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.

BACKGROUND: 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. METHODS AND
FINDINGS: 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.
CONCLUSION: 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.

Introduction

Cardiovascular diseases are one of the most common causes of death and disability in low- and middle-income countries (LMICs). Reducing premature mortality from these conditions is a key aim of the Sustainable Development Goals (SDG 3.4) [1]. To ensure that this goal is met requires identifying and adequately treating people who have conditions like diabetes and hypertension. Monitoring country progress towards meeting this goal is essential to ensuring commensurate investment in health systems to manage these conditions.

Globally, hundreds of indicators are collected or collated by governments, the World Health Organization (WHO), World Bank, United States Agency for International Development (USAID), and other multilateral and bilateral governmental and nongovernmental organisations, in order to evaluate healthcare capacity and performance. These indicators allow assessment of temporal trends, comparisons between and within countries, identification of aspects of healthcare requiring targeted investment and improvement, and signal areas in need of further research. Different organisations often request distinct indicators and have numerous and varying reporting requirements. This results in a substantial burden of collecting and reporting on healthcare providers and managers, potentially detracting time and resources from the delivery of quality clinical care to patients [2,3]. There have been efforts to rationalise the number of indicators collected, for example, with the list of 100 Core Global Health Indicators first being produced in 2015 [4]. However, thus far, adoption of such rationalised lists by the global health and donor community has been slow. Also, many of these indicators are proxies for patient outcomes that are based on conceptual frameworks, such as the Primary Healthcare Performance Initiative’s Conceptual Framework of critical elements of a strong primary healthcare system [5]. These are often based upon the Donabedian ‘structures’ and ‘processes’ needed to deliver care. Although these outcomes may be relatively easy to measure, the utility of some of them to reflect quality clinical care outcomes (the Donabedian ‘outcomes’) has been called into question, especially as many are based largely on expert opinion or feasibility of collection [2,6,7]. Although for some indicators, clinical outcomes are clear (for example, maternal mortality rate), for others, the relevance to outcomes may be remote, even if the links between metric and outcome seem superficially clear (for example, the presence of a policy or plan).

There are several indicators used to measure countries’ preparedness for reducing the burden of noncommunicable diseases (NCDs)—including cardiovascular diseases and their risk factors (cardiovascular disease risk factors [CVDRFs])—but few that are routinely collected measure whether patient outcomes indicative of quality clinical care are achieved (for example, disease diagnosis, treatment, or control). Prominent indicators recommended to reflect progress to achievement of NCD control include those collected by WHO in the National Capacity for the Prevention and Control of Noncommunicable Diseases surveys (hereafter termed ‘NCD readiness’ indicators) [8]. Additionally, there are other indicators collected by global agencies that could affect achievement of relevant patient clinical care outcomes. These range from high-level indicators reflecting health service readiness (for example, number of service providers available in any particular country) or those that have been used to reflect overall health service performance (for example, neonatal mortality rate [NMR]), to health facility readiness measures (for example, Service Availability and Readiness Assessment [SARA] and Service Provision Assessment [SPA]) [9,10]. In addition to regularly collected indicators, individual patient–level factors (e.g., demographic and socioeconomic characteristics) and markers of overall country development (e.g., gross domestic product [GDP]) are also known to impact on achievement of patient clinical care outcomes [11-14].

For the CVDRFs of hypertension and diabetes, disease burden has been estimated using modelling [15], and previous studies have associated limited health service performance or readiness measures with modelled prevalence [14]. However, author knowledge and review of the literature showed that achievement of quality clinical care to manage prevalent disease across multiple countries has not previously been associated with indicators commonly used to assess performance of the health services or NCD readiness indicators. Empiric individual-patient data for hypertension and diabetes [11-13,16] are regularly collected using the WHO Stepwise Approach to Surveillance (WHO STEPS) [17,18] and similar surveys. We have recently used individual participant data from these surveys to create cascades of care which assess whether individuals identified as having hypertension or diabetes have been diagnosed, treated, or are controlled to target [11,12].

In order to inform the evidence-based collection and use of country preparedness indicators for CVDRFs, we aimed to assess whether the NCD readiness indicators are proxies of, and thus associated with, patient-level quality clinical care outcomes as determined by progress through cascades of care (to assess whether conditions are diagnosed, treated [both proximate to patient ‘process’ measures], or controlled [an ‘outcome measure’] for individual patients). We also aimed to assess whether or not these indicators are stronger predictors of achieving success along the cascade of care than other indicators likely to effect health service provision of care for CVDRFs.

Methods

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist). None of the analyses presented in this paper were prespecified in a protocol. The decisions to exclude the indicators, (1) operational multisectoral national policy for NCDs and shared risk factors and (2) NCD surveillance and monitoring system in place to enable reporting against the nine global NCD targets, and to evaluate the correlation of country wealth and health expenditure were made during data analysis.

Indicator measures

We conceptualised a high-level working framework based upon the author’s knowledge of health systems (in particular, informed by Atun’s 2013 framework for analysis of Turkey’s health system [19]), available rigorously collected data, and CVDRF care. We then collated numerous variables collected by health and development agencies and mapped to this framework those that authors agreed were most relevant. Where there were multiple variables within these data sources of potential relevance to the study question, those selected were chosen (on discussion between JID, SKR, LMJ, LRH, PG, and JMG) based on relevance to the question, avoidance of duplication, and availability within 2 years of the data used to construct the care cascade. Given that care delivery for CVDRFs has not changed rapidly in many LMICs, it was decided that the 2-year threshold allowed reasonable temporal comparability between data sources.

Our framework was hierarchical. We started with the country context in which the health system sits, hypothesising that a strong health system to enable care for complex chronic conditions, like CVDRFs, would be more likely in countries with a higher Human Development Index (HDI) or those with a higher GDP (markers of country development). In this hierarchical framework, markers of NCD readiness are high-level factors as they reflect countries’ commitment to facilitate quality clinical outcomes for CVDRF. At the next level were high-level factors focussed on financial spending on health and equity (indicators of health service finance and equity). More proximate to the patient are indicators of health service performance (whether a health service is delivering care) or readiness (whether it is able to deliver care). Health facility readiness indicators reflect facility readiness to provide specific care for CVDRFs. Lastly are the patient-proximate indicators of individual-level patient characteristics.

Categorisation of variables, sources from which these have been extracted, and number of countries for which information on each variable was available are summarised in (and S1–S4 Texts and S1 Table).

GDP per capita (current $US) and HDI were selected as measures of overall country development. For NCD readiness indicators, we selected responses to NCD-specific health service policies of relevance for management of CVDRFs. General health service finance and equity markers were chosen to reflect country healthcare expenditure (current health expenditure as a percentage of total GDP) and the equity of funding for health (domestic private health expenditure as percentage of current health expenditure or out-of-pocket [OOP] expenditure as percentage of current health expenditure). Other health service performance or readiness indicators were chosen because they had either been previously shown to be associated with prevalent NCDs (hospital beds or human resources for health) or were targets of the Millennium Development Goals (MDGs), which may reflect performance of the health service, rather than siloed care (i.e., NMR was chosen over maternal mortality ratio [MMR] because improving NMR requires that care is provided to both the mother and neonate) [14,20]. Health facility readiness indicators to give additional granular information on care specific to diabetes were available for a subset of countries (n = 6); we considered the use of these in our analysis as exploratory.

HDI was extracted from the UN Human Development Reports [21]. GDP per capita (current $US); current health expenditure (as percentage of GDP); domestic private health expenditure (as percentage of current health expenditure); OOP expenditure (as percentage of current health expenditure); hospital beds, nurses and midwives, and physicians per 1,000 people; and NMR per 1,000 live births were extracted from the World Development Indicators [22]. Indicators were extracted from these sources for the year corresponding to the start of the survey from which cascades were derived (and within 2 years when data from the same year were not available). Note, for OOP expenditure, ‘of current’ is how the metric is reported by the source; for each country, data were extracted for the year in which the cascade survey was done; thus, ‘current’ refers to that year.

NCD readiness indicators were extracted from WHO National Capacity for the Prevention and Control of Noncommunicable Diseases surveys [8]. These were categorised as ‘yes’, positive response; ‘no’ (referent category), negative report, do not know, or question asked but no response; and ‘country did not respond to survey’. Countries with no survey available within 2 years or in which the question was not asked on the survey were set to missing.

