Neonatal mortality clustering in the central districts of Ghana.

INTRODUCTION: Identifying high risk geographical clusters for neonatal mortality is important for guiding policy and targeted interventions. However, limited studies have been conducted in Ghana to identify such clusters.
OBJECTIVE: This study aimed to identify high-risk clusters for all-cause and cause-specific neonatal mortality in the Kintampo Districts.
MATERIALS AND METHODS: Secondary data, comprising of 30,132 singleton neonates between January 2005 and December 2014, from the Kintampo Health and Demographic Surveillance System (KHDSS) database were used. Verbal autopsies were used to determine probable causes of neonatal deaths. Purely spatial analysis was ran to scan for high-risk clusters using Poisson and Bernoulli models for all-cause and cause-specific neonatal mortality in the Kintampo Districts respectively with village as the unit of analysis.
RESULTS: The study revealed significantly high risk of village-clusters for neonatal deaths due to asphyxia (RR = 1.98, p = 0.012) and prematurity (RR = 5.47, p = 0.025) in the southern part of Kintampo Districts. Clusters (emerging clusters) which have the potential to be significant in future, for all-cause neonatal mortality was also identified in the south-western part of the Kintampo Districts.
CONCLUSIONS: Study findings showed cause-specific neonatal mortality clustering in the southern part of the Kintampo Districts. Emerging cluster was also identified for all-cause neonatal mortality. More attention is needed on prematurity and asphyxia in the identified cause-specific neonatal mortality clusters. The emerging cluster for all-cause neonatal mortality also needs more attention to forestall any formation of significant mortality cluster in the future. Further research is also required to understand the high concentration of prematurity and asphyxiated deaths in the identified clusters.

Introduction

The decline of neonatal mortality rates have been slower than those of under-five mortality globally [1]. It is projected that, 27.8 million neonates will die between 2018 and 2030 if the current trend continues [2]. Globally, neonatal mortality rate fell by 51% between 1990 and 2017 as compared to 63% of postneonatal mortality rate within the same period [2, 3]. This 51% reduction in neonatal mortality translates into 37 deaths per 1000 live births in 1990 to 18 deaths per 1000 live births in 2017 [2]. In 2017 alone, 2.5 million newborns died within the first month of life which was approximately 7,000 deaths per day [3]. Meanwhile, the Sustainable Development Goal (SDG) 3 target of reducing neonatal mortality rate to 12 deaths per 1000 live births will require more than 60 countries to accelerate their progress in order to achieve the target by 2030 [2, 3].

Globally, sub-Saharan Africa has the highest rate of neonatal mortality and accounts for 38% of global neonatal deaths [4, 5]. According to global estimates report, sub-Saharan Africa accounted for 41% of total burden of neonatal deaths in 2017 alone [2]. In Ghana, 48% of under-five mortality occurs in the neonatal period and the rate of neonatal mortality is twice that of postneonatal mortality [6]. Unless concerted efforts are made, Ghana will not be able to achieve SGD 3.

There are many risk factors of neonatal mortality and that includes birth spacing, maternal age at birth, asphyxia, pneumonia, meningitis, low birth weight, sepsis and region in which the child was born [7-11]. Although there are many risk factors of neonatal mortality, each geographical area has specific risk factors that predisposes neonates to death [12, 13]. It is important therefore to consider geographical setting in the study of neonatal mortality to consequently provide the appropriate intervention. Besides, population-based interventions are good at preventing many people from morbidities and mortalities but can be a huge undertaken and expensive. Hence, when population-based interventions for neonatal mortality are expensive to implement, it is necessary to identify high-risk mortality clusters and provide them (clusters) with the needed resources which are less expensive and more useful to poor resource settings in sub-Saharan Africa [14].

In Ghana, spatial clustering of under-five mortality has been investigated in a couple of studies [15, 16]. Nettey et al. [16] and Adjuik et al. [15] studies used villages as units for clustering and were able to identify high-risk areas for under-five mortality clustering. Clustering for neonatal mortality which constitute highest proportion of under-five mortality in Ghana [6] was not considered in those studies. However, MDG 4 of reducing under-five mortality by two-thirds was not achieved by Ghana and Ghana is currently striving to achieve SDG 3 which has a target of reducing neonatal and under-five mortality rates to 12 and 25 deaths per 1000 live births respectively. Hence, knowledge of all-cause and cause-specific neonatal clustering will make intervention for neonatal mortality more focused and less expensive [17]; and subsequently contribute to the achievement of SDG 3 in Ghana.

