Hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia: An ecological study

Anna D Gage; Günther Fink; John E Ataguba; Margaret E Kruk

doi:10.1371/journal.pmed.1003843

. 2021 Dec 1;18(12):e1003843. doi: 10.1371/journal.pmed.1003843

Hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia: An ecological study

Anna D Gage ^1,^*, Günther Fink ^2,³, John E Ataguba ^4,⁵, Margaret E Kruk ¹

Editor: Jenny E Myers⁶

PMCID: PMC8635398 PMID: 34851947

Abstract

Background

Widespread increases in facility delivery have not substantially reduced neonatal mortality in sub-Saharan Africa and South Asia over the past 2 decades. This may be due to poor quality care available in widely used primary care clinics. In this study, we examine the association between hospital delivery and neonatal mortality.

Methods and findings

We used an ecological study design to assess cross-sectional associations between the share of hospital delivery and neonatal mortality across country regions. Data were from the Demographic and Health Surveys from 2009 to 2018, covering 682,239 births across all regions. We assess the association between the share of facility births in a region that occurred in hospitals (versus lower-level clinics) and early (0 to 7 days) neonatal mortality per 1,000 births, controlling for potential confounders including the share of facility births, small at birth, maternal age, maternal education, urbanicity, antenatal care visits, income, region, and survey year. We examined changes in this association in different contexts of country income, global region, and urbanicity using interaction models.

Across the 1,143 regions from 37 countries in sub-Saharan Africa and South Asia, 42%, 29%, and 28% of births took place in a hospital, clinic, and at home, respectively. A 10-percentage point higher share of facility deliveries occurring in hospitals was associated with 1.2 per 1,000 fewer deaths (p-value < 0.01; 95% CI: 0.82 to 1.60), relative to mean mortality of 22. Associations were strongest in South Asian countries, middle-income countries, and urban regions. The study’s limitations include the inability to control for all confounding factors given the ecological and cross-sectional design and potential misclassification of facility levels in our data.

Conclusions

Regions with more hospital deliveries than clinic deliveries have reduced neonatal mortality. Increasing delivery in hospitals while improving quality across the health system may help to reduce high neonatal mortality.

In an ecological study, Anna D Gage and colleagues examine the association between hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia.

Author summary

Why was this study done?

Neonatal mortality remains high in sub-Saharan Africa and South Asia despite increasing access to facilities for delivery, potentially due to poor quality of care.
Few studies have examined the relationship between the level of facility (hospital versus lower-level clinic) used for delivery and neonatal mortality.

What did the researchers do and find?

In this ecological analysis of 1,143 subnational regions across 37 countries in sub-Saharan Africa and South Asia, we estimate the association between the share of facility births in a region that occurs in a hospital and early neonatal mortality.
We compare this to the association between the proportion of facility deliveries in a region and the early neonatal mortality rate.
We find that regions with higher proportions of hospital delivery have lower levels of neonatal mortality after controlling for potential confounders. We find that this association is strongest in South Asian countries (relative to sub-Saharan Africa), middle-income countries (relative to low income), and urban regions (relative to rural).

What do these findings mean?

Delivery in hospitals, compared to lower-level clinics, may reduce the risk of early neonatal mortality in these 2 high mortality regions.
A better understanding of the relationship between facility level used for delivery and mortality could guide interventions to reduce neonatal mortality in high mortality areas.

Introduction

Despite substantial increases in facility delivery in the past 2 decades, global declines in neonatal mortality have lagged reductions in child mortality [1,2]. In 2019, of the 2.4 million neonatal deaths globally, 81% were in South Asia or in sub-Saharan Africa [3]. Despite a preponderance of evidence that good-quality delivery and newborn care can prevent most newborn deaths [4], delivering in a facility appears only weakly associated with improved neonatal survival in these regions [5,6]. This is potentially due to the poor quality of maternity care, particularly in primary care clinics, which conduct 30% or more deliveries in low-income countries [7]. Across 5 sub-Saharan African countries, for example, quality was poorer in low-volume clinics without cesarean delivery capacity than hospitals [8]. Clinics may be unable to provide high-quality maternal and newborn care that can quickly recognize and effectively treat complications when they arise because of limited supplies and equipment, a lack of providers skilled in managing complications, and low delivery volumes, which make retaining skills challenging.

While many studies have examined the relationship between facility delivery and health outcomes [9,10], few have assessed health outcomes across facility types or levels (i.e., compared outcomes between primary care clinics and hospitals). Assessing health outcomes across facility levels is challenging in cross-sectional data because women with underlying health risks or difficulties in labor typically opt for—or are referred to—hospitals, resulting in a more challenging patient mix for hospitals [7,11]. On the other hand, hospitals tend to be located in less remote and economically more developed areas, which may lead to the opposite bias in cross-sectional analysis. To address both concerns, ecological comparisons across regions can be useful. Because populations in a region will include individuals with the full range of underlying risks, comparing health outcomes across regions with different obstetric facility use patterns should allow valid inference regarding the relative contributions of different facility levels to neonatal health outcomes.

Understanding the roles that different types of health facilities play in neonatal mortality could help to elucidate new strategies for improving maternal and newborn health. We aim to answer whether there is an association between regional hospital delivery and regional neonatal mortality. This analysis uses data from 1,143 regions in 37 countries in 2 high mortality areas, sub-Saharan Africa and South Asia, to examine the ecological relationship between the share of facility deliveries occurring in a hospital and neonatal mortality. We further examined how this relationship differs in different contexts.

Methods

Ethics approval

The Harvard University Human Research Protection Program categorized this secondary analysis of data as exempt from human subjects review.

Data and settings

We used an observational ecological study design encompassing all countries in sub-Saharan Africa and South Asia with available data. This study did not have a prospective analysis plan. We compiled data from the most recent Demographic and Health Survey (DHS) [12] for each country in these 2 areas available after 2000 [13]. DHS surveys are nationally representative population surveys that include questions about healthcare utilization and child mortality for children born to women of reproductive age in selected households. Surveys from 37 countries collected between 2009 and 2018 met the inclusion criteria; the countries and survey dates are listed in Table 1. For each survey, we identified the lowest administrative level for which the survey was intended to be representative, most often the region or province level. We defined each of the variables of interest at the child level for children born 5 years before the survey, then used the household sampling weights to collapse the dataset to a regional level.