Granular health facility readiness data pertaining to diabetes (and reflecting availability of structures—for example, guidelines, equipment, staff, etc—necessary to provide care at facilities) were extracted from national summaries of reports from WHO SARA and, if SARA surveys were not available, from SPA surveys. These data were extracted from reports from within 2 years of the survey from which cascades were derived being done. For countries with both survey types available, we chose SARA rather than SPA, given that the majority of surveys used were SARA.

Finally, individual-level patient characteristics (age, sex, and education) were extracted from surveys from which the quality clinical care outcomes were derived [11,12].

Patient-level quality clinical care outcome measures

Using individual-patient data from rigorously conducted nationally representative surveys, we evaluated three care-cascade outcomes which reflect quality clinical care provision for the CVDRFs of hypertension and diabetes, thus making six clinical care variables in total. These were (1) knowledge of condition (‘diagnosed’), (2) knowledge of condition and being on treatment (‘treatment’), and (3) on treatment and condition controlled to target (‘controlled’). We conducted separate analyses for hypertension and diabetes. Methods for deriving progress through the care cascade for hypertension and diabetes have been described in full elsewhere [11,12].

In short, for hypertension, we pooled nationally representative, individual-level population-based data collected between July 2005 and November 2016 from 43 LMICs. Half of the surveys (n = 21) were WHO STEPS surveys, and the remaining 22 were Demographic and Health Surveys (DHS), Family Life Surveys, WHO Study on global AGEing and adult health (SAGE) surveys, or other national health surveys. Hypertension was defined as systolic blood pressure (BP) ≥140 mm Hg or diastolic BP ≥90 mm Hg or reporting use of medication for hypertension. We computed the percentage of all those with hypertension who had previously been diagnosed with hypertension by a healthcare provider (‘diagnosed’); those with hypertension who had been diagnosed and were taking antihypertensive medications (‘treated’); and those with hypertension who had been diagnosed, had been treated, and had a systolic BP <140 mm Hg and a diastolic BP <90 mm Hg (‘controlled’).

For diabetes, we pooled nationally representative, individual-level population-based data collected between August 2008 and November 2016 in 28 LMICs. Diabetes was defined as fasting plasma glucose ≥7.0 mmol/l (126 mg/dl), random plasma glucose ≥11.1 mmol/l (200 mg/dl), HbA1c ≥6.5%, or reporting taking medication for diabetes. Eighteen of the surveys were WHO STEPS surveys, and the remaining 10 were from DHS, Family Life Surveys, or other national health surveys. Analogous to the hypertension cascade, we computed the percentage of all those with diabetes who had previously been diagnosed with diabetes by a healthcare provider (‘diagnosed’); those with diabetes who had been diagnosed and were taking antidiabetic medications (‘treated’); and those with diabetes who had been diagnosed, had been treated, and had a plasma glucose <10.1 mmol/l or, if available (n = 4 surveys), HbA1c <8.0% (‘controlled’).

Ethics

This study received a written determination of ‘not human subjects research’ by the institutional review board of the Harvard T.H. Chan School of Public Health on 9 May 2018 (IRB16-1915). The investigators on this study had only access to deidentified data and never had any direct contact with any of the participants in the individual country studies that provided the data.

Statistical analysis

Our aim was to assess if NCD readiness or other health service performance or readiness indicators were useful proxies for quality clinical care and to describe whether they were stronger indicators of quality clinical care than general country development or health system finance and equity indicators. We did not aim to assess causation. We first summarised the country-level indicators for the 44 countries included in this sample using descriptive statistics. To determine the association of indicators with each of the six clinical care outcomes (condition diagnosed, treated, or controlled for both diabetes and hypertension), we used maximum likelihood estimation of mixed-effects logistic regression models with a binary outcome (indicating whether or not the individual achieved the clinical care outcome) and robust variance estimation (S5 Text). The sample for each regression was all individuals with diabetes or, for the three clinical care outcomes specific to hypertension, all individuals with hypertension. All models were adjusted for country, specified as a random effect, and individual-level predictors, specified as fixed effects (age, sex, and education). These individual-level predictors have previously been shown to be predictive of progress through the cascades of care [11,12]. Because only two countries with the hypertension-related outcomes and one country with the diabetes-related outcomes answered ‘yes’ to (1) operational multisectoral national policy for NCDs and shared risk factors and (2) NCD surveillance and monitoring system in place to enable reporting against the nine global NCD targets, we excluded these two indicators from the analyses for that outcome. Continuous indicators (GDP per capita [current $US], HDI, current health expenditure [percentage of GDP], domestic private health expenditure [percentage of current health expenditure], OOP expenditure [percentage of current health expenditure], NMR [per 1,000 live births], hospital beds [per 1,000 people], nurses and midwives [per 1,000 people], and physicians [per 1,000 people]) were rescaled to have a mean of zero and a standard deviation (SD) of one to ease interpretation and comparisons across variables. Regression analyses took into account sample weights, which were rescaled by dividing an individual's sample weight by the sum of weights of respondents in the estimation sample such that all sample weights within the sample summed to 1 and thus all countries contributed equally to the overall estimates. All analyses were done in Stata v. 14.2 (StataCorp, College Station, TX, USA).

Results

A total of 44 countries with data available from between 2005 and 2016 were included in the analysis; 43 countries had data on cascade steps for hypertension and country preparedness metrics and 28 countries had data available for diabetes (1 country had data on diabetes, but not hypertension). (and S1 Table) shows the availability of each metric. Country-level indicators assessed in this analysis are summarised in . Individual-level characteristics of participants are summarised in

Of note, GDP per capita and HDI were low (mean ± SD GDP per capita of $US 4,335 ± $US 3,681 and HDI 0.65 ± 0.12), reflecting the LMIC status of the countries included. Information on NCD readiness indicators of relevance to CVDRFs were available from 39 countries (). shows individual countries who responded positively (‘yes’) to survey questions on NCD readiness. Two-thirds of countries (66.7%) had an operational NCD unit/branch/department in their Ministry of Health, or equivalent, and 74.4% had at least one operational policy for CVD, diabetes, physical activity, healthy diets, tobacco, or alcohol. The most common operational policy was for tobacco control, whereas the least common was for alcohol. About two-thirds of countries (65.0%) had some funding for surveillance, prevention, or treatment. Current health expenditure was a mean ± SD of 5.9% ± 2.0% of GDP; private health expenditure was 43.2% ± 19.0% of current, and OOP expenditure was 37.0% ± 18.9% of current.

Progress through cascades of care for both hypertension and diabetes are shown in : 40.7% (95% CI 37.9%–43.5%) were diagnosed, 27.6% (95% CI 25.7%–29.7%) had received treatment, and 11.1% (95% CI 10.2%–12.0%) were controlled. Corresponding figures for diabetes were 43.4% (95% CI 39.5%–47.5%) diagnosed, 34.7% (95% CI 32.2%–37.3%) treated, and 21.1% (95% CI 19.6%–22.5%) controlled.

Association between indicators and hypertension care

Individuals with hypertension living in countries with a higher GDP per capita or higher HDI were significantly more likely to be diagnosed, treated, and achieve control (); GDP per capita was the strongest predictor of hypertension care including diagnosis (odds ratio [OR], 1.58 [95% CI 1.27–1.96], p < 0.001), treatment (OR, 1.49 [95% CI 1.15–1.93], p = 0.002), and control (OR, 1.57 [95% CI 1.22–2.02], p = 0.001).

In general, NCD readiness indicators were not associated with achieving cascade steps. However, individuals with hypertension living in countries with evidence-based national guidelines/protocols/standards for the management of major NCDs through a primary care approach were significantly more likely to have been diagnosed (OR, 1.86 [95% CI 1.08–3.21], p = 0.03). Having funding for NCD surveillance, monitoring, and evaluation; treatment and control; and/or prevention and health promotion was significantly and strongly associated with achieving hypertension control (OR, 2.26 [95% CI 1.09–4.69], p = 0.03). The association between having funding available for NCD surveillance, monitoring, and evaluation; treatment and control; and/or prevention and health promotion and being treated for hypertension did not achieve significance (OR, 1.41 [95% CI 0.83–2.41], p = 0.21).

The indicators reflecting health service finances (current health expenditure, private health expenditure, or OOP expenditures) were not significant predictors of any of the stages of the hypertension care cascade. But general health service performance or readiness indicators did reflect achievement of some cascade steps. Individuals living in countries with lower NMRs were more likely to be diagnosed (OR, 0.73 [95% CI 0.61–0.88], p = 0.001). Those with a greater number of hospital beds, nurses and midwives, and physicians were more likely to be diagnosed and treated (all p < 0.05; OR and 95% CI presented in ). These variables were less predictive of control, with only the number of physicians being a significant predictor of hypertension control in this sample (OR, 1.12 [95% CI 1.01–1.23], p = 0.03).

