

Throughout the paper, please use the style "up to 2 years", although numbers should be written out at the start of sentences.

Please remove all instances of "[online]" from the reference list.

Noting reference 13, please ensure that all references have full access details.

Please update reference 41, and if not published make the latest version available to us in confidence.

Please add a completed checklist for the most appropriate reporting guideline, which may be RECORD, labelled "S1_RECORD_Checklist" or similar and referred to as such in the Methods section.

In the checklist, individual items should be referred to by section (e.g., "Methods") and paragraph numbers, not line or page numbers as the latter generally change in the event of publication.

Comments from the reviewers:

*** Reviewer #1:

This study aims to assess the association between conflict and maternal and child health globally, in both the short and long term.

Comments:

There are just a few typos to correct within the manuscript.

"Due to the complexity of measuring conflict, four specifications were explored in the analyses."

The authors have demonstrated rigor by applying four different specifications here, including the number of battles being considered as a continuous variable.

"To adjust regression models for potential confounders, covariates were selected based on conceptual frameworks that outlined the theoretical causal pathways between conflict and health [25-27]. Ten suitable covariates were identified that are probable confounders and included in the regression models - all expressed at the country-year level. "

These are: "GDP per capita, OECD member, population density, urban living, age dependency ratio, male education, temperature, rainfall, earthquakes, and droughts"

The authors have suitably assessed the evidence base and attempted to account for various confounders within the models.

Additionally, did the authors consider a measure of accessibility to healthcare or healthcare systems as a potential confounder?

"There was missing data in four variables: GDP per capita (2.7%), age dependency ratio (3.9%), Multiplicative Polyarchy Index (11.8%), and Ethnic Fractionalisation Index (46.0%). The latter two were omitted from models due to high levels of missing data, but reintroduced in sensitivity analyses."

The authors have appropriately assessed and accounted for missing data within their analysis, including running a sensitivity analysis on this.

Did the authors consider completing a multiple imputation methodology for missing data for comparison, to further assess the robustness of their findings?

Overall, this study presents a clearly described methodology, where collinearity and outliers are adequately explored and removed from the model, and a thorough set of sensitivity analyses have been completed.

Furthermore, the study limitations have been suitably and transparently discussed.

*** Reviewer #2:

General Comments: This is an interesting and fairly straightforward paper on an important topic that is gaining much attention. While other recent papers have used the Uppsala Conflict Database to examine similar questions, this study takes a different modeling approach using different datasets. Further, instead of focusing at the sub-national level, they conduct an ecological analysis at the country level, which has some strengths and weaknesses. While the paper in general is clearly written, there are several areas where the authors could improve. Notably, the discussion section presents an overly simplistic picture of the findings and focuses primarily explaining the results they observe as stemming from decreases in government spending. Further, they provide little reflection or interpretation of the results, rather the discussion repeats much of the same conceptual rationale for why conflict is associated with increased maternal, child, and infant mortality.

Specific comments:

* Line 137: The authors made a decision to not include countries that have many minor conflicts summing to more than 1000 battle-related deaths per year with the countries that have experienced war. It would be helpful it the authors could provide more information as to what they mean by multiple minor conflicts and to provide a rationale for this distinction. If there are many minor conflicts in a country that amount to the same number of deaths, why would this not have a similar impact as a war on the health system or macro-level environment, thus leading to the same outcomes as hypothesized resulting from exposure to war? Did the authors perform any sensitivity analysis to interrogate this decision?

* In the methods, some of the co-variates are not fully defined. For example, the authors do not define how skilled birth attendance is measured. Authors should make sure that all exposures and covariates are clearly defined.

* As stated above, the discussion section presents a rather simplistic interpretation of the results and focuses primarily on reduced government spending on healthcare and damage to healthcare infrastructure. However, there are several other factors that could contribute to the increases observed and be equally important. For example, conflict likely limits a woman's ability to travel to a facility for ANC and delivery; though of note, it is interesting that the authors do not find a significant decrease in skilled birth attendance associated with conflict. To my earlier point, a clearer definition of what skilled birth attendance means in this study would be helpful to develop a more nuanced picture of the results. Are the authors able to further unpack skilled birth attendance by looking at institutional births or cadre of "skilled" attendant? The authors do state that this finding could be the result of a type II error, though it is noted that the confidence intervals around the point estimates are not particularly wide. Providing some deeper discussion here could add depth to the interpretation of these results.