Table 1. Study countries and place of delivery.

	DHS year	N regions	Area	Income group	Place of birth
Country	DHS year	N regions	Area	Income group	Hospital	Clinic	Home	Unknown facility
Afghanistan	2015	34	South Asia	Low	40%	8%	51%	1%
Angola	2015	18	sub-Saharan Africa	Middle	35%	10%	53%	1%
Bangladesh	2014	7	South Asia	Middle	37%	1%	62%	0%
Benin	2017	12	sub-Saharan Africa	Low	30%	53%	14%	2%
Burkina Faso	2010	13	sub-Saharan Africa	Low	6%	61%	33%	0%
Burundi	2017	18	sub-Saharan Africa	Low	21%	63%	12%	4%
Cameroon	2011	12	sub-Saharan Africa	Middle	34%	27%	38%	1%
Chad	2015	21	sub-Saharan Africa	Low	10%	12%	78%	0%
Congo, Dem. Rep.	2014	11	sub-Saharan Africa	Low	26%	48%	19%	7%
Congo, Rep.	2012	12	sub-Saharan Africa	Middle	68%	21%	8%	0%
Côte d’Ivoire	2012	11	sub-Saharan Africa	Middle	23%	34%	41%	1%
Ethiopia	2016	11	sub-Saharan Africa	Low	7%	18%	73%	2%
Gabon	2012	10	sub-Saharan Africa	Middle	77%	10%	7%	4%
Ghana	2014	10	sub-Saharan Africa	Middle	53%	20%	27%	0%
Guinea	2012	8	sub-Saharan Africa	Low	14%	26%	59%	0%
India	2016	634	South Asia	Middle	50%	29%	21%	0%
Kenya	2014	47	sub-Saharan Africa	Middle	39%	16%	38%	1%
Lesotho	2014	10	sub-Saharan Africa	Middle	59%	14%	23%	4%
Liberia	2013	5	sub-Saharan Africa	Low	35%	20%	44%	1%
Madagascar	2009	22	sub-Saharan Africa	Low	8%	26%	64%	2%
Malawi	2016	28	sub-Saharan Africa	Low	33%	56%	7%	4%
Mali	2018	9	sub-Saharan Africa	Low	5%	61%	33%	1%
Mozambique	2011	11	sub-Saharan Africa	Low	19%	33%	43%	3%
Namibia	2013	13	sub-Saharan Africa	Middle	83%	5%	12%	1%
Nepal	2016	7	South Asia	Low	40%	13%	41%	5%
Niger	2012	8	sub-Saharan Africa	Low	6%	23%	70%	1%
Nigeria	2013	37	sub-Saharan Africa	Middle	27%	8%	63%	0%
Pakistan	2017	6	South Asia	Middle	64%	1%	34%	1%
Rwanda	2015	5	sub-Saharan Africa	Low	27%	64%	8%	1%
Senegal	2017	14	sub-Saharan Africa	Low	15%	63%	21%	2%
Sierra Leone	2013	4	sub-Saharan Africa	Low	13%	40%	44%	1%
Swaziland	2007	4	sub-Saharan Africa	Middle	66%	8%	25%	1%
Tanzania	2016	30	sub-Saharan Africa	Low	32%	30%	36%	2%
Togo	2014	6	sub-Saharan Africa	Low	29%	44%	27%	1%
Uganda	2016	15	sub-Saharan Africa	Low	36%	37%	25%	2%
Zambia	2014	10	sub-Saharan Africa	Middle	20%	44%	31%	5%
Zimbabwe	2015	10	sub-Saharan Africa	Low	39%	31%	20%	10%

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Variables

The primary outcome is the region’s early neonatal mortality rate (death within 7 days of birth) per 1,000 births. We also considered the neonatal mortality rate (within 28 days of birth) per 1,000 births. Both outcomes are strongly influenced by the quality of care around the time of delivery [4]. As a sensitivity test, we also examined postneonatal mortality rate (between 29 days and 1 year), which should not be directly influenced by the delivery facility but the broader health system and the social determinants of health. Stillbirths were included as early neonatal deaths if they were reported by the mother as deaths occurring on the day of birth; however, they were not explicitly considered as an outcome.

The explanatory variable of interest is the share of births occurring in a hospital in each region among those occurring in any facility, whether a hospital, lower-level clinic, or unclassified facility. Hospitals were first defined using the country’s own definitions, while clinics include all nonhospitals, i.e., dispensaries, health centers, or doctors’ offices. A small percentage of births occur in an unclassified facility type. Given that there is no universal definition of a hospital across countries, we then further validated the accuracy of the hospital/clinic categorization by assessing the proportion of cesarean deliveries in each, as this service is not typically available in lower-level clinics and often signals the presence of higher-level capabilities including an operating room, an obstetrician, and blood. S1 Table presents the percent of births delivered by cesarean section by facility level and country. A category of clinics (i.e., “Upazila health complex” in Bangladesh) was recategorized as “hospital” in a country if over 10% of deliveries in that category were cesarean deliveries. No facilities were recategorized from hospital to clinic. As a reference point for the effect sizes, we also defined the percent of facility births as the sum of the hospital, clinic, and unknown facility births, as opposed to home births. Following reviewer feedback, we also conducted a robustness check in which the primary explanatory variable was defined as the share of births occurring in a hospital in each region among all births (including home births).