Association between indicators and diabetes care

In contrast to what was seen for hypertension care, GDP per capita and HDI were not consistently strong predictors of the stages of the diabetes cascade of care (); although they were both significantly associated with being diagnosed with diabetes (for GDP per capita, OR, 1.70 [95% CI 1.12–2.59], p = 0.01; and for HDI, OR, 1.21 [95% CI 1.01–1.44], p = 0.04).

Two results ran counter to our hypothesis; individuals with diabetes living in countries with an operational NCD unit/branch/department in Ministry of Health were significantly less likely to have been diagnosed (OR, 0.36 [95% CI 0.18–0.72], p = 0.004), and those living in countries that reported having evidence-based national guidelines/protocols/standards for the management of major NCDs through a primary care approach were significantly less likely to have been treated (OR, 0.34 [95% CI 0.24–0.47], p < 0.001). However, none of the other predictors were significant for any stage of the diabetes cascade. The four countries that responded that they had guidelines were Bhutan, Indonesia, Romania, and Seychelles, whereas the countries that performed highest in achieving the cascade steps, such as Costa Rica, did not report having such guidelines (). Of the health service performance or readiness indicators, only the number of physicians per 1,000 people was associated with diabetes diagnosis (OR, 1.28 [95% CI 1.09–1.51], p = 0.003).

Not responding to the NCD-specific policy questionnaires was not associated with worse performance in achieving any step of the diabetes or hypertension cascade.

In the exploratory analysis considering facility readiness, data were available for six countries: Burkina Faso, Kenya, Nepal, Togo, Tanzania, and Uganda. Tanzania had data from both surveys and SARA was used; Nepal only had SPA data. There were no significant positive associations between any indicators of health facility readiness and achieving diabetes care-cascade steps, except having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01–1.81], p = 0.04). Moreover, there were some indicators that were significantly negatively associated with achieving cascade steps (); for example, offering diabetes diagnosis and management services was associated with a lower OR of being diagnosed (OR, 0.56 [95% CI 0.39–0.82], p = 0.003) and treated (OR, 0.63 [95% CI 0.42–0.93], p = 0.02) as compared to not offering these service.

Post hoc analyses done to explore relationships between country wealth and health expenditure showed GDP was significantly associated with OOP expenditure (Pearson correlation coefficient, −0.34, p = 0.02) but not significantly associated with lower private healthcare expenditure (Pearson correlation coefficient, −0.26, p = 0.09).

Discussion

We have found that few of the indicators widely collected as proxies of NCD or health service preparedness assessed in this study were associated with progress through the CVDRF care cascade. This was especially the case for people with diabetes, in whom some of the indicators of NCD readiness and facility readiness to provide care were associated with significantly worse achievement of our markers of quality clinical care. Although these findings are superficially unintuitive, they are likely reflective of the complexity of the health service elements and their interactions that are required to deliver good-quality clinical care [23-26]. In fact, on considering the multifaceted requirements to diagnose, manage, and control individuals with chronic conditions such as diabetes and hypertension [27], it is unsurprising that these indicators were not useful to assess good-quality clinical care outcomes.

Given their ease of assessment, NCD readiness and health service performance or readiness indicators have been widely accepted proxies for quality clinical care, and, to our knowledge, very little, if any, previous research has been done to compare country preparedness and quality clinical care outcomes for CVDRFs. However, evidence from the field of maternal and newborn health is consistent with our overall finding that proxies are poor markers of the provision of quality clinical care [2,28].

Although our results suggest that health service indicators should not be used in isolation to indicate quality clinical care for management of diabetes or hypertension in LMICs and that none of these indicators were very strong predictors of outcomes, even when significance was achieved, some of our results are worthy of further exploration. We found that GDP per capita was strongly associated with achieving all hypertension care-cascade steps. This is consistent with long-standing evidence of the association between GDP and health [29]. This association was initially thought to be driven by countries with higher GDP investing more in health, but the bidirectionality of this association is also recognised, with healthier people more likely to be able to contribute to their country’s economy. It is likely, in the countries under study, that investing in health stems from a higher GDP. Indeed, our post hoc analysis of this sample of countries showed a reasonably strong association between higher GDP and lower OOP expenditures on health, although not with higher GDP and lower private healthcare expenditure. These findings indicate that access to care might be more equitable in countries with a higher GDP. Given the positive association between GDP and quality clinical care outcomes, it is not surprising that countries’ HDI—which is reflective of economic wealth in addition to life expectancy and markers of education—was also consistently associated with achievement of cascade steps for hypertension. Interestingly, however, there was no significant association between GDP or HDI and achieving cascade steps for diabetes care.

Average current health expenditure across included countries was 6.0% of GDP (range: 2.3%–10.5%). Current health expenditure (percentage of GDP) was chosen to reflect the total amount spent on health (from government [domestic government revenue], OOP expenditure, insurance, and development assistance), as we were interested in overall funding for health; examining care outcomes by source of funding for health was not part of this analysis. Chatham House [30] recommends that domestic government expenditure on health alone should amount to 5% of GDP. That our metric of total expenditure on health was only marginally above this target is consistent with low overall expenditures on health in the countries included in this study. On average, health service finance markers reflected a lack of equity of health services in the countries studied; i.e., OOP spending and private health expenditure were high. To put this in context, in countries where health service access is considered to be reasonably equitable, for example, Germany and the United Kingdom, OOP expenditure and private health expenditure as percentage of current is much lower than in our included countries. In 2016, Germany’s private health expenditure as percentage of current was 15.33 and the UK’s was 19.76; OOP expenditure in these countries as percentage of current was 12.41 and 15.12, respectively [31]. Nonetheless, in our study, none of the indicators of health financing were associated with performance for hypertension or diabetes. It might be expected that necessity to pay for health services, reflected in increasing OOP expenditure on health or private healthcare, would limit access to care and thus be associated with worse care-cascade performance. That this expected negative association was not seen might be because it is those who are wealthier who suffer from CVDRF and they can perhaps also afford to pay for their management [11-13]. However, this hypothesis requires further study, especially considering that even the relatively wealthy in many LMICs cannot afford to pay for treatment.

Interestingly, health service performance or readiness indicators—NMR, numbers of physicians, numbers of nurses and midwives—showed a reasonably consistent association with achievement of cascade steps for hypertension, especially being diagnosed and on treatment. Others have associated number of hospital beds, physicians, or skilled birth attendants with overall NCD prevalence and estimated that mortality from NCDs is likely to be largest in countries that are least prepared in these—and other readiness—domains [14]. Our results are thus in alignment with these previous findings, although in our study, the association between number of hospital beds and outcomes was less strong than with other health service indicators. This may be reflective of the outpatient-centred delivery of hypertension care. It is possible that these measures are better associated with clinical care outcomes, as they are more proximate to patient care delivery than the NCD readiness indicators; however, that associations were not consistent suggests that further work needs to be done on the utility of these indicators before accepting them as truly reflective of health service functioning. Physicians per 1,000 population (which was a significant predictor of being diagnosed for both hypertension and diabetes) may be the most promising health service metric for further investigation.

Most countries that responded to the NCD readiness surveys reported that they had an operational NCD unit/branch or department within the Ministry of Health or equivalent, or had at least one operational multisectoral national policy, strategy, or action plan that integrates several NCDs and shared risk factors. However, only half of the countries had any funding for surveillance, prevention, or treatment. Thus, it is perhaps not surprising that there was no association between achieving cascade steps for either hypertension or diabetes and having an operational NCD unit or operational policies, if there was no funding to actualise the policy or the plans of the unit. For hypertension, there were some other expected and positive associations between NCD readiness indicators and effective clinical care outcomes; however, relationships that did exist were weak and not consistent across indicators or cascade steps. Individuals with hypertension living in countries with evidence-based national guidelines/protocols/standards for the management of major NCDs through a primary care approach were significantly more likely to have been diagnosed, but not treated or controlled, suggesting that guidelines are not enough for transit through the whole care cascade and more investment is needed in other elements of the health services to ensure that those who are recognised with disease have guideline-based care to ensure treatment and control. Indeed, in the exploratory analysis for diabetes, facility availability of medications—which requires most of the WHO health service building blocks to be in place—was positively associated with care-cascade outcomes, although this association was not statistically significant.

