Constructing a malaria-related health service readiness index and assessing its association with child malaria mortality: an analysis of the Burkina Faso 2014 SARA data
verfasst von:
Ourohiré Millogo, Jean E. O. Doamba, Ali Sié, Jürg Utzinger, Penelope Vounatsou
The Service Availability and Readiness Assessment surveys generate data on the readiness of health facility services. We constructed a readiness index related to malaria services and determined the association between health facility malaria readiness and malaria mortality in children under the age of 5 years in Burkina Faso.
Methods
Data on inpatients visits and malaria-related deaths in under 5-year-old children were extracted from the national Health Management Information System in Burkina Faso. Bayesian geostatistical models with variable selection were fitted to malaria mortality data. The most important facility readiness indicators related to general and malaria-specific services were determined. Multiple correspondence analysis (MCA) was employed to construct a composite facility readiness score based on multiple factorial axes. The analysis was carried out separately for 112 medical centres and 546 peripheral health centres.
Results
Malaria mortality rate in medical centres was 4.8 times higher than that of peripheral health centres (3.5% vs. 0.7%, p < 0.0001). Essential medicines was the domain with the lowest readiness (only 0.1% of medical centres and 0% of peripheral health centres had the whole set of tracer items of essential medicines). Basic equipment readiness was the highest. The composite readiness score explained 30 and 53% of the original set of items for medical centres and peripheral health centres, respectively. Mortality rate ratio (MRR) was by 59% (MRR = 0.41, 95% Bayesian credible interval: 0.19–0.91) lower in the high readiness group of peripheral health centres, compared to the low readiness group. Medical centres readiness was not related to malaria mortality. The geographical distribution of malaria mortality rate indicate that regions with health facilities with high readiness show lower mortality rates.
Conclusion
Performant health services in Burkina Faso are associated with lower malaria mortality rates. Health system readiness should be strengthened in the regions of Sahel, Sud-Ouest and Boucle du Mouhoun. Emphasis should be placed on improving the management of essential medicines and to reducing delays of emergency transportation between the different levels of the health system.
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Abkürzungen
ACT
Artemisinin-based combination therapy
BCI
Bayesian credible interval
CSPS
Centre de Santé et de Promotion Sociale
FAOC-G
Global first axis ordering consistency
HMIS
Health management and information system
IPT
Intermittent preventive treatment of malaria
IRS
Indoor residual spraying
ITN
Insecticide-treated net
LMICs
Low- and middle-income countries
MCA
Multiple correspondence analysis
MRR
Mortality rate ratio
NCDs
Non-communicable diseases
ORS
Oral rehydration solution
PCA
Principal component analysis
RDT
Rapid diagnostic test
SARA
Service Availability and Readiness Assessment
WHO
World health Organization
Background
Over the past 20 years, considerable progress has been made in the fight against malaria. Indeed, there was an estimated reduction of 41% of clinical malaria incidence, and an estimated reduction in malaria mortality rate of 69% [1]. This success is mainly explained by the scaling up of cost-effective health interventions, such as insecticide-treated nets (ITNs), indoor residual spraying (IRS) and artemisinin-based combination therapy (ACT) [2]. Globally, 19 countries eliminated malaria and six of them have been certified malaria-free [1]. Notwithstanding, malaria remains a major public health issue in sub-Saharan Africa. Indeed, in 2017, 92% of the 219 million new cases of malaria and 93% of the 435,000 attributable deaths worldwide occurred in this part of the world. The disease burden is particularly high in children under the age of 5 years [1]. Burkina Faso accounts for 4 and 6% of the global clinical malaria incidence and malaria-related deaths, respectively. The Malaria Indicator Survey of 2014 estimated that the prevalence of malaria parasitaemia determined by rapid diagnostic tests (RDTs) was 61%, compared to 76% in 2010 [3].
The importance of health systems strengthening to reach health-related goals and targets is stressed since the early 2000s [4, 5]. Human resource shortages and inadequate training, poor supply chain management, inadequate infrastructure and equipment, and weak health information systems prevent the health facilities from responding adequately to populations needs [6‐8]. Consequently, existing tools and strategies, designs and frameworks need to be improved in order to strengthen health systems [8‐10]. In sub-Saharan Africa, only few counties regularly implement health systems assessment. In early 2010, the World Health Organization (WHO) developed the Service Availability and Readiness Assessment (SARA) survey to assess the readiness of health facilities to respond to community needs [11]. SARA surveys collect a set of binary tracer items on several domains related to the availability of basic equipment, basic amenities, essential medicines, diagnostic capacity and delivery of health interventions. The data cover readiness of health facilities to provide general services as well as services related to 20 health programmes, including malaria, HIV, tuberculosis, antenatal care, family planning and non-communicable diseases (NCDs).
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Several authors have analysed the SARA survey tool and similar methodologies proposing statistical approaches to create a measure of health facility readiness and to relate readiness to health outcomes. Shawon and colleagues (2018), in their study following WHO guidelines, calculated separate readiness scores for each tracer item as the proportion of health facilities possessing the item [11, 12]. Domain-specific readiness scores for general (e.g. basic amenities, basic equipment, standard precautions for infection prevention, diagnostic capacity and essential medicines) and for malaria-specific services (e.g. staff and guidelines, diagnostics, medicines and commodities) were also calculated as the mean availability of the tracer items belonging to the domain. A similar approach has been adopted by Kanyangarara et al. (2018) to assess obstetric service readiness in 17 low- and middle-income countries (LMICs) [13]. Ali et al. (2018) obtained a general service score as the average of domain-specific scores to compare family planning service availability and readiness in 10 African countries [14]. This average composite measure takes into account the different aspects of health facility readiness. However, it assumes an equal contribution of the tracer items to the overall readiness. Boyer and colleagues (2015) applied principal component analysis (PCA) on the tracer items and defined a readiness index based on the first principal component. The index was utilized to assess the association between facility readiness with child survival, low birth weight, maternal and neonatal death in Ghana [15]. PCA has been applied to relate general service readiness and health financing factors in 10 countries in Africa and Asia [16], health facility readiness to child delivery services and service utilization in Haiti [17] or to assess facility readiness to maternal health services over time in Nigeria [18]. Of note, Ssempiira et al. (2019) criticized the use of PCA on binary items and derived a readiness index based on multiple correspondence analysis (MCA) [19]. To obtain a meaningful readiness score ensuring that the absence of any tracer item from a facility will contribute to a lower score than its presence, the authors proposed a composite measure based on more than one MCA axis.