We identified a set of covariates associated with both birth location and neonatal mortality to control for potential confounding. Included covariates needed to be available from all DHS surveys in our sample, have known associations with neonatal mortality [14,15], are determined and knowable prior to delivery, and are relevant at the population rather than the individual level. We first controlled for facility births in the hospital share models. We also included in all adjusted models the percent of babies in the region that were small at birth (<2,500 grams or mother’s report of smaller than average); percent of multiple births; average maternal age at birth; percent of first births; percent of births with a preceding interval less than 2 years (for non-first births), the median number of antenatal care visits (only asked among most recent births); the percent of mothers with only primary education and with secondary or higher education; the percent of households that were in an urban area; the average estimated annual household income as estimated by the International Center for Equity in Health; the global region (sub-Saharan Africa or South Asia); the country’s income level (low or middle); and an indicator for the survey year [16]. The country’s income category is drawn from the World Bank Group classifications; given the small number of countries in the upper-middle-income category (just Namibia and Gabon), we combined upper-middle- and low-middle-income categories into a single middle-income group. Maternal anemia, which is also associated with perinatal and neonatal mortality, was assessed in only 28 of the 37 countries in our sample [17]. We, therefore, conducted a robustness check that also adjusted for maternal anemia prevalence in this subset of countries following reviewer feedback. An initial set of covariates included percent male and excluded maternal age and percent of first births; this was revised following reviewer feedback.

If a variable was missing at the child level, the region’s average was estimated without that child’s data, effectively imputing the missingness to be the regional average. Missingness for all variables of interest at the child level is shown by country in S2 Table. Most variables had low levels of missingness, except for small at birth in Kenya. We conduct a robustness check excluding Kenya from the analysis to account for the reduced variation in this covariate.

Analysis

We summarized the variables of interest for the study regions; the countries in each region are listed in Table 1. We used unadjusted and adjusted 2-level random intercept models, with regions nested within countries, to estimate separately the associations of regional share of hospital delivery and regional facility delivery on regional neonatal mortality outcomes. Adjusted models included the full set of covariates listed above. We did not correct for multiple testing in the main analysis; Bonferroni-adjusted p-values to account for the 3 different outcomes are presented in S3 Table [18]. Based on these initial results, we looked at 3 different contextual variables to understand how these associations may vary across contexts: low- versus middle-income countries, sub-Saharan African versus South Asian countries, and urban versus rural regions (urban defined as a population that is >50% urban). We used adjusted 2-level random intercept interaction models where hospital delivery or facility delivery share was interacted with the relevant contextual variable. All models were adjusted using the same covariates as listed above; the full model specification is included in S4 Table. We used these models to plot margins by predicting early neonatal mortality across the range of hospital or delivery share holding all other covariates at their mean values, and varying the level of the contextual variable. Robustness checks that interact the contextual variable with all the model covariates are included in the Supporting information [19]. A fourth contextual variable, time of the survey (during or before 2015 versus during or after 2016), was further considered in the Supporting information. Finally, after reviewing the contextual variables, we further explored the associations in sub-Saharan Africa with a random slope and random intercept model, allowing the relationship between hospital delivery and early neonatal mortality to vary by country.

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist). Analyses were conducted in Stata version 15.1. The Harvard University Human Research Protection Program approved this secondary data analysis as exempt from human subjects review.

Results

A total of 682,239 births across the 1,143 study regions and 37 countries were included in the analysis; summary statistics are presented in Table 2. The average early neonatal mortality rate was 22 deaths per 1,000 births, 42% of births took place in a hospital, 29% took place in a clinic, and 28% took place at home. Hospital births made up 49% of all facility deliveries in sub-Saharan Africa and 64% in South Asia. S5 Table shows the within-country variation in place of delivery, which, for most countries, was substantial; in Ethiopia, for example, the share of hospital deliveries among facility births was 16% in the minimum region and 73% in the maximum region, while the share of facility deliveries ranged from 15% to 97%.

Table 2. Characteristics of sample regions.

	Sub-Saharan Africa	South Asia	Overall
Data
N countries	32	5	37
N regions	455	688	1,143
Outcomes
Early neonatal death per 1,000	23	22	22
Neonatal death per 1,000	26	26	26
Postneonatal death per 1,000	22	11	15
Place of delivery
Hospital mean %	31	49	42
Lower facility mean %	30	27	29
Home mean %	36	23	28
Unknown facility mean %	2	0	1
Covariates
Small at birth mean %	19	21	20
Median number of antenatal care visits	3.9	4.1	4.0
Multiple births mean %	4	2	2
Maternal age mean	26.9	25.2	25.9
Urban mean %	29	25	27
First birth mean %	23	38	32
Less than 2 year birth interval mean %	17	18	17
Mother primary education only mean %	39	14	24
Mother secondary education or higher mean %	27	57	45
Average annual income^a (USD) Mean	8,907	13,101	11,431

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Early neonatal deaths are deaths between 0–7 days; neonatal deaths are deaths between 0–28 days; postneonatal deaths are deaths 29–365 days. Unclassified facility is where we could not determine the level (hospital vs. nonhospital). Small at birth is birth weight <2,500 grams or mother’s report at birth. Mother’s primary education is completed only primary education; mother’s secondary education or higher is completed secondary or higher education. Number of antenatal care visits only defined for woman’s most recent birth.

^aAverage annual income based on estimates from the International Center for Equity in Health; they are in 2011 international dollars adjusted at purchasing power parity [16].

A higher share of facility deliveries occurring in hospitals was associated with lower early neonatal deaths and neonatal deaths (Table 3) after controlling for covariates, while share of delivery in any facility was associated with higher mortality (Table 4). A 10-percentage point increase in hospital delivery among facility births was associated with 1.2 fewer early neonatal deaths per 1,000 births (p-value < 0.01; 95% CI: 0.83 to 1.58), or a 5% decrease relative to the mean early neonatal mortality rate. In contrast, a 10-percentage point increase in the share of any facility deliveries (as compared to home births) was associated with 0.89 increase in early neonatal mortality (p-value = 0.02; 95% CI: 0.17,1.61). As expected, delivery location had a lower association with postneonatal deaths than with early neonatal deaths. S6 Table presents the unadjusted models. S7 Table shows the exposure defined as the share of hospital births among all births: a 10-percentage point increase in overall hospital deliveries is associate with 1.58 fewer early neonatal deaths (p-value < 0.01; 95% CI 1.23,1.93). These associations are robust to the inclusion of anemia prevalence as a covariate in a subset of countries and the exclusion of Kenya from the analysis (S8 and S9 Tables).

Table 3. Associations between share of hospital delivery and deaths per 1,000 births in 1,143 regions.