A number of countries were sent the NCD readiness questionnaire to complete but did not respond to the request. Interestingly, there seemed to be a positive association between not returning the survey (‘no response to the survey’) and some care-cascade outcomes, especially for diabetes. The reasons behind this finding require further exploration. Additionally, for diabetes, there were a number of other health service indicators for which apparent success in achievement was associated with worse-quality clinical care outcomes. This study was not planned to explore causation or the reasons for these negative associations—our plan was to see if any positive associations existed. Further exploration of these findings goes beyond this study’s remit.

This study has several limitations. The survey data that we used to complete the care cascades are epidemiology studies, which rely on field data rather than formal clinical diagnoses of hypertension or diabetes. Formal clinical diagnoses require more detailed investigation. For example, at least two elevated BP recordings on separate, consecutive occasions are required to meet a diagnosis of hypertension, and definitive diagnosis of diabetes requires oral glucose tolerance testing. In large-scale epidemiology surveys, these methods are not feasible, and although the survey’s results are accepted in the literature as reliable estimates, we recognise that these may be over- or underestimates [11-13,16,32]. We also used self-report of individuals’ diagnoses or treatment; unfortunately, healthcare records in most LMICs included in this analysis are not well maintained or reliable; thus, self-report is currently the best available method of capturing these variables. Data were not available for care cascades and health service indicators for all LMICs, and for some analyses, sample sizes were small. That said, our country sample size was larger than others who have looked at similar associations between health services and outcomes in the field of maternal health—and with whom our findings are similar. Changes in the NCD readiness indicators [8] over time and the inconsistent availability of survey results within 2 years of collection of care-cascade data meant that not all countries in our study had data. Nevertheless, data were available for many of the questions for most countries. We aggregated funding availability into a single metric, in order to simplify the interpretation, and have not found associations between a specific funding stream and improved outcomes at care-cascade steps; assessing whether targeted funding associates with improved outcomes requires further investigation. We used data from both SARA and SPA for the exploratory analysis of health facility readiness and cascade outcomes. We preferred SARA over SPA, as more countries had SARA data available; we only used SPA data for one country. We acknowledge that the data may not be completely comparable from these two survey methodologies; however, the questions that we used in our comparisons were very similar. Additionally, SARA and SPA surveys do not capture information from retail pharmacies or other places of care delivery outside of the formal sector; these providers are often used by patients with CVDRFs, but unfortunately, our data on medications availability did not cover them. Our aggregation of individual-patient clinical outcomes and SARA or SPA data at a national level may mask some positive associations between these variables which may be seen if these were available to be analysed at a local level, but data were not available for that level of study. We only included countries who had data on indicators within 2 years of the care-cascade data being collected. This was done to ensure data were contemporaneous; however, we cannot exclude temporal confounding. Our analyses did not allow estimations of causation, and we cannot account for the effects of nonmeasured confounders on these results. Although we have found that health service indicators do not add predictive value to achieving care-cascade steps above individual participant–level factors, it may be that countries who had achieved all indicators, a larger number of indicators, or a certain combination of indicators better achieved care-cascade steps than others. Lastly, we have only assessed one of the requirements for quality care (i.e., that it is effective); our data do not allow us to assess whether it is also safe, timely, efficient, equitable, and people-centred [33].

The implications for policy are that the totality of evidence suggests that although commonly used health service assessment tools contain some of the elements required to achieve quality clinical care, positive responses to these elements may not actually reflect quality clinical care. In other words, although elements that are indicative of health service preparedness or readiness, and policies to enable these, are necessary for delivery of quality clinical care, they are not sufficient for its delivery. Given that collection and compilation of the totality of these indicators requires substantial human effort [2,3], it may be more efficient to directly capture patient care outcomes and individual person factors, like sex, age, and education, as the most reliable way of judging progress towards achieving equitable, quality clinical care [34]. Similar individual measures of quality clinical care have been the driving force behind reducing maternal and neonatal mortality and the burden of HIV [35, 36]. Although access to good antenatal care or availability of antiretrovirals is an important step to achieving these end points, the human-outcome end point (e.g., the UNAIDS 90-90-90 target for persons with HIV [35]) is unquestionably the most important to assess. To ensure the UN High-Level Commission and SDG targets for NCDs are met [1,37], it is likely that similar targets need to be put in place for CVDRFs. Proxy health service markers can then be used to define where barriers to care are in countries that do not meet quality clinical care targets—perhaps defined as those that are achievable in other settings with well-functioning health services [32]. Nevertheless, it should be noted that some of the indicators used in this study are also collected for other monitoring purposes, and although we have shown their lack of utility as a proxy for quality clinical care outcomes for CVDRFs, they may have utility for these other purposes. That utility should be tested in further studies.

In summary, country preparedness indicators to deliver CVDRF care have been collected for a number of years, and although questions have been refined over time, their focus has remained the same—they are reflective of components thought necessary to produce an improvement in NCD management in countries. Like facility-assessment surveys, these require investments of human capital and resources to complete. Our findings suggest that these surveys do not reflect quality clinical care outcomes for diabetes or hypertension. Although building blocks for health services and services reflected by these indicators are needed to provide care, to monitor if good clinical outcomes are achieved, individual-level outcomes data are needed.

STROBE Statement—checklist of items that should be included in reports of observational studies.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.

Summary of source of data for individual-level predictors and outcomes (e.g., cascades of care) relative to dates for higher-level predictors (e.g., NCD preparedness indicators).

NCD, noncommunicable disease.

(DOCX)

Click here for additional data file.

Information on data used in constructing the cascades.

(DOCX)

Click here for additional data file.

Data sources and extraction method for facility readiness data.

(DOCX)

Click here for additional data file.

Data sources and extraction method for NCD readiness indicators. NCD, noncommunicable disease.

(DOCX)

Click here for additional data file.

Data sources and extraction for indicators of country development, indicators of health service finance and equity, and general indicators of health service performance or readiness.

(DOCX)

Click here for additional data file.

Model specification and Stata code for mixed-effects logistic regression.

(DOCX)

Click here for additional data file.

17 Mar 2020

Dear Dr. Davies,

Thank you very much for submitting your manuscript "Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower and middle income countries" (PMEDICINE-D-19-04192) for consideration at PLOS Medicine.

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Comments from the reviewers:

Reviewer #1:

This well-written paper is concerned with the relevance of preparedness indicators to some clinical care outcomes. Specific comments are given below.

The model that was used in this paper is somewhat unclear, perhaps the authors could write it explicitly in an appendix, including whether the outcomes were allowed to correlate and any potential multicollinearity issues. For example, the lack of significance for some associations could possibly be attributed to collinearity issues.

Assuming a random effects logistic regression was used it would be useful to add the estimation method, since this does matter outside the linear mixed model framework.

If a Bayesian approach was used please add the priors for the variance components.

Also please add the details of the rescaling process.

There is a large numbers of comparisons/tests conducted in this paper, how was multiplicity accounted for?

In relation to this comment, please discuss the model and variable selection procedure.

Clinical care is generally hard to measure so it would be useful to discuss the relevance of the selected outcomes, the associated uncertainty any potential measurement error and their influence on the statistical analysis.

The message of the paper is clear. However, it would be useful to discuss all the reasons that the preparedness indicators are collected for and whether dropping them would affect any other current process.

Reviewer #2: Thank you for the opportunity to review this manuscript. I commend the authors for undertaking this ambitious analysis as it helps to address an important gap in the evidence base on the monitoring and evaluation of global health systems for NCD control. I have listed below several minor and major comments.

Minor comments:

1. Please clarify if the levels (%) of each outcome reported in Table 2 were also weighted in a similar way when pooling across countries for the regression models? If not, why not?

2. In the results section, some of the figures in the text do not include units, and there is inconsistent labelling of estimates.

3. On page 13 paragraph 4, it seems that reference 11 is cited in the text, but it is unclear why.

4. There are some issues with punctuation throughout.

5. For figure 1, I would suggest using a coding scheme that is suitable when printing in grey scale to help better distinguish between categories.

Major comments:

1. I agree strongly with many of the key statements in the discussion section, including that there is little evidence suggesting that indicators of service readiness are reflective of health system performance or success - which is the main motivation for this analysis, and that direct assessments of achievement of cascade steps for hypertension and diabetes management may be the most reliable ways of judging equitable care. On this second point, one publication where this has been done for hypertension is https://doi.org/10.1186/s12939-016-0478-6.

