Advertisement

SN Comprehensive Clinical Medicine

, Volume 1, Issue 11, pp 905–914 | Cite as

Prevalence and Correlates of Chronic Kidney Disease in Patients with Hypertension in Rural Malawi.

  • Chiyembekezo KachimangaEmail author
  • Lawrence Nazimera
  • Enoch Ndarama
  • Richard Kamwezi
  • Limbani Thengo
  • Emily B. Wroe
  • George C. Talama
Medicine
  • 129 Downloads
Part of the following topical collections:
  1. Topical Collection on Medicine

Abstract

The prevalence of chronic kidney disease (CKD) in patients with hypertension is very high in Africa. We investigated the prevalence and correlates of CKD in patients with hypertension (defined as currently taking hypertension drugs at enrolment or systolic blood pressure ≥ 160 mmHg and / or diastolic blood pressure ≥ 110 mmHg) attending longitudinal care in rural Malawi, where currently no data on prevalence of CKD in patients with hypertension exists. We retrospectively reviewed medical records of all hypertensive patients who were screened for CKD between January 2018 and April 2019. Screening was done using serum creatinine, and CKD epidemiology formula was used to estimate the glomerular filtration rate (eGFR). We used Kidney Disease: Improving Global Outcomes definitions of renal insufficiency and CKD. Descriptive statistics were used to describe the variables and logistic regression analysis was used to identify correlates of CKD. During the study duration, 1197 patients with hypertension were screened for CKD. The mean creatinine and eGFR was 0.90 mg/dl (Confidence Interval (CI) 0.85–0.94 mg/dl) and 84.1 ml/min/1.73m2 (CI 82.7–85.4 ml/min/1.73m2) respectively. About half of the patients had a normal eGFR (48.3%, n = 578) and 36.3% (n = 435) had mildly decreased eGFR. The prevalence of renal insufficiency was 15.4% (CI 13.4–17.5, 184/1197) and the prevalence of CKD was 7.1% (CI 5.7–8.7%). By eGFR category in the CKD patients, 41.2% (n = 35), 31.8% (n = 27), 24.7% (n = 21) and 2.3% (n = 2) had CKD stage 3a, 3b, 4 and 5 respectively. CKD was strongly associated with age and diabetes. We found moderately high renal insufficiency and CKD in this cohort. We propose investing in screening for CKD in patients with hypertension in other clinics in Malawi.

Keywords

Non-communicable diseases Chronic kidney disease Hypertension Renal insufficiency Malawi 

Abbreviations

CI

confidence interval.

CKD

Chronic kidney disease.

NCD

Non-communicable disease.

EGFR

Estimated glomerular filtration rate.

KDIGO

Kidney Disease: Improving Global Outcomes.

BMI

Body mass index.

IQR

interquartile range.

MOH

Ministry of Health.

EMR

Electronic medical record.

OR

Odds ratio

Background

Globally, the prevalence of hypertension is high, affecting over 31% of adults [1]. Low-and-middle-income countries, especially African countries, are severely affected with over 75% of patients with hypertension living in these countries [1, 2]. In Africa alone, hypertension affects over 30% of adults [3], and is the most common cause of chronic kidney disease (CKD) [4].

CKD is a growing public health problem in Africa with recent increases in CKD incidence, prevalence and mortality [5]. Whilst 15% of the general population have CKD, this is higher in patients with hypertension, with a recent systematic review reporting a prevalence of 36% [4]. Additionally, the burden of CKD in patients with hypertension varies widely. For example, studies in Cameroon and one study in Ghana reported about 50% prevalence of CKD in patients with hypertension [6, 7], but a study in Uganda and another study in Ghana report lower prevalence, at 38.5% and 13% respectively [8].

The management of CKD and its complications is expensive, and most African countries cannot afford to manage the complications of CKD, particularly end stage renal disease [9, 10]. Additionally, these countries are struggling with a high burden of communicable diseases and maternal and child health conditions, with some of these diseases such as HIV also increasing the risk of CKD [11]. Dialysis and renal replacement therapy is not affordable and feasible for all the patients that need these services in African countries [10, 12]. However, screening for CKD, which is feasible and affordable, provides an opportunity for early detection and management of CKD in Africa.

