Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data
Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs...
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| creator | Davies, Justine I Reddiar, Sumithra Krishnamurthy Hirschhorn, Lisa R Ebert, Cara Marcus, Maja-Emilia Seiglie, Jacqueline A Zhumadilov, Zhaxybay Supiyev, Adil Sturua, Lela Silver, Bahendeka K Sibai, Abla M Quesnel-Crooks, Sarah Norov, Bolormaa Mwangi, Joseph K Omar, Omar Mwalim Wong-McClure, Roy Mayige, Mary T Martins, Joao S Lunet, Nuno Labadarios, Demetre Karki, Khem B Kagaruki, Gibson B Jorgensen, Jutta M A Hwalla, Nahla C Houinato, Dismand Houehanou, Corine Guwatudde, David Gurung, Mongal S Bovet, Pascal Bicaba, Brice W Aryal, Krishna K Msaidié, Mohamed Andall-Brereton, Glennis Brian, Garry Stokes, Andrew Vollmer, Sebastian Bärnighausen, Till Atun, Rifat Geldsetzer, Pascal Manne-Goehler, Jennifer Jaacks, Lindsay M |
| description | Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care.
We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with |
| doi_str_mv | 10.1371/journal.pmed.1003268 |
| format | Article |
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We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09-4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02-1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06-1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11-1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01-1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12-2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01-1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09-1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01-1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes.
In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries' preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care.</description><identifier>ISSN: 1549-1676</identifier><identifier>ISSN: 1549-1277</identifier><identifier>EISSN: 1549-1676</identifier><identifier>DOI: 10.1371/journal.pmed.1003268</identifier><identifier>PMID: 33170842</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Blood pressure ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - epidemiology ; Care and treatment ; Clinical outcomes ; Cross-Sectional Studies ; Developing countries ; Developing Countries - statistics & numerical data ; Diabetes ; Diabetes mellitus ; Equity ; Expenditures ; Global Health - statistics & numerical data ; Health aspects ; Health facilities ; Health services ; Humans ; Hypertension ; Income - statistics & numerical data ; Maternal mortality ; Medicine and Health Sciences ; Metformin ; Neonates ; Patients ; Poverty ; Public health ; Quality of Health Care ; Risk Factors ; Social Sciences ; Surveillance ; Surveys ; Surveys and Questionnaires ; Variables</subject><ispartof>PLoS medicine, 2020-11, Vol.17 (11), p.e1003268-e1003268</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Davies et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Davies et al 2020 Davies et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c791t-5a486c7e3a4f439f736752a26b7e3b94149a837d609d9006c930d817d6b51b73</citedby><cites>FETCH-LOGICAL-c791t-5a486c7e3a4f439f736752a26b7e3b94149a837d609d9006c930d817d6b51b73</cites><orcidid>0000-0003-0622-6695 ; 0000-0001-9295-0035 ; 0000-0002-4232-1625 ; 0000-0002-5542-8712 ; 0000-0002-4182-4212 ; 0000-0002-1075-3036 ; 0000-0002-8878-5505 ; 0000-0001-8074-9393 ; 0000-0003-4861-7870 ; 0000-0002-8242-1145 ; 0000-0001-9278-4516 ; 0000-0001-6834-1838 ; 0000-0002-7791-5167 ; 0000-0002-0740-8020 ; 0000-0002-1851-5606 ; 0000-0002-7863-0462 ; 0000-0002-0242-4259 ; 0000-0001-7532-4665 ; 0000-0002-4318-1126 ; 0000-0002-1531-5983 ; 0000-0003-4904-2087 ; 0000-0001-8155-5144 ; 0000-0002-1054-8104 ; 0000-0002-8502-3636 ; 0000-0002-0685-6323 ; 0000-0001-8080-7872 ; 0000-0001-6481-6242 ; 0000-0003-3563-0224 ; 0000-0003-1870-1430</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654799/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654799/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33170842$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Davies, Justine I</creatorcontrib><creatorcontrib>Reddiar, Sumithra Krishnamurthy</creatorcontrib><creatorcontrib>Hirschhorn, Lisa R</creatorcontrib><creatorcontrib>Ebert, Cara</creatorcontrib><creatorcontrib>Marcus, Maja-Emilia</creatorcontrib><creatorcontrib>Seiglie, Jacqueline