However, to better substantiate such points, including several statements made in the manuscript's concluding paragraph, I suggest that much deeper engagement with the supposed relationships between indicators on various health system inputs and the cascade step outcomes is needed. For example, the introduction section could present information on why proxy indicators are often used in lieu of difficult to measure concepts, including patient-level measures, and how good proxy indicators are selected and justified, such as through the development and application of conceptual frameworks (e.g. the PHCPI conceptual framework: https://improvingphc.org/phcpi-conceptual-framework).

For this analysis, a conceptual framework that relates a range of health system input proxies to the gold standard indicators (i.e. cascade steps) could help to identify which are likely to be good candidates (i.e. theoretically, readiness indicators that are more proximal to the cascade steps should be better proxies), and could serve as the basis for test if these relationships are borne out empirically, which is essentially what the analysis presented in this manuscript attempts to do. A conceptual framework could also provide possible explanations for some of the observed associations readiness indicators and cascade steps. For example, a readiness indicator such as medicine availability may be strongly associated with treatment and control because it is proximal to the cascade step outcome, or weakly associated because there is lots of heterogeneity or effect modification - despite being proximal to the cascade step. Similarly, a very distal indicator, such as GDP per capita or HDI, may still be strongly associated because, much like the cascade step, it may reflect the summative effects of all the various pathways through which a health system impacts risk factor control in individuals. But ultimately, because the way that health systems achieve better levels of risk factor management both for individuals and populations is multi-factorial, with many interlinked causal pathways operating across several levels, this means how success is achieved will vary considerably by context, and an understanding of how each jurisdiction makes such achievements is needed. Therefore, a conceptual model that maps readiness indicators to cascade steps could help to identify any gaps that may be hindering better risk factor management as targets for policy intervention, which is a point that is brought up in the current introduction text. While I do not suggest that the manuscript be modified to include such in-depth analysis, I do think that addressing such points introduction and discussion could result in more nuanced, sound and meaningful interpretations of the findings.

2. In the introduction, the authors draw attention to the high level of resources needed to collect national service readiness data, but it should also be acknowledged that collecting nationally representative data on blood pressure, blood glucose levels, current medication status and health histories of individuals is also resource intensive, and may be even more so than the effort needed to collect service readiness data. In a sense, this has been used to partly justify the value of the SARA data collection, but also underscores the need to identify reliable and cost-effective proxy indicators, if possible.

3. In the methods, could you please clarify if year dummies were included in models as, in some countries, patient-level data were collected over several years, and across countries these could help to control for potential regional or global time-dependent effects (e.g. changes to treatment guidelines, such as from mono- to combination therapy) as the included survey data span 2005 to 2016. And if not, why not. Similarly, given that there are expected (unobserved) differences across countries that would likely confound the associations of interest, my preference would be to use country fixed effects, rather than a random effect for country in the multi-level specification. So some justification for the use of a random effect is needed.

4. A key issue in the discussion section is that it does not address the important role played by private health care providers, including retail pharmacies, as key sources of care for both hypertension and diabetes in LMICs. An important limitation of the supply-side data sources used is that, while both the SPA and SARA surveys do include private health facilities, they do not capture retail pharmacies and other possible sources of private care outside of formal health facilities (i.e. hospitals and clinics). Given the proximity of indicators for medicine availability to the cascade outcomes in the causal relationship, one would expect these to be among the most strongly associated indicators with diabetes treatment and control, but were only found to be 'approaching significance'. [As an important aside, such interpretations should be avoided throughout the manuscript in preference for the reporting p-values so that readers may assess the strength of the associations themselves.] Two possible explanations for this unexpected observations follow from my comments above: 1) because the supply-side data does not capture medicine availability from other important sources of private care as described above, or 2) the analytical approach used 'over aggregates' medicine availability data when producing national-level indicators from the micro-data, which are then regressed against the individual-level outcomes data. Such aggregation eliminates important cross-cluster heterogeneity of indicators within countries, thus reducing the ability to detect strong associations. Palafox et. al (https://doi.org/10.1016/j.ssmph.2019.100376) note the limitations of measuring access to care/health system performance (or even indicators of quality care) in this way, and demonstrate an alternative method that merges contemporaneous patient-and facility-level data (such as DHS and SPA data) to produce more realistic and people-centred measures. Perhaps following a similar approach (e.g. regressing aggregate measures of patient- and facility-level outcomes at cluster level in countries with DHS and SPA data) would produce stronger evidence of an association.

Reviewer #3: This was an interesting and comprehensive manuscript that could potentially inform countries preparedness on cardiovascular diseases risk factors.

I enjoyed reading your manuscript. However, as a discretionary comment I would like the authors to add a line about why some countries with availability of DHS data on hypertension and diabetes were excluded from the study for example Ukraine, Azerbaijan, Armenia, Maldives etc were not included in this study.

Reviewer #4: Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower and middle income countries

Foremost, I would like to appreciate the team for conceptualising and investing a heavy amount of time in generating evidence for association between country preparedness indicators and quality of clinical care for cardiovascular disease outcomes in 44 lower and middle income countries. This study is worth the effort and would give us a lot of insights if it is conducted rigorously. Unfortunately, I find several limitations that makes it difficult for me to agree with the current conclusion drawn by the authors.

Foremost, the authors do not give us a detailed account of how the indicators are collected, when they are collected and whether they are appropriately collected? It is possible that poorly collected data is being used to make strong inferences/conclusions? If this is the case, one would question the associations? Is it a true association or cofounded outcome due to poorly collected data for the various indicators?

Another important pitfall has to do with the cascade data. The authors use cross sectional survey data collected at community level as proxy for measuring cascade outcomes - "diagnosis", "treatment" and "control". I don't think this is correct. We know very well that cross sectional survey at the community level for estimating for example hypertension are screening activities and give a glimpse of the possible disease burden but not necessarily diagnostic. Thus, this data normally skews the estimates. I would imagine the most accurate outcomes should be drawn at the health facility level where diagnosis, care treatment etc. is provided. Unless we have cascaded programs combining community and health facilities, the current study might be interpreted as misleading, first of all, given the diverse sources of data but also the point at which the outcomes is measured.

I would also like to bring to the attention of the authors about the proximal and dismal nature of the various indicators. For example, having an operational policy, strategy or action plan to … I wonder how operational is measured? I can report "yes" or "no" to these indicators and yet in reality only a handful of facilities/communities in a country have these policies operational. This is particularly true at least for Uganda that I am familiar with. Making some of these policies operational is still a challenge as much as they might be reported as operational. So such indicators would only be helpful if we track their operationalization slightly a little further. On the other hand, we have indicators such as number of physicians, nurses, or hospital beds per 1000 people etc. These are more likely to be collected accurately. Moreover, they are proximal indicators, so linking them to quality of care takes a shorter path than the former. No wonder the authors found "number of physicians was associated with being treated and controlled (1.12 [1.06 - 1.17] and 1.10 [1.01 - 1.20], respectively). In summary the outcome - quality clinical-care has a fairly complicated causal/non causal pathway that should be well thought through to generate inferences.

Another challenge that I find with the current study is the multiple sources of data used to manipulate the associations. The authors need to clearly describe the data bases and also possible generate a schema on how the different data bases were managed for analysis. Authors infer to the field of maternal and new-born health, and use this as a basis for the current concept. However, the papers they quote are methodically incomparable, (1, 23 and 24), and thus it is difficult to draw similar conclusions. Therefore, with the current data sources as analysed, it is difficult to accurately predict country preparedness and quality clinical care outcomes for cardiovascular disease risk factors (CVDRF). More work is needed to assess the pathways. For the different indicators and how they are interlinked to each other.

I also need to state that maternal and neonatal data is currently one of the well collected data in most of these low income countries. Operationalization of guidelines is almost unquestionable and the data is fairly if not accurately tracked right through the healthcare delivery levels. It is still a struggle for NCD data though there is some effort.

Finally, I don't seem to appreciate how the authors particularly addressed the "process" strand of the Donabedian's framework. This is particularly important in measuring quality outcomes. If we say we have guidelines, it is not enough? How many of these guidelines have trickled down?

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

[LINK]

8 Apr 2020

Submitted filename: Response to reviewers comments 08-04-20.docx

Click here for additional data file.

4 May 2020

Dear Dr. Davies,

Thank you very much for submitting your manuscript "Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower and middle income countries: A multi-country analysis of survey data" (PMEDICINE-D-19-04192R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

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Requests from the editors: Please address Rev 1's report.

Comments from the reviewers:

Reviewer #1:

The authors have revised their paper and this is a substantially improved manuscript.

However, one major issue remains.

This relates to the selection of a Poisson regression model with a binary dependent variable.