Screening for CKD, especially in high risk groups like patients with hypertension, has many advantages. Screening allows for early detection as CKD may be asymptomatic in early stages. This enables early interventions to reduce progression of CKD and may help improve the quality of life and extend life expectancy in these patients [11]. Additionally, screening saves patient and health care costs as it is cheaper than managing complications of CKD [9, 13]. Far more importantly, screening helps to improve awareness of CKD, as many patients in Africa are not aware that they have CKD [14]. All of these advantages can be achieved by using relatively cheap screening tests such as urinary albumin and/or serum creatinine, which have been shown to be effective and feasible in many African countries [15].

Situated in southern Africa, Malawi currently faces an increase in non-communicable diseases (NCDs), in addition to an existing burden of HIV [16]. Up to 16% of adults aged 18 years and above have hypertension [17]. Although screening for CKD is included in the National NCD Action Plan for Prevention and management of NCDs in Malawi, most facilities, especially district and rural health facilities, have minimal capacity to screen for CKD [18, 19, 20]. Currently, limited data exists on the prevalence of CKD patients with hypertension, especially in rural Malawi where over 84% of the population resides [21]. The majority of studies on CKD were done in the context of HIV and are all from urban facilities [22, 23, 24, 25].

We present a study on the prevalence and correlates of CKD in hypertensive patients aged 18 years and above attending Integrated Chronic Care Clinics in Neno District, Malawi. This study is relevant because 1) It estimates the prevalence of undiagnosed CKD in a cohort of patients with hypertension, a key high risk group in Malawi; 2) It investigates CKD in rural Malawi, where despite the majority of Malawians living in rural areas, no data exists on CKD; and 3) It measures context specific correlates of CKD.

Methods

Setting

This is a cross-sectional study conducted between January 2018 and April 2019 in Neno District, Malawi. We retrospectively reviewed medical records of all patients with hypertension screened for CKD in Integrated Chronic Care Clinics.

Neno District is a remote and rural south-western district of Malawi with an estimated population of 138,000 in 2018 [21].Within the Neno District’s 14 health facilities (2 hospitals and 12 primary health facilities), the Ministry of Health (MOH) in collaboration with a non-governmental organization called Partners In Health has been providing integrated HIV-NCD care since 2015 [26]. In the Integrated Chronic Care Clinics, a team of mid-level providers, nurses, and other support staff provide longitudinal care to patients with HIV, hypertension, diabetes, epilepsy, chronic respiratory disease, mental illnesses, and other chronic NCDs. By April 2019, over 7850 HIV and 3400 NCD patients, of which 2184 were patients with hypertension, were receiving care in this clinic (Partners In Health internal data). The Integrated Chronic Care Clinic model has been described elsewhere [26, 27].

Hypertension Enrolment, CKD Screening and Laboratory Measurements

In the Integrated Chronic Care Clinics, patients were referred for treatment for hypertension from several places: inpatient wards, outpatient clinics, and community screening events [28]. Patients were enrolled in the clinic for treatment for hypertension either if they were currently on hypertension treatment or based on a systolic blood pressure ≥ 160 mmHg and/or diastolic blood pressure ≥ 110 mmHg, procedures initiated in Neno District in order to capture the highest risk patients first and avoid early overcrowding of the Integrated Chronic Care Clinics.

Beginning January 2018, Neno District introduced annual routine screening for CKD in patients with hypertension in the Integrated Chronic Care Clinics using two point of care chemistry analyzers. For eligible hypertensive patients, at least one milliliter of venous blood was collected in heparinized bottles by clinic nurses using aseptic techniques for the measurement of plasma creatinine (mg/dl). In order to provide immediate results and expedite patient care, the samples were analyzed immediately in the clinic using an I-Stat analyzer and chem8+ cartridges (Abbot, USA). The test was performed by support staff working in the clinic with support from a laboratory technician who provided a one day training followed by longitudinal mentorship at least once every 3 months.

In the event an I-Stat analyzer was malfunctioning or not available in the clinic, the samples were transported to one of the two hospitals in Neno for same day analysis on I-Stat analyzers located in the laboratories.