A</creatorcontrib><creatorcontrib>Zhumadilov, Zhaxybay</creatorcontrib><creatorcontrib>Supiyev, Adil</creatorcontrib><creatorcontrib>Sturua, Lela</creatorcontrib><creatorcontrib>Silver, Bahendeka K</creatorcontrib><creatorcontrib>Sibai, Abla M</creatorcontrib><creatorcontrib>Quesnel-Crooks, Sarah</creatorcontrib><creatorcontrib>Norov, Bolormaa</creatorcontrib><creatorcontrib>Mwangi, Joseph K</creatorcontrib><creatorcontrib>Omar, Omar Mwalim</creatorcontrib><creatorcontrib>Wong-McClure, Roy</creatorcontrib><creatorcontrib>Mayige, Mary T</creatorcontrib><creatorcontrib>Martins, Joao S</creatorcontrib><creatorcontrib>Lunet, Nuno</creatorcontrib><creatorcontrib>Labadarios, Demetre</creatorcontrib><creatorcontrib>Karki, Khem B</creatorcontrib><creatorcontrib>Kagaruki, Gibson B</creatorcontrib><creatorcontrib>Jorgensen, Jutta M A</creatorcontrib><creatorcontrib>Hwalla, Nahla C</creatorcontrib><creatorcontrib>Houinato, Dismand</creatorcontrib><creatorcontrib>Houehanou, Corine</creatorcontrib><creatorcontrib>Guwatudde, David</creatorcontrib><creatorcontrib>Gurung, Mongal S</creatorcontrib><creatorcontrib>Bovet, Pascal</creatorcontrib><creatorcontrib>Bicaba, Brice W</creatorcontrib><creatorcontrib>Aryal, Krishna K</creatorcontrib><creatorcontrib>Msaidié, Mohamed</creatorcontrib><creatorcontrib>Andall-Brereton, Glennis</creatorcontrib><creatorcontrib>Brian, Garry</creatorcontrib><creatorcontrib>Stokes, Andrew</creatorcontrib><creatorcontrib>Vollmer, Sebastian</creatorcontrib><creatorcontrib>Bärnighausen, Till</creatorcontrib><creatorcontrib>Atun, Rifat</creatorcontrib><creatorcontrib>Geldsetzer, Pascal</creatorcontrib><creatorcontrib>Manne-Goehler, Jennifer</creatorcontrib><creatorcontrib>Jaacks, Lindsay M</creatorcontrib><title>Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data</title><title>PLoS medicine</title><addtitle>PLoS Med</addtitle><description>Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care.
We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09-4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02-1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06-1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11-1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01-1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12-2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01-1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09-1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01-1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes.
In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries' preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care.</description><subject>Blood pressure</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Care and treatment</subject><subject>Clinical outcomes</subject><subject>Cross-Sectional Studies</subject><subject>Developing countries</subject><subject>Developing Countries - statistics & numerical data</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Equity</subject><subject>Expenditures</subject><subject>Global Health - statistics & numerical data</subject><subject>Health aspects</subject><subject>Health facilities</subject><subject>Health services</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Income - statistics & numerical data</subject><subject>Maternal mortality</subject><subject>Medicine and Health Sciences</subject><subject>Metformin</subject><subject>Neonates</subject><subject>Patients</subject><subject>Poverty</subject><subject>Public health</subject><subject>Quality of Health Care</subject><subject>Risk Factors</subject><subject>Social Sciences</subject><subject>Surveillance</subject><subject>Surveys</subject><subject>Surveys and 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between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data</title><author>Davies, Justine I ; Reddiar, Sumithra Krishnamurthy ; Hirschhorn, Lisa R ; Ebert, Cara ; Marcus, Maja-Emilia ; Seiglie, Jacqueline A ; Zhumadilov, Zhaxybay ; Supiyev, Adil ; Sturua, Lela ; Silver, Bahendeka K ; Sibai, Abla M ; Quesnel-Crooks, Sarah ; Norov, Bolormaa ; Mwangi, Joseph K ; Omar, Omar Mwalim ; Wong-McClure, Roy ; Mayige, Mary T ; Martins, Joao S ; Lunet, Nuno ; Labadarios, Demetre ; Karki, Khem B ; Kagaruki, Gibson B ; Jorgensen, Jutta