	Early neonatal death (per 1,000 births)			Neonatal death (per 1,000 births)			Postneonatal death (per 1,000 births)
	Coef.	p-value	95% CI	Coef.	p-value	95% CI	Coef.	p-value	95% CI
Hospital % among facility deliveries	−12	0.00	[−15.8,−8.3]	−14.9	0.00	[−20.0,−9.8]	−4.4	0.04	[−8.6,−0.2]
All facility %	6.3	0.04	[0.2,12.4]	7	0.06	[−0.3,14.3]	−6	0.16	[−14.4,2.3]
Small at birth %	6.6	0.07	[−0.6,13.7]	10.5	0.01	[2.8,18.3]	2.6	0.63	[−8.1,13.3]
Antenatal care visit median	−0.4	0.04	[−0.8,−0.0]	−0.6	0.01	[−1.0,−0.1]	−0.2	0.22	[−0.6,0.1]
Multiple birth %	16.4	0.00	[11.8,20.9]	18.7	0.00	[15.2,22.2]	4.4	0.05	[0.1,8.8]
Average maternal age	−0.1	0.71	[−0.7,0.5]	−0.1	0.82	[−0.7,0.6]	0.1	0.64	[−0.3,0.6]
Urban %	0.2	0.93	[−3.3,3.6]	0.1	0.94	[−3.2,3.5]	1.9	0.09	[−0.3,4.2]
First birth %	−8.4	0.19	[−21.1,4.3]	−9.3	0.16	[−22.2,3.7]	−8	0.40	[−26.5,10.5]
Less than 2-year birth interval %	21	0.03	[2.5,39.4]	26.3	0.02	[4.0,48.7]	20.1	0.01	[5.8,34.5]
Mother’s primary education %	2.1	0.54	[−4.6,8.7]	0.5	0.88	[−6.7,7.8]	3.1	0.41	[−4.2,10.5]
Mother’s secondary education or higher %	−13.6	0.00	[−21.1,−6.0]	−16.3	0.00	[−24.6,−7.9]	−1.4	0.34	[−4.2,1.4]
Average annual income	−0.1	0.93	[−2.0,1.8]	0.7	0.38	[−0.9,2.4]	−1	0.44	[−3.5,1.5]
South Asia (vs. sub-Saharan Africa)	5.6	0.11	[−1.3,12.6]	7.6	0.06	[−0.4,15.6]	−0.3	0.92	[−5.6,5.1]
Middle-income country (vs. low-income)	6.3	0.00	[2.0,10.6]	6.6	0.01	[1.9,11.2]	−3.2	0.10	[−7.1,0.7]
N	1143			1143			1143

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Models also include survey year fixed effects for years 2009–2018. Small at birth is birth weight <2,500 grams or mother’s report at birth. Number of antenatal care visits only defined for woman’s most recent birth. Mother’s primary education is completed only primary education; mother’s secondary education or higher is completed secondary or higher education. Log average annual income based on estimates from the International Center for Equity in Health; they are in log 2011 international dollars adjusted at purchasing power parity.

Table 4. Associations between share of deliveries in any facility and deaths per 1,000 births in 1,143 regions.

	Early neonatal death (per 1,000 births)			Neonatal death (per 1,000 births)			Postneonatal death (per 1,000 births)
	Coef.	p-value	95% CI	Coef.	p-value	95% CI	Coef.	p-value	95% CI
All facility %	8.9	0.02	[1.7,16.1]	10.3	0.02	[1.5,19.1]	−4.9	0.25	[−13.1,3.4]
Small at birth %	6.3	0.09	[−1.1,13.7]	10.3	0.01	[2.3,18.2]	2.5	0.67	[−8.7,13.6]
Antenatal care visit median	−0.6	0.00	[−1.0,−0.2]	−0.8	0.00	[−1.3,−0.3]	−0.3	0.09	[−0.6,0.0]
Multiple birth %	16.9	0.00	[12.6,21.2]	19.3	0.00	[16.2,22.4]	4.6	0.04	[0.2,8.9]
Average maternal age	−0.3	0.41	[−0.9,0.4]	−0.3	0.47	[−0.9,0.4]	0.1	0.80	[−0.4,0.6]
Urban %	−1.6	0.41	[−5.3,2.1]	−2	0.31	[−5.8,1.8]	1.4	0.24	[−0.9,3.6]
First birth %	−13.5	0.04	[−26.4,−0.6]	−15.6	0.02	[−28.6,−2.7]	−9.9	0.28	[−28.1,8.2]
Less than 2-year birth interval %	15.4	0.08	[−1.6,32.5]	19.8	0.05	[−0.0,39.7]	18.5	0.02	[3.2,33.8]
Mother’s primary education %	1	0.77	[−5.9,8.0]	−0.4	0.92	[−7.8,7.0]	3.1	0.41	[−4.4,10.6]
Mother’s secondary education or higher %	−15.3	0.00	[−23.2,−7.3]	−18.3	0.00	[−27.0,−9.6]	−1.9	0.18	[−4.6,0.9]
Average annual income	−1.9	0.23	[−5.1,1.2]	−1.6	0.34	[−4.8,1.6]	−1.8	0.07	[−3.7,0.1]
South Asia (vs. sub-Saharan Africa)	4.1	0.32	[−3.9,12.0]	5.7	0.22	[−3.4,14.9]	−0.7	0.81	[−6.4,5.0]
Middle-income country (vs. low-income)	5	0.06	[−0.2,10.1]	4.9	0.09	[−0.8,10.5]	−3.8	0.07	[−7.9,0.4]
N	1,143			1,143			1,143

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Fig 1 shows the marginal relationship between place of delivery and early neonatal mortality by contextual variables. Hospital delivery was protective in regions in middle-income countries, in South Asia, and in both urban and rural regions, but not within low-income countries or in sub-Saharan Africa. However, facility delivery share was not protective in any of the groups of regions examined. The full interaction models are shown in S4 Table. S1 Fig and S10 Table show the interaction by year of survey; hospital delivery was more protective in later surveys than in 2015 or before. Results from the random intercept and slope models from the sub-Saharan African region are in S11 Table; the share of hospital delivery was more protective for early neonatal mortality in Namibia and Kenya and less protective in Lesotho and Côte d’Ivoire. The results are robust to including the contextual variable and covariate interactions (S2 Fig and S12 Table).