Based on the initial serum creatinine, an estimated glomerular filtration rate (eGFR) was calculated. If the patients had renal insufficiency on their initial test, creatinine was checked again after at least 3 months in order to confirm the diagnosis of CKD. Follow up was facilitated by reminders to clinic staff, using community health workers to inform patients to come to the clinic for follow up testing, and, if necessary, home visits were conducted by the clinical team to identify and escort patients to the nearest clinic for CKD screening.

Inclusion and Exclusion Criteria of the Study

We retrospectively reviewed the electronic medical record (EMR) of patients with hypertension. All patients’ clinical information was recorded in the MOH standardized patient chart, and the data was duplicated in the EMR. All hypertensive patients aged 18 years and above who were screened for CKD were included in the study. We excluded hypertensive patients with an existing diagnosis of CKD.

Clinical Measurements and Outcomes

CKD was the main outcome and was defined based on Kidney Disease: Improving Global Outcomes (KDIGO) definition: an estimated eGFR of less than 60 ml/min/1.73m2 persistent for at least 3 months [29]. EGFR of less than 60 ml/min/1.73m2 on one occasion only was defined as renal insufficiency. In addition, we categorized eGFR in all patients based on KDIGO categories of eGFR.

We derived an eGFR from plasma creatinine using CKD Epidemiology formula without the factor of race. The CKD Epidemiology formula without race was chosen as it has been shown to better predict eGFR in Malawians than CKD epidemiology formula with black as a race factor or other formulas of predicting eGFR [30].

We extracted several other variables from the patients’ charts based on data that was routinely collected during clinical encounters and variables that have been shown to be associated with CKD in other studies. Therefore, we extracted the following data for each patient with hypertension:
  1. 1)

    Demographic variables: Age (years), gender (male or female)

     
  2. 2)

    Clinical history of hypertension: Years since diagnosis of hypertension and duration of enrolment in the clinic (less than one year, 1–2 years, more than 2 years), and most recent systolic and diastolic blood pressures (mmHg)

     
  3. 3)

    Risk factors of hypertension: Body mass index (BMI) (kg/m2), BMI over 25, BMI categories (<18.5, ≥18.5- < 25, ≥25- < 30, ≥30), proteinuria (negative and trace were coded as protein <1+, protein 1+, 2+ and 3+ were coded as protein ≥1+)

     
  4. 4)

    Comorbidities (yes or no): Patients with concurrent diabetes and/or HIV.

     

We planned to include variables that are representative of the hypertensive population. Therefore we opted to exclude from the final analysis any variable that had more than 90% of the participants’ data missing. This included history of smoking and alcohol and the following complications: stroke, cardiovascular disease, peripheral vascular disease and neuropathy. All included data were collected during routine clinical encounters, and protocols for their measurement is explained elsewhere [27].

Data Management and Statistical Analysis

All data used in this study were initially extracted from the EMR to Microsoft Excel version 2013. Data cleaning and analysis was performed using Stata 15 (Stata Corp, Texas). Descriptive statistics were used to describe the data. Depending on normality, mean and standard deviation were used for continuous variables and median and Interquartile range (IQR) were used non-normally distributed data. Depending on normality and type of variables, Chi2, t-test, Mann Whitney U test were used to explore the relationship between the independent variables and the patients with CKD.

We performed logistic regression analysis to determine correlates of CKD. Initially, we performed single predictor models to identify significant correlates of CKD. We then performed multivariable logistic regression analysis using identified significant factors from the single predictor models. Statistical significance was defined at a p value <0.05 at 95% confidence interval.

Ethics Approval and Consent to Participate

All data were collected as part of routine patient care and analyzed retrospectively. As a result, we did not obtain informed consent from patients. Access to data was given only to investigators and data were kept securely. The study received ethical clearance from Malawi National Health Sciences Research Committee protocol number 1216.

