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Erschienen in: BMC Cardiovascular Disorders 1/2021

Open Access 01.12.2021 | Research article

Associations between essential medicines and health outcomes for cardiovascular disease

verfasst von: Liane Steiner, Shawn Fraser, Darshanand Maraj, Nav Persaud

Erschienen in: BMC Cardiovascular Disorders | Ausgabe 1/2021

Abstract

Background

National essential medicines lists are used to guide medicine reimbursement and public sector medicine procurement for many countries therefore medicine listings may impact health outcomes.

Methods

Countries’ national essential medicines lists were scored on whether they listed proven medicines for ischemic heart disease, cerebrovascular disease and hypertensive heart disease. In this cross sectional study linear regression was used to measure the association between countries’ medicine coverage scores and healthcare access and quality scores.

Results

There was an association between healthcare access and quality scores and health expenditure for ischemic heart disease (p ≤ 0.001), cerebrovascular disease (p ≤ 0.001) and hypertensive heart disease (p ≤ 0.001). However, there was no association between medicine coverage scores and healthcare access and quality scores for ischemic heart disease (p = 0.252), cerebrovascular disease (p = 0.194) and hypertensive heart disease (p = 0.209) when country characteristics were accounted for.

Conclusions

Listing more medicines on national essential medicines lists may only be one factor in reducing mortality from cardiovascular disease and improving healthcare access and quality scores.
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Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1186/​s12872-021-01955-1.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
ATC
Anatomical Therapeutic Chemical Classification
GEM
Global essential medicines
HAQ
Healthcare access and quality
NCD
Non-communicable disease
NEML
National essential medicines list
WHO
World Health Organization
WHO Model List
WHO model list of essential medicines

Introduction

Approximately 29% of deaths worldwide are from cardiovascular disease specifically, ischemic heart disease, stroke and hypertensive heart disease [1]. The burden of these and other non-communicable diseases (NCD) will be associated with productivity loss and catastrophic healthcare costs [2] which has the potential to significantly undermine national macroeconomic development [3]. Deaths from cardiovascular disease are amenable to healthcare including treatments such as antihypertensives [4].
Following a 2011 United Nations meeting, the World Health Organization (WHO) released a briefing document which stated that the burden of NCD’s cannot be reduced without access to essential medicines [5]. Essential medicines are those that satisfy the priority health care needs of the population [6]. The purpose of an essential medicines list is to ensure quality medicines are available in a functioning health system, in appropriate forms, at affordable prices for both the individual and the community [6]. The WHO created a Model List of Essential Medicines (WHO Model List) which provides recommendations for minimum medicine needs for a basic health-care system. More than 100 countries have embraced the idea of essential medicines and adapted their own national essential medicines list (NEML) to address their health care priorities informed by their national burden of disease [2]. NEMLs are used to guide appropriate use of medicines, as well as medicine selection, reimbursement and public sector procurement [7, 8]. In the public sector, essential medicines are more available than other medicines, suggesting that there may be preferential attention from governments given to them, therefore carefully selecting and adopting an NEML is the first step in ensuring equitable access to pharmaceutical treatment [2]. Medication availability and accessibility plays and important role in addressing the burden of NCD’s [3] as evident by a reduction in mortality and morbidity in many countries since the implementation of essential medicines [9]. Population mortality that is amenable to care is assessed by the healthcare access and quality (HAQ) score that is available for 195 countries and that is comprised of 32 causes of death including ischemic heart disease, cerebrovascular disease and hypertensive heart disease [4].
The purpose of this study was to determine the relationship between listing essential medicines used to treat ischemic heart disease, cerebrovascular disease and hypertensive heart disease and amenable mortality related to these conditions measured by the HAQ score [4].

Methods

Dataset sources

All medications, with some exceptions, from countries’ NEMLs hosted in the WHO’s National Essential Medicines Lists Repository were extracted and recorded in an Excel database [10, 11]. NEMLs for 137 countries were identified [10].
We used the 2018 amenable mortality subscores, calculated by measuring age standardized mortality rates, for ischemic heart disease, cerebrovascular disease and hypertensive heart disease [4].