The Kintampo North Municipality and South District (Hereinafter referred to Kintampo Districts) in the middle belt of Ghana also experiences a slower decline in neonatal mortality and has 42% of neonatal mortality occurring in the districts [18, 19]. Routine data is collected on core demographic data using the health and demographic surveillance system. In addition, houses are geocoded and therefore make it feasible for neonatal mortality data to be used for spatial studies to expand the literature in this area. This study therefore aims to identify villages with high risk of all-cause and cause-specific neonatal mortality in the Kintampo Districts.

Materials and methods

Study area

This study used a secondary dataset from Kintampo Health and Demographic Surveillance System (KHDSS) which covers virtually the whole of Kintampo North Municipality (located between latitudes 8°45’N and 7°45’N and Longitudes 1°20’W and 2°1’E) and Kintampo South District (located between 1o 20’ West and 2o10’ East and latitude 8o 15’ North and 7o 45’ South) [16, 18]. Both the Municipality and District together have a total surface area of 7,162 km2. The KHDSS area is also located in the Brong Ahafo Region of Ghana.

The total population and households in the KHDSS area in 2014 were 145,000 and 32,000 respectively. The main indigenous ethnic groups are the Bonos and the Mos. There is however a large immigrant population from the three Northern Regions (Dagaaba, Dagomba and Konkomba) who are generally farmers. Some Dangbe and Ewe immigrants are mainly fisher folks and are settled along the banks of the Black Volta. The settlements are mostly concentrated in the Southern part and along the main trunk road linking the two District capitals (Kintampo and Jema) to Northern Region. Community members engage in farming of predominantly maize, yam and cassava. Livestock rearing of cattle, sheep, goats and birds are also common.

Each district in the KHDSS area has a hospital and other health facilities such as health centres, CHPS Compounds and maternity homes that perform family planning services as well as maternal, child and neonatal services. Outreach health services are also performed by CHPS compounds within the communities [20].

Determination of causes of deaths

Causes of neonatal deaths from the secondary data were determined by using a validated verbal autopsy (VA) tool with a sensitivity greater than 60% for all major causes [21]. Specificity was found to be 76% for birth asphyxia and greater than 85% for prematurity and infection [21]. The VA tool had a battery of questions together with a narration section where the immediate caregiver of the neonates gave a vivid accounts of what might have led to the death of the neonate [22]. The VA interviews were conducted by well-trained field supervisors of KHRC and it was ensured that the interviews were conducted as closely as possible to the day of death to reduce recall bias. Because field supervisors training includes psychological aspect, they were able to handle emotional and psychological trauma during the interview. Data collection were rigorously supervised and 5% of the interviews were repeated to ensure data quality. In cases where discrepancies were detected, interviews were repeated. Three clinicians trained on coding of verbal autopsies reviewed the same VA questionnaire independently to assign the possible cause of death in accordance with 3-digit code of the International Statistical Classification of Diseases and Health-Related Problems [18, 22]. The possible cause of neonatal death is established if there was concordance in the review diagnosis of at least two clinicians. In addition, VA questionnaires were submitted to two additional clinicians for review when there was discordance among all the first three clinicians. The cause of neonatal death was declared indeterminate where consensus could not be reached with regards to possible cause of death. Moreover, the possible cause of neonatal death was declared unknown where there was little or no information to making it possible to establish the possible cause of death.

Classification for neonatal causes of death

Neonatal deaths in the secondary dataset were classified into six major categories namely congenital abnormality, prematurity, birth asphyxia, infection (sepsis, meningitis, pneumonia, septicaemia, tetanus, diarrhea and other neonatal infections), other specific causes of death (malaria, injury, infant haemorrhage and other specific causes excluding the first four major causes) and unexplained cause of death. This classification method was based on Neonatal and Intrauterine Death Classification according to Etiology (NICE) and the WHO Child Health Epidemiology Reference Group (CHERG) and has been described elsewhere [23]. Table 1 depicts the classification of the six major categories. Hence the cause-specific neonatal deaths for this study were six.

Table 1

Classification of 6 major causes of death.