Results

Data Completeness

A total of 2184 hypertension patients were active in care in April 2019 (Fig. 1).We managed to completely retrieve 1995 hypertension files for this analysis. Among the 1995 patients, 798 patients did not receive CKD screening during the study period due to the convenience sampling of the study in the Chronic Care Clinics.
Fig. 1

Screening results for patients with hypertension attending Intergrated Chronic Care Clinics in Neno, malawi

We performed statistical analysis to see differences that existed between the patients that received CKD screening (n = 1197) and patients that did not receive CKD screening in this study (n = 798) as a result of convenience sampling (See supplementary Table 1). There was no differences in age, most recent systolic and diastolic blood pressures, body mass index, or co-morbidities with diabetes or HIV. 7 out of 10 patients in this cohort were females, with slightly more females in the patients that received CKD screening (78.7%) than the females that did not receive CKD screening (72.2%).

Baseline Characteristics of the Participants

Between January 2018 and April 2019, 1197 out of 2184 (54.8%) patients with hypertension were screened for CKD (Fig. 1). Seventy nine percent (n = 942) were females and the median age was 61 years (IQR 51–70 years). About half of the patients had been diagnosed and enrolled in Integrated Chronic Care Clinics for more than 2 years. The median body mass index (BMI) was 22.9 kg/m2 (IQR 20.3–26.6 kg/m2) and only 5% (n = 46) had proteinuria > = 1+. 5.3% (n = 63) and 10.9% (n = 112) had diabetes and HIV as comorbidities of hypertension respectively (Table 1).
Table 1

Characteristics of patients with hypertension screened for CKD

 

Total hypertension patients

No CKD

CKD

Total number of patients screened (%)

1197

1112 (92.9)

85 (7.1)

Median age(IQR)-years *

61 (51–70)

60 (50–69)

70 (62–78)

Female gender (%)

942 (78.7)

878(79.0)

64(75.3)

Years since diagnosis of hypertension (%)

Less than 1

290 (24.3)

275 (24.7)

15 (17.6)

1–2

274 (22.9)

252 (22.7)

22 (25.9)

Over 2

632 (52.8)

584 (52.6)

48(56.5)

Duration in clinic (%)-years

Less than 1

291(24.3)

276 (24.8)

15 (17.6)

1–2

276 (23.1)

254 (22.9)

22 (25.9)

Over 2

630 (52.6)

582 (52.3)

48 (56.5)

Median most recent systolic blood pressure (IQR)-mmHg*

135 (123–148)

135 (123–148)

140.5 (124.5–157)

Median most recent diastolic blood pressure (IQR)-mmHg

84 (76.5–93)

84 (77–93)

83 (73–92)

Risk factors

Median BMI (IQR)-kg/m2

22.9 (20.3–26.6)

22.8 (20.3–26.5)

23.7 (20.3–27.4)

BMI over 25

(median, IQR)-kg/m2

29.1(27.3–32.9)

27.8 (26.0–31.6)

29.3 (27.3–33.2)

BMI category (%)-kg/m2

<18.5

115 (9.7)

105 (9.5)

10 (11.9)

18.5- < 25

684 (57.4)

641(57.9)

43 (51.2)

25- < 30

270 (22.7)

245 (22.1)

25 (29.8)

30 and above

122 (10.2)

116 (10.5)

6 (7.1)

Proteinuria (%)

Protein <1+

946 (95.4)

883 (95.5)

63 (94.0)

Protein > = 1

46 (4.6)

42 (4.5)

4 (6.0)

Diabetes (%)*

No

1134 (94.7)

1058 (95.1)

76 (89.4)

Yes

63 (5.3)

54 (4.9)

9 (10.6)

HIV (%)

Negative

916 (89.1)

845 (88.6)

71 (95.9)

Positive

112 (10.9)

109 (11.4)

3 (4.1)

Table 1 Baseline demographic and clinical characteristics of patients with hypertension who were screened for CKD in Neno district, Malawi. CKD Chronic Kidney Disease % Percentage.

IQR interquartile range BMI Body mass index *significant differences between CKD patients and patients without CKD.