SARA survey data from Burkina Faso have been used to assess readiness of surgical [20], obstetric [13] and family planning services [14]. However, no studies have been carried out to date to investigate the relationship between health service readiness and health outcomes in Burkina Faso. Hence, to fill this gap, we focused our research on malaria-related services and determined the extent to which malaria services readiness is effective and able to prevent malaria deaths in children under the age of 5 years. Our findings will help to optimize resources allocation and improve SARA survey analyses for Burkina Faso and other LMICs.
Methods
Study area and national health system
Malaria is endemic in Burkina Faso. It is the leading cause of health care consultation, hospitalisation and mortality in under 5-year-old children [21]. The health system of Burkina Faso is pyramidal and consists of three levels [22]. The peripheral level is formed by the health district and includes the “Centre de Santé et de Promotion Sociale” (CSPS), medical centres, isolate dispensaries, delivery centres and district hospitals. The latter serve as referral centres of the former health facilities. The second level is made of the regional hospitals, which are the reference structures for the district hospitals. The third level comprises the national and teaching hospitals and is the highest level of referral care providing specialized services. In 2016, there were approximately 1760 CSPS, 47 district hospitals, eight regional hospitals and five national and teaching hospitals.
Data sources
The 2014 SARA survey
We analysed health facility data from the Burkina Faso SARA survey carried out in 2014 that included 786 health facilities grouped in three strata: (i) 19 teaching hospitals, private polyclinics and regional hospitals (stratum 1); (ii) 90 district hospitals and medical centres (stratum 2); and (iii) 671 CSPS, isolate dispensaries and delivery centres (stratum 3). Strata 1 and 2 correspond to a rather homogeneous group as they are staffed with physicians (in most cases), and hence, we combined them to increase the sample size and created two hierarchical levels of health facilities: medical centres (highest level) consisting of strata 1 and 2 and peripheral health centres (lowest level), including those of stratum 3. Of note, medical centres are usually staffed by physicians, while peripheral health centres are primarily managed by nurses.
The items in the SARA questionnaire are specific to the services provided by the health facilities and remain the same across health facility levels for a specific service. As facility levels differ in terms of the services and health programmes they offer, the items have different importance or weights depending on the facility level. For example, access to power grid is mostly found in medical centres as they are situated mainly in urban areas, while solar power is the main source of energy in rural areas. Medicines for chronic diseases or surgery, anesthesia and X-ray equipment are mainly part of the medical centres rather than peripheral health centres.
We defined as tracer items readiness indicator (i) for the general services and (ii) for the malaria-specific services, the proportion of health facilities having the tracer item available. The services were defined as binary variables taking the value “1” if the tracer item was available in the facility and “0” otherwise. Furthermore, we created domain readiness indicators for general (i.e. basic amenities, basic equipment, standard precautions for infection prevention, diagnostic capacity and essential medicines) and malaria services (i.e. staff and guidelines, diagnostics, medicines and commodities). Domain readiness indicators correspond to the proportion of health facilities having the whole set of tracer items belonging in the domain. We used “1” if all tracer items belonging to the domain where available at the health facility and “0” otherwise.
Health outcome: malaria-related mortality among under-5-year-old
Mortality data were extracted from the Health Management Information System (HMIS) for a full year (January–December 2014). Malaria mortality in children below the age of 5 years was defined as the number of malaria-related deaths among all in-patient visits to a health facility of that age group. The mortality outcome was linked to the SARA database according to the health facility.
Statistical analysis
Bayesian negative binomial models were fitted on the number of malaria-related deaths at the health facility. We assumed that the number of malaria-related deaths at the health facility follows a negative binomial count distribution, and hence, Bayesian negative binomial models were fitted on the malaria deaths data. The total number of children below the age of 5 years visiting the facility (i.e. the denominator of the mortality rate outcome) was considered as an offset term in the model, that is the logarithmic transformation of it was introduced as a covariate with fixed regression coefficient equal to 1. The tracer items were included as covariates in the model. Bayesian variable selection was applied to determine the most important tracers associated with the malaria mortality rate. A separate analysis was carried out for each facility level, i.e. medical centres and peripheral health centres.
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MCA was applied to the most important tracers, adhering to an approach put forth by Ssempiira et al. (2018) [19]. In short, let K be the set of selected tracers, Xk, k = 1, …. K and \( {X}_{0,i}^k \) and \( {X}_{1,i}^k \) be two binary indicators corresponding to the presence or absence of the Xk from the facility i, respectively, that is, \( {X}_{0,i}^k \) takes value 1 when the tracer k is absent (\( {X}_i^k=0 \)) and 0 otherwise. Likewise, \( {X}_{1,i}^k \) takes value 1 when the tracer k is present in health facility i (i.e. \( {X}_i^k=1 \)) and 0 otherwise.
The readiness score for health facility i, based on the ath factorial axis is defined by \( {F}_i^a=\frac{1}{K}\sum \limits_{k=1}^K\sum \limits_{j_k=0}^1{\mathsf{W}}_{j_k}^{a,k}{X}_{j_k,i}^k, \) where jk indicates the value of Xk and the weights \( {\mathsf{W}}_{j_k}^{a,k} \) are the columns standards coordinates on the ath factorial axis corresponding to \( {X}_{j_k,i}^k. \) Following the procedure of Asselin (2009), we define a composite readiness score as \( {F}_i^a=\frac{1}{K}\sum \limits_{k=1}^K\ \sum \limits_{j_k\in \left\{0,1\right\}}^1\sum \limits_{a=1}^L\delta \left(k-a\right)\ {\mathsf{W}}_{j_k}^{a,k}{X}_{j_k,i}^k, \) where L is the number of factorial axes used in the composite score and δ(k − a) is the Dirac delta function, which takes the value 1 when the weights related to \( {X}_{j_k,i}^k \) are selected from the factorial axis and 0 otherwise, that is, δ(k − a) = 1 if k = a and δ(k − a) = 0 if k ≠ a. The factorial axes that will represent the Xk tracer are identified based on a discrimination measure, which is calculated for each tracer and axis and measures the contribution of the tracer to the total variance explained by the axis. To improve interpretation of the score, we translated the weights so that the absence category jk = 0 of the Xk tracer received a zero weight and the presence one jk = 1 received a strictly positive weight indicating the gain in the readiness increase measured by the axis a when a facility i acquires the kth tracer. Hence, the \( {\mathsf{W}}_{j_k}^{a,k} \) in Fi is replaced by \( {\mathsf{W}}_{j_k}^{+a,k} \), where \( {\mathsf{W}}_0^{+a,k}=0 \) and \( {\mathsf{W}}_1^{+a,k}={\mathsf{W}}_1^{+a,k}-{\mathsf{W}}_1^{+a,k} \) [23]. The composite readiness score was converted into a readiness index with three categories by dividing the ordered distribution of the score values into three parts, each containing a third of the values.