M A ; Hwalla, Nahla C ; Houinato, Dismand ; Houehanou, Corine ; Guwatudde, David ; Gurung, Mongal S ; Bovet, Pascal ; Bicaba, Brice W ; Aryal, Krishna K ; Msaidié, Mohamed ; Andall-Brereton, Glennis ; Brian, Garry ; Stokes, Andrew ; Vollmer, Sebastian ; Bärnighausen, Till ; Atun, Rifat ; Geldsetzer, Pascal ; Manne-Goehler, Jennifer ; Jaacks, Lindsay M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c791t-5a486c7e3a4f439f736752a26b7e3b94149a837d609d9006c930d817d6b51b73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Blood pressure</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular Diseases - epidemiology</topic><topic>Care and treatment</topic><topic>Clinical outcomes</topic><topic>Cross-Sectional Studies</topic><topic>Developing countries</topic><topic>Developing Countries - statistics & numerical data</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Equity</topic><topic>Expenditures</topic><topic>Global Health - statistics & numerical data</topic><topic>Health aspects</topic><topic>Health facilities</topic><topic>Health services</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Income - statistics & numerical data</topic><topic>Maternal mortality</topic><topic>Medicine and Health Sciences</topic><topic>Metformin</topic><topic>Neonates</topic><topic>Patients</topic><topic>Poverty</topic><topic>Public health</topic><topic>Quality of Health Care</topic><topic>Risk Factors</topic><topic>Social Sciences</topic><topic>Surveillance</topic><topic>Surveys</topic><topic>Surveys and Questionnaires</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Davies, Justine I</creatorcontrib><creatorcontrib>Reddiar, Sumithra Krishnamurthy</creatorcontrib><creatorcontrib>Hirschhorn, Lisa R</creatorcontrib><creatorcontrib>Ebert, Cara</creatorcontrib><creatorcontrib>Marcus, Maja-Emilia</creatorcontrib><creatorcontrib>Seiglie, Jacqueline A</creatorcontrib><creatorcontrib>Zhumadilov, Zhaxybay</creatorcontrib><creatorcontrib>Supiyev, Adil</creatorcontrib><creatorcontrib>Sturua, Lela</creatorcontrib><creatorcontrib>Silver, Bahendeka K</creatorcontrib><creatorcontrib>Sibai, Abla M</creatorcontrib><creatorcontrib>Quesnel-Crooks, Sarah</creatorcontrib><creatorcontrib>Norov, Bolormaa</creatorcontrib><creatorcontrib>Mwangi, Joseph K</creatorcontrib><creatorcontrib>Omar, Omar Mwalim</creatorcontrib><creatorcontrib>Wong-McClure, Roy</creatorcontrib><creatorcontrib>Mayige, Mary T</creatorcontrib><creatorcontrib>Martins, Joao S</creatorcontrib><creatorcontrib>Lunet, Nuno</creatorcontrib><creatorcontrib>Labadarios, Demetre</creatorcontrib><creatorcontrib>Karki, Khem B</creatorcontrib><creatorcontrib>Kagaruki, Gibson B</creatorcontrib><creatorcontrib>Jorgensen, Jutta M A</creatorcontrib><creatorcontrib>Hwalla, Nahla C</creatorcontrib><creatorcontrib>Houinato, Dismand</creatorcontrib><creatorcontrib>Houehanou, Corine</creatorcontrib><creatorcontrib>Guwatudde, David</creatorcontrib><creatorcontrib>Gurung, Mongal S</creatorcontrib><creatorcontrib>Bovet, Pascal</creatorcontrib><creatorcontrib>Bicaba, Brice W</creatorcontrib><creatorcontrib>Aryal, Krishna K</creatorcontrib><creatorcontrib>Msaidié, Mohamed</creatorcontrib><creatorcontrib>Andall-Brereton, Glennis</creatorcontrib><creatorcontrib>Brian, Garry</creatorcontrib><creatorcontrib>Stokes, Andrew</creatorcontrib><creatorcontrib>Vollmer, Sebastian</creatorcontrib><creatorcontrib>Bärnighausen, Till</creatorcontrib><creatorcontrib>Atun, Rifat</creatorcontrib><creatorcontrib>Geldsetzer, Pascal</creatorcontrib><creatorcontrib>Manne-Goehler, Jennifer</creatorcontrib><creatorcontrib>Jaacks, Lindsay M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central 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Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Medicine</collection><jtitle>PLoS medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Davies, Justine I</au><au>Reddiar, Sumithra Krishnamurthy</au><au>Hirschhorn, Lisa R</au><au>Ebert, Cara</au><au>Marcus, Maja-Emilia</au><au>Seiglie, Jacqueline A</au><au>Zhumadilov, Zhaxybay</au><au>Supiyev, Adil</au><au>Sturua, Lela</au><au>Silver, Bahendeka K</au><au>Sibai, Abla M</au><au>Quesnel-Crooks, Sarah</au><au>Norov, Bolormaa</au><au>Mwangi, Joseph K</au><au>Omar, Omar Mwalim</au><au>Wong-McClure, Roy</au><au>Mayige, Mary T</au><au>Martins, Joao S</au><au>Lunet, Nuno</au><au>Labadarios, Demetre</au><au>Karki, Khem