Inclusion criteria

Countries were included if they had a NEML captured by the Global Essential Medicines (GEM) database and a HAQ score for ischemic heart disease, cerebrovascular disease and hypertensive heart disease.

Data collection

In order to identify which medications were relevant to the three causes of interest (ischemic heart disease, cerebrovascular disease and hypertensive heart disease), we searched for guidelines for ischemic heart disease, cerebrovascular disease and hypertensive heart disease on the WHO website in June 2019. Four international guidelines distributed by the WHO, an internationally recognized health authority, were selected: Prevention and Control of Non-communicable Diseases: Guidelines for primary health care in low-resource settings [12], WHO Package of Essential Non-communicable Diseases Interventions for Primary Health Care in Low-Resource Settings [13], Technical Package for cardiovascular disease management in primary health care- evidence-based treatment protocols [7], Tackling NCDs: “Best Buys” and other recommended interventions for the prevention and control of non-communicable diseases [14]. Although it is not an internationally recognized guideline, additional guidance from the American Heart Association’s website was used to ensure all relevant medicines were captured [15]. These guidelines along with the WHO Model List 20th edition [16] were used to identify medicines used for treatment of ischemic heart disease, cerebrovascular disease and hypertensive heart disease. Guidelines were searched using the causes and associated International Classification of Diseases 10th revision codes provided by the HAQ score [4].
Population size, health expenditure and life expectancy were retrieved from the Global Health Observatory [17]; prevalence for ischemic heart disease, cerebrovascular disease and hypertensive heart disease was retrieved from the Global Burden of Disease Study [1]. Most data was for the year 2016; if 2016 data was not available, data from the closest year to 2016 was retrieved. Country characteristics can be found in Table 1.
Table 1
Country Characteristics
Country
ISO code
Geographic region
Income group
Ischemic heart disease medicine coverage score
Cerebrovascular disease medicine coverage score
Hypertensive hear disease medicine coverage score
Health expenditure for 2015 (per capita in PPP intl$)
Population for 2016 (in thousands)
Life expectancy for 2016 (in years)
Year of NEML publication
Afghanistan
AFG
Eastern Mediterranean
Low
22
15
14
183.9
34,656
62.7
2014
Albania
ALB
Europe
Upper middle
34
30
26
773.7
2926
76.4
2011
Algeria
DZA
Africa
Upper middle
47
44
36
1031.2
40,606
76.4
2016
Angola
AGO
Africa
Lower middle
5
3
2
195.5
28,813
62.6
2007
Antigua and Barbuda
ATG
The Americas
High
28
21
17
1105.1
101
75
2008
Argentina
ARG
The Americas
High
35
27
21
1389.8
43,847
76.9
2011
Armenia
ARM
Europe
Upper middle
24
17
13
883.2
2925
74.8
2010
Bahrain (Kingdom of)
BHR
Eastern Mediterranean
High
37
31
26
2453.2
1425
79.1
2015
Bangladesh
BGD
South-East Asia
Lower middle
19
12
10
88
162,952
72.7
2008
Barbados
BRB
The Americas
High
57
51
42
1233.6
285
75.6
2011
Belarus
BLR
Europe
Upper middle
33
28
21
1084.6
9480
74.2
2012
Belize
BLZ
The Americas
Upper middle
30
23
20
523.7
367
70.5
2008
Bhutan
BTN
South-East Asia
Lower middle
30
23
18
287.1
798
70.6
2016
Bolivia
BOL
The Americas
Lower middle
28
20
16
445.8
10,888
71.5
2011
Bosnia and Herzegovina
BIH
Europe
Upper middle
28
24
19
1101.8
3517
77.3
2009
Botswana
BWA
Africa
Upper middle
28
22
17
970
2250
66.1
2012
Brazil
BRA
The Americas
Upper middle
31
24
18
1391.5
207,653
75.1
2014
Bulgaria
BGR
Europe
Upper middle
55
53
45
1491.9
7131
74.8
2011
Burkina Faso
BFA
Africa
Low
24
17
13
96.1
18,646
60.3
2014
Burundi
BDI
Africa
Low
24
18
13
63.7
10,524
60.1
2012
Cabo (Cape) Verde
CPV
Africa
Lower middle
42
35
28
310.4
540
73.2
2009
Cambodia
KHM
South-East Asia
Lower middle
2
1
0
209.6
15,762
69.4
2003
Cameroon
CMR
Africa
Lower middle
27
20
14
162.8
23,439
58.1
2010
Central African Republic
CAF
Africa
Low
25
19
14
31.9
4595
53
2009
Chad
TCD
Africa
Low
17
12
9
99.8
14,453
54.3
2007
Chile
CHL
The Americas
High
28
21
18
1903.1
17,910
79.5
2005
China
CHN
Western Pacific
Upper middle
30
25
19
762.2
1,411,415
76.4
2012
Colombia
COL
The Americas
Upper middle
30
22
17
852.8
48,653
75.1
2011
Congo
COG
Africa
Lower middle
22
16
13
202.7
5126
64.3
2013
Costa Rica
CRI
The Americas
Upper middle
27
20
15
1286.5
4857
79.6
2014
Côte d’Ivoire
CIV
Africa
Lower middle
39
32
25
189.6
23,696
54.6
2014
Croatia
HRV
Europe
High
48
41
31
1656.4
4213
78.3
2010
Cuba
CUB
The Americas
Upper middle
31
26
20
2478.8
11,476
79
2012
Czech Republic
CZE
Europe
High
65
60
49
2469.9
10,611
79.2
2012
Democratic Peoples Republic of Korea
PRK
South-East Asia
Low
20
13
11
 