Furthermore, we assessed the association between malaria mortality rate and the readiness index described above, using a geostatistical Bayesian negative binomial model. Locational random effects were included in the model to take into account spatial correlation. We assumed a Gaussian process with an exponential correlation function of the distance between health facilities. The analysis was adjusted for the type of health facility location (urban or rural) and of administrative status (publicor private). Further details of the statistical methods are provided in Additional file 1.
The descriptive analyses were carried out in STATA version 14 (StataCorp.; College Station, TX, USA) and Bayesian models were fitted in OpenBUGS version 3.2.3 (Imperial College and Medical Research Council; London, UK). Maps were produced in ArcGIS version 10.2.1 (Esri Inc.; Redlands, CA, USA).
Results
Health facility characteristics and malaria mortality
The SARA survey carried out in Burkina Faso in 2014 included 786 health facilities. Among these health facilities, 658 (83.7%) reported complete malaria mortality data, and hence, they were used for subsequent analyses. Seventeen percent of the facilities (n = 112) belonged to medical centres. Around 80% of medical centres are located in urban areas, while in peripheral health centres, more than 80% of the facilities are in rural zones (Table 1). Most of the facilities are managed by the government (77% of medical centres and 93% of peripheral health centres). The malaria mortality rate in medical centres is 4.8 times higher than that of peripheral health centres (3.5% vs 0.7%, p < 0.0001).
Table 1
Health facility characteristics and malaria mortality rates according to the SARA survey of 2014 in Burkina Faso
Characteristics
Medical centres
(n = 112)
n (%)
Peripheral health centres (n = 546)
n (%)
Location
Urban
90 (80.4)
83 (15.2)
Rural
22 (19.6)
463 (84.8)
Administrative management
Public
86 (76.8)
510 (93.4)
Private
26 (23.2)
36 (6.6)
Regions
Boucle du Mouhoun
9 (8.0)
65 (11.9)
Cascades
4 (3.6)
25 (4.6)
Centre
27 (24.1)
54 (9.9)
Centre-Est
10 (8.9)
38 (7.0)
Centre-Nord
6 (5.4)
41 (7.5)
Centre-Ouest
11 (9.8)
53 (9.7)
Centre-Sud
4 (3.6)
30 (5.6)
Est
9 (8.0)
40 (7.3)
Hauts Bassins
9 (8.0)
55 (10.1)
Nord
8 (7.1)
53 (9.7)
Plateau Central
4 (3.6)
38 (7.0)
Sahel
4 (3.6)
27 (5.0)
Sud-Ouest
7 (6.4)
27 (5.0)
Malaria
Number of deaths (a)
1860
347
Number of consultations (b)
53,768
48,524
Mortality rate = a/b
3.5%
0.7%
Domains and tracer items readiness’ indicators
Table 2 summarises the domains and tracer items readiness indicators of the general and malaria-specific services. Among the general service domains, basic equipment readiness was the most attainable domain (reached by 64.2 and 48.4% of medical centres and peripheral health centres, respectively). On the other hand, essential medicines was the domain with the lowest readiness (only 0.1% of medical centres and 0% of peripheral health centres had the whole set of essential medicines tracer items). Malaria services consisted of nine tracer items covering three domains. Apart of the diagnostic domain, which had one tracer, readiness of the staff and guidelines domain was higher in peripheral health centres compare to medical centres (57.7 and 45.5%, p = 0.027). Medicines and the commodities domain readiness was also higher in peripheral health centres but the difference to medical centres was borderline significant (31.5% vs 18.8%, p = 0.051).
Table 2
Frequency distribution of domains and tracer items readiness indicators as well as posterior inclusion probabilities of general and malaria-specific tracers estimated from the Bayesian variable selection. Tracers with inclusion probabilities higher than 50% were selected for the MCA
Domain/tracer items
Medical centres (n = 112)
Peripheral health centres (n = 546)
Availability (%)
Posterior inclusion probability2 (%)
Availability (%)
Posterior inclusion probability (%)
General service
Basic amenities1
39 (34.8)
6 (1.1)
Power (electric or solar device)
86 (76.8)
8.5
362 (66.3)
21.4
Improved water source inside or within the ground of the facility
110 (98.2)
−3
476 (87.2)
60.9
Room with auditory and visual privacy for patient consultations
81 (72.3)
100
284 (52.0)
39.2
Access to adequate sanitation facilities for clients
109 (97.3)
–
519 (95.1)
–
Communication equipment (phone or SW radio)
111 (99.1)
–
535 (98.0)
–
Facility has access to computer with E-mail/Internet access
56 (50.0)
6.9
10 (1.8)
–
Emergency transportation
106 (94.6)
61.7
515 (94.3)
88.0
Basic equipment
72 (64.2)
264 (48.4)
Adult scale
108 (96.4)
–
527 (96.5)
–
Child scale
82 (73.2)
13.2
428 (78.4)
15.1
Thermometer
112 (100)
–
544 (99.6)
–
Stethoscope
112 (100)
–
540 (98.9)
–
Blood pressure apparatus
109 (97.3)
–
533 (97.6)
–
Light source
92 (82.1)
100
349 (63.9)
16.2
Standard precautions for infection prevention
52 (46.4)
223 (40.8)
Safe final disposal of sharp materials
85 (75.9)
84.7
422 (77.3)
28.2
Safe final disposal of infectious wastes
82 (73.2)
62.9
336 (61.5)
18.2
Appropriate storage of sharp waste
110 (98.2)
–
535 (98.0)
–
Appropriate storage of infectious waste
103 (92.0)
85.3
494 (90.5)
