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Introduction: Target 3.8 of the Sustainable Development Goals calls for the guaranteeing of universal health service coverage without generating financial risks for households and individuals. In Colombia, there is no up-to-date information on the proportion of households that suffer catastrophic health expenditure (CHE), nor about how these expenses are associated with the place of residence. To contribute to an understanding of these issues, this study analyses the differences in the levels of CHE among Colombian households, and their association with the province and area (urban or rural) of residence. Methods: This is a descriptive and analytical cross-sectional study using the 2016–2017 National Household Budget Survey, the household and population Census 2018, and the Register of Health Providers 2017. We used the definition of CHE proposed by the World Health Organization, with a threshold of 20%. We estimated the percentage of households facing CHE, and its intensity, and estimated a multi-level logistic regression model, using as the dependent variable the question of whether a household experienced CHE, and the province as a second level, where explanatory variables related to the province were included. Results: We found differences in CHE levels according to the province of residence. At the national level, 1.77% of households experienced CHE, and households in the provinces of Boyacá (5.04%), Nariño (4.04%), Cauca (3.82%), and Chocó (3.78%) faced the highest CHE. For most households with CHE in these provinces, spending on medicines and medical consultations represented close to 50% of their out-of-pocket spending. The multi-level logistic regression model indicated that there are significant variations in CHE attributed to the provinces under study, where the contextual variables of hospital-bed density (AOR = 0.91; 95% CI 0.86–0.96) and incidence of multi-dimensional poverty (AOR = 1.13; 95% CI 1.01–1.30) were factors associated with CHE. For an urban household, 6.58% of the CHE variation is attributed to the province in question, while for a rural household the corresponding variation is 1.56%. Conclusions: The geographical location of the household is a key factor when studying CHE in Colombia, where rural households present higher levels of CHE, mainly in the delivery of medicines and medical consultations. The findings reveal the need to analyse financial protection at the local level and establish policies to protect households, especially poor households, from CHE. [ABSTRACT FROM AUTHOR]

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities. [ABSTRACT FROM AUTHOR]

In the global context, the health and quality of life of people are adversely affected by either one or more types of chronic diseases. The chronic pain associated with diagnosed patients may include heavy medical expenditure along with the physical and mental suffering they undergo. Usually, unbearable amounts of medical expenses are incurred, to improve or sustain the health condition of the patient. Consequently, the heavy financial burden tends to push households from a comfortable or secure life, or even from bad to worse, towards the probability of becoming poor. Hence, this study is conducted to identify the impact chronic illnesses have on poverty using data from a national survey referred as the Household Income and Expenditure Survey (HIES), with data gathered by the Department of Census and Statistics (DCS) of Sri Lanka in 2016. As such, this study is the first of its kind in Sri Lanka, declaring the originality of the study based on data collected from the local arena. Accordingly, the study discovered that married females who do not engage in any type of economic activity, in the age category of 40–65, having an educational level of tertiary level or below and living in the urban sector have a higher likelihood of suffering from chronic diseases. Moreover, it was inferred that, if a person is deprived from access to basic education in the level of education, lives in the rural or estate sector, or suffers from a brain disease, cancer, heart disease or kidney disease, he is highly likely to be poor. Some insights concluded from this Sri Lankan case study can also be applied in the context of other developing countries, to minimise chronic illnesses and thereby the probability of falling into poverty. [ABSTRACT FROM AUTHOR]

Country-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a "bottom-up"-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population "top-down" from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years. [ABSTRACT FROM AUTHOR]

Accurate smoking prevalence data is critical for monitoring, surveillance, and evaluation. However, estimates of prevalence vary across surveys due to various factors. This study examines smoking prevalence estimates for 18–21 year olds across six U.S. national telephone, online and in-person surveys for the years 2013 and 2014. Estimates of ever smoking ranged from 35% to 55%. Current smoking ranged from 16% to 30%. Across the three modalities, household surveys were found to yield the highest estimates of smoking prevalence among 18 to 21 year olds while online surveys yielded the lowest estimates, and this was consistent when stratifying by gender and race/ethnicity. Assessments of the joint effect of gender, race/ethnicity, educational attainment and survey mode indicated that the relative differences in the likelihood of smoking were consistent across modes for gender and education groups. However, the relative likelihood of smoking among minority groups compared with non-Hispanic Whites varied across modes. Gender and racial/ethnic distributions for most surveys significantly differed from the U.S. Census. Over and underrepresentation of certain demographic subpopulations, variations in survey question wording, and social desirability effects may explain modality differences in smoking estimates observed in this study. Further research is needed to evaluate the effect of survey mode on variation in smoking prevalence estimates across national surveys, particularly for young adult populations. [ABSTRACT FROM AUTHOR]