25,369
71.9
2012
Democratic Republic of Congo
COD
Africa
Low
22
16
13
34
78,736
60.5
2010
Djibouti
DJI
Eastern Mediterranean
Lower middle
19
13
10
146.7
942
63.8
2007
Dominica
DMA
The Americas
Upper middle
27
20
16
585.7
74
 
2007
Dominican Republic
DOM
The Americas
Upper middle
31
24
19
873.1
10,649
73.5
2015
Ecuador
ECU
The Americas
Upper middle
28
19
15
980.2
16,385
76.5
2013
Egypt
EGY
Eastern Mediterranean
Lower middle
27
19
15
495.2
95,689
70.5
2012
El Salvador
SLV
The Americas
Lower middle
32
25
19
578.5
6345
73.7
2009
Eritrea
ERI
Africa
Low
23
16
15
56.2
4955
65
2010
Estonia
EST
Europe
High
48
45
36
1886.8
1312
77.8
2012
Ethiopia
ETH
Africa
Low
50
40
32
65.6
102,403
65.5
2014
Fiji
FJI
Western Pacific
Upper middle
21
15
12
331.4
899
69.9
2015
Gambia
GMB
Africa
Low
15
9
9
114.1
2039
61.9
2001
Georgia
GEO
Europe
Lower middle
21
14
10
717.7
3925
72.6
2007
Ghana
GHA
Africa
Lower middle
26
20
18
249.3
28,207
63.4
2010
Grenada
GRD
The Americas
Upper middle
28
21
17
677.5
107
73.4
2007
Guinea
GIN
Africa
Low
33
26
23
57.2
12,396
59.8
2012
Guyana
GUY
The Americas
Upper middle
25
19
16
336.1
773
66.2
2010
Haiti
HTI
The Americas
Low
24
17
13
120.1
10,847
63.5
2012
Honduras
HND
The Americas
Lower middle
29
23
18
353.4
9113
75.2
2009
India
IND
South-East Asia
Lower middle
30
22
17
237.7
1,324,171
68.8
2015
Indonesia
IDN
South-East Asia
Lower middle
23
16
12
369.3
261,115
69.3
2011
Iran (Islamic Republic of)
IRN
Eastern Mediterranean
Upper middle
47
41
29
1261.7
80,277
75.7
2014
Iraq
IRQ
Eastern Mediterranean
Upper middle
46
40
33
481
37,203
69.8
2010
Jamaica
JAM
The Americas
Upper middle
40
34
26
511.4
2881
76
2012
Jordan
JOR
Eastern Mediterranean
Upper middle
51
46
37
568.1
9456
74.3
2011
Kenya
KEN
Africa
Lower middle
26
21
15
157.2
48,462
66.7
2016
Kiribati
KIR
Western Pacific
Lower middle
22
15
13
151.8
114
66.1
2009
Kyrgyzstan
KGZ
Europe
Lower middle
36
29
21
286.6
5956
71.4
2009
Latvia
LVA
Europe
High
46
43
36
1429.3
1971
75
2012
Lebanon
LBN
Eastern Mediterranean
Upper middle
30
24
18
1117.3
6007
76.3
2014
Lesotho
LSO
Africa
Lower middle
20
13
11
251.1
2204
52.9
2005
Liberia
LBR
Africa
Low
16
11
9
127.8
4614
62.9
2011
Lithuania
LTU
Europe
High
49
46
40
1874.6
2908
75
2012
Madagascar
MDG
Africa
Low
16
10
10
76.7
24,895
66.1
2008
Malawi
MWI
Africa
Low
25
18
16
108.2
18,092
64.2
2015
Malaysia
MYS
Western Pacific
Upper middle
25
18
15
1063.9
31,187
75.3
2014
Maldives
MDV
South-East Asia
Upper middle
43
36
27
1513.9
428
78.4
2011
Mali
MLI
Africa
Low
24
17
12
118.5
17,995
58
2012
Malta
MLT
Europe
High
51
44
38
3470.9
429
81.5
2008
Marshall Islands
MHL
Western Pacific
Upper middle
21
16
13
862.8
53
 