50.8
Disinfectant
111 (99.1)
–
544 (99.6)
–
Single use (standard disposable or auto-disable syringes)
111 (99.1)
–
543 (99.5)
–
Soap and running water or alcohol based hand rub
105 (93.8)
33.6
518 (94.9)
99.2
Latex gloves
100 (89.9)
56.1
499 (91.4)
99.3
Guidelines for standard precautions
98 (87.5)
98.3
469 (85.9)
21.2
Diagnostic capacity
37 (33.0)
3 (0.6)
Haemoglobin
72 (64.3)
100
9 (1.7)
–
Blood glucose
50 (44.6)
48.2
6 (1.1)
–
Malaria diagnostic capacity
101 (90.2)
17.5
467 (85.5)
21.3
Urine dipstick-protein
103 (92.0)
49.0
501 (91.8)
50.1
Urine dipstick-glucose
104 (92.9)
80.6
491 (89.9)
31.4
HIV diagnostic capacity
106 (94.6)
32.9
512 (93.8)
39.8
Urine test for pregnancy
96 (85.7)
26.0
412 (75.5)
42.3
Essential medicines
2 (0.1)
0 (0)
Amoxicillin tablet
101 (90.2)
40.6
523 (95.8)
–
Ampicillin for inject
104 (92.9)
21.7
519 (95.1)
–
Gentamicin injectable
101 (90.2)
77.7
472 (86.5)
30.3
Oxytocin injectable
98 (87.5)
100
502 (91.9)
77.8
Amoxicillin dispersible
94 (83.9)
10.6
475 (87.0)
20.1
Oral rehydration solution (ORS)
95 (84.8)
16.8
476 (87.2)
20.3
Zinc
77 (68.8)
100
418 (76.6)
14.9
Aspirin
94 (83.9)
100
377 (69.1)
19.6
Magnesium sulfate
78 (69.6)
100
121 (22.2)
20.9
Amlodipine
25 (22.3)
100
12 (2.2)
–
Enalapril
20 (17.9)
26.1
6 (1.1)
–
Insulin injectable
8 (7.1)
35.9
5 (0.9)
–
Betablockers
20 (17.9)
100
8 (1.5)
–
Beclomethasone inhaler
14 (12.5)
100
9 (1.7)
–
Ceftriaxone injection
103 (92.0)
93.8
492 (90.1)
58.4
Thiazidic
25 (22.3)
14.2
41 (7.5)
50.6
Glibenclamide tablet
39 (34.8)
100
10 (1.8)
–
Metformin
41 (36.6)
22.9
9 (1.7)
–
Omeprazole
65 (58.0)
10.1
110 (20.2)
20.2
Salbutamol inhaler
86 (76.8)
63.3
288 (52.8)
24.9
Carbamazepine
28 (25.0)
69.9
0 (0.0)
–
Haloperidol
27 (24.1)
96.6
0 (0.0)
–
Simvastatin
4 (3.6)
–
Fluoxetin
3 (2.7)
–
–
Malaria-specific service
Staff and guidelines
41 (45.5)
313 (57.7)
Guidelines for diagnosis and treatment of malaria
105 (93.8)
22.4
536 (98.2)
–
Guidelines for intermittent preventive treatment
75 (67.0)
13.0
481 (88.1)
31.1
Staff trained in malaria diagnosis and treatment
79 (70.5)
97.5
453 (83.0)
40.9
Staff trained in intermittent preventive treatment
First-line antimalarial in stock (artemether+lumefantrine, artesunate+amodiaqune)
99 (88.4)
58.8
526 (96.3)
–
Paracetamol cap/tab
104 (92.9)
100
418 (76.2)
34.6
Intermittent preventive treatment of malaria in pregnancy (IPTp) drug (sulfadoxine pyrimethamine)
62 (55.4)
28.4
356 (65.2)
17.1
ITNs
29 (25.9)
73.2
185 (33.9)
26.2
1Domain readiness indicators were defined as availability of all tracer items belonging to the domain
2Posterior inclusion probability: gives the probability of the tracer to be included in the final model and it is calculated by the proportion of all possible models in the variable selection procedure that include the specific tracer. For example, the posterior inclusion probability of 21.4 estimated for the power tracer indicates that this tracer was included in 21.4% of all possible models generated from all general services-related tracers
3Item not included in the variable selection procedure due to low relative frequency i.e. < 5%
Bayesian variable selection identified 29 tracers that are related to malaria deaths out of the 49 items across all domains of the general service offered by medical centres (Table 2). These are privacy room and emergency transportation (under basic amenities), light source (basic equipment), safe disposal of sharp materials, safe disposal and storage of infectious wastes, latex gloves and precaution guidelines (standard precautions for infection prevention), haemoglobin and glucose in urine (diagnostic), medicines for the management of NCDs (diabetes, cardiovascular and respiratory chronic diseases) and availability of two antibiotics (gentamycin and ceftriaxone) commonly used in medical centres (essential medicines). Five out of nine tracer items were selected in the malaria-specific service of medical centres (i.e. staff trained in malaria diagnostic and treatment, trained in intermittent preventive treatment of malaria, the first line of malaria treatment, paracetamol and ITNs).
For peripheral health centres, 29% (10/34) tracers were selected in the general service. These are similar to those in medical centres with the exception of the essential medicines, as most of them were not available in peripheral health centres. Regarding malaria-specific services offered by peripheral health centres, readiness to the first line of antimalarial drugs (96.3%) and to malaria diagnostics (85.5%) was similar as observed in medical centres.
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Health facility readiness index
MCA was applied on the tracers items selected from the variable selection procedure to obtain a readiness score. Fourteen and six factorial axes were sufficient to build the composite indices for medical centres and peripheral health centres, respectively. Standard coordinates of the selected tracers are provided in Table 3 (medical centres) and Table 4 (peripheral health centres).
Table 3
Standard coordinates of tracer items on the first 14 factorial axes (medical centres) derived from the SARA survey in 2014 in Burkina Faso.