Discussion

This analysis of place of facility delivery and neonatal mortality in the highest mortality regions of the world found that subnational regions that had a higher share of facility births in hospitals had lower early neonatal mortality, controlling for income and sociodemographic factors. Regions where all facility births occurred in hospitals had an estimated 12.1 fewer early neonatal deaths per 1,000 births than regions with all facility delivery in clinics. By contrast, a greater proportion of births in all facilities was associated with greater newborn mortality in our analysis. These associations were seen most strongly in regions in South Asia and middle-income countries. The associations did not differ between urban and rural regions; hospital delivery was protective in both.

Hospitals may be protective against mortality than other facilities because they have more advanced equipment, infrastructure, and health workers to manage emergencies if they arise; higher-volume facilities may also allow greater practice in managing facilities [8,20]. Regions with high hospital delivery may also have stronger health systems, including more health financing or better transportation, facilitating both hospital delivery and lower mortality. These hypotheses may account for the different associations seen across contexts. First, the quality of hospitals may be higher in middle-income countries than in low-income countries, making delivery in them more protective. Cesarean section capacity is more common in middle-income country hospitals than in hospitals in low-income countries; care for small or sick newborns may also be more advanced in middle-income country hospitals [21–23]. Second, a greater proportion of health spending is pooled in middle-income countries in comparison to low-income countries, which may remove financial barriers to hospital delivery for a greater percentage of women [24]. As a majority of the low-income regions were in countries in sub-Saharan Africa, these reasons may also explain the differences between South Asia and sub-Saharan Africa. Across all the categories examined, however, the share of hospital delivery among facility deliveries was more protective than the share of facility delivery. Furthermore, we found that while the unadjusted relationship between any facility delivery and mortality was negative, once we controlled for confounders, particularly regional income, secondary education and urbanicity, any facility delivery share was actually positively associated with neonatal mortality. Facility delivery was found to be only weakly associated with neonatal mortality reductions in the literature [5,6].

The estimated size of the association between hospital delivery and early neonatal delivery is comparable to those seen in other studies examining the level or quality of facility conducting deliveries. An analysis from Malawi found that higher-quality facilities (the majority of which were hospitals) were associated with a 23 fewer neonatal deaths per 1,000 births than other facilities in the country [25]; similarly, in Ghana, reduction in stillbirth was only found among women who live near higher-quality CEmONC facilities but not lower-level BEmONC facilities [26]. Relocation of care to high-quality facilities following a transportation intervention in Ghana was also found to reduce facility-based maternal mortality [27]. Some interventions have also found similar levels of reductions in mortality: A study from Rwanda also found a 12.9 deaths per 1,000 reduction in neonatal mortality following an intensive quality improvement intervention in 2 rural districts [28], while a neonatal resuscitation intervention in Nepal reduced intrapartum-related mortality by 3 deaths per 1,000 [29]. However, many other health system–oriented neonatal health interventions have failed to reduce mortality [30–32]. New strategies such as supporting hospital delivery should therefore be explored further.

There are several limitations to this analysis. First, although we attempted to control for confounding and conduct sensitivity tests, this is an observational and ecological analysis that may not have been able to control for all confounding; for example, areas with more hospitals may also have more staff or supplies per facility. Second, we relied on women’s self-report of the level of facility where they delivered. Women may not know the level of the facility or use the term “hospital” to colloquially mean a health facility; furthermore, there are no global definitions of what constitutes a hospital, meaning that the exposure differs from country to country. We addressed this by conducting random and fixed effects models with regions nested within countries, but there may still be within-country variations in these definitions. It is likely that our hospital category includes smaller, nonsurgical facilities, which would bias our hospital association downward. Third, while we hypothesize that improved quality of care in hospitals may be a key factor for the observed associations, limited cross-country data on quality of care prevent direct measurement. Finally, this analysis did not consider stillbirths as an outcome because they were inconsistently recorded in versions of the DHS that relied on a full birth history rather than a full pregnancy history [33]; neonatal deaths by cause were also not considered as that information is unavailable.

Efforts to improve primary care facilities have proliferated in the past 2 decades; despite this, newborn mortality remains stubbornly high in many regions [2,34]. A recent large randomized controlled trial found that a package of interventions to improve delivery care at primary care facilities in India failed to reduce maternal and newborn mortality in the absence of onsite operative delivery and other advanced services [30]. Shifting deliveries out of primary care clinics and into facilities that have advanced services to manage complications and sufficient delivery volume to maintain clinical expertise has been proposed [35]. To be effective, such a policy would require additional investment in hospitals to ensure excellent lifesaving and respectful midwifery and obstetric care, improving the quality of antenatal and postnatal care quality in clinics, decreasing geographic and financial barriers to care, and igniting population demand for high-quality maternity care [35]. Prospective evaluation using implementation science methods can provide needed evidence on how to best achieve successful health system redesign, in which contexts health system redesign may be most beneficial, and how to mitigate unintended consequences.

Health system innovations are required to meet the ambitious targets in the Sustainable Development Goals for neonatal mortality (12 deaths/1,000 live births). As countries work to meet these goals, structural redesign of health systems that places a premium on high-quality obstetric care is a potential opportunity to reduce mortality that warrants further exploration.

Supporting information

S1 STROBE Checklist. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

(DOC)

Click here for additional data file.^{(83KB, doc)}

S1 Table. C-section rates by facility level and country.

(DOCX)

Click here for additional data file.^{(20.5KB, docx)}

S2 Table. Percent of births missing key variables by country.

(DOCX)

Click here for additional data file.^{(22.1KB, docx)}

S3 Table. Bonferroni-adjusted models.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

S4 Table. Interaction models specification and full model results.

(DOCX)

Click here for additional data file.^{(25.1KB, docx)}

S5 Table. Within-country variation in place of delivery.