2007
Mauritania
MRT
Africa
Lower middle
19
13
11
177.1
4301
63.9
2008
Mexico
MEX
The Americas
Upper middle
50
44
31
1008.7
127,540
76.6
2011
Mongolia
MNG
South-East Asia
Lower middle
24
17
14
469.6
3027
69.8
2009
Montenegro
MNE
Europe
Upper middle
41
36
25
957
629
76.8
2011
Morocco
MAR
Eastern Mediterranean
Lower middle
31
24
18
435.3
35,277
76
2012
Mozambique
MOZ
Africa
Low
21
15
12
63.7
28,829
60.1
2016
Myanmar (Burma)
MMR
South-East Asia
Lower middle
30
22
18
267.2
52,885
66.8
2010
Namibia
NAM
Africa
Upper middle
27
20
16
942.5
2480
63.7
2016
Nepal
NPL
South-East Asia
Low
22
15
11
150.6
28,983
70.2
2011
Nicaragua
NIC
The Americas
Lower middle
26
19
16
406
6150
75.5
2011
Nigeria
NGA
Africa
Lower middle
21
15
13
215.2
185,990
55.2
2010
Oman
OMN
Eastern Mediterranean
High
38
32
23
1635.9
4425
77
2009
Pakistan
PAK
Eastern Mediterranean
Lower middle
26
19
14
134.4
193,203
66.5
2016
Papua New Guinea
PNG
Western Pacific
Lower middle
20
13
11
98.6
8085
65.9
2012
Paraguay
PRY
The Americas
Upper middle
28
21
16
724.3
6725
74.2
2009
Peru
PER
The Americas
Upper middle
33
25
18
671
31,774
75.9
2012
Philippines
PHL
Western Pacific
Lower middle
46
39
31
322.8
103,320
69.3
2008
Poland
POL
Europe
High
47
45
34
1704.2
38,224
77.8
2017
Portugal
PRT
Europe
High
71
67
50
2661.4
10,372
81.5
2011
Republic of Moldova
MDA
Europe
Lower middle
37
31
24
515.3
4060
71.5
2011
Romania
ROU
Europe
Upper middle
55
49
41
1090.4
19,778
75.2
2012
Russian Federation
RUS
Europe
Upper middle
36
31
21
1414
143,965
71.9
2014
Rwanda
RWA
Africa
Low
21
14
12
143.2
11,918
68
2010
Saint Lucia
LCA
The Americas
Upper middle
28
21
17
681.4
178
75.6
2007
Saint Vincent and the Grenadines
VCT
The Americas
Upper middle
26
18
14
469.5
110
72
2010
Senegal
SEN
Africa
Low
24
17
13
97.1
195
75.1
2013
Serbia
SRB
Europe
Upper middle
50
42
31
1323.7
8820
76.3
2010
Seychelles
SYC
Africa
High
24
18
13
867.3
94
73.3
2010
Slovakia
SVK
Europe
High
73
65
54
2062
5444
77.4
2012
Slovenia
SVN
Europe
High
56
54
41
2733.8
2078
80.9
2017
Solomon Islands
SLB
Western Pacific
Lower middle
23
17
13
173
599
71.1
2017
Somalia
SOM
Africa
Low
8
6
7
 