Tracers
Category
Frequency, n(%)
Factorial axes*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Privacy room
No
31 (27.7)
−0.281a
−1.196
0.358
−2.537
1.257
−1.370
−0.195
0.746
0.171
−5.265
−3.066
1.560
0.252
1.213
Yes
81 (72.3)
0.107
0.458
−0.137
0.971
−0.481
0.524
0.075
−0.285
− 0.065
2.015
1.173
−0.597
−0.097
− 0.464
Emergency transportation
No
6 (5.4)
0.063
−3.332
−2.716
4.195
0.012
−7.946
4.844
−11.193
−0.109
− 1.787
1.657
3.354
−2.609
2.616
Yes
106 (94.6)
−0.004
0.189
0.154
−0.237
−0.001
0.450
−0.274
0.634
0.006
0.101
−0.094
−0.190
0.148
−0.148
Light power
No
20 (17.9)
−0.925
−3.350b
0.885
0.805
0.298
2.439
−4.057
−1.751
−0.649
−3.378
2.240
−4.542
−0.070
− 0.001
Yes
92 (82.1)
0.201
0.728
−0.192
−0.175
− 0.065
−0.530
0.882
0.381
0.141
0.734
−0.487
0.987
0.015
0.000
Safe final disposal of sharps
No
27 (24.1)
1.254
−0.656
−4.856
−2.973
− 0.352
0.664
− 0.421
− 0.995
1.152
− 0.588
2.051
− 0.445
− 0.888
− 0.100
Yes
85 (75.9)
−0.398
0.208
1.542
0.944
0.112
−0.211
0.134
0.316
−0.366
0.187
−0.652
0.142
0.282
0.032
Safe final disposal of infectious wastes
No
30 (26.8)
0.859
−0.684
−4.958
−2.727
0.017
0.163
−1.408
− 0.591
1.160
0.798
−0.069
0.185
−0.102
− 0.391
Yes
82 (73.2)
−0.314
0.250
1.814
0.998
−0.006
−0.060
0.515
0.216
−0.424
−0.292
0.025
−0.068
0.037
0.143
Appropriate storage of infectious waste
No
9 (8.0)
1.198
−2.643
0.035
1.036
−1.691
−9.057
− 1.236
− 0.570
− 1.376
2.107
5.636
−5.990
3.825
3.190
Yes
103 (92.0)
−0.105
0.231
− 0.003
− 0.091
0.148
0.791
0.108
0.050
0.120
−0.184
−0.492
0.523
−0.334
− 0.279
Latex gloves
No
12 (10.1)
−0.252
−3.347
0.867
0.983
−4.095
−0.858
−6.782
3.797
−3.464
0.525
2.358
0.705
−3.537
−1.541
Yes
100 (89.9)
0.030
0.402
−0.104
− 0.118
0.491
0.103
0.814
−0.456
0.416
−0.063
−0.283
− 0.085
0.424
0.185
Guidelines for standard precautions
No
14 (22.5)
−3.610
0.980
−1.949
−1.532
− 1.850
− 1.734
4.077
1.023
−2.052
− 3.909
− 0.936
− 3.326
− 1.546
1.195
Yes
98 (87.5)
0.516
−0.140
0.278
0.219
0.264
0.248
−0.582
− 0.146
0.293
0.558
0.134
0.475
0.221
−0.171
Haemoglobin test
No
40 (35.7)
−1.086
−1.396
−0.563
0.630
−2.200
0.331
0.568
−0.251
− 2.686
1.100
− 0.855
1.830
−3.237
0.473
Yes
72 (64.3)
0.603
0.775
0.313
−0.350
1.222
−0.184
− 0.316
0.139
1.492
−0.611
0.475
−1.017
1.798
−0.263
Glucose dipstick
No
8 (7.1)
−2.850
0.482
−4.354
0.289
−6.397
2.569
3.601
4.457
−1.015
−5.601
0.354
−5.051
−0.402
− 0.182
Yes
104 (92.9)
0.219
−0.037
0.335
−0.022
0.492
− 0.198
− 0.277
− 0.343
0.078
0.431
− 0.027
0.389
0.031
0.014
Amlopdipin
No
87 (77.7)
−0.329
− 0.723
0.354
− 0.378
− 0.280
− 0.416
− 0.848
0.125
1.106
0.339
− 0.459
0.036
0.424
0.184
Yes
25 (22.3)
1.144
2.515
−1.231
1.315
0.974
1.448
2.953
−0.436
− 3.848
−1.180
1.597
−0.125
− 1.477
−0.640
Aspirin
No
18 (16.1)
−3.484
3.093
−0.109
− 0.623
1.267
−1.229
0.818
2.121
−2.599
1.476
0.217
− 2.705
1.985
1.667
Yes
94 (83.9)
0.667
−0.592
0.021
0.119
−0.243
0.235
−0.157
− 0.406
0.498
− 0.283
− 0.042
0.518
− 0.380
− 0.319
Beclomethasone inhaler
No
98 (87.5)
−0.205
−0.683
0.214
−0.707
− 0.065
− 0.013
0.293
− 0.313
− 0.362
0.313
0.484
0.025
0.342
−0.079
Yes
14 (12.5)
1.435
4.781
−1.495
4.948
0.452
0.094
−2.052
2.190
2.536
− 2.189
−3.386
−0.177
− 2.394
0.555
Beta-blockers
No
92 (82.1)
−0.433
− 0.793
0.614
−0.473
− 0.185
0.294
0.561
− 0.134
0.132
−0.074
0.098
0.032
0.439
0.587
Yes
20 (17.9)
1.990
3.648
−2.824
2.176
0.849
−1.355
−2.581
0.616
−0.609
0.339
− 0.450
− 0.145
− 2.021
− 2.701
Ceftriaxone
No
9 (8.0)
−6.392
1.090
0.991
−0.869
3.199
−1.868
−2.313
−2.918
2.354
−0.844
3.647
1.036
−6.562
−1.918
Yes
103 (92.0)
0.558
−0.095
− 0.087
0.076
− 0.280
0.163
0.202
0.255
−0.206
0.074
−0.319
− 0.090
0.573
0.168
Gentamicin
No
11 (9.8)
−4.331
−1.279
−2.234
4.478
2.838
2.256
−0.629
− 0.754
1.864
1.838
−1.003
−2.284
−0.105
6.498
Yes
101 (90.2)
0.472
0.139
0.243
−0.488
− 0.309
− 0.246
0.069
0.082
− 0.203
− 0.200
0.109
0.249
0.011
−0.708
Glibenclamide
No
73 (65.2)
−0.724
−0.211
− 0.581
0.607
0.655
0.053
−0.176
−0.305
−1.378
− 0.786
0.346
0.540
1.845
−1.922
Yes
39 (34.8)
1.356
0.395
1.088
−1.137
− 1.225
− 0.099
0.329
0.571
2.580
1.471
−0.648
−1.011
−3.453
3.598
Insulin injectable
No
104 (92.9)
−0.123
−0.424
0.222
−0.186
− 0.106
−0.147
0.493
0.038
0.378
0.233
−0.339
− 0.364
− 0.207
−0.641
Yes
8 (7.1)
1.596
5.512
−2.880
2.420
1.375
1.909
−6.404
−0.489
−4.911
−3.035
4.404
4.737
2.688
8.330
Magnesium
No
34 (30.4)
−2.083
− 2.028
−0.340
1.431
0.424
0.597
−1.968
0.063
0.339
−0.313
−2.323
0.905
0.300
−0.988
Yes
78 (69.6)
0.908
0.884
0.148
−0.624
− 0.185
− 0.260
0.858
−0.028
− 0.148
0.136
1.013
−0.395
− 0.131
0.431
Oxytocin
No
14 (12.5)
−3.089
−0.951
−3.370
5.102
−0.779
2.837
1.260
−2.386
2.970
1.110
−1.926
− 2.982
0.951
−0.272
Yes
98 (87.5)
0.441
0.136
0.481
−0.729
0.111
−0.405
− 0.180
0.341
− 0.424
− 0.159
0.275
0.426
− 0.136
0.039
Salbutamol
No
26 (23.2)
−3.000
− 0.517
−1.091
−1.330
−0.915
− 0.662
− 0.223
1.480
−2.038
1.850
− 0.362
1.727
0.879
1.003
Yes
86 (76.8)
0.907
0.156
0.330
0.402
0.277
0.200
0.067
−0.448
0.616
− 0.559
0.109
− 0.522
− 0.266
− 0.303
Zinc
No
35 (31.3)
−2.157
0.502
−0.495
−0.975
0.011
2.273
0.503
−0.816
1.147
0.610
1.572
2.790
1.487
0.844
Yes
77 (68.8)
0.980
−0.228
0.225
0.443
−0.005
−1.033
− 0.229
0.371
−0.522