Number	Neonatal death	Infant born alive and cried, moved or breathed after birth and then died within the first 28 days of life
1	Congenital abnormality	Neonatal death due to one or more of the following:Major or lethal congenital abnormalityUnspecified; specific abnormality e.g neurological, neural tube defect
2	Prematurity	Neonatal death due to one or more of the following:Severe immaturity (<33 weeks), birthweight<1.8 kg where gestation is unknown, specific severe complications of prematurity such as surfactant deficiency or necrotising enterocolitis
3	Birth asphyxia	Neonatal death in an infant ≥33 weeks of gestation due to one or more of the following:Obstetric complications, maternal haemorrhage, or clinical diagnosis of birth asphyxia (no cry soon after birth plus either convulsion/spasms or not able to suckle normally after birth)
4	Infection	Neonatal death in an infant ≥33 weeks of gestation due to one or more of the following:Tetanus, meningitis, pneumonia, diarrheoa, septicaemia, other infection
5	Other	Neonatal death in an infant ≥33 weeks of gestation due to a cause not included in first 4 selected causes including:Accident/injury, infant haemorrhage respiratory distress syndrome, severe neonatal jaundice
6	Unexplained	Neonatal death due to unknown cause including sudden infant death syndrome

Data management

This study used secondary data from the KHDSS database comprising of all singleton neonates (dead or alive) and their corresponding VA dataset between January 2005 and December 2014. The data were assessed for any discrepancies and errors found were corrected with the assistance of a Data Manager from Kintampo Health Research Centre (KHRC). The cleaned dataset was exported to SaTScan software for spatial analysis.

Spatial analysis

Due to low proportions of neonatal mortality in each year, purely spatial analysis based on the total mortality over the study period was employed. The Poisson and Bernoulli models were used to run a purely spatial analysis scanning for clusters (village or group of villages) with high rates of all-cause and cause-specific neonatal mortality respectively. Regarding the Bernoulli model, definition of cases were all neonatal deaths that were caused by a specific morbidity. Controls were all other deaths caused by other morbidities together with other neonates who were alive. Scanning was set to identify villages with high proportion of all-cause and cause-specific neonatal deaths because the interest was to search for a village or a group of villages with proportion of all-cause and cause-specific neonatal mortality that was higher than average. Geographic overlap was not considered in the setting so secondary clusters did not overlap the most significant (or likely) cluster. Maximum cluster size was set to 50% of the total population at risk in order to scan for small to large clusters. In order to ensure sufficient statistical power, the number of Monte Carlo replications was set to 999 and clusters with p<0.05 were considered as statistically significant.

Ethical consideration

This study which has minimal risk was carried out by analysing secondary dataset. Study participants confidentiality was ensured since unique ID codes were used to identify them. The core KHDSS activities (births, deaths, migration) have been given ethical clearance from the Research Development and Division (of the Ghana Health Service) Ethical Review Board.

Results

All-cause neonatal mortality clustering

The Poisson model was used in this analysis that involved 30,132 population at risk with 634 neonatal deaths. Table 2 depicts the villages in each all-cause neonatal mortality cluster while Table 3 presents the results of the spatial scan statistics using the Poisson model. No statistically significant cluster was detected in the model (Table 3). However, four potential clusters were detected for all-cause neonatal mortality with the first cluster (most likely or primary cluster) containing eleven villages, second cluster (secondary cluster) containing eleven villages and the third (secondary cluster) and fourth (secondary cluster) clusters containing only one village (Table 3). The first potential cluster which was in the south-western part of the Kintampo HDSS area had a radius of 8.22 km with 71 all-cause neonatal deaths and a relative risk of 1.51 (p = 0.333). The second potential cluster can also be located in the south-eastern part of the KHDSS area with a radius of 10.83 km, forty-eight cases and a relative risk of 1.56 (p = 0.640). The third potential cluster was in the north-western part of the KHDSS area but since it contains only one village (Yaara) and the villages are represented by their centroids, its radius was denoted as 0 km. The number of all-cause neonatal deaths identified in this cluster was five with a relative risk of 3.51 (p = 0.967). The fourth cluster also contains only one village (Techira No.1) and had five cases with a relative risk of 3.23 (p = 0.993). This cluster is located in the north-western part of the KHDSS area. Fig 1 depicts the four potential clusters in the Kintampo Districts.

Table 2

Identified potential clusters and their villages in the Kintampo Districts.

Cluster number	Village(s) within a cluster
1	Bawa Akura 1, Krabonso, Aworata, Bobrobo, Akruma, Adiembra, Nante, NanteZongo, Tanokrom, Hyireso, Ampoma
2	Abom Basare, Abom Kokonba, Jerusalem, Apesika, Anokyekrom, Akora Nkwanta, Akora, Nyamebekyere, Brechakrom, Attakrom, Asuogya No. 1
3	Yaara
4	Techira No. 1

Table 3

Detected clusters from purely spatial analysis using the Poisson model.