14,318
55.4
2006
South Africa
ZAF
Africa
Upper middle
21
14
12
1086.4
56,015
63.6
2014
Sri Lanka
LKA
South-East Asia
Lower middle
22
18
11
353.1
20,798
75.3
2013
Sudan
SDN
Africa
Lower middle
35
28
22
277
39,579
65.1
2014
Suriname
SUR
The Americas
Upper middle
25
18
14
1016.9
558
71.8
2014
Sweden
SWE
Europe
High
31
29
16
5298.6
9838
82.4
2016
Syrian Arab Republic
SYR
Eastern Mediterranean
Low
64
57
46
 
18,430
63.8
2008
Tajikistan
TJK
Europe
Low
26
19
15
192.7
8735
70.8
2009
Thailand
THA
South-East Asia
Upper middle
34
28
21
610.2
68,864
75.5
2013
The former Yugoslav Republic of Macedonia
MKD
Europe
Upper middle
35
28
17
857.1
2081
75.9
2008
Timor-Leste
TLS
South-East Asia
Lower middle
24
17
13
141.3
1269
68.6
2015
Togo
TGO
Africa
Low
28
21
15
95.6
7606
60.6
2012
Tonga
TON
Western Pacific
Upper middle
23
16
12
323.8
107
73.4
2007
Trinidad & Tobago
TTO
The Americas
High
44
39
32
2204.1
1365
71.8
2010
Tunisia
TUN
Eastern Mediterranean
Lower middle
58
52
43
774.1
11,403
76
2012
Uganda
UGA
Africa
Low
26
19
16
138.5
41,488
62.5
2012
Ukraine
UKR
Europe
Lower middle
20
13
10
469.4
44,439
72.5
2009
United Republic of Tanzania
TZA
Africa
Low
36
28
21
96.5
55,572
63.9
2013
Uruguay
URY
The Americas
High
41
35
28
1747.8
3444
77.1
2011
Vanuatu
VUT
Western Pacific
Lower middle
18
12
9
106.1
270
72
2006
Venezuela (Bolivarian Republic of)
VEN
The Americas
Upper middle
25
19
14
579.4
31,568
74.1
2004
Viet Nam
VNM
Western Pacific
Lower middle
60
53
43
334.3
94,569
76.3
2008
Yemen
YEM
Eastern Mediterranean
Low
21
14
11
144.5
27,584
65.3
2009
Zambia
ZMB
Africa
Lower middle
26
19
17
203
16,591
62.3
2013
Zimbabwe
ZWE
Africa
Low
26
18
14
182.3
16,150
61.4
2011
Geographic region was retrieved from the World Health Organization; Income group was retrieved from the World Bank; Population size, health expenditure and life expectancy were retrieved from the Global Health Observatory; ISO: The International Organization for Standardization- ISO-3166 Alpha-3 country code (Source: https://​www.​iso.​org/​iso-3166-country-codes.​html)