(DOCX)

Click here for additional data file.^{(19.2KB, docx)}

S6 Table. Unadjusted main model results.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S7 Table. Association between percent of all deliveries in hospital and neonatal mortality.

(DOCX)

Click here for additional data file.^{(19.3KB, docx)}

S8 Table. Robustness check including anemia as covariate in subset of study countries.

(DOCX)

Click here for additional data file.^{(22.9KB, docx)}

S9 Table. Robustness check excluding Kenya from analysis.

(DOCX)

Click here for additional data file.^{(22.5KB, docx)}

S10 Table. Interaction model comparing associations in early versus late surveys.

(DOCX)

Click here for additional data file.^{(18.7KB, docx)}

S11 Table. Random intercept and slopes in sub-Saharan Africa.

(DOCX)

Click here for additional data file.^{(18.5KB, docx)}

S12 Table. Interaction models including contextual variable and covariate interactions.

(DOCX)

Click here for additional data file.^{(29.1KB, docx)}

S1 Fig. Interaction model comparing associations in early versus late surveys.

Each graph shows a different multivariable random intercept model that is fully adjusted. Points show the marginal estimate of early neonatal mortality for the given level of hospital deliveries or facility deliveries; bars show 95% confidence intervals. Mortality defined as death within 7 days per thousand births.

(TIF)

Click here for additional data file.^{(10.6MB, tif)}

S2 Fig. Interaction models including contextual variable and covariate interactions.

Each graph shows a different multivariable random intercept model that is fully adjusted with all covariates as well as interactions between each of the covariates and contextual variable. Points show the marginal estimate of early neonatal mortality for the given level of hospital deliveries or facility deliveries; bars show 95% confidence intervals. Country income groups defined by the World Bank Group, with low-middle- and upper-middle-income countries combined. Rural regions defined as those with less than 50% of the population classified as urban, urban regions defined as the converse.

(TIF)

Click here for additional data file.^{(10.6MB, tif)}

Data Availability

All data files are available from the Demographic and Health Surveys Program, https://dhsprogram.com/.

Funding Statement

This work was supported by funding from the Bill and Melinda Gates Foundation (INV-005254, MK). The funder played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003843.r001

Decision Letter 0

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25 May 2021

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Decision Letter 1

Beryne Odeny

2 Aug 2021

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Comments from the reviewers:

Reviewer #1: "The association between hospital delivery and neonatal mortality: an ecological analysis in 37 countries in sub-Saharan Africa and South Asia" presents an positive association between hospital delivery and reduced neonatal mortality, in particular that a 10% higher share of hospital deliveries was associated with a 6% decrease in early neonatal mortality, or 1.21 per 1000 fewer deaths, from an ecological study design on DHS data from some 680,000 births over 1,143 regions in 37 countries in sub-Saharan Africa and South Asia.

These findings appear not in total agreement with prior studies on much the same DHS source data cited as [5,6], with [6] in particular investigating the same broad regions of sub-Saharan Africa and South Asia also through ecological analyses. These prior work suggest weak/non-significant associations between institutional deliveries/health facilities and neonatal mortality (e.g. from Figure 2B in [6]). The main methodological distinction for this paper appears to be in recognizing different tiers/qualities of health institutions/facilities, in particular between "hospitals" (with capability to perform advanced procedures such as Caesarean deliveries) and "primary-care clinics" (with fewer such capabilities). Increased significance could then be found for neonatal mortality once the comparison is based on (higher-tier) hospital delivery, as suggested from Table S2, rather than on all facilities (which appears correlated with early neonatal births; Table 4). These associations however appear mixed when analyzed by contextual variables (e.g. Figure 1), and between countries (e.g. Table S4).

Overall, the main conclusions appear plausible, and of importance in motivating better quality of maternal/natal care, especially in less-developed countries. The methodological design also appears appropriate. However, the various analyses might stand to be described in greater detail to facilitate potential replication, and there remain some concerns over the validity of the hospital/clinic definitions used:

1. In Page 5, it is stated that "The explanatory variable of interest is the share of births occurring in a hospital in each region among those occurring in any facility, whether a hospital, lower-level clinic, or unclassified facility" (i.e. hospital/all facilities); is there a reason why it was not "share of births occuring in a hospital in each region among all births whether in a facility or not" (i.e. hospital/all births)? This might be considered as a secondary analysis.

2. In Page 5, it is stated that "We validated the accuracy of the hospital/clinic categorization by assessing the proportion of Caesarean deliveries in each, as this service is not typically available in lower-level clinics". It might be explained in much greater detail as to how this validation influenced the categorization. For example, was there some threshold of Caesarean deliveries above which the facility would be categorized for purposes of the analyses as a hospital, and otherwise a clinic, and if so, what threshold? And if so, was a single universal threshold applied, since it is later stated that "Caesarean section capacity may in particularly be more common in middle income country hospitals, than in hospitals in low income countries"? Or were facilities not matching their self-definition ignored? In either case, by what extent did any re-categorization differ from the initial categorization, or if no re-categorization was done, by what extent did the original self-categorization differ from the expected categorization from Caesarean deliveries?

3. Related to the above, since the ability to perform Caesarean deliveries (and related advanced services) appears a major factor suggested for the protectiveness of hospital delivery (e.g. "Across five sub-Saharan African countries, for example, quality was poorer in low-volume clinics without Caesarean delivery capacity than hospitals", Page 3), it might be considered to perform brief statistical presentation/analyses relating specifically to Caesarean delivery, if at all possible.

For example, while it is stated that "we relied on women's self-report of the level of facility where they delivered. Women may not know the level of the facility or use the term 'hospital' to colloquially mean a health facility" (Page 9), might it be determined as to whether the facilities mentioned did or did not have Caesarean delivery capability, and perform brief analyses according to that firm criteria? Or alternatively perform sensitivity analyses on only facilities for which the status of such capability is known for certain?

4. From Page 5, the set of covariates appears relatively limited, and many reasonable population-level demographic/health confounders apparently not included (e.g. average age of mother at birth, average prevalence of various common diseases/conditions known to affect neonatal mortality, etc.); these might be discussed more comprehensively, if possible.