EGFR, Renal Insufficiency and CKD

Among all hypertensive patients that were screened for CKD, the mean creatinine was 0.90 mg/dl (Confidence Interval (CI) 0.85–0.94 mg/dl) and the mean eGFR was 84.1 ml/min/1.73m2 (CI 82.7–85.4 ml/min/1.73m2). About half of the patients had a normal eGFR (48.3%, n = 578) and 36.3% (n = 435) had mildly decreased eGFR (Table 2).The prevalence of renal insufficiency was 15.4% (CI 13.4–17.5, 184/1197).
Table 2

Estimated GFR stages in all patients with hypertension screened for chronic kidney disease

Stages of eGFR

eGFR

(ml/min/1.73m2)

N (%)

Normal eGFR

≥ 90

578 (48.3)

G2 (Mildly decreased)

60–89

435 (36.3)

G3a (Mildly to Moderately decreased)

45–59

85 (7.1)

G3b (Moderately to severely decreased)

30–44

57 (4.8)

G4 (Severely decreased)

15–29

33 (2.8)

G5 (Kidney failure)

<15

9 (0.7)

Total

 

1197 (100)

Table 2 Distribution of eGFR in all patients screened for renal insufficiency and CKD eGFR Estimated GFR CKD Chronic kidney disease.

Among the 184 hypertensive patients with renal insufficiency, 126 patients were successfully re-tested after a minimum of 3 months (Fig. 1). Of these, 85 had persistent eGFR <60 ml/min/1.73m2, hence the prevalence of CKD in this study was 7.1% (CI 5.7–8.7%). By eGFR category in the CKD patients, 41.2% (n = 35), 31.8% (n = 27), 24.7% (n = 21) and 2.3% (n = 2) had CKD stage 3a, 3b, 4 and 5 respectively.

Correlates of CKD

After univariate logistic regression model, increasing age (unadjusted odds ratio (OR) 1.06, CI 1.04–1.08, p < 0.001), higher systolic blood pressure (unadjusted OR 1.01, CI 1.004–1.02, p < 0.001) and a concurrent diagnosis of diabetes (unadjusted OR 2.32, CI 1.10–4.88, p = 0.03) were all associated with CKD (Table 3). In multivariable logistic regression, older age (adjusted OR 1.06, CI 1.04–1.08, p < 0.001) and a history of diabetes (adjusted OR 2.63, CI 1.21–5.69, p = 0.01) were strongly associated with CKD. Most recent systolic blood pressure was weakly associated with CKD (adjusted OR 1.01 CI 1.00–1.02, p = 0.048) (Table 4).
Table 3

Univariate Logistic Regression of Correlates of CKD

Independent Variable

Odds ratio (CI)

P value

Age (years)

1.06 (1.04–1.08)

<0.001*

Gender

Female

ref

ref

Male

1.23 (0.74–2.06)

0.43

Years since diagnosis

<1 year

ref

 

1–2 years

1.60 (0.81–3.15)

0.17

Over 2 years

1.51 (0.83–2.73)

0.19

Diagnosis duration

<1 year

ref

 

1–2 years

1.59 (0.81–3.14)

0.19

Over 2 years

1.52 (0.83–2.76)

0.17

Most recent systolic blood pressure

1.01 (1.004–1.02)

<0.001*

Most recent diastolic blood pressure

0.99 (0.98–1.01)

0.60

BMI

1.02 (0.99–1.06)

0.40

Overweight or obesity

1.01 (0.93–1.09)

0.82

BMI category

<18.5

1.42 (0.69–2.91)

0.34

18.5- < 25

Ref

Ref

25- < 30

1.52 (0.91–2.5)

0.11

30 and above

0.77 (0.32–0.85)

0.56

Proteinuria

Less than 1+

Ref

 

1+ and above

1.33 (0.46–3.80)

0.59

Diabetes diagnosis

No

Ref

 

Yes

2.32 (1.10–4.88)

0.03*

HIV diagnosis

Negative

Ref

 

Positive

0.33 (0.10–1.06)

0.06

Table 3 Single predictor logistic regression model of correlates of CKD *significant associations.

Table 4

Multivariable analysis of Correlates of CKD

Independent Variable

Adjusted Odds ratio (CI)

P value

Age (years)

1.06 (1.04–1.08)

<0.001

Most recent systolic blood pressure

1.01 (1.00–1.02)

0.048

Diabetes diagnosis

No

Ref

 

Yes

2.63 (1.21–5.69)

0.01

Table 4 multivariable logistic regression analysis for Correlates of CKD CKD Chronic kidney disease.