Data extraction

Using the identified guidelines for ischemic heart disease, cerebrovascular disease and hypertensive heart disease, medications used to treat these conditions were abstracted. If a guideline indicated a therapeutic class of medicines, that class was fully expanded to include all medicines because medicines within the same chemical subgroup may be considered therapeutically similar. The WHO Model List recognizes interchangeability of certain medicines on their list for others within the same therapeutic class [16]. Using this principle, 4th level Anatomical Therapeutic Chemical Classification (ATC) codes [18] were used to guide which medicines are in the same therapeutic class. If a therapeutic class was mentioned and specific alternatives were stated, only those medicines were included (no therapeutic class expansion was done).
Medicines listed on the WHO Model List or those from guidelines appearing on the WHO Model List (in a form that is usable for the conditions or cause), with a square box symbol, were fully expanded based on the 4th level, chemical subgroup of the ATC code to include all medicines within that therapeutic class. If the medicine is not denoted with a square box it was not expanded. If specific medicines considered equivalent were stated, only those medicines were included. A medicine coverage score was created by summing the number of medicines on a country’s NEML that were also listed on our list of medicines used to treat each HAQ cause.

Data analysis

Data was analyzed using IBM SPSS Statistics version 26 (IBM Corp., 2018), and a p-value ≤ 0.05 was considered significant. An ordinary least squares linear regression model was used to test the hypothesis that there would be a positive relationship between listing medicines (medicine coverage score) and HAQ scores. HAQ score was used as the dependent variable and the previously calculated medicine coverage score was used as the independent variable. Linear regression results are reported for both unadjusted and adjusted with health expenditure, population, life expectancy and prevalence as covariates.

Results

In total, 131 countries were included in the analysis having both a NEML and HAQ score (Table 1). WHO regions represented by countries were the Eastern Mediterranean (14 countries), Europe (26 countries), Africa (38 countries), the Americas (29 countries), South-East Asia (13 countries) and the Western Pacific (11 countries) [17]. Using the World Bank categorization, included countries represented a range of income levels with 28 low income countries, 40 lower-middle income countries, 43 upper middle countries and 20 high income countries [19]. Three countries (Democratic Peoples Republic of Korea, Somalia and Syrian Arab Republic) were excluded from the regression analysis because they were missing values for healthcare expenditure.
The total number of medicines identified through guideline searches for each cause was 103 medicines for ischemic heart disease, 96 medicines for cerebrovascular disease and 73 medicines for hypertensive heart disease (see Additional file 1 for list of medicines). Figure 1 graphs the association between medicine coverage score and HAQ score, with health expenditure represented by bubble size.

Ischemic heart disease

For ischemic heart disease, medicine coverage scores ranged from 2 to 73 (median: 28, IQR: 23 to 37). Results of the unadjusted linear regression model show that listing ischemic heart disease medicines only explained 0.5% of the variability in the HAQ scores across countries. After adjusting for population size, health expenditure, life expectancy and prevalence, approximately 18% of differences in the HAQ score for ischemic heart disease were explained. In the adjusted regression, there was no association between medicine coverage score and HAQ score for ischemic heart disease (p = 0.252), however other variables showed an association with HAQ score. Health expenditure was associated with a 0.011 point increase in HAQ score for each additional per capita dollar (p < 0.001) and prevalence of ischemic heart disease was associated with a 0.007 point decrease in HAQ score for each additional 100, 000 people diagnosed with ischemic heart disease (p < 0.001) (Table 2).
Table 2
Ischemic Heart Disease: Medicine Coverage Score
 
Variable
B
95% CI lower bound
95% CI upper bound
Beta
P value
Pearson correlation
Unadjusted
Medicine coverage score
0.109
− 0.164
0.382
0.69
0.43
0.069
Adjusted
Medicine coverage score
0.194
− 0.14
0.528
0.123
0.252
0.108
Health expenditure
0.011
0.005
0.017
0.467
< 0.001
0.232
Population
− 1.056E−7
0
0
0.001
0.991
− 0.008
Life expectancy
0.058
− 0.636
0.752
0.02
0.869
0.093
Prevalence
− 0.007
− 0.01
− 0.004
− 0.49
< 0.001
− 0.108
R2unadjusted = 0.005 (F = 0.626, (df 130), p = 0.43). R2adjusted = 0.176 (F = 5.131, (df 125), p < 0.001)
B, unstandardized coefficient; Beta, standardized coefficient; CI, confidence interval