− 0.277
− 0.715
− 1.268
− 0.676
− 0.383
ITNs
No
83 (74.1)
−0.220
0.045
−0.747
− 0.247
0.762
−1.081
−0.434
− 0.005
− 0.520
0.795
− 1.332
− 0.377
0.161
− 0.081
Yes
29 (25.9)
0.628
−0.128
2.139
0.708
−2.180
3.093
1.242
0.015
1.487
−2.276
3.811
1.079
−0.461
0.231
Staff trained in malaria diagnosis and treatment
No
33 (29.5)
−1.091
1.604
−0.341
− 0.226
−3.506
− 0.728
− 1.149
1.928
2.217
− 0.102
0.246
0.246
1.343
−0.185
Yes
79 (70 5)
0.456
−0.670
0.143
0.094
1.465
0.304
0.480
− 0.806
−0.926
0.043
− 0.103
− 0.103
− 0.561
0.077
Staff trained in intermittent preventive treatment in pregnancy (IPTp)
No
38 (33.9)
−0.158
−0.459
− 0.829
2.575
−1.426
−2.856
0.619
0.713
1.757
−1.492
1.060
2.415
0.922
0.162
Yes
74 (66.1)
0.081
0.236
0.426
−1.322
0.732
1.467
−0.318
−0.366
− 0.902
0.766
− 0.544
−1.240
− 0.474
− 0.083
First line treatment of malaria
No
13 (11.6)
−4.606
2.906
0.766
−1.353
0.142
−1.435
0.786
0.550
2.369
1.904
2.504
0.379
−0.856
−4.781
Yes
99 (88.4)
0.605
−0.382
− 0.101
0.178
− 0.019
0.188
− 0.103
− 0.072
−0.311
− 0.250
−0.329
− 0.050
0.112
0.628
IPTp drug
No
50 (44.6)
−5.589
3.721
1.822
−2.447
5.322
−2.872
− 2.985
0.352
1.426
−2.683
3.527
−0.455
−4.909
2.079
Yes
62 (55.4)
0.430
−0.286
− 0.140
0.188
− 0.409
0.221
0.230
−0.027
− 0.110
0.206
−0.271
0.035
0.378
−0.160
Carbamazepine
No
84 (75.0)
−0.144
0.764
0.367
−0.450
−1.050
0.110
−0.594
−1.146
−0.364
0.136
−0.686
0.030
0.566
0.637
Yes
28 (25.0)
0.432
−2.292
−1.101
1.349
3.149
−0.331
1.782
3.438
1.093
−0.408
2.057
−0.091
−1.698
−1.911
Haloperidol
No
85 (75.9)
−0.183
0.825
0.330
−0.199
− 0.894
− 0.172
− 0.410
−1.475
− 0.025
−0.361
− 0.402
− 0.354
− 0.078
− 0.660
Yes
27 (24.1)
0.577
−2.599
−1.039
0.627
2.815
0.541
1.292
4.644
0.079
1.136
1.266
1.116
0.244
2.078
Inertia explained by the factorial axis (%)
14.5
8.9
6.7
6.1
5.9
5.3
4.7
4.4
4.0
3.7
3.6
3.4
3.0
2.9
*First 14 factorial axes to build the composite readiness score as there is no information gain beyond axis 14
aFour tracers consistent with the FAOC-G in negative direction (not bold) and 25 consistent in positive direction (bold)
bHighlighted in bold and italic are the weights of tracers from factorial axes selected to build the composite readiness score
Table 4
Standard coordinates of tracer items on the first six factorial axes (peripheral health centres)
Tracers
Category
Frequency
Factorial axes*
1
2
3
4
5
6
Improved water source
No
0.457a
0.048
−5.424
0.301
−5.699
0.095
Yes
476 (87.2)
−0.067
−0.007
0.798
−0.044
0.838
− 0.014
Emergency transportation
No
−5.770b
−0.594
−1.284
−0.830
−1.483
3.543
Yes
515 (94.3)
0.347
0.036
0.077
0.050
0.089
−0.213
Soap or running water
No
−1.239
−0.895
8.502
−5.784
−7.725
−0.620
Yes
518 (94.9)
0.067
0.048
−0.460
0.313
0.418
0.034
Storage infectious waste
No
0.602
−6.612
− 0.633
1.732
−0.529
0.274
Yes
494 (90.5)
−0.063
0.696
0.067
−0.182
0.056
−0.029
Latex gloves
No
0.418
−7.016
1.337
1.780
−0.218
0.025
Yes
499 (91.4)
−0.039
0.661
−0.126
− 0.168
0.021
− 0.002
Urine dipstick
No
−4.999
0.574
−0.718
1.596
−1.019
2.063
yesYes
501 (91.8)
0.449
−0.052
0.065
−0.143
0.092
−0.185
Ceftriaxone
No
−3.772
−1.310
− 1.489
− 1.567
3.602
1.638
Yes
492 (90.1)
0.414
0.144
0.163
0.172
−0.395
− 0.180
Oxytocin
No
−5.750
−0.119
1.406
0.905
−0.920
0.604
Yes
502 (91.9)
0.504
0.010
−0.123
− 0.079
0.081
− 0.053
Thiazidic
No
0.048
−0.190
−0.263
− 0.751
0.136
0.041
Yes
41 (7.5)
−0.586
2.338
3.235
9.250
−1.672
−0.510
IPTg training
No
− 1.518
−0.266
− 0.552
− 0.122
0.163
−4.223
Yes
370 (67.8)
0.722
0.127
0.263
0.058
−0.077
2.009
Inertia explained by the factorial axis (%)
20.0
14.2
10.5
10.3
9.9
8.8
*First 6 factorial axes to build the composite readiness score as there is no information gain beyond axis 6
aFour tracers consistent with the FAOC-G in negative direction (not bold) and 6 consistent in positive direction (bold)
bHighlighted in bold and italic are the weights of tracers from factorial axes selected to build the composite readiness score
For medical centres, the factorial axis 1 accounted for 10 tracer items, followed by axis 2 with five tracer items. The most weighted rescaled tracer items were the emergency transportation and appropriate storage of infectious waste picked from factorial axes eight and six, respectively. On the first factorial axis, a subset of four tracers met the Global First Axis Ordering Consistency (FAOC-G) requirement in the positive direction, while a second subset of 25 tracer items met this condition in the negative direction (i.e. the score monotonically increases/decreases for all tracer items) [23]. Hence, there are two subsets of tracer items that are inconsistent and one subset should have been discarded, leading to a loss of information if we had constructed the score using the first factorial axis. With regard to peripheral health centres, four tracer items showed a high discrimination measure on factorial axis 1. The highest weighted tracers are “thiazidic” and “running water source or soap” from axes 4 and 5, respectively. The discrimination measures of the tracers and the rescaled weights are given in Tables 2.1 and 2.2 (in Additional file 2) for medical centres and peripheral health centres, respectively.