Discussion

As far as we know, this is the first study to investigate CKD among patients with hypertension attending longitudinal care in rural Malawi. Approximately half of the patients with hypertension tested had abnormal eGFR, and at-least 15% of the patients had evidence of renal insufficiency. This demonstrates the clinical importance of routine screening for CKD in hypertension clinics. Most patients with renal insufficiency benefitted from follow up after three months for confirmation of CKD. The screening and dissemination of their results may also have helped with raising the awareness of CKD.

The confirmation of CKD by KDIGO guidelines requires re-testing after at least 3 months, and in rural impoverished settings, effort is required to find these patients for repeat testing. This challenge has been reported in a previous study that used KDIGO guidelines [31]. Despite our efforts to track the patients, we were not able to re-test all patients with renal insufficiency; 126 out of 184 (68%) patients with hypertension were reached and re-tested. It was impossible to track other patients; some had died or transferred outside the district, and others could not be located for testing within our study period. Therefore, we may have underestimated the prevalence of CKD. Other programs implementing screening for CKD should consider how to strengthen the ways used to track patients in this study, which also has benefits for patient care beyond screening, or try other strategies that can be used to effectively track patients for confirmation of CKD.

We found a lower prevalence of CKD in patients with hypertension in comparison to most studies published in Africa [13, 32]. The prevalence may have been underestimated since we could not find all the patients who needed a repeat test. Additionally, we had used KDIGO guidelines, and therefore some people had an initially low eGFR which normalized after repeat testing.

Additionally, most of the patients in this study are females, which reflects health service utilization in Malawi, where more females utilize services than men. After controlling for multiple factors, only older age and diabetes were strongly associated with CKD in patients with hypertension, and this has also been shown in other studies in Africa [8, 33].

There are many clinical and research implications from these results. First, we concluded that all patients with hypertension should be screened for CKD, and Neno District continues to gradually screen all patients and plans to repeat CKD screening annually. Additionally, Neno District has opened two Advanced NCD Clinics where patients with CKD stage 4 and 5 are routinely managed alongside patients with other severe chronic NCDs. Beyond Neno, hypertension clinics need to invest in CKD screening for early detection and management of kidney disease. Additionally, more research needs to be generated, particularly in urban areas, to compare with the results from this study.

There are a few key limitations in this study. We used only eGFR only to define patients with CKD, and we were unable to measure albumin-creatinine ratio or other measures of CKD as defined by KDIGO guidelines. The study is also a facility based audit and was conducted in a rural area. As a result, the study may not be generalizable to populations outside Neno District. We also did not investigate CKD in patients with hypertension less than 18 years old. In addition, to be enrolled for hypertension treatment in clinics in Neno, patients have blood pressures ≥160/110, so the results are not generalizable to patients with Stage I hypertension. Finally, in Integrated Chronic Care Clinics in Neno district, we measured prevalence and not incidence of CKD hence we could not differentiate the temporal sequence of hypertension and CKD.

Conclusions

The study found moderately high prevalence of renal insufficiency and CKD in a rural cohort of severe hypertension patients (15.4% and 7.1%) respectively. Hypertension in this study was defined as currently taking hypertension drugs at enrolment or systolic blood pressure ≥ 160 mmHg and / or diastolic blood pressure ≥ 110 mmHg. Additionally, CKD was significantly associated with older age and a history of diabetes. We advocate for investing in CKD screening among patients with hypertension attending longitudinal care in order to optimize early diagnosis and management in this population .

Notes

Acknowledgements

We acknowledge all of our patients who receives care at all the Integrated Chronic Care clinics in Neno District, Malawi. Special acknowledgment to all the staff working in Neno health facilities, their efforts continues to contribute to better quality care of the patients.

Authors’ Contributions

CK, LN, EN, GCT and EBW conceptualized the study. LT, RK, CK and GCT performed data curation. CK performed data cleaning and analysis. CK wrote the first draft. All authors provided feedback and approved the final manuscript for publication.