* Further, the authors do not reflect on some of their more interesting findings in the discussion. For example, it is quite interesting that under-5 mortality increases only between three and eight years since the onset of war. Why might this be the case and why might the effects not be immediately observed?

* Similarly, I think one of the more important findings of this paper is the significant lagged effects observed with regard to several of the outcome variables. While the authors reference this finding in their conclusions; they give little interpretation of these results in the discussion section.

* Given that other studies have recently looked at maternal mortality (several of which are cited), and other related indicators, such as institutional delivery, using the same database to assess conflict exposure, it would be helpful if the authors could contextualize the results of this study in the existing literature.

* The authors mention in the limitations that "DHS does not provide a representative picture of conflict exposure and so can over- or underestimate the effects of conflict depending on the sampling approach [39]. Additionally, DHS analyses are limited in their ability to assess trends and impact of shocks such as conflict [40]- which is a key strength of this study." These statements are not clear. In particular, DHS does not measure conflict and is not conducted during times of conflict, thus it is not clear how the DHS would under or over-estimate the effects of conflict. Further, it is not clear what the authors are referring to their improved ability over these other studies to "assess trends and shocks such as conflict," given that the data used to measure conflict is this analysis is the same as that used in the analyses that use DHS. More clarity here is needed. The papers referenced by the authors Further, these statements are not appropriately referenced. Reference 39 is not directly relevant to the DHS while reference 40 appears to have been made in error.

* Given that several other papers have examined conflict exposure using smaller scale geographies, and this paper takes a country-level ecological approach, it would be helpful to see a more nuanced discussion of why this approach is particularly meaningful, what new can be learned from it beyond what has already been published, etc. beyond the little that is mentioned in the limitations section.

* Page 21 (no line numbers): what do the authors mean by "data manipulation?" This is not clear.

* In conclusion, the authors state "This study shines light on the major challenges that countries in conflict face in meeting SDG targets for maternal and child health, especially in settings where conflict is of high intensity and long lasting." I do not see where the authors examine interactions between duration and intensity of conflict. It would seem that this would be a particularly salient dimension to explore—especially given that the effect of prolonged conflict on systems-level deterioration may compound over time as opposed to conflicts that are more brief in duration.

*** Reviewer #3:

This is an interesting and relevant study that assesses the association between conflict and maternal and child health, analyzing six indicators in 180 countries between 2000 and 2019 with data from the Uppsala Conflict Data Program and World Bank. The statistical methods are adequate and robust to draw the conclusions.

Some minor comments that may help to improve the manuscript are:

Introduction: I suggest to add a paragraph that introduce some of the other variables associated with the outcomes of interest that are taken into account as potential confounders in the analysis and how some of this are highly correlated with conflict.

Methods:

I suggest expanding the explanation of the population mean temperature and rainfall, it is not very clear.

Page 9, statistical analysis: "Four data points (Rwanda 1994, Bosnia and Herzegovina 183 1995, Congo 1997, and Eritrea 1999) were considered outliers due to having high leverage 184 and influence, and were omitted from the model.." please expand or clarify exactly to what variable are you referring.

Results: Table 2: improve the title to include that it is an adjusted model.

It seems that there is a mistake in the number of the figure captions.

Is there a way to account for possible correlation or collinearity between conflict and confounding variables?

I suggest including a brief description of the findings related to the confounding variables in the models.

Discussion:

The Branch consortium has a series of recent publications that could help to expand the comparisons with other studies.

https://www.biomedcentral.com/collections/branchconsortium

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

7 Jun 2021

Submitted filename: Response to reviewers.docx

Click here for additional data file.

10 Jul 2021

Dear Dr. Jawad,

Thank you very much for submitting your revised manuscript "Implications of armed conflict for maternal and child health: A regression analysis of data for 180 countries from 2000-2019" (PMEDICINE-D-21-00734R2) for consideration at PLOS Medicine for our upcoming Special Issue.