Cerebrovascular disease

For cerebrovascular disease, medicine coverage scores ranged from 1 to 67 (median: 21, IQR 17–31). Results of the unadjusted linear regression model show that listing cerebrovascular disease medicines explained approximately 15% of the variation in the HAQ scores. After adjusting for covariates approximately 44% of differences in the HAQ score for cerebrovascular disease were explained. In the unadjusted regression, there was an association between medicine coverage score and HAQ score for cerebrovascular disease (p < 0.001), however the relationship was not present when covariates were included (p = 0.194). Similar to ischemic heart disease, other variables in the adjusted analysis showed a significant association with HAQ scores. Health expenditure was associated with a 0.014 point increase in HAQ score for each additional per capita dollar (p < 0.001), life expectancy was associated with a 0.557 point increase with each additional year of life (p = 0.042) and prevalence of cerebrovascular disease was associated with a 0.008 point decrease in HAQ score for each additional 100, 000 people diagnosed with cerebrovascular disease (p = 0.001) (Table 3).
Table 3
Cerebrovascular disease: medicine coverage score
 
Variable
B
95% CI lower bound
95% CI upper bound
Beta
P-value
Pearson correlation
Unadjusted
Medicine coverage score
0.565
0.333
0.796
0.391
< 0.001
0.391
Adjusted
Medicine coverage score
0.173
− 0.089
0.435
0.117
0.194
0.393
Health expenditure
0.014
0.009
0.018
0.587
< 0.001
0.609
Population
− 3.977E−06
0
0
− 0.037
0.596
− 0.092
Life expectancy
0.557
0.02
1.095
0.2
0.042
0.476
Prevalence
− 0.008
− 0.013
− 0.004
− 0.319
0.001
0.185
R2unadjusted = 0.153 (F = 23.225, (df 130), p < 0.001). R2adjusted = 0.443 (F = 19.071, (df 125), p < 0.001)
B, unstandardized coefficient; Beta, standardized coefficient; CI, confidence interval

Hypertensive heart disease

For hypertensive heart disease, medicine coverage scores ranged from 0 to 54 (median 17, IQR 13–25). Results of the unadjusted linear regression model show that listing hypertensive heart disease medicines explained approximately 11% of the variation in the HAQ score. Results of the adjusted analysis show that approximately 45% of differences in HAQ score were explained. Similar to cerebrovascular disease, an association between medicine coverage score and the HAQ score was observed for hypertensive heart disease (p < 0.001), however the multivariate relationship was not present when covariates were included (p = 0.209). Other variables in the adjusted analysis showed a significant association with HAQ scores. Health expenditure was associated with a 0.008 point increase in HAQ score for each additional per capita dollar (p < 0.001), life expectancy was associated with a 1.371 point increase with each additional year of life (p < 0.001) and prevalence of hypertensive heart disease was associated with a 0.044 point decrease in HAQ score for each additional 100,000 people diagnosed with hypertensive heart disease (p < 0.001) (Table 4).
Table 4
Hypertensive heart disease: medicine coverage score
 
Variable
B
95% CI lower bound
95% CI upper bound
Beta
P-value
Pearson correlation
Unadjusted
Medicine coverage score
0.621
0.312
0.929
0.331
< 0.001
0.331
Adjusted
Medicine coverage score
0.204
− 0.116
0.524
0.11
0.209
0.324
Health expenditure
0.008
0.004
0.013
0.346
< 0.001
0.533
Population
2.073E−06
0
0
0.019
0.782
− 0.009
Life expectancy
1.371
0.829
1.913
0.484
< 0.001
0.554
Prevalence
− 0.044
− 0.063
− 0.026
− 0.402
< 0.001
0.084
R2unadjusted = 0.109 (F = 15.846, (df 130), p < 0.001). R2adjusted = 0.454 (F = 19.963, (df 125), p < 0.001)
B, unstandardized coefficient; Beta, standardized coefficient; CI, confidence interval