5. It would be highly encouraged to provide more details (e.g. full specification, equations, covariates adjusted for, coefficients found, etc.) about the various analyses and models (two-level random intercept, interaction [also by contextual variables], etc.), as mentioned on Page 6 and Page 7, possibly in the supplementary material. Some details seem already provided (e.g. Table S3 for the interaction model), and might be expanded upon.

6. The treatment of stillbirths in these analyses might be explicitly stated, since it appears a significant consideration in prior related studies.

Reviewer #2: Thank you very much for inviting me to review this interesting piece of secondary analysis of demographic health survey in low and middle income countries.

Researchers aim to explore the difference in neonatal mortality (early or late) by the place of birth and provide explanation on what might have been the possible reasons. Researchers have adjusted the possible confounders to assess the association with neonatal mortality and have come to the conclusion that the mortality is high in clinic or primary health care center. From the 32 countries in Sub-saharan Africa and South Asia, using the DHS dataset, 1143 regions were analyzed. The mortality in these 1143 regions were analyzed and also the proportion of hospital birth, primary health care unit and home was analysed. The estimation of the neonatal mortality rate in region was then associated using a linear regression model with the proportion of birth in hospital, primary health care unit and home.

To adjust the confounders, maternal education, average annual income, urban/rural, birth interval, multiple birth, antenatal care visit and sex of baby was done.

This is an interesting way of analyzing the DHS which collects data at individual level using a PPS method and extracting data at sub-national level or regional level to assess mortality with place of birth. I have two major observations and two major feedback to the work

1. The regional mortality estimate does not take into consideration the obstetric complication that is the major factor for early neonatal mortality. As for the late neonatal mortality, there are other postnatal exposures such as infection practices and care of small and sick newborns. I know this is difficult to assess however, DHS does provide information on complication during pregnancy and delivery based on self reporting. Can this been done

2. The regionalized mortality estimate of each region and facility birth takes into a fixed model consideration that all women go to the hospital directly from home, and there is no intra-facility referral in place, which increases the risk of mortality. I know this is not provided in DHS, but a proxy measure is available in DHS questionnaires.

3. The high mortality in primary health facilities is postulated, yet not shown in the data might have been due to poor staffing pattern and competency in health facility. Using the three delay care model, the delay in arrival to health facility is one of the major factor for mortality in first referral units. Can the researchers analyse the birth preparedness indicator.

4. There has been recent global debates on whether all births should take place in hospitals and primary health care facility should be at outreach clinic, which ANC and PNC by , Roder-DeWan and colleagues in BMJ Global Health. As shown by Ashish KC and colleagues in Journal of global health (Perfect Storm, 2021), that quality of intrapartum care is associated with number of facility birth. I think this has not been considered.

Reviewer #3: This is a critically important topic and although DHS data have limitations in answering the question, this is a well conducted analysis and the adjusted models have controlled for a reasonable set of confounders.

Which countries introduced of voucher schemes or free maternity care, and when? Could this be incorporated in the model? The rationale for this is that introduction of such schemes has commonly increased the number of facility-based deliveries without the corresponding provision of staffing and resources to cope with the increased numbers.

A '10 percentage point increase in hospital delivery among facility births' is not quite the same thing as suggested by the article title 'The association between hospital delivery and neonatal mortality'. All facility births are compared to home births, for completeness I think the same needs to be presented for hospital births.

In the same light, the sentence in the Discussion 'Across all the categories examined however, share of hospital delivery was more protective than share of facility delivery' really should read 'Across all the categories examined however, share of hospital delivery among facility deliveries was more protective than the overall share of facility delivery'.

Please state how much data were missing. Imputing the missingness to be the regional average artificially reduces the width of the distribution and thus potentially increases power of analyses. This is not much of an issue if missingness is rare, but if more than 5 to 10%, could have impacted the findings.

The analysis is a strong approach, however using DHS data does not allow us to know whether the facility births were those of the small size babies, multiple births etc. Nor were there data on the quality of facilities or quality improvement schemes. Some of these are mentioned in the discussion, but lack of such data is a limitation that should be mentioned. A potential bias is that stillbirths may not be reported as early neonatal mortality at facilities and both stillbirths and early neonatal deaths may also occur at home and not be reported in national statistics or at DHS rounds (https://pophealthmetrics.biomedcentral.com/articles/10.1186/s12963-020-00225-0).

The argument seems strongest for reducing births at small facilities where resources, staffing and training may be limited - this point is well made in the discussion.

Reviewer #4: Thank you for submitting this interesting article on the association between hospital delivery and neonatal mortality.

As you state in your introduction, there have been substantial increases in the proportion of facility-based deliveries, without the corresponding decreases in neonatal mortality. It's important to try to understand why this is the case. This article provides some insight into a potential reason for this.

The following are some general comments/suggestions:

Abstract:

You should state in the background section that you are examining the association between place of delivery and neonatal mortality. Specifically, you seem to be interested in whether delivering a baby in the hospital, clinic or at home is associated with a reduction neonatal mortality. You should also state in the abstract the basis for your analysis and your hypothesis - quality of care in primary care clinics may be sub-optimal.

What you state in your conclusions is not reflecting your findings. Indeed, your findings suggests that deliveries at hospital are associated with a reduction in early neonatal mortality rates. The data you have obtained don't have any information on the quality of care at these hospitals. Future research should try to understand why tertiary level facilities had lower early neonatal mortality rates.

Introduction:

You need to describe what constitutes high quality maternal and newborn care.

You need to ensure that your objective is accurately stated before your methods - whether neonatal mortality is associated with place of delivery including primary, lower level clinics and home deliveries.

Methods

* Substantial changes in the proportion of women delivering at health facilities took place during data collection. You should consider adjusting for this in your analysis, or perhaps stratifying on different time periods.

* The same applies to improving the quality of care between 2009 and 2018, so I feel stratification on time may be warranted.

* Some variables you have included as confounders are not correct. All confounders should be variables that take place before delivery, and not after delivery. If you select variables that include after delivery, they will be on the causal pathway. So the following variables are not appropriate: small at birth, multiple births, male babies. If the DHS data has information on difficulties in the antenatal period, this may be a relevant confounder. Also, previous neonatal deaths, stillbirths, miscarriages could be considered.