Your paper was discussed with the guest editors for the issue, and re-seen by our independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will be unable to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a further revised version that addresses the reviewers' and editors' comments fully. We will not be able to make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript within two weeks. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

Please let me know if you have any questions, and we look forward to receiving your revised manuscript.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

-----------------------------------------------------------

Requests from the editors:

Please address the issues of selection bias and missing data for each of the main outcomes (with summary data on each provided for review)

We ask you to add discussion to put your findings in context of other related results from the BRANCH and PRIO consortia.

We also suggest devoting some discussion to the findings on deliveries with skilled birth attendants and neonatal mortality, where the observed impacts may be less than would be expected.

Please adapt the data statement (submission form) to provide the links where study data can be obtained.

Thank you for describing the origin of your analysis plan. Are you able to attach a document or documents as a supplementary file, referred to in the methods section?

Please use the journal name abbreviation "PLoS ONE" in the reference list; and use other abbreviations, e.g., "Confl Health", consistently.

Comments from the reviewers:

*** Reviewer #1:

The authors have satisfactorily considered and responded to each comment in turn.

*** Reviewer #2:

The authors have generally done a good job addressing my previous comments; however, there are still some issues that need to be addressed that have emerged as a result of some of the revisions.

1. The authors mention that over half of the countries are missing data on SBAs and that more conflict-affected countries are missing data on this variable than non-conflict affected countries. This seems like a substantial limitation in the paper and it is difficult for me to understand how this offers a robust estimate of the association given the extent of missing data, but the authors only make a passing comment about this issue. Are any of the other variables of interest missing data? It would be helpful for the authors to provide this additional information for other important variables in the paper that may also be subject to selection bias in this same way due to missing data.

2. The authors discuss the limitation with the DHS data as it being unable to assess trends. "A key strength of our study is therefore the use of data from the World Bank, which unlike the DHS, enables an assessment of trends and the impacts of shocks such as armed conflict." However, the limitation that they describe really only seems to be relevant to estimates of maternal mortality given the DHS use of the sisterhood method, and it is hard to understand how this would apply to other other outcomes of interest investigated by the authors. The authors should be more precise in discussing these limitations.

*** Reviewer #3:

The authors have addressed the comments satisfactorily.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

7 Aug 2021

Submitted filename: Response to reviewers R2.docx

Click here for additional data file.

2 Sep 2021

Dear Dr. Jawad,

Thank you very much for re-submitting your manuscript "Implications of armed conflict for maternal and child health: A regression analysis of data for 180 countries from 2000-2019" (PMEDICINE-D-21-00734R3) for consideration at PLOS Medicine for our upcoming Special Issue.

I have discussed the paper with editorial colleagues and the guest editors, and it was also seen again by one reviewer. I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

Please let me know if you have any questions, and we look forward to receiving the revised manuscript.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

------------------------------------------------------------

Requests from Editors:

At line 31, prior to the presentation of the findings, we suggest adding a sentence to summarize the number of observations, number of minor conflicts and number of wars, for example.

Parenthetically, why are 192 countries specified in table 1 when you highlight 180 countries in the title etc?

At line 36 and elsewhere in the paper, please avoid the word "effects" given the observational study design, substituting "impacts" or similar.

At line 36 we suggest amending the wording to: "Long term impacts of war were demonstrated by associated increases in maternal mortality observed for up to 7 years; in under 5 mortality ..." or similar.

At line 46, "... relating to"?

Please spell out "DPT" at first use in abstract, author summary and main text.

Comments from Reviewers:

Reviewer #2:

The authors have addressed all my concerns. This is an interesting paper that will make an important contribution.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

9 Sep 2021

Submitted filename: Response to reviewers R3.docx

Click here for additional data file.

13 Sep 2021

Dear Dr Jawad,

On behalf of my colleagues and the Academic Editor, Dr Bhutta, I am pleased to inform you that we have agreed to publish your manuscript "Implications of armed conflict for maternal and child health: A regression analysis of data for 180 countries from 2000-2019" (PMEDICINE-D-21-00734R4) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

Prior to final acceptance, please:

Around line 126 (Methods), adapt the text to "Although a written plan is not available, the analysis plan was based ..." or similar (assuming this is correct);

Adapt the text to "... continued to increase linearly" (soon after the call-out for reference 42);

And remove "forthcoming" from reference 25 (if this is available online, please include a URL with accessed date).

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org