Compliance with Ethical Standards

Ethics Approval and Consent to Participate

All data were collected as part of routine patient care and was analyzed retrospectively. As a result, we did not obtain informed consent from patients. Access to data was given only to investigators and data were kept securely. The study received ethical clearance from Malawi National Health Sciences Research Committee protocol number 1216.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Supplementary material

42399_2019_154_MOESM1_ESM.docx (18 kb)
ESM 1 (DOCX 17 kb)

References

  1. 1.
    Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global disparities of hypertension prevalence and control:a systematic analysis of population-based studies from 90 countries. Circulation. 2016;134:441–50.CrossRefGoogle Scholar
  2. 2.
    NCD Countdown 2030 collaborators. NCD Countdown 2030 : worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3. 4. Lancet. 2018;392:1072–88.CrossRefGoogle Scholar
  3. 3.
    Ataklte F, Erqou S, Kaptoge S, Taye B, Echouffo-tcheugui JB, Kengne AP. Burden of undiagnosed hypertension in sub-Saharan Africa a systematic review and meta-analysis. Hypertension. 2015;65:291–8.CrossRefGoogle Scholar
  4. 4.
    Kaze AD, Ilori T, Jaar BG, Echouffo-tcheugui JB. Burden of chronic kidney disease on the African continent : a systematic review and meta-analysis. BMC Nephrol. 2018;19(125):125.CrossRefGoogle Scholar
  5. 5.
    Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, et al. Analysis of the global burden of disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94:567–81.CrossRefGoogle Scholar
  6. 6.
    Osafo C, Mate-kole M, Affram K, Adu D. Prevalence of chronic kidney disease in hypertensive patients in Ghana prevalence of chronic kidney disease in hypertensive patients in Ghana. Ren Fail. 2011;33(4):388–92.CrossRefGoogle Scholar
  7. 7.
    Kaze FF, Kengne A, Magatsing CT, Halle M, Yiagnigni E, Ngu KB. Prevalence and determinants of chronic kidney disease among hypertensive Cameroonians according to three common estimators of the glomerular filtration rate. J Clin Hypertens. 2016;18:408–14.CrossRefGoogle Scholar
  8. 8.
    Ajayi S, Manmak M, Oji D. EGFR and chronic kidney disease stages among newly diagnosed asymptomatic hypertensives and diabetics seen in a tertiary health center in Nigeria. Ethn Dis. 2014;24.Google Scholar
  9. 9.
    George C, Mogueo A, Okpechi I, Echouffo-Tcheugui JB, Kengne AP. Chronic kidney disease in low-income to middle-income countries: the case for increased screening. BMJ Glob Health. 2017;2(2):e000256.CrossRefGoogle Scholar
  10. 10.
    Bamgboye EL. The challenges of ESRD care in developing economies : sub-Saharan African opportunities for significant improvement. Clin Nephrol. 2016;86(supplementary 1):18–22.CrossRefGoogle Scholar
  11. 11.
    Perico N, Remuzzi G. Prevention programs for chronic kidney disease in low-income countries. Intern Emerg Med. 2016;11:385–9.CrossRefGoogle Scholar
  12. 12.
    Barsoum RS, Khalil SS, Arogundade FA. Fifty years of Dialysis in Africa: challenges and Progress. Am J Kidney Dis. 2015;65(3):502–12.CrossRefGoogle Scholar
  13. 13.
    Stanifer JW, Jing B, Tolan S, Helmke N, Mukerjee R, Naicker S, et al. The epidemiology of chronic kidney disease in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health. 2014;2:e174–81.CrossRefGoogle Scholar
  14. 14.
    Ene-iordache B, et al. Chronic kidney disease and cardiovascular risk in six regions of the world ( ISN-KDDC ): a cross-sectional study. Lancet Glob Health. 2016;4:e307–19.CrossRefGoogle Scholar
  15. 15.
    Levey AS, Becker C, Inker LA. Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: a systematic review. JAMA. 2015;313(8):837–46.CrossRefGoogle Scholar
  16. 16.