Background: With India preparing for the next decennial Census in 2021, we compared the disability estimates and data collection methodology between the Census 2011 and the most recent population-level survey for India and its states, to highlight the issues to be addressed to improve robustness of the disability estimates in the upcoming Census. Methods: Data from the Census 2011 and from two complementary nationally representative household surveys that covered all Indian states with the same methodology and survey instruments–the District-Level Household Survey-4 (DLHS-4, 2012–2013) and the Annual Health Surveys (AHS three rounds, 2010–11, 2011–12 and 2012–13) were used. Data from DLHS-4 and AHS 2012–13 round were pooled to generate estimates for the year 2012–13. Data collection methodology between the sources was compared, including the review of definitions of each type of disability. The overall, mental, visual, hearing, speech, and movement disability rate (DR) per 100,000 population were compared between the sources for India and for each state, and the percent difference in the respective rates was calculated. We explored the reliability of these estimates comparing yearly data from the AHS for three successive rounds. Results: Survey data were collected through proxy reporting, however, it is not entirely clear whether the data were proxy- or self-reported or a mix of both in the Census. The overall DR was 25.1% higher in the Census (2,242; 95% CI 2,241–2,243) than the survey (1,791; 95% CI 1,786–1,797) per 100,000 population, with the state-level difference ranging from -64% in Tamil Nadu to 107% in Sikkim state. Despite both sources using nearly similar definitions for overall disability and disability by type, the difference in DR was 125.5%, 54.2%, -25.7%, -19.7%, and 21.9% for hearing, speech, mental, movement, and visual DR, respectively. At the state-level, the difference in disability-specific estimates ranged from -84% to 450%. The extent of variations in the disability-specific estimates in AHS successive rounds ranged from -25% to 929% at the state-level. Conclusions: There is momentum globally towards building disability measurement that is consistent with the data required for monitoring of the Sustainable Development Goals to ensure robust estimation of disability. The current estimates from the Census and surveys seem much lower than would be expected at the population level. We make recommendations that India needs to take serious note of in order to improve the validity and reliability of India’s disability estimates. [ABSTRACT FROM AUTHOR]

Introduction: Lahe Township belongs to Myanmar`s Naga Self-administered Zone, which is one of the most remote and mountainous areas in Myanmar. However, the limited health data available for the region suggests that there could be neglected health needs that require attention. The purpose of this study was to assess the health status of the population of Lahe Township. Methods: A cross-sectional study design incorporating a two-stage cluster sampling methodology recommended by the WHO was used to conduct a household level survey. In the first stage, 30 village clusters were selected from all villages situated in the Lahe Township through systematic sampling with probability of selection proportional to the population size of each village based on the 2014 Myanmar census. In the second stage, a GPS-based sampling method was used to select 30 households within a village cluster. The head of the household completed the survey for all members of the household. Questionnaires inquired about maternal health, mortality, morbidities, childhood nutritional status, access to health care, and water & sanitation. The resulting data was stratified by urban/rural status. Results: Data was collected on 5,929 individuals living in 879 households, of which 993 individuals (16.7%) were children 5 years old or younger. The median age was 18.0 (IQR 8.0–35.0). Children 15 years old or younger represented 44.7% of the population. 19.8% of households reported at least 1 household member sick during the previous 30 days. The crude mortality rate per 10,000 people per day was 0.58 (95% CI: 0.48–0.69). The under 5 mortality per 10,000 people per day was 0.74 (95% CI: 0.50–1.06). Only 46.7% of households could access a hospital if there was a need. Conclusion: Our results demonstrate a high rate of mortality and the inability to access healthcare in Lahe Township, which should be addressed to prevent further deterioration of health. [ABSTRACT FROM AUTHOR]

Background: Violence against women is often exacerbated by war, but most civilian research has investigated short term impact. We describe the conflict experiences of men and women from the general population of Bougainville Papua New Guinea, perceptions of the enduring impact of conflict, and the associations between these and the major health and development problems on the islands: mental ill-health and violence against women. Methods: Fourteen years after the end of the decade long civil war, we conducted a household survey with a random sample of adult (n = 864) men and (n = 879) women living in Bougainville. The interviews were mostly conducted face-to-face, with very sensitive questions self-completed. Results: Mental ill-health was highly prevalent, 37.8% of women and 32% of men had high levels of depressive symptomatology, 34.4% of men abused alcohol and 15.1% of women and 24.6% of men had high levels of PTSD symptoms. Among women, 23.3% had been raped in the year prior to the interview and 33.3% had experience physical or sexual partner violence. The prevalence of exposure to trauma during the civil war was very high and many of the men and women experienced lingering impact of conflict. Multiple logistic regression models showed that war trauma was associated with PTSD symptoms in women and PTSD symptoms, alcohol abuse and depressive symptoms in men. The perceived enduring impact of conflict was associated with depressive symptoms in men and women, problem drinking and suicidal thoughts in women and drug use in men. The perceived enduring conflict impact was associated with perpetration of past year rape and physical and/or sexual partner violence. Discussion: The Bougainville civil war had a devastating impact on the population’s lives. Reversing this legacy is essential but requires addressing what is perceived as the enduring social, economic and psychological impact of the conflict and a major focus on prevention of violence against women. [ABSTRACT FROM AUTHOR]