* During the time period that data was collected, women could have more than one delivery. It is important to account for this in the analysis. Perhaps some type of GEE model?

* You should include the statistical software you used for your analysis as well as the commands you used for your models. It seems as though you used marginal models. What command did you use for these models?

* This is an important paper you should probably reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5463806/

Minor comments:

Some copy editing is required.

Your discussion includes a recommendation of using implementation science methods to achieve health system redesign. Could you elaborate further how implementation science could be used to help improve the quality of care at health facilities?

Reviewer #5: The authors have presented a comprehensive analysis of the association between hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia. The manuscript is detailed, well-written, and has the potential to make a valuable contribution to the literature. I have one minor comment about the limitations of the study. A good number of babies born at health facility dies at the facility without leaving (the facility). Unfortunately, the DHS questionnaires do not single these deaths out. Besides, to be effective in improving neonatal survival in those countries, adding the causes of deaths module to the survey instruments can provided much needed credence to the conclusions of this study.

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

[LINK]

PLoS Med. 2021 Dec 1;18(12):e1003843. doi: 10.1371/journal.pmed.1003843.r003

Author response to Decision Letter 1

25 Aug 2021

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PLoS Med. doi: 10.1371/journal.pmed.1003843.r004

Decision Letter 2

Beryne Odeny

22 Sep 2021

Dear Dr. Gage,

Thank you very much for re-submitting your manuscript "Hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia: An ecological study" (PMEDICINE-D-21-02269R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning 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:

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

Requests from Editors:

1) Author summary: Please trim the content under “What did the researchers do and find?” to 2-3 sentences per bullet point.

2) Please remove the ‘Funding’, “Data availability statement” and “Conflict of interest” from the end of the main text. In the event of publication, this information will be published as metadata based on your responses to the submission form.

3) Please define the abbreviation, ANC, in the table footnotes

4) Please provide the meaning of the bars and whiskers in Figure S1.

5) Please label the Y axis in figure S1 more clearly, i.e., early neonatal mortality (deaths per 1000) or similar\\

6) References: Please ensure that journal name abbreviations consistently match those found in the National Center for Biotechnology Information (NCBI) databases

Comments from Reviewers:

Reviewer #1: We thank the authors for considering our previous suggestions, and note that the various additional sensitivity analyses generally support the initial claims. A couple of points remain for consideration:

1. The interaction model specification in S4 Table is much appreciated. There is however a concern that the covariates should be further controlled for potential alternative interactions (see: https://towardsdatascience.com/interaction-analyses-appropriately-adjusting-for-control-variables-d34dfbdd781a); this might be considered as another robustness check.

2. The formatting of Tables 3 and 4 might however be reconsidered in the latest manuscript, since the final column for post-neonatal death appears to be partially cut off.

Reviewer #2: The researchers have addressed most of the comments made and highlighted the limitation on examining the quality of care from DHS. However, can the immediate newborn care practice such as skin to skin contact and immediate breast feeding be used as a proxy to quality of care.

Reviewer #3: My comments have been adequately addressed.

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

[LINK]

PLoS Med. 2021 Dec 1;18(12):e1003843. doi: 10.1371/journal.pmed.1003843.r005

Author response to Decision Letter 2

29 Sep 2021

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PLoS Med. doi: 10.1371/journal.pmed.1003843.r006

Decision Letter 3

Beryne Odeny

8 Oct 2021

Dear Dr Gage,

On behalf of my colleagues and the Academic Editor, Dr. Jenny E Myers, I am pleased to inform you that we have agreed to publish your manuscript "Hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia: An ecological study" (PMEDICINE-D-21-02269R3) 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.

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PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 STROBE Checklist. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

(DOC)

Click here for additional data file.^{(83KB, doc)}

S1 Table. C-section rates by facility level and country.

(DOCX)

Click here for additional data file.^{(20.5KB, docx)}

S2 Table. Percent of births missing key variables by country.

(DOCX)

Click here for additional data file.^{(22.1KB, docx)}

S3 Table. Bonferroni-adjusted models.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

S4 Table. Interaction models specification and full model results.

(DOCX)

Click here for additional data file.^{(25.1KB, docx)}

S5 Table. Within-country variation in place of delivery.

(DOCX)

Click here for additional data file.^{(19.2KB, docx)}

S6 Table. Unadjusted main model results.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S7 Table. Association between percent of all deliveries in hospital and neonatal mortality.

(DOCX)

Click here for additional data file.^{(19.3KB, docx)}

S8 Table. Robustness check including anemia as covariate in subset of study countries.

(DOCX)

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S9 Table. Robustness check excluding Kenya from analysis.

(DOCX)

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S10 Table. Interaction model comparing associations in early versus late surveys.

(DOCX)

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S11 Table. Random intercept and slopes in sub-Saharan Africa.

(DOCX)

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S12 Table. Interaction models including contextual variable and covariate interactions.

(DOCX)

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S1 Fig. Interaction model comparing associations in early versus late surveys.

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S2 Fig. Interaction models including contextual variable and covariate interactions.

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Data Availability Statement

All data files are available from the Demographic and Health Surveys Program, https://dhsprogram.com/.

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PERMALINK

Hospital delivery and neonatal mortality in 37 countries in sub-Saharan Africa and South Asia: An ecological study

Anna D Gage

Günther Fink

John E Ataguba

Margaret E Kruk

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Ethics approval

Data and settings

Table 1. Study countries and place of delivery.

Variables

Analysis

Results

Table 2. Characteristics of sample regions.

Table 3. Associations between share of hospital delivery and deaths per 1,000 births in 1,143 regions.

Table 4. Associations between share of deliveries in any facility and deaths per 1,000 births in 1,143 regions.

Fig 1. Early neonatal mortality and place of delivery by country income, country area, and urban region.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Beryne Odeny

Roles

Decision Letter 1

Beryne Odeny

Roles

Author response to Decision Letter 1

Decision Letter 2

Beryne Odeny

Roles

Author response to Decision Letter 2

Decision Letter 3

Beryne Odeny

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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