Figure 1 shows the proportion of variation in the tracers explained by the first factorial axis and the composite readiness score based on (i) the whole set of tracers and (ii) the subset of tracers identified by the Bayesian variable selection. The results show that the composite score explains more than twice the variance explained by the first factorial axis (medical centres: 30% vs. 15%; peripheral health centres 53% vs. 18%). Furthermore, the composite score based on the subset of tracers explained more variation than the composite score based on the whole set (medical centres: 30% vs. 26%; peripheral health centres: 53% vs. 30%).
Fig. 1
Proportion of variance explained by the first factorial axis (red) and the composite readiness score (blue) based on the whole set and the subset of tracers identified by the Bayesian geostatistical negative binomial models.
×
Association between health facility readiness and malaria mortality
The composite readiness score was converted into a categorical index with three categories defined by the tertiles of its distribution. Results of the Bayesian geostatistical negative binomial model fitted on malaria mortality indicated that medical centres with the highest and moderate readiness experienced a lower mortality rate by 19 and 6%, respectively, compared to the facilities with the lowest readiness (Table 5). However, this difference lacked statistical significance. The type of management and the location of health facilities do not influence malaria mortality.
Table 5
Posterior estimates (median and 95% BCI) of the association between health facility readiness and malaria mortality obtained from a Bayesian geostatistical negative binomial model
Medical centres
Peripheral health centres
Readiness index
MRRa (95% BCI)
MRR (95% BCI)
Low
1.00
1.00
Middle
0.94 (0.76–1.25)
0.74 (0.54–1.00)
High
0.81 (0.74–2.51)
0.41 (0.19–0.91)*
Location
Rural
1.00
1.00
Urban
0.97 (0.48–1.77)
0.49 (0.31–0.78)*
Administrative status
Private
1.00
1.00
Public
1.12 (0.51–2.17)
0.69 (0.46–1.01)
Spatial parameters
Spatial variance
0.26 (0.14–0.53)
0.46 (0.29–0.67)
Spatial range (km)
43.3 (13.6–89.9)
26.32 (6.39–83.1)
aMRR Mortality rate ratio
*: Statistically important association
Anzeige
Peripheral health centres at the highest readiness category had a mortality rate ratio (MRR) of 0.41 (95% Bayesian credible interval (BCI): 0.19–0.91) compared to those with the lowest readiness. Furthermore, urban health facilities were associated with a statistically important reduction of malaria mortality compared to those in rural areas (MRR: 0.49, 95% BCI: 0.31–0.78). The median spatial range distance (distance over which the spatial correlation is no more important) was higher in medical centres compared to peripheral health centres.
The geographical distribution of malaria mortality rate showed a similar pattern with that of the proportion of health facilities with lowest readiness (Fig. 2), indicating that regions with high malaria mortality rate have high proportion of facilities with low readiness and vice versa. In particular, the region of Centre (first region in terms of health infrastructure and population) showed for both health facility levels low malaria mortality rates, while Sud-Ouest, Sahel and Boucle du Mouhoun were those among the highest mortality and highest proportion of low performing facilities.
Fig. 2
Spatial distribution of malaria-related mortality rate among children under the age of 5 years in Burkina Faso for medical centres (a) and peripheral health centres (b) and the proportion of health facilities medical centres (c) and peripheral health centres (d) in the lowest category of the corresponding composite readiness index
×
Discussion
Malaria services readiness and malaria-related mortality
The aim of our study was to estimate the extent to which malaria services readiness in Burkina Faso was associated with malaria mortality. Service delivery is an essential building block of the WHO health systems framework [8]. Our research indicated that the higher the readiness index, the lower the mortality in peripheral health centres. Hence, the index is sensitive enough to identify some of the barriers in the quality of the management of malaria cases. Information from Malaria Indicator Surveys and of the HMIS can be included as additional components of this index to look into other aspects of case management, such as delays of seeking care, the severity of cases consulting or the quality of care provided. Our results corroborate with previous investigations done in Bangladesh, Ghana, Haiti, Mozambique, Nigeriaand Tanzania that also used SARA or similar survey data and revealed a positive effect of readiness on health outcomes [15‐18, 24, 25].
The lack of a statistically important association between facility readiness and malaria mortality in medical centres might be explained by the severity of malaria cases seeking treatment in medical centres. Indeed, peripheral health centres refer complicated cases to medical centres. Hence, although the latter are better equipped and staffed, the mortality rate is partially influenced by the seriousness of their cases. On the other hand, the reduced mortality rate in peripheral health centres with highest readiness was certainly related to prompt diagnosis and adequate treatment, since peripheral health centres receive patients at an early stage of the disease. This is consistent with the important association of the emergency transportation tracer with malaria mortality. In medical centres, emergency transportation obtained the highest weight. Reducing the delay of reference from peripheral health centres to medical centres will reduce the probability of deaths due to a severe malaria [26‐29]. In addition, training health workers of peripheral health centres would allow for early reference decisions. At community level, populations must be encouraged to consult very early. In peripheral health centres, we noticed that medicines for NCDs management had low availability, although one drug devoted to chronic diseases had the highest weight. The low availability could be explained by an insufficiency in the supply of this type of drug and thus a low quality of the management of chronic diseases. On the contrary, its presence may mean competent health workers in the provision of drugs and thus a better quality of care and therefore to the management of malaria cases as well.