Discussion

The number of medicines used to treat cerebrovascular disease and hypertensive heart disease included in national essential medicines lists was associated with amenable mortality, but the association was not present when country characteristics such as health spending were accounted for.
Our findings suggest that increases in a country’s health expenditure may improve HAQ scores for cardiovascular disease. Fullman et al., (2018) found that health spending per capita was strongly correlated with HAQ Index performance, however there was a large variation in score within similar levels of spending [4]. Government spending as a fraction of total health spending was also positively correlated with HAQ Index performance [4]. Per-capita health expenditure is inadequate to pay for basic healthcare interventions in some low-income countries [20, 21]. For the countries included in this study, 62 countries’ (of the 131 total countries; one country had no data) per-capita government expenditure on health was less than the minimum required for basic effective public-health system [20]. A modest increase in public spending, efficient resource use and an investment in prevention programs is necessary for addressing inequity in healthcare [21]. It is also possible that higher healthcare spending would allow countries to purchase a better selection of medicines which may, in turn, lead to better health outcomes or higher spending could increase the availability of essential medicines.
We suspect that barriers within the healthcare system are particularly important for cardiovascular health. Inequity exists within the implementation of cost-effective interventions and the provision of care for cardiovascular disease predominantly in low-income countries where health systems may not be adequately equipped for providing chronic disease care [21]. For example, in Kenya, cardiovascular medicines can only be prescribed by physicians, [22] however it can be difficult for patients to access physicians due to a lack of effective referral networks [23] and a shortage of physicians making it difficult to contend with the disease burden [22]. Therefore, patients may be entering the healthcare system but not receiving proper cardiovascular care.
Other factors, such as quality of care, may impact mortality from cardiovascular disease. A study of 137 low- and middle-income countries found that amenable mortality outcomes were predominantly due to poor quality healthcare (84% of cardiovascular deaths amenable to healthcare), while the remaining 16% was due to non-utilization of healthcare [24]. This study shows that cardiovascular deaths for people entering the healthcare system are predominantly driven by poor quality of care. Therefore quality of care may account for some of the observed differences in amendable mortality and this would attenuate any real relationship between medicine selection and health outcomes.

Strengths and limitations

This was the first study we are aware of to compare NEML medications listings for cardiovascular diseases on a large scale. As a cross-sectional study, it would be inappropriate to draw causal conclusions about a relationship between medicine coverage scores and HAQ scores. Studying these associations over time may help solidify the conclusions drawn in this cross-sectional study. Applying a global medicine coverage score calculation represents a number of challenges. The score does not account for medicines that are therapeutically interchangeable within a class; theoretically, only one medicine in the class needs to be present for treatment, and the others are redundant. However, listing more than one medicine in a class can be beneficial in certain circumstances, for example in the case of drug recalls or shortages. In addition, there are no guidelines for the number of medicines needed in a class for proper coverage so we opted to include any that were listed in the country score. Although there are limitations to creating a medicine coverage score, our approach that was based on total medicines listed on NEMLs, allowed for an overall score that could be compared across many countries. The HAQ Index and GEM database both have their own limitations, which can be found in their respective articles [4, 10].

Conclusions

The number of medicines relevant to cardiovascular disease included in NEMLs is associated with amenable cardiovascular mortality but this association is not present when accounting for country attributes such as national healthcare spending. Country attributes may influence essential medicine listing which can impact health outcomes.

Acknowledgements

Not applicable.

Declarations

Not applicable.
Not applicable.

Competing interests

NP reports grants from the Canadian Institutes of Health Research (CIHR), Ontario SPOR Support Unit, St Michael’s Hospital Foundation, and Canada Research Chairs Program. LS, SF and DM have no competing interests to declare.
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Supplementary Information

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Metadaten
Titel
Associations between essential medicines and health outcomes for cardiovascular disease
verfasst von
Liane Steiner
Shawn Fraser
Darshanand Maraj
Nav Persaud
Publikationsdatum
01.12.2021
Verlag
BioMed Central
Erschienen in
BMC Cardiovascular Disorders / Ausgabe 1/2021
Elektronische ISSN: 1471-2261
DOI
https://doi.org/10.1186/s12872-021-01955-1

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