Urban-rural differences in hypertension prevalence in low-income and middle-income countries, 1990-2020: A systematic review and meta-analysis.

BACKGROUND: The influence of urbanicity on hypertension prevalence remains poorly understood. We conducted a systematic review and meta-analysis to assess the difference in hypertension prevalence between urban and rural areas in low-income and middle-income countries (LMICs), where the most pronounced urbanisation is underway. METHODS AND FINDINGS: We searched PubMed, Web of Science, Scopus, and Embase, from 01/01/1990 to 10/03/2022. We included population-based studies with ≥400 participants 15 years and older, selected by using a valid sampling technique, from LMICs that reported the urban-rural difference in hypertension prevalence using similar blood pressure measurements. We excluded abstracts, reviews, non-English studies, and those with exclusively self-reported hypertension prevalence. Study selection, quality assessment, and data extraction were performed by 2 independent reviewers following a standardised protocol. Our primary outcome was the urban minus rural prevalence of hypertension. Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure as ≥90 mm Hg and could include use of antihypertensive medication, self-reported diagnosis, or both. We investigated heterogeneity using study-level and socioeconomic country-level indicators. We conducted meta-analysis and meta-regression using random-effects models. This systematic review and meta-analysis has been registered with PROSPERO (CRD42018091671). We included 299 surveys from 66 LMICs, including 19,770,946 participants (mean age 45.4 ± SD = 9 years, 53.0% females and 63.1% from rural areas). The pooled prevalence of hypertension was 30.5% (95% CI, 28.9, 32.0) in urban areas and 27.9% (95% CI, 26.3, 29.6) in rural areas, resulting in a pooled urban-rural difference of 2.45% (95% CI, 1.57, 3.33, I-square: 99.71%, tau-square: 0.00524, Pheterogeneity < 0.001). Hypertension prevalence increased over time and the rate of change was greater in rural compared to urban areas, resulting in a pooled urban-rural difference of 5.75% (95% CI, 4.02, 7.48) in the period 1990 to 2004 and 1.38% (95% CI, 0.40, 2.37) in the period 2005 to 2020, p < 0.001 for time period. We observed substantial heterogeneity in the urban-rural difference of hypertension, which was partially explained by urban-rural definition, probably high risk of bias in sampling, country income status, region, and socioeconomic indicators. The urban-rural difference was 5.67% (95% CI, 4.22, 7.13) in low, 2.74% (95% CI, 1.41, 4.07) in lower-middle and -1.22% (95% CI, -2.73, 0.28) in upper-middle-income countries in the period 1990 to 2020, p < 0.001 for country income. The urban-rural difference was highest for South Asia (7.50%, 95% CI, 5.73, 9.26), followed by sub-Saharan Africa (4.24%, 95% CI, 2.62, 5.86) and reversed for Europe and Central Asia (-6.04%, 95% CI, -9.06, -3.01), in the period 1990 to 2020, p < 0.001 for region. Finally, the urban-rural difference in hypertension prevalence decreased nonlinearly with improvements in Human Development Index and infant mortality rate. Limitations included lack of data available from all LMICs and variability in urban and rural definitions in the literature. CONCLUSIONS: The prevalence of hypertension in LMICs increased between 1990 and 2020 in both urban and rural areas, but with a stronger trend in rural areas. The urban minus rural hypertension difference decreased with time, and with country-level socioeconomic development. Focused action, particularly in rural areas, is needed to tackle the burden of hypertension in LMICs.

Ambient Air Pollution in Relation to SARS-CoV-2 Infection, Antibody Response, and COVID-19 Disease: A Cohort Study in Catalonia, Spain (COVICAT Study).

BACKGROUND: Emerging evidence links ambient air pollution with coronavirus 2019 (COVID-19) disease, an association that is methodologically challenging to investigate. OBJECTIVES: We examined the association between long-term exposure to air pollution with SARS-CoV-2 infection measured through antibody response, level of antibody response among those infected, and COVID-19 disease. METHODS: We contacted 9,605 adult participants from a population-based cohort study in Catalonia between June and November 2020; most participants were between 40 and 65 years of age. We drew blood samples from 4,103 participants and measured immunoglobulin M (IgM), IgA, and IgG antibodies against five viral target antigens to establish infection to the virus and levels of antibody response among those infected. We defined COVID-19 disease using self-reported hospital admission, prior positive diagnostic test, or more than three self-reported COVID-19 symptoms after contact with a COVID-19 case. We estimated prepandemic (2018-2019) exposure to fine particulate matter [PM with an aerodynamic diameter of ≤2.5µm (PM2.5)], nitrogen dioxide (NO2), black carbon (BC), and ozone (O3) at the residential address using hybrid land-use regression models. We calculated log-binomial risk ratios (RRs), adjusting for individual- and area-level covariates. RESULTS: Among those tested for SARS-CoV-2 antibodies, 743 (18.1%) were seropositive. Air pollution levels were not statistically significantly associated with SARS-CoV-2 infection: Adjusted RRs per interquartile range were 1.07 (95% CI: 0.97, 1.18) for NO2, 1.04 (95% CI: 0.94, 1.14) for PM2.5, 1.00 (95% CI: 0.92, 1.09) for BC, and 0.97 (95% CI: 0.89, 1.06) for O3. Among infected participants, exposure to NO2 and PM2.5 were positively associated with IgG levels for all viral target antigens. Among all participants, 481 (5.0%) had COVID-19 disease. Air pollution levels were associated with COVID-19 disease: adjusted RRs=1.14 (95% CI: 1.00, 1.29) for NO2 and 1.17 (95% CI: 1.03, 1.32) for PM2.5. Exposure to O3 was associated with a slightly decreased risk (RR=0.92; 95% CI: 0.83, 1.03). Associations of air pollution with COVID-19 disease were more pronounced for severe COVID-19, with RRs=1.26 (95% CI: 0.89, 1.79) for NO2 and 1.51 (95% CI: 1.06, 2.16) for PM2.5. DISCUSSION: Exposure to air pollution was associated with a higher risk of COVID-19 disease and level of antibody response among infected but not with SARS-CoV-2 infection. https://doi.org/10.1289/EHP9726.

The COVID-19 pandemic and global environmental change: Emerging research needs.

The outbreak of COVID-19 raised numerous questions on the interactions between the occurrence of new infections, the environment, climate and health. The European Union requested the H2020 HERA project which aims at setting priorities in research on environment, climate and health, to identify relevant research needs regarding Covid-19. The emergence and spread of SARS-CoV-2 appears to be related to urbanization, habitat destruction, live animal trade, intensive livestock farming and global travel. The contribution of climate and air pollution requires additional studies. Importantly, the severity of COVID-19 depends on the interactions between the viral infection, ageing and chronic diseases such as metabolic, respiratory and cardiovascular diseases and obesity which are themselves influenced by environmental stressors. The mechanisms of these interactions deserve additional scrutiny. Both the pandemic and the social response to the disease have elicited an array of behavioural and societal changes that may remain long after the pandemic and that may have long term health effects including on mental health. Recovery plans are currently being discussed or implemented and the environmental and health impacts of those plans are not clearly foreseen. Clearly, COVID-19 will have a long-lasting impact on the environmental health field and will open new research perspectives and policy needs.