Cluster type	No. of villages within a cluster	Radius (km)	Observed cases	Expected cases	Relative risk	p-value
Most likely	11	8.22	71	48.96	1.51	0.333
Secondary	11	10.83	48	31.69	1.56	0.640
Secondary	1	0.00	5	1.43	3.51	0.967
Secondary	1	0.00	5	1.56	3.23	0.993

Fig 1

Most likely and secondary clusters for all-cause neonatal mortality in the Kintampo Districts.

Cause-specific neonatal mortality clustering

Table 4 depicts the villages in each detected cluster and Table 5 summarises the cause-specific neonatal mortality results from the Bernoulli model. The analysis involved 474 neonatal deaths attributed to specific causes. This constitutes 74.8% of the 634 neonatal deaths considered in the study. The missing data were due to verbal autopsy interviews which could not be conducted as a result of out-migration and also outstanding verbal autopsy coding yet to be done by physicians.

Table 4

Detected clusters and villages for neonatal cause-specific mortality.

Cause-specific mortality	Cluster number	Village(s) within a cluster
Asphyxia	1	Anyima, Alhassan Akura No. 2, Yaw Amoakrom, Boadi No1, Hyireso, Boadi No. 2, Ampoma, Adiembra, Krabonso, Beposo, Jema Nkwanta, Ajina, Paninamisa, Jema, Mansie, Pamdu, Tanokrom
	2	Asuogya No 1, Attakrom, Brechakrom
Congenital	-	No cluster detected
Infection	1	Nante Zongo, Nante, Pumpuatifi
	2	Kandige, Busuama, Yaara, Old Longoro, Mansra, Gbuonyonga, Tuffoboi, Sogliboi, Ntareban, Dwere, Ahenakrom, Gomboi, Baniantwe, Bewele, Bug Nkwanta, Gong, Nyabia, Weila, New Longoro, Techira No.1, Basabasa, Asantekwa, Techira2, Sabule, Yabraso, Taningni, Chara, Babiledor Konkonba, Babildor, Soronuase, Babator, Chingakrom, Punpuano, Nkwanta, Sora, Aworata, Tanokrom, Ayorya, Bobrobo, Tahiru Akura
	3	Asuogya No. 1
Prematurity	1	Bawa Akura 1, Krabonso, Aworata, Bobrobo, Akruma, Adiembra, Nante, Nante Zongo
	2	Kwabia, Yepemso, Oforikrom, Drepo, Yeboah, Abudwuom, Fokuokrom, Nyamebekyere, Brechakrom, Attakrom, Akora, Bosomkai, Kobeda1, Asuogya No. 1, Akora Nkwanta, Ntankro
	3	Ajina, Amoma
Other	1	Brechakrom, Attakrom, Nyamebekyere, Asuogya No 1, Fokuokrom, Akora, Akora Nkwanta, Agyegyemakunu, Kofiekrom, Kwabia
	2	Sora, Mansie, Chara, Sabule, Babiledor Konkonba, Babildor, Tanokrom, Taningni, Nkwanta, Babukrom, Dakore, Boadi No. 2, Adiembra, Basabasa, Boadi No. 1, Nyabia, Yaw Amoakrom, Aworata, Weila, Bug Nkwanta, Gazienya, Hyireso, Asantekwa, Krabonso, Ayorya, Alhassan Akura No. 2, Bawa Akura 1, Anyima, Yabraso, Gomboi, Bobrobo, Techira No. 1, Ahenakrom,Dwere
	3	Atta Akura, Chiranda
Unexplained	1	Gong, Baniantwe, Techira No. 1
	2	Agyegyemakunu, Kofiekrom

Table 5

Detected clusters for neonatal cause-specific mortality using the Bernoulli model.

Cause-specific mortality	Cluster type	No. of villages within a cluster	Radius (km)	Observed cases	Expected cases	Relative risk	p-value
Birth asphyxia	Most likely	18	12.23	56	32.62	1.98	0.012
	Secondary	3	1.23	5	1.07	4.77	0.608
Congenital	No cluster detected	-	-	-	-	-	-
Infection	Most likely	3	3.46	10	3.94	2.68	0.630
	Secondary	41	28.09	29	18.07	1.79	0.650
	Secondary	1	0	2	0.24	8.60	0.960
Prematurity	Most likely	8	7.03	10	2.28	5.47	0.025
	Secondary	16	6.47	6	2.11	3.16	0.891
	Secondary	2	4.57	4	1.43	3.00	0.993
Other	Most likely	10	4.83	7	2.37	3.20	0.810
	Secondary	34	16.35	15	7.85	2.20	0.810
	Secondary	3	3.00	4	0.97	4.35	0.850
Unexplained	Most likely	3	3.69	3	0.21	15.33	0.222
	Secondary	2	0.91	3	0.41	7.82	0.689