Background: Disability affects approximately 15% of the world’s population, and has adverse socio-economic effects, especially for the poor. In Rwanda, there are a number of government compensation programs that support the poor, but not specifically persons with disability (PWDs). This study investigates the relationship between poverty and government compensation on disability among working-age adults in Rwanda. Methods: This was a secondary analysis of 35,114 adults aged 16 to 65 interviewed in the 2010/2011 Rwanda Household Wealth and Living Conditions survey, a national cross-sectional two-stage cluster survey, stratified by district. This study estimated self-reported major disability, and used chi-square tests to estimate associations (p<0.1) with income, government compensation, occupation type, participation in public works programs, and household poverty status. Non-collinear economic variables were included in a multivariate logistic regression, along with socio-demographic confounders that modified the relationship between any economic predictor and the outcome by 10% or more. All analyses adjusted for sampling weights, stratification, and clustering of households. Results: Over 4% of working-age adults reported having a major disability and the most prevalent types of disability in order were physical, mental, and then sensory disability. In bivariate analysis, annual income, occupation type, and poverty status were associated with major disability (p<0.001 for all). Occupation type was dropped because it was collinear with income. Age, education, and urban/rural residence were confounders. In the multivariate analysis, adults in all income groups had about half the odds of disability compared to adults with no income (Rwf1-120,000 OR = 0.57; Rwf120,000–250,000 OR = 0.61; Rwf250,000–1,000,000 OR = 0.59; Rwf1,000,000+ OR = 0.66; p<0.05 for all), and non-poor adults had 0.77 the odds of disability compared to poor adults (p = 0.001). Conclusion: Given that personal income rather than government programming is associated with disability in Rwanda, we recommend deliberately targeted services to those with disability via cash transfers, placements in disability-appropriate employment, and micro-savings programs. [ABSTRACT FROM AUTHOR]

Mobile telephone numbers are increasingly being included in household surveys samples. As approach letters cannot be sent because many do not have address details, alternatives approaches have been considered. This study assesses the effectiveness of sending a short message service (SMS) to a random sample of mobile telephone numbers to increase response rates. A simple random sample of 9000 Australian mobile telephone numbers: 4500 were randomly assigned to be sent a pre-notification SMS, and the remaining 4500 did not have a SMS sent. Adults aged 18 years and over, and currently in paid employment, were eligible to participate. American Association for Public Opinion Research formulas were used to calculated response cooperation and refusal rates. Response and cooperation rate were higher for the SMS groups (12.4% and 28.6% respectively) than the group with no SMS (7.7% and 16.0%). Refusal rates were lower for the SMS group (27.3%) than the group with no SMS (35.9%). When asked, 85.8% of the pre-notification group indicated they remembered receiving a SMS about the study. Sending a pre-notification SMS is effective in improving participation in population-based surveys. Response rates were increased by 60% and cooperation rates by 79%. [ABSTRACT FROM AUTHOR]

Background/Objectives: Growing evidence indicates that self-reported height and weight are biased, but little is known about systematic errors in the general adult population in Japan. This study takes advantage of the unique opportunity to examine this issue provided by the 1986 National Nutrition Survey. Subjects/Methods: Individual-level data on a nationally representative sample aged 20–89 years from the National Nutrition Survey (November 1986) were merged with Comprehensive Survey of Living Conditions (September 1986) data to obtain a dataset containing both self-reported and measured data on height and weight for each person (n = 10,469). Discrepancies between self-reported and measured means of height, weight, and body mass index (BMI) were tested across measured BMI categories (<18.5, 18.5–24.9, 25.0–27.4, 27.5–29.9, and ≥30.0 kg/m2), age groups (20–44, 45–64, and 65–89 years), and sexes. Reporting bias in mean BMI was decomposed into the contributions of misreporting height and weight. The sensitivity and specificity of self-reported BMI categories were estimated. Results: Mean self-reported BMI was substantially underestimated in older women (P<0.001; Cohen’s d, -0.4), and the major contributor to the bias was their over-reported height. Mean self-reported BMI was also considerably underestimated in both men and women who were overweight and obese (P<0.001; Cohen’s d, -1.0 to -0.6), due mainly to their underreported weight. In contrast, mean self-reported BMI was considerably overestimated in underweight men (P<0.001; Cohen’s d, 0.5), due largely to their over-reported weight. The sensitivity of self-reported BMI categories was particularly low for individuals who had a measured BMI of 27.5–29.9 kg/m2 (40.9% for men and 26.8% for women). Conclusions: Self-reported anthropometric data were not sufficiently accurate to assert the validity of their use in epidemiological studies on the general adult population in Japan in the late 1980s. [ABSTRACT FROM AUTHOR]