B</au><au>Kagaruki, Gibson B</au><au>Jorgensen, Jutta M A</au><au>Hwalla, Nahla C</au><au>Houinato, Dismand</au><au>Houehanou, Corine</au><au>Guwatudde, David</au><au>Gurung, Mongal S</au><au>Bovet, Pascal</au><au>Bicaba, Brice W</au><au>Aryal, Krishna K</au><au>Msaidié, Mohamed</au><au>Andall-Brereton, Glennis</au><au>Brian, Garry</au><au>Stokes, Andrew</au><au>Vollmer, Sebastian</au><au>Bärnighausen, Till</au><au>Atun, Rifat</au><au>Geldsetzer, Pascal</au><au>Manne-Goehler, Jennifer</au><au>Jaacks, Lindsay M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data</atitle><jtitle>PLoS medicine</jtitle><addtitle>PLoS Med</addtitle><date>2020-11-10</date><risdate>2020</risdate><volume>17</volume><issue>11</issue><spage>e1003268</spage><epage>e1003268</epage><pages>e1003268-e1003268</pages><issn>1549-1676</issn><issn>1549-1277</issn><eissn>1549-1676</eissn><abstract>Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care.
We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09-4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02-1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06-1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11-1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01-1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12-2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01-1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09-1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01-1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes.
In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries' preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33170842</pmid><doi>10.1371/journal.pmed.1003268</doi><orcidid>https://orcid.org/0000-0003-0622-6695</orcidid><orcidid>https://orcid.org/0000-0001-9295-0035</orcidid><orcidid>https://orcid.org/0000-0002-4232-1625</orcidid><orcidid>https://orcid.org/0000-0002-5542-8712</orcidid><orcidid>https://orcid.org/0000-0002-4182-4212</orcidid><orcidid>https://orcid.org/0000-0002-1075-3036</orcidid><orcidid>https://orcid.org/0000-0002-8878-5505</orcidid><orcidid>https://orcid.org/0000-0001-8074-9393</orcidid><orcidid>https://orcid.org/0000-0003-4861-7870</orcidid><orcidid>https://orcid.org/0000-0002-8242-1145</orcidid><orcidid>https://orcid.org/0000-0001-9278-4516</orcidid><orcidid>https://orcid.org/0000-0001-6834-1838</orcidid><orcidid>https://orcid.org/0000-0002-7791-5167</orcidid><orcidid>https://orcid.org/0000-0002-0740-8020</orcidid><orcidid>https://orcid.org/0000-0002-1851-5606</orcidid><orcidid>https://orcid.org/0000-0002-7863-0462</orcidid><orcidid>https://orcid.org/0000-0002-0242-4259</orcidid><orcidid>https://orcid.org/0000-0001-7532-4665</orcidid><orcidid>https://orcid.org/0000-0002-4318-1126</orcidid><orcidid>https://orcid.org/0000-0002-1531-5983</orcidid><orcidid>https://orcid.org/0000-0003-4904-2087</orcidid><orcidid>https://orcid.org/0000-0001-8155-5144</orcidid><orcidid>https://orcid.org/0000-0002-1054-8104</orcidid><orcidid>https://orcid.org/0000-0002-8502-3636</orcidid><orcidid>https://orcid.org/0000-0002-0685-6323</orcidid><orcidid>https://orcid.org/0000-0001-8080-7872</orcidid><orcidid>https://orcid.org/0000-0001-6481-6242</orcidid><orcidid>https://orcid.org/0000-0003-3563-0224</orcidid><orcidid>https://orcid.org/0000-0003-1870-1430</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1549-1676 |
| ispartof | PLoS medicine, 2020-11, Vol.17 (11), p.e1003268-e1003268 |
| issn | 1549-1676 1549-1277 1549-1676 |
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| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Blood pressure Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - epidemiology Care and treatment Clinical outcomes Cross-Sectional Studies Developing countries Developing Countries - statistics & numerical data Diabetes Diabetes mellitus Equity Expenditures Global Health - statistics & numerical data Health aspects Health facilities Health services Humans Hypertension Income - statistics & numerical data Maternal mortality Medicine and Health Sciences Metformin Neonates Patients Poverty Public health Quality of Health Care Risk Factors Social Sciences Surveillance Surveys Surveys and Questionnaires Variables |
| title | Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data |
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