If I'm guessing correctly (given that the authors still did not explicitly added their model) the logarithm of the probability of achieving the clinical care outcome is linked to the fixed and random effects of the model.

This creates an unnecessary constrain as the log-probability is inherently negative, likely yielding biased estimates.

One would think that the canonical version, assuming a Bernoulli distribution with a logit (or probit or cloglog) link ought to be used instead, avoiding the potential biases of a Poisson/binary model.

If this guess is correct it seems reasonable to re-run all the analyses using the canonical model.

This may or may not significantly affect the results, but it will remove some biases from what is an already challenging model.

Also, please add the details of the estimation method, including for the variance components (was that done using restricted maximum likelihood?), and write the model explicitly in the supplement, to avoid any further confusion

Reviewer #3: Thank you for taking time to address the reviewers' comments, the manuscript is much stronger now and my comments have been addressed satisfactorily.

Reviewer #4: The authors have reasonably addressed my comments. They may still need to check for typos e.g line 152 should be rely and not reply.

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

[LINK]

8 Jun 2020

Submitted filename: Response to reviewers comments 08-06-20.docx

Click here for additional data file.

25 Jun 2020

Dear Dr. Davies,

Thank you very much for re-submitting your manuscript "Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower and middle income countries: A multi-country analysis of survey data" (PMEDICINE-D-19-04192R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by the statistical reviewer. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

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We look forward to receiving the revised manuscript by Jul 02 2020 11:59PM.

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Requests from Editors:

Please add p values to the abstract and elsewhere where 95% Cis are indicated; please be more specific with dates in the abstract (add in months) and also in the main text for example line 318.

Data - PLOS does not permit "data not shown” or “data available on request” Please remove this claim, or do one of the following: a) If you are the owner of the data relevant to this claim, please provide the data in accordance with the PLOS data policy, and update your Data Availability Statement as needed. b) If the data not shown refer to a study from another group that has not been published, please cite personal communication in your manuscript text (it should not be included in the reference section). Please provide the name of the individual, the affiliation, and date of communication. The individual must provide PLOS Medicine written permission to be named for this purpose. c) For any other circumstance, please contact me ASAP. Please note an author cannot be a point of contact for requesting access to data.

Author summary – I would suggest removing “(LMICs, or so-called, “developing” countries).”

Please ensure ref calls outs appear before rather than after punctuation.

Please ensure any questionnaires are provided as supp files.

Please ensure that the study is reported according to the [STROBE] guideline, and include the completed [STROBE or other] checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)." Please report your study according to the relevant guideline, which can be found here: http://www.equator-network.org/

I note that there seems to be a bit of disagreement in the methods section where you state "all analyses were not prespecified" and then go on to say "all other analyses were planned". Please clarify and be consistent.

Comments from Reviewers:

Reviewer #1: The authors have sufficiently revised their paper and this now represents a substantially improved manuscript, acceptable for publication

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

[LINK]

18 Sep 2020

Dear Prof Davies,

On behalf of my colleagues and the academic editor, Dr. Margaret E Kruk, I am delighted to inform you that your manuscript entitled "Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower and middle income countries: A multi-country analysis of survey data" (PMEDICINE-D-19-04192R3) has been accepted for publication in PLOS Medicine.

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Clare Stone, PhD

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PLOS Medicine

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Table 1

Summary of independent variables included in analysis.

Level of Analysis	Variable	Data Source*	Number of Countries With Available Data out of 44 Countries Included
Country development	GDP per capita (current $US)	WDI [22]	44
	Human Development Index†	UNDP [21]	41
NCD readiness indicators	Has an operational NCD unit/branch or department within the Ministry of Health, or equivalent	WHO National Capacity for NCDs Report [8]	39
	Has evidence-based national guidelines/protocols/standards for the management of major NCDs through a primary care approach	WHO National Capacity for NCDs Report	24
	Has an operational multisectoral national policy, strategy, or action plan that integrates several NCDs and shared risk factors	WHO National Capacity for NCDs Report	24
	Funding available for NCD surveillance, monitoring, and evaluation OR for NCD treatment and control OR for NCD prevention and health promotion	WHO National Capacity for NCDs Report	20
	NCD surveillance and monitoring service in place to enable reporting against the nine global NCD targets	WHO National Capacity for NCDs Report	24
	Has an integrated or topic-specific policy, programme, or action plan that is currently operational for cardiovascular diseases	WHO National Capacity for NCDs Report	20
	Has an integrated or topic-specific policy, programme, or action plan that is currently operational for diabetes	WHO National Capacity for NCDs Report	20
	Has an operational policy, strategy, or action plan to reduce physical inactivity and/or promote physical activity	WHO National Capacity for NCDs Report	39
	Has an operational policy, strategy, or action plan to reduce unhealthy diet and/or promote healthy diets	WHO National Capacity for NCDs Report	39
	Has an operational policy, strategy, or action plan to reduce the burden of tobacco use	WHO National Capacity for NCDs Report	39
	Has an operational policy, strategy, or action plan to reduce the harmful use of alcohol	WHO National Capacity for NCDs Report	39
General health service finance	Current health expenditure (percentage of GDP)	WDI	44
	Domestic private health expenditure (percentage of current health expenditure)	WDI	44
	Out-of-pocket expenditure (percentage of current health expenditure)	WDI	44
Health service performance and readiness	NMR (per 1,000 live births)	WDI	44
	Hospital beds (per 1,000 people)	WDI	29
	Nurses and midwives (per 1,000 people)	WDI	36
	Physicians (per 1,000 people)	WDI	35
Health facility readiness for diabetes	Percent of facilities with insulin	SARA/SPA [9,10]	6
	Percent of facilities with metformin	SARA/SPA	6
	Percent of facilities with glibenclamide	SARA/SPA	5
	Percent of facilities offering diabetes diagnostic and management services	SARA/SPA	4
	Among facilities offering diabetes diagnosis and management services, percent with at least one trained staff in diabetes diagnostic and management	SARA/SPA	4
	Among facilities offering diabetes diagnosis and management services, percent with guidelines for diagnosis and management	SARA/SPA	4
	Among facilities offering diabetes diagnosis and management services, percent with blood pressure apparatus	SARA/SPA	4
	Among facilities offering diabetes diagnosis and management services, percent with blood glucose measurement capacity	SARA/SPA	4
	Among facilities offering diabetes diagnosis and management services, percent with adult weighing scale	SARA/SPA	4
Individual participant characteristics	Age	Nationally representative survey data	44
	Sex	Nationally representative survey data	44
	Education	Nationally representative survey data	44

*WDI data downloaded between 23 and 25 September 2018 and the Human Development Index extracted on 30 December 2018.

†Human Development Index is a composite of GNI per capita, education variables, and life expectancy.

Abbreviations: GDP, gross domestic product; GNI, gross national income; NCD, noncommunicable disease; NMR, neonatal mortality rate; SARA, Service Availability and Readiness Assessment; SPA, Service Provision Assessment; UNDP, United Nations Development Program; WDI, World Development Indicators; WHO, World Health Organization.

Table 2

Summary of country-level indicators assessed in this analysis, presented for countries for which hypertension or diabetes outcomes were available to study.