Two clusters were identified for birth asphyxia related deaths. All the clusters were located in the southern part of the KHDSS area with the first cluster consisting of eighteen villages while the second cluster was made up of three villages. The first cluster (most likely cluster) which covered a radius of 12.23 km was statistically significant (p = 0.012) with fifty-six cases and relative risk of 1.98. On the contrary, the second cluster (secondary cluster) was not statistically significant (p = 0.608) but had a relative risk of 4.77 and covered a radius of 1.23 km. Fig 2 depicts the primary and secondary clusters for deaths due to birth asphyxia.

Fig 2

Most likely cluster and secondary cluster for asphyxiated deaths in the Kintampo Districts.

There was no cluster detected for the neonatal deaths attributed to congenital cases as there were just two cases in total. Three clusters were detected for infections but none of them was statistically significant. The first cluster (most likely cluster) which contains three villages and had a radius of 3.46 km was in the south-western part of the KHDSS. Ten observed infection-related deaths were identified in the cluster which had a relative risk of 2.68 (p = 0.63). The second potential cluster (secondary cluster) for infection-related deaths contains forty-one villages covering a radius of 28.09 km in the central part of the KHDSS area. The number of cases in this cluster was 29 and had a relative risk of 1.79 (p = 0.65). The third cluster (secondary cluster) for neonatal deaths attributed to infection was also in the north-eastern part of the KHDSS area and also contains only one village (Asuogya No. 1). It had two cases and a relative risk of deaths due to infections to be 8.60 (p = 0.96). Fig 3 shows the potential clusters for neonatal deaths attributed to infections.

Fig 3

Most likely and secondary clusters for neonatal deaths due to infections in the Kintampo Districts.

Three clusters were detected in the southern part of the KHDSS area as a result of deaths due to prematurity. The first cluster was significant (p = 0.025) and consists of eight villages covering a radius of 7.03 km. Ten deaths were identified from premature births and the relative risk of cases in the cluster was 5.47. The second cluster had sixteen villages, 6.47 km radius, ten cases and a relative risk of 3.16 (p = 0.891). In addition, the third cluster consists of two villages and covering a radius of 4.57 km. Four cases of premature-related deaths were identified in the cluster and a relative risk of 3.00 (p = 0.993) being the risk of cases. The primary and secondary clusters for premature deaths are depicted in Fig 4.

Fig 4

Most likely and secondary clusters for premature deaths in the Kintampo Districts.

Regarding neonatal deaths attributed to other causes such as infant haemorrhage, injury etc., three potential clusters were identified; the first cluster (most likely cluster) was in the southern part, the second cluster (secondary cluster) in the western part and the third one (secondary cluster) in the northern part of the KHDSS area. The first cluster had ten villages within a radius of 4.83 km. The number of deaths attributed to other causes in this cluster was seven and the relative risk was 3.20 (p = 0.810). The second cluster was made up of thirty-four villages and had a 16.35 km radius. The number of cases identified in this cluster was fifteen and had a relative risk of 2.20 (p = 0.81). There were three villages in the third cluster which had a 3.00 km radius, four cases and a relative risk of 4.35 (p = 0.85). Three potential clusters attributed to other causes of neonatal deaths are depicted in Fig 5.

Fig 5

Most likely and secondary clusters for other causes of neonatal deaths in the Kintampo Districts.

Two potential clusters were identified for unexplained causes of neonatal deaths; the first cluster (most likely cluster) was in the north-western part and the other cluster (secondary cluster) in the southern part of the KHDSS area. The first cluster had a 3.69 km radius with three villages. The number of cases in this cluster was three with a corresponding relative risk of 15.33 (p = 0.222). The second cluster covered a radius of 0.91 km and consists of two villages. The number of cases in the cluster was three and the relative risk was 7.82 (p = 0.689). The primary and secondary clusters for unexplained causes are shown in Fig 6.

Fig 6

Most likely cluster and secondary cluster for unexplained deaths in the Kintampo Districts.

Kintampo Districts’ map which forms part of the figures were extracted from elsewhere [24].