    Malawi Government. Health sector strategic plan II 2017-2022, Towards Universal Health Coverage. 2017.Google Scholar
  17. 17.
    Price AJ, Crampin AC, Amberbir A, Kayuni-Chihana N, Musicha C, Tafatatha T, et al. Prevalence of obesity , hypertension , and diabetes , and cascade of care in sub-Saharan Africa : a cross-sectional , population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol. 2018;6(3):208–22.CrossRefGoogle Scholar
  18. 18.
    Ministry of Health [Malawi]. National Action plan for prevention and management of non-communicable diseases in Malawi; 2013. p. 2012–106.Google Scholar
  19. 19.
    Wood R, Viljoen V, Van Der Merwe L, Mash R. Quality of care for patients with non-communicable diseases in the Dedza District, Malawi. Afr J Prim Health Care Fam Med. 2015;7(1):1–8.CrossRefGoogle Scholar
  20. 20.
    Chikowe I, Mwapasa V, Kengne AP. Analysis of rural health centres preparedness for the management of diabetic patients in Malawi. BMC Res Notes. 2018;11(1):1–6.CrossRefGoogle Scholar
  21. 21.
    National Statistical Office. 2018 Malawi population and housing Cencus-preliminary report. 2018.Google Scholar
  22. 22.
    Mwanza ZV, McCulloch M, Drayson M, Plant T, Milford DV, Dreyer G. Proteinuric kidney disease in children at queen Elizabeth central hospital, Malawi. BMC Nephrol. 2018;19(1):1–8.CrossRefGoogle Scholar
  23. 23.
    Glaser N, et al. The prevalence of renal impairment in individuals seeking HIV testing in urban Malawi. BMC Nephrol. 2016;17(1):1–6.CrossRefGoogle Scholar
  24. 24.
    Struik GM, et al. The prevalence of renal impairment among adults with early HIV disease in Blantyre, Malawi. pp 457–462; 2011.Google Scholar
  25. 25.
    Divala OH, et al. The burden of hypertension , diabetes mellitus , and cardiovascular risk factors among adult Malawians in HIV care : consequences for integrated services. BMC Public Health. 2016;6:1–11.Google Scholar
  26. 26.
    Wroe EB, Kalanga N, Mailosi B, Mwalwanda S, Kachimanga C, Nyangulu K, et al. Leveraging HIV platforms to work toward comprehensive primary care in rural Malawi: the integrated chronic care clinic. Healthcare. 2015;3(4):270–6.CrossRefGoogle Scholar
  27. 27.
    Partners In Health. Integrated care Cascade toolkit:an implementation to guide screening, Treatment & Follow-up for HIV and NCDs. 2017.Google Scholar
  28. 28.
    Kachimanga C, Cundale K, Wroe E, Nazimera L, Jumbe A, Dunbar E, et al. Novel approaches to screening for noncommunicable diseases: lessons from Neno, Malawi. Malawi Med J. 2017;29(2):78–83.CrossRefGoogle Scholar
  29. 29.
    Kidney Disease Improving Global outcomes. KDIGO 2012 clinical practice guideline for the evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl. 2013;3(1):136–50.CrossRefGoogle Scholar
  30. 30.
    Glaser N, Deckert A, Phiri S, Rothenbacher D, Neuhann F. Comparison of various equations for estimating GFR in Malawi: how to determine renal function in resource limited settings? PLoS One. 2015;10(6):1–17.CrossRefGoogle Scholar
  31. 31.
    Kaze FF, Meto DT, Halle M, Ngogang J, Kengne A. Prevalence and determinants of chronic kidney disease in rural and urban Cameroonians : a cross-sectional study. BMC Nephrol. 2015;16.Google Scholar
  32. 32.
    Abd Elhafeez S, Bolignano D, D’Arrigo G, Dounousi E, Tripepi G, Zoccali C. Prevalence and burden of chronic kidney disease among the general population and high-risk groups in Africa: a systematic review. BMJ Open. 2018;1:8.Google Scholar
  33. 33.
    Ladi-Akinyemi TW, Ajayi I. Risk factors for chronic kidney disease among patients at Olabisi Onabanjo University teaching Hospital in Sagamu, Nigeria: a retrospective cohort study. Malawi Med J. 2017;29(2):166–70.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Partners In HealthNenoMalawi
  2. 2.Ministry of HealthNenoMalawi

Personalised recommendations