Indicator	Countries with Hypertension Outcomes(n = 43), Presented as Mean (SD) or % (n)	Countries with Diabetes Outcomes(n = 28), Presented as Mean (SD) or % (n)
Country development
GDP per capita (current $US)	4,357 ± 3,722	3,886 ± 3,615
Human Development Index	0.64 ± 0.12	0.62 ± 0.12
NCD readiness
Operational NCD unit/branch/department in Ministry of Health
Yes	65.8 (25)	73.1 (19)
No	21.1 (8)	11.5 (3)
No response	13.2 (5)	15.4 (4)
National guidelines for NCD management with primary care approach
Yes	33.3 (8)	25.0 (4)
No	45.8 (11)	50.0 (8)
No response	20.8 (5)	25.0 (4)
Operational multisectoral national policy for NCDs and shared risk factors
Yes	8.3 (2)	6.3 (1)
No	70.8 (17)	68.8 (11)
No response	20.8 (5)	25.0 (4)
Funding available for NCD surveillance, monitoring, and evaluation; treatment and control; and/or prevention and health promotion
Yes	63.2 (12)	57.1 (8)
No	10.5 (2)	14.3 (2)
No response	26.3 (5)	28.6 (4)
NCD surveillance and monitoring service
Yes	8.3 (2)	6.3 (1)
No	70.8 (17)	68.8 (11)
No response	20.8 (5)	25.0 (4)
Integrated or topic-specific policy currently operational for CVDs
Yes	31.6 (6)	35.7 (5)
No	42.1 (8)	35.7 (5)
No response	26.3 (5)	28.6 (4)
Integrated or topic-specific policy operational for diabetes
Yes	31.6 (6)	35.7 (5)
No	42.1 (8)	35.7 (5)
No response	26.3 (5)	28.6 (4)
Operational policy to promote physical activity
Yes	36.8 (14)	34.6 (9)
No	50.0 (19)	50.0 (13)
No response	13.2 (5)	15.4 (4)
Operational policy to promote healthy diets
Yes	44.7 (17)	42.3 (11)
No	42.1 (16)	42.3 (11)
No response	13.2 (5)	15.4 (4)
Operational policy to reduce tobacco
Yes	50.0 (19)	50.0 (13)
No	36.8 (14)	34.6 (9)
No response	13.2 (5)	15.4 (4)
Operational policy to reduce the harmful use of alcohol
Yes	31.6 (12)	34.6 (9)
No	55.3 (21)	50.0 (13)
No response	13.2 (5)	15.4 (4)
Health service finance
Current health expenditure (percentage of GDP)	6.0 ± 2.0	5.7 ± 2.2
Private health expenditure (percentage current health expenditure)	43.5 ± 19.1	41.8 ± 19.4
Out-of-pocket expenditure (percentage of current health expenditure)	37.3 ± 19.0	35.1 ± 19.5
Health service performance and readiness
NMR (per 1,000 live births)	17.3 ± 10.1	18.3 ± 9.1
Hospital beds (per 1,000 people)	3.0 ± 2.7	2.1 ± 1.8
Nurses and midwives (per 1,000 people)	2.9 ± 2.8	2.0 ± 1.8
Physicians (per 1,000 people)	1.3 ± 1.4	0.9 ± 1.2
Facility-level readiness*
Have insulin		26.2 ± 26.5
Have metformin		38.4 ± 29.7
Have glibenclamide		37.3 ± 34.1
Offer diabetes diagnostic and management services		28.5 ± 15.0
Among those who offer diabetes diagnostic and management services
Have trained staff in diabetes diagnostic and management		19.7 ± 14.0
Have guidelines for diagnosis and management		24.5 ± 14.8
Have blood pressure apparatus		95.6 ± 3.0
Have blood glucose measurement capacity		25.2 ± 17.7
Have adult weighing scale		90.7 ± 5.8

All countries contributed equally to these estimates.

For health service policy, NCD specific, ‘yes’ indicates a positive response to the question; ‘no’ indicates a negative response to the question, do not know, or question asked but no response; and ‘no response’ indicates country did not respond to survey.

*All values are percent of facilities nationally.

Abbreviations: CVD, cardiovascular disease; NCD, noncommunicable disease; NMR, neonatal mortality rate; SD, standard deviation

Table 3

Summary of individual-level data used in this analysis.

The hypertension sample includes data from 43 countries, and the diabetes sample includes data from 28 countries.

Indicator	Hypertension SampleUnweighted n = 187,552	Diabetes SampleUnweighted n = 40,795
Age (years)	48.1 (47.4–48.7)	52.2 (51.4–53.1)
Sex
Male	46.5 (45.5–47.6)unweighted n = 57,784	42.3 (40.1–44.6)unweighted n = 9,693
Female	53.5 (52.4–54.5)unweighted n = 129,768	57.7 (55.4–59.9)unweighted n = 31,102
Education
No formal schooling	19.4 (17.8–21.2)unweighted n = 45,342	16.5 (14.6–18.5)unweighted n = 9,873
Primary school	31.3 (29.3–33.4)unweighted n = 46,743	38.4 (36.2–40.6)unweighted n = 7,887
Secondary school or above	49.3 (47.5–51.1)unweighted n = 95,467	45.1 (41.8–48.5)unweighted n = 23,035
Outcomes
Diagnosis of hypertension among hypertensives (percent yes)	40.7 (37.9–43.5)unweighted n = 68,458
Treatment of hypertension among hypertensives diagnosed (percent yes)	27.6 (25.7–29.7)unweighted n = 48,735
Control of hypertension among hypertensives diagnosed and treated (percent yes)	11.1 (10.2–12.0)unweighted n = 23,599
Diagnosis of diabetes among diabetics (percent yes)		43.4 (39.5–47.5)unweighted n = 12,931
Treatment of diabetes among diabetics diagnosed (percent yes)		34.7 (32.2–37.3)unweighted n = 11,932
Control of diabetes among diabetics diagnosed and treated (percent yes)		21.1 (19.6–22.5)unweighted n = 7,107

Values are weighted mean (95% CI) or weighted percent (95% CI) and unweighted n. All countries contributed equally to estimates.

Table 4

Pattern of countries reporting to NCD-specific policy questions.

Countries	NCD Branch	NCD Guidelines	Operational NCD Policy	NCD Funding	NCD Surveillance Service	Operational CVD Policy	Operational Diabetes Policy	Operational Physical Activity Policy	Operational Policy for Promotion of Healthy Diets	Operational Tobacco Control Policy	Operational Alcohol Control Policy
Albania
Bangladesh
Belize
Benin
Bhutan
Brazil
Burkina Faso
Chile
China
Comoros
Costa Rica
Ecuador
Egypt
Fiji
Ghana
Grenada
India
Indonesia
Kazakhstan
Kenya
Kyrgyz Republic
Lebanon
Lesotho
Liberia
Mexico
Mongolia
Namibia
Nepal
Peru
Romania
Russia
St. Vincent and the Grenadines
Seychelles
South Africa
Swaziland
Tanzania
Timor-Leste
Togo
Uganda

Countries shaded in light orange were included in diabetes cascades. All countries except Fiji (for which blood pressure data were not available) were included in the hypertension cascades. Responses to questions are shaded as follows: white, ‘no’; orange, ‘yes’; grey, ‘no response to survey’; blue, ‘responded to survey, but question not asked in survey’. N = 5 countries (Azerbaijan, Georgia, Guyana, Mozambique, and Ukraine) were missing data and are not listed in table.

Abbreviations: CVD, cardiovascular disease; NCD, noncommunicable disease

Table 5

Association between indicators and care for hypertension among individuals with hypertension.

Indicator	Diagnosis (OR [95% CI])	Treatment (OR [95% CI])	Control (OR [95% CI])
Country development
GDP per capita (per SD)	1.58 (1.27–1.96)	1.49 (1.15–1.93)	1.57 (1.22–2.02)
Human Development Index (per SD)	1.26 (1.14–1.40)	1.19 (1.02–1.38)	1.23 (1.04–1.46)
NCD readiness
Operational NCD unit/branch/department in Ministry of Health
Yes	0.83 (0.41–1.67)	1.78 (0.61–5.22)	1.42 (0.56–3.63)
No	Ref.	Ref.	Ref.
No response	0.75 (0.31–1.83)	1.52 (0.47–4.88)	1.10 (0.42–2.93)
National guidelines for NCD management with primary care approach
Yes	1.86 (1.08–3.21)	0.95 (0.36–2.45)	0.95 (0.39–2.27)
No	Ref.	Ref.	Ref.
No response	1.05 (0.55–2.01)	0.97 (0.50–1.88)	0.80 (0.45–1.43)
Funding available for NCD surveillance, monitoring, and evaluation; treatment and control; and/or prevention and health promotion
Yes	1.17 (0.65–2.11)	1.41 (0.83–2.41)	2.26 (1.09–4.69)
No	Ref.	Ref.	Ref.
No response	1.07 (0.55–2.06)	1.21 (0.69–2.12)	1.76 (1.07–2.91)
Integrated or topic-specific policy currently operational for CVD
Yes	1.51 (0.76–3.00)	1.33 (0.65–2.74)	1.12 (0.38–3.31)
No	Ref.	Ref.	Ref.
No response	1.12 (0.49–2.53)	1.01 (0.43–2.42)	0.88 (0.30–2.58)
Integrated or topic-specific policy operational for diabetes
Yes	1.51 (0.76–3.00)	1.33 (0.65–2.74)	1.12 (0.38–3.31)
No	Ref.	Ref.	Ref.
No response	1.12 (0.49–2.53)	1.01 (0.43–2.42)	0.88 (0.30–2.58)
Operational policy to promote physical activity
Yes	1.13 (0.69–1.83)	1.55 (0.85–2.83)	1.72 (0.90–3.28)
No	Ref.	Ref.	Ref.
No response	0.92 (0.47–1.79)	1.17 (0.58–2.34)	1.09 (0.60–1.97)
Operational policy to promote healthy diets
Yes	1.33 (0.86–2.06)	1.13 (0.64–1.98)	1.32 (0.71–2.46)
No	Ref.	Ref.	Ref.
No response	1.02 (0.54–1.94)	1.01 (0.52–1.96)	0.97 (0.54–1.74)
Operational policy to reduce tobacco
Yes	1.42 (0.88–2.30)	1.05 (0.57–1.95)	1.04 (0.50–2.17)
No	Ref.	Ref.	Ref.
No response	1.08 (0.52–2.22)	0.97 (0.46–2.07)	0.84 (0.39–1.83)
Operational policy to reduce the harmful use of alcohol
Yes	1.22 (0.75–1.97)	1.42 (0.76–2.63)	1.56 (0.77–3.14)
No	Ref.	Ref.	Ref.
No response	0.94 (0.48–1.83)	1.08 (0.54–2.16)	0.99 (0.53–1.84)
Health service finance
Current health expenditure (per SD)	1.02 (0.87–1.20)	1.15 (0.93–1.43)	1.23 (0.95–1.60)
Private health expenditure (per SD)	0.92 (0.78–1.09)	1.05 (0.85–1.29)	1.03 (0.83–1.29)
Out-of-pocket expenditure (per SD)	0.95 (0.80–1.12)	1.04 (0.85–1.26)	1.01 (0.82–1.23)
Health service performance and readiness
NMR (per SD)	0.73 (0.61–0.88)	0.80 (0.63–1.00)	0.77 (0.58–1.02)
Hospital beds (per SD)	1.14 (1.02–1.27)	1.17 (1.02–1.33)	0.98 (0.83–1.15)
Nurses and midwives (per SD)	1.24 (1.06–1.44)	1.21 (1.00–1.48)	1.11 (0.88–1.40)
Physicians (per SD)	1.21 (1.11–1.32)	1.20 (1.10–1.30)	1.12 (1.01–1.23)