Discussion

The study identified emerging and statistically significant mortality clusters in the Kintampo districts. The most likely cluster for all-cause neonatal mortality was in the south-western part of the Kintampo Districts and contains eleven villages. Of the eleven villages, Nante (32.4%), Ampoma (18.3%), Krabonso (15.5%), Hyireso (15.5%) and Nante Zongo (11.3%) are the villages that contributed most to the 71 deaths that occurred in the cluster. Neonatal deaths from Nante, Ampoma and Nante Zongo could be due to lack of well-equipped healthcare facilities such as Neonatal Intensive Care Unit (NICU) and well-trained healthcare personnel such as neonatologists and paediatricians in the district hospital which members of these communities highly patronised. Apart from Nante, Ampoma and Nante Zongo which are close to the district hospital in Jema and are linked to Jema by the main highway, Hyireso and Krabonso are far from Jema and their road linking the main highway to Jema is bad. Means of transport are also not readily available. Perhaps the high proportion of all-cause neonatal deaths in these two villages are due to delay in transporting neonates to the district hospital during emergency situations. The common factor that may be attributed to a higher all-cause neonatal mortality in the five villages is a lower proportion of mothers with secondary or higher level of education. Further analysis of the data revealed that the proportion of mothers with secondary or higher educational level in the five villages constitute only 2.4% of all mothers with that level of education. Neonates whose mothers have secondary or higher level of education are supported by literature to have low risk of mortality [10, 25]. The possible contribution of these category of mothers to neonatal deaths is their inability to adhere to instructions and prescriptions from clinicians, non-adherence to prompt vaccination schedules, and their inability to defy strict negative cultural norms associated with neonatal deaths [11, 26, 27].

With regards to cause-specific neonatal mortality, the most likely cluster with eighteen villages had a statistically significant effect on asphyxiated deaths. Nine of the villages namely Jema (19.6%), Anyima (16.1%), Pamdu (12.5%), Mansie (8.9%), Ajina (7.1%), Paninamisa (7.1%), Ampoma (7.1%), Krabonso (7.1%) and Hyireso (5.4%) contributed a significant proportion of the asphyxia-related deaths. Ampoma, Krabonso and Hyireso that contributed significantly to all-cause mortality are also included in this cluster. Apart from Jema, Ampoma, Paninamisa and Pamdu which do not have bad road and transportation problems, the others have these problems. Since asphyxia-related deaths usually occur on the day of birth, the possible reason for the villages with unmotorable roads and the lack of transportation might have been delays in reaching the hospital when there were labour complications. Further analysis of the study data supports the afore-mentioned reason since nearly half (48%) of the asphyxiated deaths in these villages occurred in the hospital and this may be due to delay in reaching the hospital. The other probable explanation is the delay in referral of high-risk pregnancies from health centres, CHPS compounds, and traditional birth attendants in some of these villages to the referral hospital in the district. It may also be due to lack of basic neonatal resuscitation and provision of suboptimal Emergency Obstetric and Neonatal Care (EmoNC) [28] at the referral hospital.

The most likely cluster for deaths attributed to infections has three villages with Nante (50%) and Nante Zongo (40%) contributing most to the ten cases that occurred in the cluster. Neonatal deaths in these villages may be related to inadequate continuum of care and lack of adequate supply of medicines and poor coverage of services that targets neonatal survival [29]. Additionally, health facilities in these villages are not well-equipped and may not be able to effectively treat neonatal infections. Lack of prompt identification of danger signs associated with these infections by mothers may also contribute to these deaths [30].

There was significant clustering for premature neonatal deaths. Villages that contributed high proportions of these deaths are Nante (60%) and Krabonso (30%). Premature babies need intensive neonatal care services for their survival [31] but the villages in this cluster lack such services and will ultimately contribute to these deaths. Other reasons such as lack of well-equipped health facilities, skilled birth attendance, inadequate prenatal care, delay in attending to obstetric complications may be the underlying reasons for the deaths in this cluster. Malaria is a correlate of prematurity [32] and the villages in the cluster are malaria endemic. Therefore malaria could contribute to the underlying causes of the premature deaths.

The most likely cluster for other-related deaths contains villages that are in the outskirts of southern part of Kintampo Districts. Therefore, deaths of this nature may be due to, lack of skilled birth attendants, lack of health personnel, lack of well-equipped health facilities, poor socio-economic status, unmotorable roads, poor housing conditions and lack of transportation.