Anzeige
Tracer items and domains readiness
Results of the individual tracers and domain readiness indicators are consistent with the role assigned to each level. Peripheral health centres are the first contact with any health issues and thus they provide the so called “minimum package” of health care and services, while medical centres provide the “complementary package”. Basic equipment was the most available domain for both levels of health care and for general services. The most widely available items within this domain were thermometer, stethoscope, adult scale and blood pressure apparatus, which represent minimum essential equipment to manage patients. However, their availability was almost 50% in peripheral health centres meaning that the quality of health care is not guaranteed in about half of the peripheral health centres, suggesting lack of financial resources and of management of supplies in peripheral health centres.
The weakest domain for both levels for general services was the essential medicine with an availability of less than 1%. Two types of medicines appeared in this domain; medicines for infectious diseases (availability > 80%) and medicines for chronic diseases (availability < 10%). The situation depicts the epidemiological profile of Burkina Faso, where infectious diseases are still predominant, but also indicates that services towards chronic diseases and NCDs in 2014 were inadequate, particularly in view of NCDs rapidly gaining importance in LMICs [21, 30‐32]. This also indicates the weakness in the drug supply circuit of health facilities from the expression of adequate needs, to the availability of drugs at the point of purchase [33, 34].
The diagnostic capacity domain was very weak in peripheral health centres (0.6%) compared to medical centres (33%) even though in peripheral health centres, large number of biological diagnostic tests do not need sophisticated equipment. Peripheral health centres generally refer patients who need further biological testing. Nevertheless, the level of availability of malarial diagnosis capacities was > 80% appreciable in both levels and reflects the high workload relative to malaria in consultations [22].
The basic amenities domain is related to the health infrastructure investment and depends heavily on the financial support of the government. At the time of the SARA survey in 2014, only 1.9% of peripheral health centres had a computer. Hence, computers were the exception rather than the norm in peripheral health centres.
Regarding malaria-specific services, the average availability of “staff and guidelines” and the “medicine and commodity” domains was higher in peripheral health centres than medical centres. More than 80% of them had their staff trained and knew the guidelines for malaria management. In addition, more than 95% in these facilities possessed first-line treatment for malaria. Malaria is the most important cause of morbidity and mortality in under 5-year-old children, which explains that substantial efforts are being made to train peripheral health facility workers, render medicines and other medical supplies available for malaria case management at all levels of the health system. In recent years, there has been a shift from first-line medicines to ACTs, introduction of RDTs, and ITN campaigns [35, 36]. However, the availability of ITNs in health facilities had reduced the availability of malaria readiness in general because it is mostly during mass campaign that ITNs are distributed to pregnant women.
Variables selection
The variable selection highlighted facts that are consistent with the health system in Burkina Faso. In both health facility levels and for general service readiness, “emergency transportation” was selected. In general, emergency transportation (ambulances) which reduces the delay to reach a health centre is available in medical centres. Peripheral health centres use mainly motorcycles for transportation. The malaria management policy in Burkina Faso requests that cases are confirmed before treatment; yet, there is still considerable empiric treatment [21]. Without a diagnostic test, malaria might be confused with other infectious diseases, which has ramifications on disease management, including treatment [37, 38]. This may explain the heavy prescription not only of antimalarials but also antibiotics, such as “gentamicin”and “ceftriaxone”.
Geographical distribution of readiness and mortality rate
The geographical distribution of the under-5 malaria-related mortality corresponds almost to the HMIS statistics in 2014 suggesting that the regions of the Boucle du Mouhoun, Sahel and Sud-Ouest had the highest mortality rates and that malaria was the leading cause of deaths in this age group at that time. Regions with low mortality rates are concentrated in the central and eastern parts of the country for both levels. Apart from the fact that there is a greater concentration of health workers around the central region, there is no evidence to explain this distribution of mortality [21]. Similarly, to the mortality rate, the geographical distribution of the readiness index is heterogeneous for both levels. Nevertheless, the regions of Centre and Hauts Bassins are the best equipped and have the highest numbers of health facilities. They gather more than half of health human resources in Burkina Faso and possess most performant medical centres.
Strengths and limitations
Our findings clearly favoured the construction of a composite readiness indicator rather than one derived from the first factorial axis. Indeed, the proportion of variance explained has more than doubled in both health facility levels compared to the first component. The composite index takes also into account the multifactorial and multidimensionality of the readiness allowing capturing tracers items that are represented better by high order axes. The variable selection identifies the subset of the most important tracers that are related to malaria mortality producing a score which explains even more variation in the tracers and it is directly related to a specific health outcome and thus, can led comprehensive policy decisions to strengthen the specific health services and care. The methodology can be applied on SARA or SARA-like survey in other countries.
However, SARA survey assess availability of items the day of the survey and thus do not take into account the variability over time of the items and 1 day may not be sufficient to get the mean availability of an item in a health facility longitudinally. The SARA proposed methodology weights all tracer items equally in the construction of readiness index; however, our proposed approach addresses this limitation. Unfortunately, mortality data in the HMIS were not available for several health facilities; therefore, we could not include data from those facilities in the analysis. Our results reflect the readiness of malaria services in Burkina Faso in 2014. The country has performed two more surveys in 2016 and 2018. Our methodology can be easily extended to construct a temporally varying readiness index and therefore assess potential improvements in the health facility malaria service provision.
Conclusion
Our results indicate that investing in health services is an effective means for reducing the burden of malaria in Burkina Faso. The broad implication is that resources and efforts must be maintained and strengthened, particularly at medical centres where mortality rate is high and at weak peripheral health centres. The emergency transportation mechanisms between the different levels of the health system need to be further enhanced. The composite readiness score created by exploiting more than one MCA factorial axis produces a more informative and consistent health facility readiness measure that captures all aspects of readiness unlike the index based on only the first axis.
Acknowledgements
We are grateful to the Ministry of Health of Burkina Faso and its Department of Statistics for providing the SARA survey database and allowing us access to the Health Management Information System database. This study obtained financial support by the Swiss National Science Foundation (SNSF) Swiss Programme for Research on Global Issues for Development (R4D).
Ethics approval and consent to participate
We used secondary data of the Service Availability and Readiness Assessment (SARA) survey and the Health Management and Information System (HMIS) that were made available by the “Direction Générale des Études et des Statistiques Sectorielles”, Burkina Faso. The research was approved by the National Ethics Committee for Health Research of Burkina Faso under the deliberation N°2014–7-072. All data were anonymized.
Consent for publication
Not applicable.
Competing interests
The authors have no competing interest to declare.
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Constructing a malaria-related health service readiness index and assessing its association with child malaria mortality: an analysis of the Burkina Faso 2014 SARA data
verfasst von
Ourohiré Millogo Jean E. O. Doamba Ali Sié Jürg Utzinger Penelope Vounatsou