Values are OR (95% CI), adjusted for individual-level age, sex, and education and taking into account sample weights, which were rescaled by the survey’s sample size such that all countries contributed equally to the overall estimates.

Abbreviations: CVD, cardiovascular disease; GDP, gross domestic product; NCD, noncommunicable disease; NMR, neonatal mortality rate; OR, odds ratio; Ref., reference

Table 6

Association between indicators and care for diabetes among individuals with diabetes.

Indicator	Diagnosis (OR [95% CI])	Treatment (OR [95% CI])	Control (OR [95% CI])
Country development
GDP per capita (per SD)	1.70 (1.12–2.59)	1.36 (0.88–2.10)	1.29 (0.75–2.23)
Human Development Index (per SD)	1.21 (1.01–1.44)	1.09 (0.90–1.32)	1.01 (0.82–1.25)
NCD readiness
Operational NCD unit/branch/department in Ministry of Health
Yes	0.36 (0.18–0.72)	1.80 (0.81–4.04)	0.97 (0.47–2.00)
No	Ref.	Ref.	Ref.
No response	0.66 (0.23–1.90)	3.52 (1.20–10.36)	2.35 (0.83–6.65)
National guidelines for NCD management with primary care approach
Yes	1.62 (0.51–5.14)	0.34 (0.24–0.47)	0.72 (0.35–1.49)
No	Ref.	Ref.	Ref.
No response	1.60 (0.65–3.95)	1.56 (0.65–3.71)	2.22 (0.76–6.49)
Funding available for NCD surveillance, monitoring, and evaluation; treatment and control; and/or prevention and health promotion
Yes	1.79 (0.31–10.42)	1.75 (0.27–11.46)	1.16 (0.15–8.75)
No	Ref.	Ref.	Ref.
No response	3.05 (0.46–20.46)	3.47 (0.50–24.30)	2.74 (0.41–18.53)
Integrated or topic-specific policy currently operational for CVD
Yes	0.75 (0.24–2.31)	0.55 (0.17–1.78)	0.31 (0.08–1.27)
No	Ref.	Ref.	Ref.
No response	1.52 (0.38–6.05)	1.44 (0.36–5.80)	1.17 (0.31–4.48)
Integrated or topic-specific policy operational for diabetes
Yes	0.75 (0.24–2.31)	0.55 (0.17–1.78)	0.31 (0.08–1.27)
No	Ref.	Ref.	Ref.
No response	1.52 (0.38–6.05)	1.44 (0.36–5.80)	1.17 (0.31–4.48)
Operational policy to promote physical activity
Yes	0.87 (0.41–1.85)	1.41 (0.68–2.92)	0.79 (0.33–1.90)
No	Ref.	Ref.	Ref.
No response	1.50 (0.50–4.50)	2.62 (0.97–7.08)	2.07 (0.73–5.89)
Operational policy to promote healthy diets
Yes	1.24 (0.57–2.69)	1.06 (0.47–2.39)	0.71 (0.28–1.80)
No	Ref.	Ref.	Ref.
No response	1.95 (0.64–5.90)	2.16 (0.73–6.38)	1.84 (0.59–5.73)
Operational policy to reduce tobacco
Yes	0.90 (0.35–2.35)	0.65 (0.24–1.72)	0.41 (0.15–1.11)
No	Ref.	Ref.	Ref.
No response	1.52 (0.43–5.35)	1.48 (0.43–5.13)	1.26 (0.35–4.55)
Operational policy to reduce the harmful use of alcohol
Yes	0.87 (0.41–1.85)	1.41 (0.68–2.92)	0.79 (0.33–1.90)
No	Ref.	Ref.	Ref.
No response	1.50 (0.50–4.50)	2.62 (0.97–7.08)	2.07 (0.73–5.89)
Health service finance
Current health expenditure (per SD)	1.07 (0.75–1.53)	1.13 (0.79–1.63)	1.19 (0.76–1.84)
Private health expenditure (per SD)	0.94 (0.72–1.23)	1.08 (0.82–1.43)	1.02 (0.70–1.47)
Out-of-pocket expenditure (per SD)	1.04 (0.81–1.33)	1.14 (0.89–1.47)	1.11 (0.77–1.60)
Health service performance and readiness
NMR (per SD)	0.85 (0.59–1.21)	0.99 (0.70–1.40)	1.12 (0.76–1.65)
Hospital beds (per SD)	1.30 (0.74–2.27)	1.31 (0.72–2.37)	1.50 (0.74–3.07)
Nurses and midwives (per SD)	1.44 (0.91–2.28)	0.94 (0.65–1.35)	0.94 (0.67–1.32)
Physicians (per SD)	1.28 (1.09–1.51)	1.14 (0.97–1.34)	1.16 (0.97–1.40)
Facility-level readiness*
Have insulin (per SD)	1.26 (0.91–1.74)	1.29 (0.97–1.70)	1.23 (0.98–1.53)
Have metformin (per SD)	1.34 (0.98–1.84)	1.35 (1.01–1.81)	1.27 (0.97–1.66)
Have glibenclamide (per SD)	1.07 (0.78–1.47)	1.13 (0.86–1.49)	1.05 (0.81–1.37)
Offer diabetes diagnostic and management services	0.56 (0.39–0.82)	0.63 (0.42–0.93)	0.84 (0.60–1.19)
Among those who offer diabetes diagnostic and management services:
Have trained staff in diabetes diagnostic and management (per SD)	0.83 (0.62–1.12)	0.88 (0.73–1.06)	0.90 (0.82–0.99)
Have guidelines for diagnosis and management (per SD)	0.75 (0.63–0.89)	0.81 (0.71–0.93)	0.90 (0.77–1.05)
Have blood pressure apparatus (per SD)	0.92 (0.56–1.52)	0.87 (0.58–1.30)	0.79 (0.66–0.94)
Have blood glucose measurement capacity (per SD)	0.95 (0.61–1.48)	1.01 (0.70–1.46)	1.01 (0.77–1.32)
Have adult weighing scale (per SD)	0.59 (0.40–0.86)	0.66 (0.44–1.00)	0.89 (0.64–1.24)

Values are OR (95% CI), adjusted for individual-level age, sex, and education and taking into account sample weights, which were rescaled by the survey’s sample size such that all countries contributed equally to the overall estimates.

Abbreviations: CVD, cardiovascular diseases; GDP, gross domestic product; NCD, noncommunicable disease; NRM, neonatal mortality rate; OR, odds ratio; Ref., reference; SD, standard deviation