The most likely cluster for unexplained deaths is in the northern part of the Kintampo Districts. Though not statistically significant, it has the potential for significant mortality clustering. It contains three villages namely Gong, Baniatwe and Techira No. 1 which are in the outskirts of the northern part of the Kintampo Districts. The northern part has few but unequipped health facilities, inadequate health personnel, inadequate skilled birth attendants, and few modern amenities such as electricity and good sanitation facilities. The nearest hospital of these villages in the north is the Kintampo North Municipal Hospital which is far from these villages and moreover transportation is not readily available in these villages during emergency situations. Socio-economic status in these villages within the cluster is also poor and road networks are also poor. All the enumerated factors may lead to clustering for unexplained neonatal deaths.

This study has, however, some few limitations. Using the Kulldorf method has a limitation of including low-risk areas surrounded by high-risk areas in a cluster, since it uses a circular window to scan for high-risk clusters [33]. However, the spatial analysis results for this study identified all the low-risk villages which were surrounded by high-risk villages. Therefore, any future intervention rolled out would be able to identify all the high-risk villages. Neonatal deaths are also likely to be under-enumerated due to the difficulty in the collection of information related to neonatal deaths in developing countries [34, 35]. However, this will be reduced in this study since the study has Community Key Informants (CKIs) who report any events of births and deaths in-between the scheduled household visits. There were some missing records which are also a limitation to this study. Finally, the VA instrument used to determine the causes of death is not as accurate as the autopsy performed in the hospital.

Conclusions

The study suggests higher risk of asphyxia-related deaths in some cluster of villages in the Kintampo Districts. Also, premature deaths appears to be higher than average in some cluster of villages in the Kintampo Districts. It will be appropriate to conduct a qualitative study to elicit factors explaining these high neonatal cause-specific mortality clustering.

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10 Sep 2020

PONE-D-20-22599

Neonatal Mortality Clustering in the Kintampo Districts

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is an interesting paper aimed to identify high-risk clusters for neonatal mortality in Kintampo districts, Ghana. The paper is clearly written and makes an important contribution to the knowledge in the area, describing the methods and application of the identification of clusters of neonatal deaths. l just add a few comments for the authors´consideration.

1. In the title, it would be good to highlight the study takes place in Ghana.

2. The introduction clearly presents the background and objectives of the study. I would suggest to incorporate some estimates of the Global Burden of Disease study in the description of neonatal mortality levels and trends in Ghana and sub-Saharan Africa.

3. The study uses verbal autopsy to ascertain the cause of death. It seems to me the authors used an ad hoc instrument, and the cause of death was ascertained by a panel of physicians, what is OK. Was there any chance to use any of the standardized VA instruments around (e.g. WHO or PHMRC), and analyze the information using any of the automated methods available (Tariff/SmartVA, InSylico, etc.)?

4. The clustering presented by the authors can be very useful in the planning of health services to prevent neonatal deaths. A crucial role in these deaths is the action of the health system, especially in the attention of neonatal emergencies. Is it possible to discuss the results in relation to the quality of care of services available in these clusters, and not only in terms of the risk factors faced by women and children?

Reviewer #2: Thank you for inviting me to review this paper. I think this paper will be beneficial in identifying clusters of neonatal mortality in the Kintampo district of Ghana. This manuscript could be a candidate for publication, but I have some suggestions that I think would improve the quality:

- What are some other potential reasons for particular clusters that have high cause-specific mortality? Distance and maternal mortality seemed to be the only factors mentioned for each cluster. I understand this is a quantitative study, but I found it difficult to understand the difference in cause-specific mortality when the reasons for the differences were essentially the same for each cluster.

- Verbal autopsy should be listed as a potential limitation to these analyses. Predictive performance of VA is not 100% relative to true autopsy and would impact the interpretation of these findings. I also think the fact that verbal autopsy was used as a diagnostic method should be mentioned in the abstract.

- Finally, the figures with the maps are informative. I wonder if it would be possible to have a combined map that identifies the clusters with the highest all-cause and cause-specific mortality. However, I appreciate this may be technically difficult and messy.

**********

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Reviewer #1: Yes: BERNARDO HERNANDEZ

Reviewer #2: No

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28 Feb 2021

Dear Sir/Madam,

RESPONSE TO REVIEWERS AND ACADEMIC EDITOR

Many thanks for the invaluable and insightful comments from the Reviewers and the Academic Editor. I think the comments will really help to improve this manuscript. Please find below the point-by-point responses to the useful comments raised by Reviewers and the Academic Editor. Please refer to the file labelled “Manuscript” for the point-by-point responses. The Reviewers’ and Academic Editors’ comments are italised to distinguish them from the Authors’ responses.

Academic Editor

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Please, the URL provided was accessed and the manuscript has now been put in PLoS ONE format.

2.We note that the figures in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf)

Response: Please, the original Fig 1 has been deleted from the manuscript due to editorial issues regarding copyright policy. The manuscript has therefore been revised accordingly and Fig 2 is currently Fig 1, Fig 3 currently Fig 2 and so on. However, a copy of the approved content permission form for the Kintampo Districts’ map which forms part of the current figures has been submitted to this reputable journal.

Reviewer #1

This is an interesting paper aimed to identify high-risk clusters for neonatal mortality in Kintampo districts, Ghana. The paper is clearly written and makes an important contribution to the knowledge in the area, describing the methods and application of the identification of clusters of neonatal deaths. l just add a few comments for the authors´consideration.

Response: Many thanks

1. In the title, it would be good to highlight the study takes place in Ghana.

Response: Well appreciated. The title of the manuscript has been revised to read: “Neonatal mortality clustering in the central districts of Ghana”

2. The introduction clearly presents the background and objectives of the study. I would suggest to incorporate some estimates of the Global Burden of Disease study in the description of neonatal mortality levels and trends in Ghana and sub-Saharan Africa.

Response: Many thanks for the good suggestion. Please, the Authors looked at the Global Burden of Disease paper but realised Hug et al. (2019) global and regional estimates (citation number is 2) were more recent. Therefore, the Authors have expatiated it to include your suggestion (please refer to page 3, lines 61-63 and page 4, lines 68-69).

3. The study uses verbal autopsy to ascertain the cause of death. It seems to me the authors used an ad hoc instrument, and the cause of death was ascertained by a panel of physicians, what is OK. Was there any chance to use any of the standardized VA instruments around (e.g. WHO or PHMRC), and analyze the information using any of the automated methods available (Tariff/SmartVA, InSylico, etc.)?

Response: Thanks for this comment. Please looking at the data collection period (January 2005-December 2014), the 2016 WHO VA instrument was not available by then. However, currently the Kintampo HDSS is using the afore-mentioned VA instrument and making use of the InterVA software for coding.

4. The clustering presented by the authors can be very useful in the planning of health services to prevent neonatal deaths. A crucial role in these deaths is the action of the health system, especially in the attention of neonatal emergencies. Is it possible to discuss the results in relation to the quality of care of services available in these clusters, and not only in terms of the risk factors faced by women and children?

Response: Well appreciated, the discussion has been revised to include the health system (Please refer to page 17, lines 289-291; page 18, lines 304-310, lines 312-314, lines 316-320; and page 19, lines 324-325, lines 330-331)

Reviewer #2:

Thank you for inviting me to review this paper. I think this paper will be beneficial in identifying clusters of neonatal mortality in the Kintampo district of Ghana. This manuscript could be a candidate for publication, but I have some suggestions that I think would improve the quality:

Response: Many thanks

- What are some other potential reasons for particular clusters that have high cause-specific mortality? Distance and maternal mortality seemed to be the only factors mentioned for each cluster. I understand this is a quantitative study, but I found it difficult to understand the difference in cause-specific mortality when the reasons for the differences were essentially the same for each cluster.

Response: Well appreciated, the discussion has been revised to incorporate the suggestion (Please refer to page 17, lines 289-291; page 18, lines 304-310, lines 312-314, lines 316-320; and page 19, lines 324-325, lines 330-331).

- Verbal autopsy should be listed as a potential limitation to these analyses. Predictive performance of VA is not 100% relative to true autopsy and would impact the interpretation of these findings. I also think the fact that verbal autopsy was used as a diagnostic method should be mentioned in the abstract.

Response: Thanks, the Authors agree with you. This has been stated as a limitation (please refer to page 20, lines 346-347). Verbal autopsy has also been mentioned as a diagnostic method in the abstract (please refer to page 2, line 35).

- Finally, the figures with the maps are informative. I wonder if it would be possible to have a combined map that identifies the clusters with the highest all-cause and cause-specific mortality. However, I appreciate this may be technically difficult and messy.

Response: Your suggestion is a good one and well appreciated but because some of the villages were in the highest all-cause and cause-specific mortalities, it was not feasible when we tried to do it.

Submitted filename: Response to Reviewers.pdf

Click here for additional data file.

9 Jun 2021

Neonatal mortality clustering in the central districts of Ghana

PONE-D-20-22599R1

Dear Dr. ADJEI,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Kazumichi Fujioka

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: BERNARDO HERNANDEZ

17 Jun 2021

PONE-D-20-22599R1

Neonatal mortality clustering in the central districts of Ghana

Dear Dr. Adjei:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Kazumichi Fujioka

Academic Editor

PLOS ONE